System

Abstract

A system is provided.SOLUTION: A system includes a means for collecting learning data of a learner, a means for utilizing an artificial intelligence model for analyzing the learning data, a means for designing an optimum learning route on the basis of an analysis result, a means for transmitting the learning route to a terminal of the learner and displaying it, and a means for collecting learning progress data of the learner again and analyzing improvement points.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's educational environment, income disparities between families have a significant impact on children's educational opportunities. Low-income families, in particular, find it difficult to get their children to university or preparatory schools, limiting their opportunities for high-quality education. This problem is a serious social issue that affects future career choices and income disparities. The purpose of this invention is to eliminate educational disparities caused by income disparities and ensure that everyone has equal educational opportunities. [Means for solving the problem]

[0005] The present invention provides a system that collects learner learning data and analyzes it using an artificial intelligence model. Specifically, it first provides a means for collecting learning data entered by learners. It then provides a means for using an artificial intelligence model to analyze this learning data and design an optimal learning route. It also provides a means for transmitting the designed learning route to the learner's device and displaying it. It then provides a means for re-collecting and analyzing the learner's learning progress data to identify areas for improvement. This enables low-cost, effective learning design and can eliminate educational disparities caused by income disparities.

[0006] "Learning data" refers to information about learning progress and results, such as test results and self-assessment data entered by learners.

[0007] An "artificial intelligence model" refers to a program equipped with an algorithm that analyzes collected learning data to identify optimal learning routes and areas for improvement.

[0008] "Learning route" refers to the order of learning content and materials suggested to help learners progress efficiently based on the analysis results of an artificial intelligence model.

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

[0010] "Learning progress data" refers to data that records the progress and results of a learner's learning. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0012] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0015] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0016] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0017] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0024] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0031] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0032] The present invention is a system for collecting and analyzing a learner's learning data to propose an optimal learning route and provide continuous learning support. Specific embodiments for carrying out the present invention will be described below.

[0033] Data collection

[0034] User

[0035] First, users log in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they took a math test on quadratic equations, they enter their accuracy rate and self-evaluation. This generates learning data.

[0036] Terminal

[0037] The device transmits the learning data entered by the user to the server in real time, including the learning content, test results, correct answer rate, and self-evaluation.

[0038] Data analysis

[0039] server

[0040] The server receives the learning data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the data of many other learners in the past, and by comparing it with data with similar patterns, it proposes the optimal learning route.

[0041] Designing learning routes

[0042] server

[0043] The server then designs the optimal learning path for the user based on the analysis results obtained from the AI ​​model. During this process, it determines the next content to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each).

[0044] Providing learning routes

[0045] server

[0046] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[0047] Terminal

[0048] The terminal displays the received learning route, reference materials, and learning schedule to the user so that the learner can check them at any time.

[0049] Running training and collecting progress

[0050] User

[0051] The user begins studying according to the study plan provided by the server, and periodically enters their study progress and new test results into the platform.

[0052] Terminal

[0053] The terminal again transmits the new learning progress data input by the user to the server.

[0054] Implementing the PDCA cycle

[0055] server

[0056] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. It then sends the user an optimized learning route again, preparing for the next learning cycle.

[0057] In this way, continuous learning improvement will be achieved through data collection and analysis, and learning based on the proposed learning pathways, making it possible to provide an environment in which even low-income families can receive effective, high-quality education.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] User

[0061] The user logs in to the learning platform and enters the content of today's learning in the learning content input form, for example, the results of a math quadratic equation test (60% correct answer rate) or self-evaluation.

[0062] Step 2:

[0063] Terminal

[0064] The device transmits the learning data (test results, accuracy rate, self-evaluation) entered by the user to the server in real time.

[0065] Step 3:

[0066] server

[0067] The server receives the learning data sent from the terminal and stores it in a database.

[0068] Step 4:

[0069] server

[0070] The server inputs the saved learning data into an AI model, which analyzes the user's learning data while referencing the data of many other past learners.

[0071] Step 5:

[0072] server

[0073] The server then designs the optimal learning path for the user based on the analysis results of the AI ​​model. For example, it identifies the basics of factorization as the next thing to learn and identifies the necessary study materials and reference books.

[0074] Step 6:

[0075] server

[0076] The server adds the designed learning route, recommended reference books and learning schedule to the user profile and transmits it to the terminal.

[0077] Step 7:

[0078] Terminal

[0079] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[0080] Step 8:

[0081] User

[0082] Users study according to the study plan provided by their device and periodically enter their study progress into the platform.

[0083] Step 9:

[0084] Terminal

[0085] The terminal transmits the learning progress data newly input by the user to the server in real time.

[0086] Step 10:

[0087] server

[0088] The server inputs the retransmitted learning progress data into the AI ​​model for tracking and analysis. Based on the results of the reanalysis, it proposes new learning content and areas for improvement.

[0089] Step 11:

[0090] server

[0091] The server adds the improved study plan to the user profile and sends it to the terminal to prepare for the next study cycle.

[0092] Step 12:

[0093] User

[0094] The user starts a new learning cycle based on the updated learning plan and periodically enters their progress into the platform, and this process is repeated as a PDCA cycle.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] Conventional learning support systems have difficulty efficiently and effectively providing learners with appropriate learning routes based on their progress. Furthermore, learning data is often collected and analyzed manually, which can lead to delays in real-time feedback and improvement. This raises concerns about a decline in learner motivation and a decrease in learning effectiveness.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes means for collecting learner learning data, means for transmitting the learning data from the terminal to the server, means for storing the learning data in a database, means for utilizing a generative artificial intelligence model to analyze the learning data, means for designing an optimal learning route, recommended materials, and learning schedule based on the analysis results, means for transmitting the learning route, recommended materials, and learning schedule to the learner's terminal and displaying them, means for re-collecting the learner's learning progress data and analyzing areas for improvement, and means for providing an improved learning route based on the re-analysis results. This enables the collection, analysis, and feedback of learning data in real time, making it possible to continuously provide learners with an optimal learning route.

[0100] "Learning data" refers to data entered by a learner, including learning content, test results, and self-assessments.

[0101] A "terminal" is a digital device through which a user inputs learning data and receives instructions from a server.

[0102] "Server" means a central computer system for receiving and analyzing learning data, designing learning routes, and providing feedback to learners.

[0103] A "database" is a digital storage system in which the server stores learning data.

[0104] "Generative AI model" is a general term for machine learning algorithms and related technologies used to analyze training data and propose optimal learning routes.

[0105] A "learning route" is a guideline for the specific learning content and schedule that a learner should follow, proposed based on the analysis results.

[0106] "Recommended materials" are resources such as reference books and teaching materials that are provided to learners based on their learning route.

[0107] A "learning schedule" is a specific learning plan that a learner should follow based on their learning route.

[0108] "Study progress data" refers to data that indicates the progress of a learner's learning, which is generated in the process of the learner progressing with their studies according to the study plan.

[0109] "Reanalysis" refers to the process of re-analyzing data using learning progress data.

[0110] The present invention is a system that collects and analyzes a learner's learning data to propose an optimal learning route and provide continuous learning support.

[0111] This system begins when the user logs in to the learning platform. The user enters learning data, such as their learning content, test results, and self-evaluation, into a learning content input form. The device then transmits this learning data to the server in real time. This is achieved by using, for example, the HTTPS protocol to ensure secure communications.

[0112] The server stores the training data received from the device in a database. The server then uses a generative AI model to analyze the stored training data. This AI model is built using machine learning algorithms such as TensorFlow, and performs analysis by comparing it with past training data.

[0113] Based on the analyzed data, the server designs an optimal learning path for each learner, including what to study next (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a specific study schedule (three times a week, one hour each).

[0114] The designed learning route, recommended reference books, and learning schedule are sent from the server to the learner's device. The device displays this information to the user so that the learner can check it at any time. The user begins studying according to the learning plan provided by the server. Learning progress and new test results are periodically entered into the platform and sent back to the server via the device.

[0115] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. The user is then provided with an optimized learning route again. In this way, continuous learning improvement is achieved.

[0116] As a concrete example, consider the case where a user takes a math test on "quadratic equations" and enters the results into the platform. The user enters data such as "Quadratic equations test result: 70%. Self-assessment: Medium level of understanding." This data is then sent to the server via the device, where an artificial intelligence model analyzes it based on past data, and designs study content such as "The Basics of Factorization," a recommended textbook, "High School Mathematics I: Quadratic Equations," and a schedule of "one hour of study three times a week."

[0117] An example of a prompt to input to a generative AI model is as follows:

[0118] "Please suggest the best learning path for the user based on their learning data. The data is as follows: Learning content - Quadratic equations, Test result - 70%, Self-assessment - Medium."

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] A user logs into the learning platform.

[0122] Input: Username, Password

[0123] Output: Authentication token

[0124] Specific operation: The user enters their username and password into the login form and presses the submit button. The server receives this and performs authentication. If authentication is successful, it generates an authentication token and returns it to the user.

[0125] Step 2:

[0126] The user inputs the training data.

[0127] Input: Study content, test results, self-assessment

[0128] Output: Training data (e.g., math test result on quadratic equations: 75%, self-assessment: moderate)

[0129] Specific operation: The user enters the day's learning content, test results, and self-evaluation into the learning platform's input form, and presses the submit button. This data is saved on the device.

[0130] Step 3:

[0131] The device sends the learning data to the server.

[0132] Input: Training data

[0133] Output: Training data sent to the server

[0134] Specific operation: The device transmits the saved learning data to the server in real time using the HTTPS protocol to ensure data security.

[0135] Step 4:

[0136] The server stores the learning data in a database.

[0137] Input: Training data

[0138] Output: Training data stored in a database

[0139] Specific operation: The server stores the received training data in a database. This storage process uses SQL queries.

[0140] Step 5:

[0141] The server inputs the learning data into the artificial intelligence model and begins analysis.

[0142] Input: Training data stored in a database

[0143] Output: Analysis results (e.g., what to study next, recommended reference books, study schedule, etc.)

[0144] How it works: The server retrieves training data from the database and inputs it into an AI model. The AI ​​model (for example, a model built with TensorFlow) analyzes the data and proposes the optimal training route.

[0145] Step 6:

[0146] The server designs the optimal learning route.

[0147] Input: Analysis results

[0148] Output: Optimal study route, recommended reference books, study schedule

[0149] Specific operation: Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route for the learner. For example, it sets "Fundamentals of Factorization" as a learning item, recommends "High School Mathematics I: Quadratic Equations" as a reference book, and determines a learning schedule of "One hour of study three times a week."

[0150] Step 7:

[0151] The server sends the learned route to the user's terminal.

[0152] Input: optimal study route, recommended reference books, study schedule

[0153] Output: Study route, recommended study books, and study schedule sent to the device

[0154] Specific operation: The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends them to the terminal.

[0155] Step 8:

[0156] The device displays the learned route.

[0157] Input: Study route, recommended reference books, study schedule sent from the server

[0158] Output: User-confirmable learning route, recommended reference books, and study schedule

[0159] Specific operation: The device displays the received study route, recommended reference books, and study schedule to the user, who then confirms this and begins studying.

[0160] Step 9:

[0161] The user enters their learning progress.

[0162] Input: New learning progress data (progress, new test results, etc.)

[0163] Output: Learning progress data saved on the device

[0164] What it does: Users enter their learning progress and new test results into the learning platform, and this data is stored on their device.

[0165] Step 10:

[0166] The terminal again transmits the learning progress data to the server.

[0167] Input: Learning progress data

[0168] Output: Learning progress data sent to the server

[0169] Specific operation: The device transmits the saved learning progress data to the server in real time.

[0170] Step 11:

[0171] The server re-analyzes the learning progress data.

[0172] Input: Learning progress data

[0173] Output: Reanalysis results (further improved learning route)

[0174] Specific operation: The server inputs the retransmitted learning progress data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content.

[0175] Step 12:

[0176] The server provides the improved learning route to the user.

[0177] Input: Reanalysis result

[0178] Output: Improved study route, recommended reference books, study schedule

[0179] Specific operation: Based on the reanalysis results, the server designs an improved learning route and provides it to the user, who then proceeds to the next learning cycle.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] Conventional learning support systems and factory equipment management systems did not adequately collect and analyze data, resulting in insufficient proposals for optimal learning or operation routes. Furthermore, it was difficult for learners and factory operators to take appropriate action individually, making continuous performance improvement difficult.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes means for collecting learning data from learners, means for utilizing an AI model to analyze the learning data, means for designing an optimal learning route based on the analysis results, means for transmitting the learning route to the learner's terminal and displaying it, means for re-collecting the learner's learning progress data and analyzing areas for improvement, means for collecting operation data of factory equipment, means for utilizing an AI model to analyze the operation data, means for designing an optimal operation route or maintenance route based on the analysis results, and means for transmitting the operation route or maintenance route to an equipment operation terminal and displaying it. This enables continuous learning improvement for learners and efficient operation management of factory equipment.

[0185] A "learner" is a person receiving education or training.

[0186] "Learning data" refers to information entered by learners regarding test results, self-assessments, and learning content.

[0187] An "artificial intelligence model" is a computational system that uses machine learning and data analysis algorithms to analyze data and make predictions and classifications.

[0188] A "learning route" is a series of learning content and schedules suggested to help a learner optimally learn.

[0189] "Devices" are devices used by learners, such as computers, smartphones, and tablets.

[0190] "Study progress data" is information that indicates how far a learner has progressed in their studies.

[0191] "Factory equipment" refers to production facilities and machinery used in factories.

[0192] "Operational data" refers to information relating to the operating status and performance of factory equipment.

[0193] A "maintenance route" is a plan or procedure for the proper operation and maintenance of factory equipment.

[0194] An "equipment operation terminal" is a device for monitoring and operating factory equipment.

[0195] The present invention is a system for realizing efficient management of factory equipment and continuous learning support for learners. This system consists of three main elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[0196] Data collection

[0197] User

[0198] Users first log in to the system through a terminal, then enter their learning content and the operating status of factory equipment into a designated interface. For example, students enter math test results and self-evaluations, while factory equipment collects temperature and vibration data.

[0199] Terminal

[0200] The device sends the user-entered learning and operational data to the server in real time, including learning content, test results, accuracy rate, self-evaluation, and the operating status of factory equipment (e.g., temperature, vibration, and operating time).

[0201] Data analysis

[0202] server

[0203] The server receives the data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the operation data of many past learners and factory equipment, and by comparing it with data with similar patterns, it proposes optimal learning and operation routes.

[0204] Designing learning and maintenance routes

[0205] server

[0206] Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route and factory equipment maintenance route for the user. In this process, it determines the next learning content, reference books, learning schedule, factory equipment maintenance schedule, and necessary maintenance actions.

[0207] Providing learning and maintenance routes

[0208] server

[0209] The server adds the designed learning route and maintenance route to the user's profile and sends it to the terminal.

[0210] Terminal

[0211] The terminal displays the received learning route and maintenance route to the user, allowing the learner or factory operator to check them at any time.

[0212] Learning execution and operation management, and progress collection

[0213] User

[0214] The user begins to act according to the learning plan and maintenance route provided by the server, and periodically inputs learning progress, new test results, and the operating and maintenance status of factory equipment into the system via a terminal.

[0215] Terminal

[0216] The terminal again transmits the new learning progress data and operation data input by the user to the server.

[0217] Implementing the PDCA cycle

[0218] server

[0219] The server then inputs the retransmitted data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement in the learning and maintenance routes and makes new suggestions. It then sends the optimized route back to the user for the next cycle.

[0220] Hardware and software used

[0221] In this embodiment, the following hardware and software are used:

[0222] Hardware: Computers, smartphones, smart glasses, factory equipment.

[0223] Software: AI analysis libraries (TensorFlow and PyTorch), databases (MySQL and PostgreSQL), and cloud data processing services (AWS Lambda and Google Cloud Functions).

[0224] Specific examples

[0225] As a specific example, the temperature and vibration data of factory equipment can be analyzed, and if an abnormality is detected, a notification such as "The temperature of this robot is abnormal. Please perform maintenance immediately" can be displayed on the smart glasses.

[0226] Prompt sentence for generative AI model

[0227] Develop a model that detects anomalies and proposes optimal maintenance schedules based on operational data (temperature, vibration, timestamp) from factory equipment. See the following example:

[0228] Temperature: [65.0, 70.2, 75.1, 69.8, 72.3]

[0229] Vibration: [2.5, 2.7, 3.0, 2.8, 3.2]

[0230] Timestamps: ['2023-09-01T12:00:00', '2023-09-01T12:01:00', '2023-09-01T12:02:00', '2023-09-01T12:03:00', '2023-09-01T12:04:00']

[0231] This prompt enables the AI ​​model to analyze the operating data of factory equipment, detect abnormalities, and propose optimal maintenance schedules.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] A user logs in to the system through a terminal. As input, the user ID and login information are entered and authentication is performed. As output, the user is able to access the system.

[0235] Step 2:

[0236] Users input learning data or factory equipment operation data. Specifically, learners input test results or self-assessments, and factory operators input equipment data such as temperature and vibration. The input data is collected and sent to the server.

[0237] Step 3:

[0238] The terminal sends the collected data to the server. As input, it receives learning data and operational data entered by the user, and as output, it transfers the data to the server.

[0239] Step 4:

[0240] The server stores the received data in a database. As input, it receives data sent from the terminal and stores it in a database. As output, data is accumulated in a database.

[0241] Step 5:

[0242] The server inputs the data collected from the database into an artificial intelligence model for analysis. It uses the learning data and operational data obtained from the database as input, and generates optimal learning and operational route proposals as output. Specifically, it performs analysis by comparing the data with past data to find patterns.

[0243] Step 6:

[0244] The server designs optimal learning and maintenance routes based on the analysis results of the AI ​​model. As input, it formulates the necessary content and schedules based on the analysis results, and as output, it generates detailed learning plans and maintenance plans.

[0245] Step 7:

[0246] The server sends the designed learning route and maintenance route to the terminal. It uses the designed plan as input and sends data to the user's terminal as output.

[0247] Step 8:

[0248] The terminal displays the received learning route and maintenance route to the user. It receives the plan sent from the server as input and displays it on the screen as output. Specifically, it displays the learning content and schedule to the learner and the maintenance actions to the factory operator.

[0249] Step 9:

[0250] The user begins to act according to the learning plan and maintenance route provided by the server. As input, the user refers to the plan displayed on the terminal and performs the actual learning and maintenance work. As output, new learning progress data and operation data are generated.

[0251] Step 10:

[0252] The terminal again transmits new learning progress data and operation data input by the user to the server. It receives new learning data and operation data as input and transfers the data to the server as output.

[0253] Step 11:

[0254] The server then feeds the resubmitted data into its artificial intelligence model for additional analysis, using the newly submitted data as input and generating new analysis results as output, thereby identifying areas for improvement and suggesting the next best route.

[0255] Step 12:

[0256] The server sends the new proposal to the user's terminal, using the new analysis results as input and sending the new learning and maintenance routes as output to the terminal, where the user confirms it and prepares for the next cycle.

[0257] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0258] The present invention is a system for proposing and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[0259] Data collection

[0260] User

[0261] First, a user logs in to the learning platform. Then, they enter their learning content and test results for the day into a learning content input form. For example, if they take a math test on quadratic equations, they enter their accuracy rate and self-evaluation. Furthermore, the emotion engine uses a camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotion data.

[0262] Terminal

[0263] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[0264] Data analysis

[0265] server

[0266] The server receives the learning data and emotion data sent from the device and stores them in a database. Next, it inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current learning data and emotion data while referring to the data and emotion data of many other past learners.

[0267] Designing learning routes

[0268] server

[0269] The server then uses the analysis results from the AI ​​model to design the optimal learning path for the user. In this process, it not only determines the next topic to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each), but also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[0270] Providing learning routes

[0271] server

[0272] The server adds the designed learning route, recommended reference books and learning schedule to the user's profile and transmits them to the terminal.

[0273] Terminal

[0274] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[0275] Running training and collecting progress

[0276] User

[0277] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During study, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[0278] Terminal

[0279] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0280] Implementing the PDCA cycle

[0281] server

[0282] The server inputs the retransmitted learning progress data and emotional data into an AI model for tracking and analysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account.

[0283] In this way, by collecting and analyzing data and utilizing emotional data, learning support is provided that takes into account the user's psychological state, which makes it possible to maintain learner motivation and provide effective, high-quality education.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] User

[0287] Users log in to the learning platform and enter their learning content and test results for the day into the learning content input form. For example, they enter their math test results for quadratic equations (60% correct answer rate) and their self-evaluation. At the same time, the emotion engine collects emotional data during the learning process.

[0288] Step 2:

[0289] Terminal

[0290] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[0291] Step 3:

[0292] server

[0293] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[0294] Step 4:

[0295] server

[0296] The server inputs the stored learning data and emotion data into the AI ​​model, which has been trained using a large amount of data, including data from other past learners and emotion data.

[0297] Step 5:

[0298] server

[0299] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. Specifically, it determines the next topic to learn (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (one hour of study three times a week). It also adjusts the difficulty and pace of the study based on the user's emotional data.

[0300] Step 6:

[0301] server

[0302] The server adds the designed learning route, recommended reference books, and learning schedule to the user profile and transmits it to the terminal.

[0303] Step 7:

[0304] Terminal

[0305] The device will then notify the user of the received learning route, recommended textbooks, and study schedule, and display it to them. For example, a notification might appear saying, "Next, learn the basics of factorization, use the recommended textbooks, and study for one hour three times a week."

[0306] Step 8:

[0307] User

[0308] Users study according to the study plan provided by their device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine analyzes the user's facial expressions and voice to collect emotional data in real time.

[0309] Step 9:

[0310] Terminal

[0311] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0312] Step 10:

[0313] server

[0314] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, for example, to evaluate whether the user's level of understanding has improved and whether the learning content was appropriate.

[0315] Step 11:

[0316] server

[0317] Based on the results of the reanalysis, the server determines what to learn next and what areas to improve, and also takes into account the user's emotional data to further optimize the pace and difficulty of the learning process.

[0318] Step 12:

[0319] server

[0320] The server adds the improved study plan to the user profile and transmits it again to the terminal.

[0321] Step 13:

[0322] User

[0323] Users start a new learning cycle based on their updated learning plan and periodically enter their progress into the platform. This provides continuous learning support as a PDCA cycle, thereby narrowing the educational gap among learners and enabling the provision of high-quality education.

[0324] Example 2

[0325] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] Conventional learning support systems only analyze learners' learning data and suggest optimal learning routes. However, because learners' emotions and psychological state also have a significant impact on learning effectiveness, appropriate support that takes this information into consideration is needed. In addition, there was a need for a system that could maintain learning motivation and design effective learning plans by linking the recollection of learning progress data with the analysis of emotional data.

[0327] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0328] In this invention, the server includes a means for collecting learning data from learners, a means for utilizing an AI model that analyzes the learning data and emotional data of learners, and a means for designing an optimal learning route based on the analysis results, thereby enabling effective learning support that takes into account the learner's psychological state.

[0329] Key Word Definitions

[0330] "Learning data" refers to data including test results and self-assessment data entered by learners.

[0331] "Emotion data" is data that includes facial expressions and voice data generated by the emotion engine.

[0332] An "artificial intelligence model" is an algorithm or system that analyzes and determines the optimal learning route by studying the learning data and emotional data of many other past learners.

[0333] A "learning route" is a learning plan designed based on the analysis results, including the next content to be learned, recommended reference books, and a learning schedule.

[0334] "Learning progress data" refers to data that indicates a learner's learning progress, including test results, self-assessments, and learning achievement levels.

[0335] An "emotion engine" is a system or software for collecting and analyzing learners' facial expressions and voice data via a camera or microphone.

[0336] The "server" is a central processing unit that receives learning data and emotion data, analyzes them using an artificial intelligence model, and designs learning routes.

[0337] A "terminal" is a device operated by a learner to input learning data, display learning routes, and collect emotional data.

[0338] MODE FOR CARRYING OUT THE INVENTION

[0339] The present invention is a system for suggesting and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[0340] Data collection

[0341] First, the user logs in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they take a test on quadratic equations in mathematics, they enter their accuracy rate and self-evaluation. The emotion engine then uses the necessary camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotional data. The device then sends the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice) to the server in real time. The emotion engine uses the PC's built-in camera and headset microphone.

[0342] Data analysis

[0343] The server receives the training data and emotion data sent from the device and stores them in a database. It then inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current training data and emotion data while referencing the data and emotion data of many other past learners. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis.

[0344] Designing learning routes

[0345] The server designs the optimal learning route for the user based on the analysis results obtained from the AI ​​model. Based on the analysis results, it determines the next learning content (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a learning schedule (three times a week, one hour each). It also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[0346] Providing learning routes

[0347] The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends it to the terminal. The terminal notifies the user of the received learning route, recommended reference books, and learning schedule and displays them.

[0348] Running training and collecting progress

[0349] The user studies according to the study plan notified by the device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine continues to analyze the user's facial expressions and voice to collect emotion data in real time. The device then transmits the newly entered study progress data and emotion data to the server in real time.

[0350] Implementing the PDCA cycle

[0351] The server inputs the retransmitted learning progress data and emotional data into the AI ​​model and performs a reanalysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account. This makes it possible to maintain learners' motivation and provide effective, high-quality education.

[0352] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0353] Processing flow

[0354] Step 1: Data collection

[0355] User

[0356] Users log in to the learning platform and enter their learning content and test results into a learning content input form. For example, they take a math test on quadratic equations and record their accuracy rate and self-evaluation. Furthermore, the platform collects facial and voice data using a camera and microphone, which are necessary for the emotion engine.

[0357] input

[0358] Training data (test results, accuracy rate, self-evaluation) and emotional data (facial expressions, voice).

[0359] output

[0360] Training data and emotion data are generated.

[0361] Specific actions

[0362] Users log in using their smartphone or PC and enter information into a data entry form. Emotional data is collected using the PC's built-in camera and headset microphone.

[0363] Step 2: Send data

[0364] Terminal

[0365] The terminal transmits the learning data and emotion data input by the user to the server in real time.

[0366] input

[0367] Training data and emotion data.

[0368] output

[0369] The data is sent to the server.

[0370] Specific actions

[0371] The terminal packetizes the input data and sends it to the server using a communication protocol (e.g., HTTP / HTTPS).

[0372] Step 3: Receiving and storing data

[0373] server

[0374] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[0375] input

[0376] Training data and sentiment data.

[0377] output

[0378] Data stored in a database.

[0379] Specific actions

[0380] The server listens on the incoming port and stores the data in a database (e.g. MongoDB or MySQL) in the appropriate format.

[0381] Step 4: Data analysis

[0382] server

[0383] The server inputs the data stored in the database into an artificial intelligence model and begins analysis, using machine learning libraries such as TensorFlow and PyTorch.

[0384] input

[0385] Saved training and sentiment data.

[0386] output

[0387] Analysis results.

[0388] Specific actions

[0389] The server periodically inputs new data into the model using a scheduled task, performs the analysis, and saves the analysis results in the appropriate format.

[0390] Step 5: Design your learning route

[0391] server

[0392] The server designs the optimal learning route for the user based on the analysis results of the artificial intelligence model.

[0393] input

[0394] Analysis results.

[0395] output

[0396] Designing your study route (what to study next, recommended reference books, study schedule).

[0397] Specific actions

[0398] The server references pre-registered teaching material data and schedule data and generates a learning path that reflects the analysis results.

[0399] Step 6: Providing a learning route

[0400] server

[0401] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[0402] input

[0403] Designed study route, recommended reference books, and study schedule.

[0404] output

[0405] Data sent to the device.

[0406] Specific actions

[0407] The server sends data to the terminal using communication methods such as RESTful API.

[0408] Step 7: View your learned route and schedule

[0409] Terminal

[0410] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[0411] input

[0412] Data received from the server.

[0413] output

[0414] The study route, recommended study materials, and study schedule displayed to the user.

[0415] Specific actions

[0416] The device displays the received data on the application screen and notifies the user via push notification.

[0417] Step 8: Run training and collect progress

[0418] User

[0419] Users study according to the study plan provided by their device, and periodically enter their progress and new test results into the platform. The emotion engine continues to analyze their facial expressions and voices while they study.

[0420] input

[0421] Informed learning plan.

[0422] output

[0423] Learning progress data and updated emotion data.

[0424] Specific actions

[0425] Users study on their PCs or smartphones, inputting their progress into the platform as needed, and keeping their cameras and microphones on while studying.

[0426] Step 9: Data retransmission

[0427] Terminal

[0428] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0429] input

[0430] New learning progress and emotion data.

[0431] output

[0432] Data resubmitted to the server.

[0433] Specific actions

[0434] At the end of each session, the device packets the progress data and emotion data and sends them to the server.

[0435] Step 10: Implement the PDCA cycle

[0436] server

[0437] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model for analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content and improvements.

[0438] input

[0439] Resubmitted learning progress and emotion data.

[0440] output

[0441] Suggestions for improvements and new learning content.

[0442] Specific actions

[0443] The server schedules periodic re-analysis tasks to re-analyze progress and emotion data, logs the results, and generates new learning paths and improvements.

[0444] (Application example 2)

[0445] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0446] Conventional learning support systems propose optimal learning routes based solely on learning data without considering the learner's emotional state, resulting in problems such as a decline in learner motivation and reduced learning efficiency. Furthermore, it was difficult to provide learning support in physical stores, and the provision of appropriate learning materials and services was insufficient. This resulted in the quality of the learning environment in physical stores not improving, and learners were not able to learn effectively.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0448] In this invention, the server includes a means for collecting learning data and emotional data of the learner, a means for utilizing an AI model for analyzing the learning data and emotional data, and a means for designing an optimal learning route based on the analysis results, thereby making it possible to propose an optimal learning route that also takes into account the learner's psychological state.

[0449] Furthermore, by utilizing smart devices in physical stores and providing appropriate learning materials and services, learners can receive effective learning support even in physical stores, which will help maintain learners' motivation and improve their learning efficiency.

[0450] "Learning data" refers to numerical and textual information that indicates the progress of learning, such as test results and self-assessment data entered by the learner.

[0451] "Emotional data" is information that indicates the learner's psychological state, such as the analysis results of the learner's facial expressions and voice.

[0452] An "artificial intelligence model" is an algorithm or system that learns from past data and analyzes new data to find patterns.

[0453] A "learning route" is a series of learning content and methods recommended for learners to progress effectively through their studies.

[0454] "Devices" refer to information and communication devices such as smartphones and tablets used by learners.

[0455] A "physical store" is a physical store or commercial establishment that learners actually visit.

[0456] A "smart device" is a device that can connect to the Internet and run various applications.

[0457] "Analysis results" are analytical information output by the artificial intelligence model based on collected data.

[0458] "Services" is a general term for teaching materials, educational support, technical support, etc. provided to learners.

[0459] "Data collection means" refers to a method or device for collecting learning data, emotional data, etc.

[0460] To implement this invention, it is necessary to build a system in which the server, terminal, and user elements interact with each other. The system of the present invention provides effective learning support by collecting learning data and emotional data from learners and proposing optimal learning routes through analysis using an artificial intelligence model.

[0461] Data collection

[0462] User

[0463] Users log in to the system using devices such as smartphones or tablets, enter their learning content and test results, and provide facial and voice data using the device's camera and microphone so that emotional data can be collected.

[0464] Terminal

[0465] The device transmits the learning data entered by the user (e.g., test results, self-evaluation) and the emotional data collected by the emotion engine (EmotionEngine) to the server in real time.

[0466] Data analysis

[0467] server

[0468] The server receives the learning data and emotion data sent from the device and stores them in a database. It then analyzes this data based on an artificial intelligence model (LearningAIModel) and designs the optimal learning path. Specifically, it analyzes the current learning data and emotion data while comparing it with the data of many past learners.

[0469] Designing and delivering learning routes

[0470] server

[0471] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. It determines the next content to study, recommended reference books, and study schedule, and makes adjustments based on the learner's psychological state. This information is sent to the device.

[0472] Terminal

[0473] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[0474] Running training and collecting progress

[0475] User

[0476] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During the study, the emotion engine continuously collects the user's emotional data.

[0477] Terminal

[0478] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0479] Implementing the PDCA cycle

[0480] server

[0481] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis. Based on the new analysis results, it identifies areas for improvement and optimizes the learning content and methods.

[0482] A concrete example of this system is its use in a study space in a bookstore. Users use devices in the bookstore's study area and provide the system with learning data and emotional data. Based on this, the bookstore will provide the most appropriate books and materials. Examples of prompts for the generative AI model that assists this process include:

[0483] Example prompt sentence:

[0484] "If a student's test score is 85, their self-assessment is good, and their sentiment data indicates 'Satisfied,' suggest the next learning content or material they should study."

[0485] Such a system can maintain learners' motivation and provide a high-quality learning environment.

[0486] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0487] Step 1:

[0488] The user inputs training data and emotion data.

[0489] Users log in to the learning platform using a smartphone or tablet, input their learning content and test results, and provide facial and voice data using a camera and microphone. This generates learning data (e.g., test results, self-evaluation) and emotional data.

[0490] Input: Training data, emotion data

[0491] Output: A set of training data and emotion data

[0492] Step 2:

[0493] The device sends the data to the server

[0494] The device sends the learning data entered by the user and the emotion data collected by the emotion engine to the server, which stores the data in real time.

[0495] Input: A set of training data and emotion data

[0496] Output: Training data and emotion data recorded on the server

[0497] Step 3:

[0498] The server analyzes the data

[0499] The server receives the learning data and emotion data sent from the device and stores them in a database. This data is then input into an artificial intelligence model (LearningAIModel) for analysis. The AI ​​model analyzes the current learning data and emotion data while comparing it with the data of many other learners in the past.

[0500] Input: A set of training data and emotion data, past learner data

[0501] Output: Analysis results

[0502] Step 4:

[0503] The server designs the optimal learning route

[0504] The server designs the optimal learning route for each user based on the analysis results of the AI ​​model. This route includes the next content to study, recommended reference books, and a study schedule. It also adjusts the difficulty and pace of the learning content based on the learner's emotional data.

[0505] Input: Analysis results

[0506] Output: Optimized learning route

[0507] Step 5:

[0508] The server sends the learned route to the device.

[0509] The server transmits the designed learning route to the user's terminal.

[0510] Input: Optimized learning route

[0511] Output: Learned routes sent to the device

[0512] Step 6:

[0513] The device displays and notifies you of the learned route.

[0514] The terminal notifies and displays the received learned route to the user, who can then confirm it and proceed to the next learning step.

[0515] Input: Received learned routes

[0516] Output: The learning route displayed to the user

[0517] Step 7:

[0518] Users conduct learning and update data

[0519] The user follows the provided learning plan and inputs their learning progress data and new test results into the system. The emotion engine continues to collect data on the user's facial expressions and voice.

[0520] Input: New learning progress data, emotion data

[0521] Output: Updated training data and emotion data set

[0522] Step 8:

[0523] The device sends new data to the server

[0524] The terminal again transmits the learning progress data and emotion data newly input by the user to the server.

[0525] Input: Updated training data and emotion data set

[0526] Output: New training data and emotion data recorded on the server

[0527] Step 9:

[0528] The server executes the PDCA cycle

[0529] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, thereby identifying new areas for improvement and optimizing the user's learning content and methods.

[0530] Input: A new set of training data and emotion data

[0531] Output: Improvements, optimized next learning route

[0532] This process ensures that learners always follow the learning path that best suits them, providing an effective and motivating learning environment.

[0533] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0534] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0535] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0536] [Second embodiment]

[0537] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0538] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0539] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0540] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0541] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0542] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0543] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0544] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0545] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0546] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0547] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0548] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0549] The present invention is a system for collecting and analyzing a learner's learning data to propose an optimal learning route and provide continuous learning support. Specific embodiments for carrying out the present invention will be described below.

[0550] Data collection

[0551] User

[0552] First, users log in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they took a math test on quadratic equations, they enter their accuracy rate and self-evaluation. This generates learning data.

[0553] Terminal

[0554] The device transmits the learning data entered by the user to the server in real time, including the learning content, test results, correct answer rate, and self-evaluation.

[0555] Data analysis

[0556] server

[0557] The server receives the learning data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the data of many other learners in the past, and by comparing it with data with similar patterns, it proposes the optimal learning route.

[0558] Designing learning routes

[0559] server

[0560] The server then designs the optimal learning path for the user based on the analysis results obtained from the AI ​​model. During this process, it determines the next content to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each).

[0561] Providing learning routes

[0562] server

[0563] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[0564] Terminal

[0565] The terminal displays the received learning route, reference materials, and learning schedule to the user so that the learner can check them at any time.

[0566] Running training and collecting progress

[0567] User

[0568] The user begins studying according to the study plan provided by the server, and periodically enters their study progress and new test results into the platform.

[0569] Terminal

[0570] The terminal again transmits the new learning progress data input by the user to the server.

[0571] Implementing the PDCA cycle

[0572] server

[0573] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. It then sends the user an optimized learning route again, preparing for the next learning cycle.

[0574] In this way, continuous learning improvement will be achieved through data collection and analysis, and learning based on the proposed learning pathways, making it possible to provide an environment in which even low-income families can receive effective, high-quality education.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] User

[0578] The user logs in to the learning platform and enters the content of today's learning in the learning content input form, for example, the results of a math quadratic equation test (60% correct answer rate) or self-evaluation.

[0579] Step 2:

[0580] Terminal

[0581] The device transmits the learning data (test results, accuracy rate, self-evaluation) entered by the user to the server in real time.

[0582] Step 3:

[0583] server

[0584] The server receives the learning data sent from the terminal and stores it in a database.

[0585] Step 4:

[0586] server

[0587] The server inputs the saved learning data into an AI model, which analyzes the user's learning data while referencing the data of many other past learners.

[0588] Step 5:

[0589] server

[0590] The server then designs the optimal learning path for the user based on the analysis results of the AI ​​model. For example, it identifies the basics of factorization as the next thing to learn and identifies the necessary study materials and reference books.

[0591] Step 6:

[0592] server

[0593] The server adds the designed learning route, recommended reference books and learning schedule to the user profile and transmits it to the terminal.

[0594] Step 7:

[0595] Terminal

[0596] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[0597] Step 8:

[0598] User

[0599] Users study according to the study plan provided by their device and periodically enter their study progress into the platform.

[0600] Step 9:

[0601] Terminal

[0602] The terminal transmits the learning progress data newly input by the user to the server in real time.

[0603] Step 10:

[0604] server

[0605] The server inputs the retransmitted learning progress data into the AI ​​model for tracking and analysis. Based on the results of the reanalysis, it proposes new learning content and areas for improvement.

[0606] Step 11:

[0607] server

[0608] The server adds the improved study plan to the user profile and sends it to the terminal to prepare for the next study cycle.

[0609] Step 12:

[0610] User

[0611] The user starts a new learning cycle based on the updated learning plan and periodically enters their progress into the platform, and this process is repeated as a PDCA cycle.

[0612] Example 1

[0613] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0614] Conventional learning support systems have difficulty efficiently and effectively providing learners with appropriate learning routes based on their progress. Furthermore, learning data is often collected and analyzed manually, which can lead to delays in real-time feedback and improvement. This raises concerns about a decline in learner motivation and a decrease in learning effectiveness.

[0615] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0616] In this invention, the server includes means for collecting learner learning data, means for transmitting the learning data from the terminal to the server, means for storing the learning data in a database, means for utilizing a generative artificial intelligence model to analyze the learning data, means for designing an optimal learning route, recommended materials, and learning schedule based on the analysis results, means for transmitting the learning route, recommended materials, and learning schedule to the learner's terminal and displaying them, means for re-collecting the learner's learning progress data and analyzing areas for improvement, and means for providing an improved learning route based on the re-analysis results. This enables the collection, analysis, and feedback of learning data in real time, making it possible to continuously provide learners with an optimal learning route.

[0617] "Learning data" refers to data entered by a learner, including learning content, test results, and self-assessments.

[0618] A "terminal" is a digital device through which a user inputs learning data and receives instructions from a server.

[0619] "Server" means a central computer system for receiving and analyzing learning data, designing learning routes, and providing feedback to learners.

[0620] A "database" is a digital storage system in which the server stores learning data.

[0621] "Generative AI model" is a general term for machine learning algorithms and related technologies used to analyze training data and propose optimal learning routes.

[0622] A "learning route" is a guideline for the specific learning content and schedule that a learner should follow, proposed based on the analysis results.

[0623] "Recommended materials" are resources such as reference books and teaching materials that are provided to learners based on their learning route.

[0624] A "learning schedule" is a specific learning plan that a learner should follow based on their learning route.

[0625] "Study progress data" refers to data that indicates the progress of a learner's learning, which is generated in the process of the learner progressing with their studies according to the study plan.

[0626] "Reanalysis" refers to the process of re-analyzing data using learning progress data.

[0627] The present invention is a system that collects and analyzes a learner's learning data to propose an optimal learning route and provide continuous learning support.

[0628] This system begins when the user logs in to the learning platform. The user enters learning data, such as their learning content, test results, and self-evaluation, into a learning content input form. The device then transmits this learning data to the server in real time. This is achieved by using, for example, the HTTPS protocol to ensure secure communications.

[0629] The server stores the training data received from the device in a database. The server then uses a generative AI model to analyze the stored training data. This AI model is built using machine learning algorithms such as TensorFlow, and performs analysis by comparing it with past training data.

[0630] Based on the analyzed data, the server designs an optimal learning path for each learner, including what to study next (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a specific study schedule (three times a week, one hour each).

[0631] The designed learning route, recommended reference books, and learning schedule are sent from the server to the learner's device. The device displays this information to the user so that the learner can check it at any time. The user begins studying according to the learning plan provided by the server. Learning progress and new test results are periodically entered into the platform and sent back to the server via the device.

[0632] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. The user is then provided with an optimized learning route again. In this way, continuous learning improvement is achieved.

[0633] As a concrete example, consider the case where a user takes a math test on "quadratic equations" and enters the results into the platform. The user enters data such as "Quadratic equations test result: 70%. Self-assessment: Medium level of understanding." This data is then sent to the server via the device, where an artificial intelligence model analyzes it based on past data, and designs study content such as "The Basics of Factorization," a recommended textbook, "High School Mathematics I: Quadratic Equations," and a schedule of "one hour of study three times a week."

[0634] An example of a prompt to input to a generative AI model is as follows:

[0635] "Please suggest the best learning route based on the user's learning data. The data is as follows: Learning content - Quadratic equations, Test result - 70%, Self-assessment - Medium."

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] A user logs into the learning platform.

[0639] Input: Username, Password

[0640] Output: Authentication token

[0641] Specific operation: The user enters their username and password into the login form and presses the submit button. The server receives this and performs authentication. If authentication is successful, it generates an authentication token and returns it to the user.

[0642] Step 2:

[0643] The user inputs the training data.

[0644] Input: Study content, test results, self-assessment

[0645] Output: Training data (e.g., math test result on quadratic equations: 75%, self-assessment: moderate)

[0646] Specific operation: The user enters the day's learning content, test results, and self-evaluation into the learning platform's input form, and presses the submit button. This data is saved on the device.

[0647] Step 3:

[0648] The device sends the learning data to the server.

[0649] Input: Training data

[0650] Output: Training data sent to the server

[0651] Specific operation: The device transmits the saved learning data to the server in real time using the HTTPS protocol to ensure data security.

[0652] Step 4:

[0653] The server stores the learning data in a database.

[0654] Input: Training data

[0655] Output: Training data stored in a database

[0656] Specific operation: The server stores the received training data in a database. This storage process uses SQL queries.

[0657] Step 5:

[0658] The server inputs the learning data into the artificial intelligence model and begins analysis.

[0659] Input: Training data stored in a database

[0660] Output: Analysis results (e.g., what to study next, recommended reference books, study schedule, etc.)

[0661] How it works: The server retrieves training data from the database and inputs it into an AI model. The AI ​​model (for example, a model built with TensorFlow) analyzes the data and proposes the optimal training route.

[0662] Step 6:

[0663] The server designs the optimal learning route.

[0664] Input: Analysis results

[0665] Output: Optimal study route, recommended reference books, study schedule

[0666] Specific operation: Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route for the learner. For example, it sets "Fundamentals of Factorization" as a learning item, recommends "High School Mathematics I: Quadratic Equations" as a reference book, and determines a learning schedule of "One hour of study three times a week."

[0667] Step 7:

[0668] The server sends the learned route to the user's terminal.

[0669] Input: optimal study route, recommended reference books, study schedule

[0670] Output: Study route, recommended study books, and study schedule sent to the device

[0671] Specific operation: The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends them to the terminal.

[0672] Step 8:

[0673] The device displays the learned route.

[0674] Input: Study route, recommended reference books, study schedule sent from the server

[0675] Output: User-confirmable learning route, recommended reference books, and study schedule

[0676] Specific operation: The device displays the received study route, recommended reference books, and study schedule to the user, who then confirms this and begins studying.

[0677] Step 9:

[0678] The user enters their learning progress.

[0679] Input: New learning progress data (progress, new test results, etc.)

[0680] Output: Learning progress data saved on the device

[0681] What it does: Users enter their learning progress and new test results into the learning platform, and this data is stored on their device.

[0682] Step 10:

[0683] The terminal again transmits the learning progress data to the server.

[0684] Input: Learning progress data

[0685] Output: Learning progress data sent to the server

[0686] Specific operation: The device transmits the saved learning progress data to the server in real time.

[0687] Step 11:

[0688] The server re-analyzes the learning progress data.

[0689] Input: Learning progress data

[0690] Output: Reanalysis results (further improved learning route)

[0691] Specific operation: The server inputs the retransmitted learning progress data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content.

[0692] Step 12:

[0693] The server provides the improved learning route to the user.

[0694] Input: Reanalysis result

[0695] Output: Improved study route, recommended reference books, study schedule

[0696] Specific operation: Based on the reanalysis results, the server designs an improved learning route and provides it to the user, who then proceeds to the next learning cycle.

[0697] (Application example 1)

[0698] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0699] Conventional learning support systems and factory equipment management systems did not adequately collect and analyze data, resulting in insufficient proposals for optimal learning or operation routes. Furthermore, it was difficult for learners and factory operators to take appropriate action individually, making continuous performance improvement difficult.

[0700] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0701] In this invention, the server includes means for collecting learning data from learners, means for utilizing an AI model to analyze the learning data, means for designing an optimal learning route based on the analysis results, means for transmitting the learning route to the learner's terminal and displaying it, means for re-collecting the learner's learning progress data and analyzing areas for improvement, means for collecting operation data of factory equipment, means for utilizing an AI model to analyze the operation data, means for designing an optimal operation route or maintenance route based on the analysis results, and means for transmitting the operation route or maintenance route to an equipment operation terminal and displaying it. This enables continuous learning improvement for learners and efficient operation management of factory equipment.

[0702] A "learner" is a person receiving education or training.

[0703] "Learning data" refers to information entered by learners regarding test results, self-assessments, and learning content.

[0704] An "artificial intelligence model" is a computational system that uses machine learning and data analysis algorithms to analyze data and make predictions and classifications.

[0705] A "learning route" is a series of learning content and schedules suggested to help a learner optimally learn.

[0706] "Devices" are devices used by learners, such as computers, smartphones, and tablets.

[0707] "Study progress data" is information that indicates how far a learner has progressed in their studies.

[0708] "Factory equipment" refers to production facilities and machinery used in factories.

[0709] "Operational data" refers to information relating to the operating status and performance of factory equipment.

[0710] A "maintenance route" is a plan or procedure for the proper operation and maintenance of factory equipment.

[0711] An "equipment operation terminal" is a device for monitoring and operating factory equipment.

[0712] The present invention is a system for realizing efficient management of factory equipment and continuous learning support for learners. This system consists of three main elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[0713] Data collection

[0714] User

[0715] Users first log in to the system through a terminal, then enter their learning content and the operating status of factory equipment into a designated interface. For example, students enter math test results and self-evaluations, while factory equipment collects temperature and vibration data.

[0716] Terminal

[0717] The device sends the user-entered learning and operational data to the server in real time, including learning content, test results, accuracy rate, self-evaluation, and the operating status of factory equipment (e.g., temperature, vibration, and operating time).

[0718] Data analysis

[0719] server

[0720] The server receives the data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the operation data of many past learners and factory equipment, and by comparing it with data with similar patterns, it proposes optimal learning and operation routes.

[0721] Designing learning and maintenance routes

[0722] server

[0723] Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route and factory equipment maintenance route for the user. In this process, it determines the next learning content, reference books, learning schedule, factory equipment maintenance schedule, and necessary maintenance actions.

[0724] Providing learning and maintenance routes

[0725] server

[0726] The server adds the designed learning route and maintenance route to the user's profile and sends it to the terminal.

[0727] Terminal

[0728] The terminal displays the received learning route and maintenance route to the user, allowing the learner or factory operator to check them at any time.

[0729] Learning execution and operation management, and progress collection

[0730] User

[0731] The user begins to act according to the learning plan and maintenance route provided by the server, and periodically inputs learning progress, new test results, and the operating and maintenance status of factory equipment into the system via a terminal.

[0732] Terminal

[0733] The terminal again transmits the new learning progress data and operation data input by the user to the server.

[0734] Implementing the PDCA cycle

[0735] server

[0736] The server then inputs the retransmitted data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement in the learning and maintenance routes and makes new suggestions. It then sends the optimized route back to the user for the next cycle.

[0737] Hardware and software used

[0738] In this embodiment, the following hardware and software are used:

[0739] Hardware: Computers, smartphones, smart glasses, factory equipment.

[0740] Software: AI analysis libraries (TensorFlow and PyTorch), databases (MySQL and PostgreSQL), and cloud data processing services (AWS Lambda and Google Cloud Functions).

[0741] Specific examples

[0742] As a specific example, the temperature and vibration data of factory equipment can be analyzed, and if an abnormality is detected, a notification such as "The temperature of this robot is abnormal. Please perform maintenance immediately" can be displayed on the smart glasses.

[0743] Prompt sentence for generative AI model

[0744] Develop a model that detects anomalies and proposes optimal maintenance schedules based on operational data (temperature, vibration, timestamp) from factory equipment. See the following example:

[0745] Temperature: [65.0, 70.2, 75.1, 69.8, 72.3]

[0746] Vibration: [2.5, 2.7, 3.0, 2.8, 3.2]

[0747] Timestamps: ['2023-09-01T12:00:00', '2023-09-01T12:01:00', '2023-09-01T12:02:00', '2023-09-01T12:03:00', '2023-09-01T12:04:00']

[0748] This prompt enables the AI ​​model to analyze the operating data of factory equipment, detect abnormalities, and propose optimal maintenance schedules.

[0749] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0750] Step 1:

[0751] A user logs in to the system through a terminal. As input, the user ID and login information are entered and authentication is performed. As output, the user is able to access the system.

[0752] Step 2:

[0753] Users input learning data or factory equipment operation data. Specifically, learners input test results or self-assessments, and factory operators input equipment data such as temperature and vibration. The input data is collected and sent to the server.

[0754] Step 3:

[0755] The terminal sends the collected data to the server. As input, it receives learning data and operational data entered by the user, and as output, it transfers the data to the server.

[0756] Step 4:

[0757] The server stores the received data in a database. As input, it receives data sent from the terminal and stores it in a database. As output, data is accumulated in a database.

[0758] Step 5:

[0759] The server inputs the data collected from the database into an artificial intelligence model for analysis. It uses the learning data and operational data obtained from the database as input, and generates optimal learning and operational route proposals as output. Specifically, it performs analysis by comparing the data with past data to find patterns.

[0760] Step 6:

[0761] The server designs optimal learning and maintenance routes based on the analysis results of the AI ​​model. As input, it formulates the necessary content and schedules based on the analysis results, and as output, it generates detailed learning plans and maintenance plans.

[0762] Step 7:

[0763] The server sends the designed learning route and maintenance route to the terminal. It uses the designed plan as input and sends data to the user's terminal as output.

[0764] Step 8:

[0765] The terminal displays the received learning route and maintenance route to the user. It receives the plan sent from the server as input and displays it on the screen as output. Specifically, it displays the learning content and schedule to the learner and the maintenance actions to the factory operator.

[0766] Step 9:

[0767] The user begins to act according to the learning plan and maintenance route provided by the server. As input, the user refers to the plan displayed on the terminal and performs the actual learning and maintenance work. As output, new learning progress data and operation data are generated.

[0768] Step 10:

[0769] The terminal again transmits new learning progress data and operation data input by the user to the server. It receives new learning data and operation data as input and transfers the data to the server as output.

[0770] Step 11:

[0771] The server then feeds the resubmitted data into its artificial intelligence model for additional analysis, using the newly submitted data as input and generating new analysis results as output, thereby identifying areas for improvement and suggesting the next best route.

[0772] Step 12:

[0773] The server sends the new proposal to the user's terminal, using the new analysis results as input and sending the new learning and maintenance routes as output to the terminal, where the user confirms it and prepares for the next cycle.

[0774] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0775] The present invention is a system for proposing and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[0776] Data collection

[0777] User

[0778] First, a user logs in to the learning platform. Then, they enter their learning content and test results for the day into a learning content input form. For example, if they take a math test on quadratic equations, they enter their accuracy rate and self-evaluation. Furthermore, the emotion engine uses a camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotion data.

[0779] Terminal

[0780] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[0781] Data analysis

[0782] server

[0783] The server receives the learning data and emotion data sent from the device and stores them in a database. Next, it inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current learning data and emotion data while referring to the data and emotion data of many other past learners.

[0784] Designing learning routes

[0785] server

[0786] The server then uses the analysis results from the AI ​​model to design the optimal learning path for the user. In this process, it not only determines the next topic to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each), but also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[0787] Providing learning routes

[0788] server

[0789] The server adds the designed learning route, recommended reference books and learning schedule to the user's profile and transmits them to the terminal.

[0790] Terminal

[0791] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[0792] Running training and collecting progress

[0793] User

[0794] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During study, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[0795] Terminal

[0796] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0797] Implementing the PDCA cycle

[0798] server

[0799] The server inputs the retransmitted learning progress data and emotional data into an AI model for tracking and analysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account.

[0800] In this way, by collecting and analyzing data and utilizing emotional data, learning support is provided that takes into account the user's psychological state, which makes it possible to maintain learner motivation and provide effective, high-quality education.

[0801] The processing flow will be explained below.

[0802] Step 1:

[0803] User

[0804] Users log in to the learning platform and enter their learning content and test results for the day into the learning content input form. For example, they enter their math test results for quadratic equations (60% correct answer rate) and their self-evaluation. At the same time, the emotion engine collects emotional data during the learning process.

[0805] Step 2:

[0806] Terminal

[0807] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[0808] Step 3:

[0809] server

[0810] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[0811] Step 4:

[0812] server

[0813] The server inputs the stored learning data and emotion data into the AI ​​model, which has been trained using a large amount of data, including data from other past learners and emotion data.

[0814] Step 5:

[0815] server

[0816] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. Specifically, it determines the next topic to learn (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (one hour of study three times a week). It also adjusts the difficulty and pace of the study based on the user's emotional data.

[0817] Step 6:

[0818] server

[0819] The server adds the designed learning route, recommended reference books, and learning schedule to the user profile and transmits it to the terminal.

[0820] Step 7:

[0821] Terminal

[0822] The device will then notify the user of the received learning route, recommended textbooks, and study schedule, and display it to them. For example, a notification might appear saying, "Next, learn the basics of factorization, use the recommended textbooks, and study for one hour three times a week."

[0823] Step 8:

[0824] User

[0825] Users study according to the study plan provided by their device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine analyzes the user's facial expressions and voice to collect emotional data in real time.

[0826] Step 9:

[0827] Terminal

[0828] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0829] Step 10:

[0830] server

[0831] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, for example, to evaluate whether the user's level of understanding has improved and whether the learning content was appropriate.

[0832] Step 11:

[0833] server

[0834] Based on the results of the reanalysis, the server determines what to learn next and what areas to improve, and also takes into account the user's emotional data to further optimize the pace and difficulty of the learning process.

[0835] Step 12:

[0836] server

[0837] The server adds the improved study plan to the user profile and transmits it again to the terminal.

[0838] Step 13:

[0839] User

[0840] Users start a new learning cycle based on their updated learning plan and periodically enter their progress into the platform. This provides continuous learning support as a PDCA cycle, thereby narrowing the educational gap among learners and enabling the provision of high-quality education.

[0841] Example 2

[0842] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0843] Conventional learning support systems only analyze learners' learning data and suggest optimal learning routes. However, because learners' emotions and psychological state also have a significant impact on learning effectiveness, appropriate support that takes this information into consideration is needed. In addition, there was a need for a system that could maintain learning motivation and design effective learning plans by linking the recollection of learning progress data with the analysis of emotional data.

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

[0845] In this invention, the server includes a means for collecting learning data from learners, a means for utilizing an AI model that analyzes the learning data and emotional data of learners, and a means for designing an optimal learning route based on the analysis results, thereby enabling effective learning support that takes into account the learner's psychological state.

[0846] Key Word Definitions

[0847] "Learning data" refers to data including test results and self-assessment data entered by learners.

[0848] "Emotion data" is data that includes facial expressions and voice data generated by the emotion engine.

[0849] An "artificial intelligence model" is an algorithm or system that analyzes and determines the optimal learning route by studying the learning data and emotional data of many other past learners.

[0850] A "learning route" is a learning plan designed based on the analysis results, including the next content to be learned, recommended reference books, and a learning schedule.

[0851] "Learning progress data" refers to data that indicates a learner's learning progress, including test results, self-assessments, and learning achievement levels.

[0852] An "emotion engine" is a system or software for collecting and analyzing learners' facial expressions and voice data via a camera or microphone.

[0853] The "server" is a central processing unit that receives learning data and emotion data, analyzes them using an artificial intelligence model, and designs learning routes.

[0854] A "terminal" is a device operated by a learner to input learning data, display learning routes, and collect emotional data.

[0855] MODE FOR CARRYING OUT THE INVENTION

[0856] The present invention is a system for suggesting and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[0857] Data collection

[0858] First, the user logs in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they take a test on quadratic equations in mathematics, they enter their accuracy rate and self-evaluation. The emotion engine then uses the necessary camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotional data. The device then sends the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice) to the server in real time. The emotion engine uses the PC's built-in camera and headset microphone.

[0859] Data analysis

[0860] The server receives the training data and emotion data sent from the device and stores them in a database. It then inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current training data and emotion data while referencing the data and emotion data of many other past learners. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis.

[0861] Designing learning routes

[0862] The server designs the optimal learning route for the user based on the analysis results obtained from the AI ​​model. Based on the analysis results, it determines the next learning content (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a learning schedule (three times a week, one hour each). It also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[0863] Providing learning routes

[0864] The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends it to the terminal. The terminal notifies the user of the received learning route, recommended reference books, and learning schedule and displays them.

[0865] Running training and collecting progress

[0866] The user studies according to the study plan notified by the device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine continues to analyze the user's facial expressions and voice to collect emotion data in real time. The device then transmits the newly entered study progress data and emotion data to the server in real time.

[0867] Implementing the PDCA cycle

[0868] The server inputs the retransmitted learning progress data and emotional data into the AI ​​model and performs a reanalysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account. This makes it possible to maintain learners' motivation and provide effective, high-quality education.

[0869] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0870] Processing flow

[0871] Step 1: Data collection

[0872] User

[0873] Users log in to the learning platform and enter their learning content and test results into a learning content input form. For example, they take a math test on quadratic equations and record their accuracy rate and self-evaluation. Furthermore, the platform collects facial and voice data using a camera and microphone, which are necessary for the emotion engine.

[0874] input

[0875] Training data (test results, accuracy rate, self-evaluation) and emotional data (facial expressions, voice).

[0876] output

[0877] Training data and emotion data are generated.

[0878] Specific actions

[0879] Users log in using their smartphone or PC and enter information into a data entry form. Emotional data is collected using the PC's built-in camera and headset microphone.

[0880] Step 2: Send data

[0881] Terminal

[0882] The terminal transmits the learning data and emotion data input by the user to the server in real time.

[0883] input

[0884] Training data and emotion data.

[0885] output

[0886] The data is sent to the server.

[0887] Specific actions

[0888] The terminal packetizes the input data and sends it to the server using a communication protocol (e.g., HTTP / HTTPS).

[0889] Step 3: Receiving and storing data

[0890] server

[0891] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[0892] input

[0893] Training data and sentiment data.

[0894] output

[0895] Data stored in a database.

[0896] Specific actions

[0897] The server listens on the incoming port and stores the data in a database (e.g. MongoDB or MySQL) in the appropriate format.

[0898] Step 4: Data analysis

[0899] server

[0900] The server inputs the data stored in the database into an artificial intelligence model and begins analysis, using machine learning libraries such as TensorFlow and PyTorch.

[0901] input

[0902] Saved training and sentiment data.

[0903] output

[0904] Analysis results.

[0905] Specific actions

[0906] The server periodically inputs new data into the model using a scheduled task, performs the analysis, and saves the analysis results in the appropriate format.

[0907] Step 5: Design your learning route

[0908] server

[0909] The server designs the optimal learning route for the user based on the analysis results of the artificial intelligence model.

[0910] input

[0911] Analysis results.

[0912] output

[0913] Designing your study route (what to study next, recommended reference books, study schedule).

[0914] Specific actions

[0915] The server references pre-registered teaching material data and schedule data and generates a learning path that reflects the analysis results.

[0916] Step 6: Providing a learning route

[0917] server

[0918] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[0919] input

[0920] Designed study route, recommended reference books, and study schedule.

[0921] output

[0922] Data sent to the device.

[0923] Specific actions

[0924] The server sends data to the terminal using communication methods such as RESTful API.

[0925] Step 7: View your learned route and schedule

[0926] Terminal

[0927] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[0928] input

[0929] Data received from the server.

[0930] output

[0931] The study route, recommended study materials, and study schedule displayed to the user.

[0932] Specific actions

[0933] The device displays the received data on the application screen and notifies the user via push notification.

[0934] Step 8: Run training and collect progress

[0935] User

[0936] Users study according to the study plan provided by their device, and periodically enter their progress and new test results into the platform. The emotion engine continues to analyze their facial expressions and voices while they study.

[0937] input

[0938] Informed learning plan.

[0939] output

[0940] Learning progress data and updated emotion data.

[0941] Specific actions

[0942] Users study on their PCs or smartphones, inputting their progress into the platform as needed, and keeping their cameras and microphones on while studying.

[0943] Step 9: Data retransmission

[0944] Terminal

[0945] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0946] input

[0947] New learning progress and emotion data.

[0948] output

[0949] Data resubmitted to the server.

[0950] Specific actions

[0951] At the end of each session, the device packets the progress data and emotion data and sends them to the server.

[0952] Step 10: Implement the PDCA cycle

[0953] server

[0954] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model for analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content and improvements.

[0955] input

[0956] Resubmitted learning progress and emotion data.

[0957] output

[0958] Suggestions for improvements and new learning content.

[0959] Specific actions

[0960] The server schedules periodic re-analysis tasks to re-analyze progress and emotion data, logs the results, and generates new learning paths and improvements.

[0961] (Application example 2)

[0962] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0963] Conventional learning support systems propose optimal learning routes based solely on learning data without considering the learner's emotional state, resulting in problems such as a decline in learner motivation and reduced learning efficiency. Furthermore, it was difficult to provide learning support in physical stores, and the provision of appropriate learning materials and services was insufficient. This resulted in the quality of the learning environment in physical stores not improving, and learners were not able to learn effectively.

[0964] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0965] In this invention, the server includes a means for collecting learning data and emotional data of the learner, a means for utilizing an AI model for analyzing the learning data and emotional data, and a means for designing an optimal learning route based on the analysis results, thereby making it possible to propose an optimal learning route that also takes into account the learner's psychological state.

[0966] Furthermore, by utilizing smart devices in physical stores and providing appropriate learning materials and services, learners can receive effective learning support even in physical stores, which will help maintain learners' motivation and improve their learning efficiency.

[0967] "Learning data" refers to numerical and textual information that indicates the progress of learning, such as test results and self-assessment data entered by the learner.

[0968] "Emotional data" is information that indicates the learner's psychological state, such as the analysis results of the learner's facial expressions and voice.

[0969] An "artificial intelligence model" is an algorithm or system that learns from past data and analyzes new data to find patterns.

[0970] A "learning route" is a series of learning content and methods recommended for learners to progress effectively through their studies.

[0971] "Devices" refer to information and communication devices such as smartphones and tablets used by learners.

[0972] A "physical store" is a physical store or commercial establishment that learners actually visit.

[0973] A "smart device" is a device that can connect to the Internet and run various applications.

[0974] "Analysis results" are analytical information output by the artificial intelligence model based on collected data.

[0975] "Services" is a general term for teaching materials, educational support, technical support, etc. provided to learners.

[0976] "Data collection means" refers to a method or device for collecting learning data, emotional data, etc.

[0977] To implement this invention, it is necessary to build a system in which the server, terminal, and user elements interact with each other. The system of the present invention provides effective learning support by collecting learning data and emotional data from learners and proposing optimal learning routes through analysis using an artificial intelligence model.

[0978] Data collection

[0979] User

[0980] Users log in to the system using devices such as smartphones or tablets, enter their learning content and test results, and provide facial and voice data using the device's camera and microphone so that emotional data can be collected.

[0981] Terminal

[0982] The device transmits the learning data entered by the user (e.g., test results, self-evaluation) and the emotional data collected by the emotion engine (EmotionEngine) to the server in real time.

[0983] Data analysis

[0984] server

[0985] The server receives the learning data and emotion data sent from the device and stores them in a database. It then analyzes this data based on an artificial intelligence model (LearningAIModel) and designs the optimal learning path. Specifically, it analyzes the current learning data and emotion data while comparing it with the data of many past learners.

[0986] Designing and delivering learning routes

[0987] server

[0988] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. It determines the next content to study, recommended reference books, and study schedule, and makes adjustments based on the learner's psychological state. This information is sent to the device.

[0989] Terminal

[0990] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[0991] Running training and collecting progress

[0992] User

[0993] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During the study, the emotion engine continuously collects the user's emotional data.

[0994] Terminal

[0995] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[0996] Implementing the PDCA cycle

[0997] server

[0998] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis. Based on the new analysis results, it identifies areas for improvement and optimizes the learning content and methods.

[0999] A concrete example of this system is its use in a study space in a bookstore. Users use devices in the bookstore's study area and provide the system with learning data and emotional data. Based on this, the bookstore will provide the most appropriate books and materials. Examples of prompts for the generative AI model that assists this process include:

[1000] Example prompt sentence:

[1001] "If a student's test score is 85, their self-assessment is good, and their sentiment data indicates 'Satisfied,' suggest the next learning content or material they should study."

[1002] Such a system can maintain learners' motivation and provide a high-quality learning environment.

[1003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1004] Step 1:

[1005] The user inputs training data and emotion data.

[1006] Users log in to the learning platform using a smartphone or tablet, input their learning content and test results, and provide facial and voice data using a camera and microphone. This generates learning data (e.g., test results, self-evaluation) and emotional data.

[1007] Input: Training data, emotion data

[1008] Output: A set of training data and emotion data

[1009] Step 2:

[1010] The device sends the data to the server

[1011] The device sends the learning data entered by the user and the emotion data collected by the emotion engine to the server, which stores the data in real time.

[1012] Input: A set of training data and emotion data

[1013] Output: Training data and emotion data recorded on the server

[1014] Step 3:

[1015] The server analyzes the data

[1016] The server receives the learning data and emotion data sent from the device and stores them in a database. This data is then input into an artificial intelligence model (LearningAIModel) for analysis. The AI ​​model analyzes the current learning data and emotion data while comparing it with the data of many other learners in the past.

[1017] Input: A set of training data and emotion data, past learner data

[1018] Output: Analysis results

[1019] Step 4:

[1020] The server designs the optimal learning route

[1021] The server designs the optimal learning route for each user based on the analysis results of the AI ​​model. This route includes the next content to study, recommended reference books, and a study schedule. It also adjusts the difficulty and pace of the learning content based on the learner's emotional data.

[1022] Input: Analysis results

[1023] Output: Optimized learning route

[1024] Step 5:

[1025] The server sends the learned route to the device.

[1026] The server transmits the designed learning route to the user's terminal.

[1027] Input: Optimized learning route

[1028] Output: Learned routes sent to the device

[1029] Step 6:

[1030] The device displays and notifies you of the learned route.

[1031] The terminal notifies and displays the received learned route to the user, who can then confirm it and proceed to the next learning step.

[1032] Input: Received learned routes

[1033] Output: The learning route displayed to the user

[1034] Step 7:

[1035] Users conduct learning and update data

[1036] The user follows the provided learning plan and inputs their learning progress data and new test results into the system. The emotion engine continues to collect data on the user's facial expressions and voice.

[1037] Input: New learning progress data, emotion data

[1038] Output: Updated training data and emotion data set

[1039] Step 8:

[1040] The device sends new data to the server

[1041] The terminal again transmits the learning progress data and emotion data newly input by the user to the server.

[1042] Input: Updated training data and emotion data set

[1043] Output: New training data and emotion data recorded on the server

[1044] Step 9:

[1045] The server executes the PDCA cycle

[1046] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, thereby identifying new areas for improvement and optimizing the user's learning content and methods.

[1047] Input: A new set of training data and emotion data

[1048] Output: Improvements, optimized next learning route

[1049] This process ensures that learners always follow the learning path that best suits them, providing an effective and motivating learning environment.

[1050] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1051] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1052] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1053] [Third embodiment]

[1054] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1055] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1056] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1057] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1058] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1059] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1060] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1061] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1062] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1063] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1064] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1065] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1066] The present invention is a system for collecting and analyzing a learner's learning data to propose an optimal learning route and provide continuous learning support. Specific embodiments for carrying out the present invention will be described below.

[1067] Data collection

[1068] User

[1069] First, users log in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they took a math test on quadratic equations, they enter their accuracy rate and self-evaluation. This generates learning data.

[1070] Terminal

[1071] The device transmits the learning data entered by the user to the server in real time, including the learning content, test results, correct answer rate, and self-evaluation.

[1072] Data analysis

[1073] server

[1074] The server receives the learning data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the data of many other learners in the past, and by comparing it with data with similar patterns, it proposes the optimal learning route.

[1075] Designing learning routes

[1076] server

[1077] The server then designs the optimal learning path for the user based on the analysis results obtained from the AI ​​model. During this process, it determines the next content to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each).

[1078] Providing learning routes

[1079] server

[1080] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[1081] Terminal

[1082] The terminal displays the received learning route, reference materials, and learning schedule to the user so that the learner can check them at any time.

[1083] Running training and collecting progress

[1084] User

[1085] The user begins studying according to the study plan provided by the server, and periodically enters their study progress and new test results into the platform.

[1086] Terminal

[1087] The terminal again transmits the new learning progress data input by the user to the server.

[1088] Implementing the PDCA cycle

[1089] server

[1090] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. It then sends the user an optimized learning route again, preparing for the next learning cycle.

[1091] In this way, continuous learning improvement will be achieved through data collection and analysis, and learning based on the proposed learning pathways, making it possible to provide an environment in which even low-income families can receive effective, high-quality education.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] User

[1095] The user logs in to the learning platform and enters the content of today's learning in the learning content input form, for example, the results of a math quadratic equation test (60% correct answer rate) or self-evaluation.

[1096] Step 2:

[1097] Terminal

[1098] The device transmits the learning data (test results, accuracy rate, self-evaluation) entered by the user to the server in real time.

[1099] Step 3:

[1100] server

[1101] The server receives the learning data sent from the terminal and stores it in a database.

[1102] Step 4:

[1103] server

[1104] The server inputs the saved learning data into an AI model, which analyzes the user's learning data while referencing the data of many other past learners.

[1105] Step 5:

[1106] server

[1107] The server then designs the optimal learning path for the user based on the analysis results of the AI ​​model. For example, it identifies the basics of factorization as the next thing to learn and identifies the necessary study materials and reference books.

[1108] Step 6:

[1109] server

[1110] The server adds the designed learning route, recommended reference books and learning schedule to the user profile and transmits it to the terminal.

[1111] Step 7:

[1112] Terminal

[1113] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[1114] Step 8:

[1115] User

[1116] Users study according to the study plan provided by their device and periodically enter their study progress into the platform.

[1117] Step 9:

[1118] Terminal

[1119] The terminal transmits the learning progress data newly input by the user to the server in real time.

[1120] Step 10:

[1121] server

[1122] The server inputs the retransmitted learning progress data into the AI ​​model for tracking and analysis. Based on the results of the reanalysis, it proposes new learning content and areas for improvement.

[1123] Step 11:

[1124] server

[1125] The server adds the improved study plan to the user profile and sends it to the terminal to prepare for the next study cycle.

[1126] Step 12:

[1127] User

[1128] The user starts a new learning cycle based on the updated learning plan and periodically enters their progress into the platform, and this process is repeated as a PDCA cycle.

[1129] Example 1

[1130] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1131] Conventional learning support systems have difficulty efficiently and effectively providing learners with appropriate learning routes based on their progress. Furthermore, learning data is often collected and analyzed manually, which can lead to delays in real-time feedback and improvement. This raises concerns about a decline in learner motivation and a decrease in learning effectiveness.

[1132] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1133] In this invention, the server includes means for collecting learner learning data, means for transmitting the learning data from the terminal to the server, means for storing the learning data in a database, means for utilizing a generative artificial intelligence model to analyze the learning data, means for designing an optimal learning route, recommended materials, and learning schedule based on the analysis results, means for transmitting the learning route, recommended materials, and learning schedule to the learner's terminal and displaying them, means for re-collecting the learner's learning progress data and analyzing areas for improvement, and means for providing an improved learning route based on the re-analysis results. This enables the collection, analysis, and feedback of learning data in real time, making it possible to continuously provide learners with an optimal learning route.

[1134] "Learning data" refers to data entered by a learner, including learning content, test results, and self-assessments.

[1135] A "terminal" is a digital device through which a user inputs learning data and receives instructions from a server.

[1136] "Server" means a central computer system for receiving and analyzing learning data, designing learning routes, and providing feedback to learners.

[1137] A "database" is a digital storage system in which the server stores learning data.

[1138] "Generative AI model" is a general term for machine learning algorithms and related technologies used to analyze training data and propose optimal learning routes.

[1139] A "learning route" is a guideline for the specific learning content and schedule that a learner should follow, proposed based on the analysis results.

[1140] "Recommended materials" are resources such as reference books and teaching materials that are provided to learners based on their learning route.

[1141] A "learning schedule" is a specific learning plan that a learner should follow based on their learning route.

[1142] "Study progress data" refers to data that indicates the progress of a learner's learning, which is generated in the process of the learner progressing with their studies according to the study plan.

[1143] "Reanalysis" refers to the process of re-analyzing data using learning progress data.

[1144] The present invention is a system that collects and analyzes a learner's learning data to propose an optimal learning route and provide continuous learning support.

[1145] This system begins when the user logs in to the learning platform. The user enters learning data, such as their learning content, test results, and self-evaluation, into a learning content input form. The device then transmits this learning data to the server in real time. This is achieved by using, for example, the HTTPS protocol to ensure secure communications.

[1146] The server stores the training data received from the device in a database. The server then uses a generative AI model to analyze the stored training data. This AI model is built using machine learning algorithms such as TensorFlow, and performs analysis by comparing it with past training data.

[1147] Based on the analyzed data, the server designs an optimal learning path for each learner, including what to study next (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a specific study schedule (three times a week, one hour each).

[1148] The designed learning route, recommended reference books, and learning schedule are sent from the server to the learner's device. The device displays this information to the user so that the learner can check it at any time. The user begins studying according to the learning plan provided by the server. Learning progress and new test results are periodically entered into the platform and sent back to the server via the device.

[1149] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. The user is then provided with an optimized learning route again. In this way, continuous learning improvement is achieved.

[1150] As a concrete example, consider the case where a user takes a math test on "quadratic equations" and enters the results into the platform. The user enters data such as "Quadratic equations test result: 70%. Self-assessment: Medium level of understanding." This data is then sent to the server via the device, where an artificial intelligence model analyzes it based on past data, and designs study content such as "The Basics of Factorization," a recommended textbook, "High School Mathematics I: Quadratic Equations," and a schedule of "one hour of study three times a week."

[1151] An example of a prompt to input to a generative AI model is as follows:

[1152] "Please suggest the best learning route based on the user's learning data. The data is as follows: Learning content - Quadratic equations, Test result - 70%, Self-assessment - Medium."

[1153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1154] Step 1:

[1155] A user logs into the learning platform.

[1156] Input: Username, Password

[1157] Output: Authentication token

[1158] Specific operation: The user enters their username and password into the login form and presses the submit button. The server receives this and performs authentication. If authentication is successful, it generates an authentication token and returns it to the user.

[1159] Step 2:

[1160] The user inputs the training data.

[1161] Input: Study content, test results, self-assessment

[1162] Output: Training data (e.g., math test result on quadratic equations: 75%, self-assessment: moderate)

[1163] Specific operation: The user enters the day's learning content, test results, and self-evaluation into the learning platform's input form, and presses the submit button. This data is saved on the device.

[1164] Step 3:

[1165] The device sends the learning data to the server.

[1166] Input: Training data

[1167] Output: Training data sent to the server

[1168] Specific operation: The device transmits the saved learning data to the server in real time using the HTTPS protocol to ensure data security.

[1169] Step 4:

[1170] The server stores the learning data in a database.

[1171] Input: Training data

[1172] Output: Training data stored in a database

[1173] Specific operation: The server stores the received training data in a database. This storage process uses SQL queries.

[1174] Step 5:

[1175] The server inputs the learning data into the artificial intelligence model and begins analysis.

[1176] Input: Training data stored in a database

[1177] Output: Analysis results (e.g., what to study next, recommended reference books, study schedule, etc.)

[1178] How it works: The server retrieves training data from the database and inputs it into an AI model. The AI ​​model (for example, a model built with TensorFlow) analyzes the data and proposes the optimal training route.

[1179] Step 6:

[1180] The server designs the optimal learning route.

[1181] Input: Analysis results

[1182] Output: Optimal study route, recommended reference books, study schedule

[1183] Specific operation: Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route for the learner. For example, it sets "Fundamentals of Factorization" as a learning item, recommends "High School Mathematics I: Quadratic Equations" as a reference book, and determines a learning schedule of "One hour of study three times a week."

[1184] Step 7:

[1185] The server sends the learned route to the user's terminal.

[1186] Input: optimal study route, recommended reference books, study schedule

[1187] Output: Study route, recommended study books, and study schedule sent to the device

[1188] Specific operation: The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends them to the terminal.

[1189] Step 8:

[1190] The device displays the learned route.

[1191] Input: Study route, recommended reference books, study schedule sent from the server

[1192] Output: User-confirmable learning route, recommended reference books, and study schedule

[1193] Specific operation: The device displays the received study route, recommended reference books, and study schedule to the user, who then confirms this and begins studying.

[1194] Step 9:

[1195] The user enters their learning progress.

[1196] Input: New learning progress data (progress, new test results, etc.)

[1197] Output: Learning progress data saved on the device

[1198] What it does: Users enter their learning progress and new test results into the learning platform, and this data is stored on their device.

[1199] Step 10:

[1200] The terminal again transmits the learning progress data to the server.

[1201] Input: Learning progress data

[1202] Output: Learning progress data sent to the server

[1203] Specific operation: The device transmits the saved learning progress data to the server in real time.

[1204] Step 11:

[1205] The server re-analyzes the learning progress data.

[1206] Input: Learning progress data

[1207] Output: Reanalysis results (further improved learning route)

[1208] Specific operation: The server inputs the retransmitted learning progress data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content.

[1209] Step 12:

[1210] The server provides the improved learning route to the user.

[1211] Input: Reanalysis result

[1212] Output: Improved study route, recommended reference books, study schedule

[1213] Specific operation: Based on the reanalysis results, the server designs an improved learning route and provides it to the user, who then proceeds to the next learning cycle.

[1214] (Application example 1)

[1215] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1216] Conventional learning support systems and factory equipment management systems did not adequately collect and analyze data, resulting in insufficient proposals for optimal learning or operation routes. Furthermore, it was difficult for learners and factory operators to take appropriate action individually, making continuous performance improvement difficult.

[1217] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1218] In this invention, the server includes means for collecting learning data from learners, means for utilizing an AI model to analyze the learning data, means for designing an optimal learning route based on the analysis results, means for transmitting the learning route to the learner's terminal and displaying it, means for re-collecting the learner's learning progress data and analyzing areas for improvement, means for collecting operation data of factory equipment, means for utilizing an AI model to analyze the operation data, means for designing an optimal operation route or maintenance route based on the analysis results, and means for transmitting the operation route or maintenance route to an equipment operation terminal and displaying it. This enables continuous learning improvement for learners and efficient operation management of factory equipment.

[1219] A "learner" is a person receiving education or training.

[1220] "Learning data" refers to information entered by learners regarding test results, self-assessments, and learning content.

[1221] An "artificial intelligence model" is a computational system that uses machine learning and data analysis algorithms to analyze data and make predictions and classifications.

[1222] A "learning route" is a series of learning content and schedules suggested to help a learner optimally learn.

[1223] "Devices" are devices used by learners, such as computers, smartphones, and tablets.

[1224] "Study progress data" is information that indicates how far a learner has progressed in their studies.

[1225] "Factory equipment" refers to production facilities and machinery used in factories.

[1226] "Operational data" refers to information relating to the operating status and performance of factory equipment.

[1227] A "maintenance route" is a plan or procedure for the proper operation and maintenance of factory equipment.

[1228] An "equipment operation terminal" is a device for monitoring and operating factory equipment.

[1229] The present invention is a system for realizing efficient management of factory equipment and continuous learning support for learners. This system consists of three main elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[1230] Data collection

[1231] User

[1232] Users first log in to the system through a terminal, then enter their learning content and the operating status of factory equipment into a designated interface. For example, students enter math test results and self-evaluations, while factory equipment collects temperature and vibration data.

[1233] Terminal

[1234] The device sends the user-entered learning and operational data to the server in real time, including learning content, test results, accuracy rate, self-evaluation, and the operating status of factory equipment (e.g., temperature, vibration, and operating time).

[1235] Data analysis

[1236] server

[1237] The server receives the data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the operation data of many past learners and factory equipment, and by comparing it with data with similar patterns, it proposes optimal learning and operation routes.

[1238] Designing learning and maintenance routes

[1239] server

[1240] Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route and factory equipment maintenance route for the user. In this process, it determines the next learning content, reference books, learning schedule, factory equipment maintenance schedule, and necessary maintenance actions.

[1241] Providing learning and maintenance routes

[1242] server

[1243] The server adds the designed learning route and maintenance route to the user's profile and sends it to the terminal.

[1244] Terminal

[1245] The terminal displays the received learning route and maintenance route to the user, allowing the learner or factory operator to check them at any time.

[1246] Learning execution and operation management, and progress collection

[1247] User

[1248] The user begins to act according to the learning plan and maintenance route provided by the server, and periodically inputs learning progress, new test results, and the operating and maintenance status of factory equipment into the system via a terminal.

[1249] Terminal

[1250] The terminal again transmits the new learning progress data and operation data input by the user to the server.

[1251] Implementing the PDCA cycle

[1252] server

[1253] The server then inputs the retransmitted data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement in the learning and maintenance routes and makes new suggestions. It then sends the optimized route back to the user for the next cycle.

[1254] Hardware and software used

[1255] In this embodiment, the following hardware and software are used:

[1256] Hardware: Computers, smartphones, smart glasses, factory equipment.

[1257] Software: AI analysis libraries (TensorFlow and PyTorch), databases (MySQL and PostgreSQL), and cloud data processing services (AWS Lambda and Google Cloud Functions).

[1258] Specific examples

[1259] As a specific example, the temperature and vibration data of factory equipment can be analyzed, and if an abnormality is detected, a notification such as "The temperature of this robot is abnormal. Please perform maintenance immediately" can be displayed on the smart glasses.

[1260] Prompt sentence for generative AI model

[1261] Develop a model that detects anomalies and proposes optimal maintenance schedules based on operational data (temperature, vibration, timestamp) from factory equipment. See the following example:

[1262] Temperature: [65.0, 70.2, 75.1, 69.8, 72.3]

[1263] Vibration: [2.5, 2.7, 3.0, 2.8, 3.2]

[1264] Timestamps: ['2023-09-01T12:00:00', '2023-09-01T12:01:00', '2023-09-01T12:02:00', '2023-09-01T12:03:00', '2023-09-01T12:04:00']

[1265] This prompt enables the AI ​​model to analyze the operating data of factory equipment, detect abnormalities, and propose optimal maintenance schedules.

[1266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1267] Step 1:

[1268] A user logs in to the system through a terminal. As input, the user ID and login information are entered and authentication is performed. As output, the user is able to access the system.

[1269] Step 2:

[1270] Users input learning data or factory equipment operation data. Specifically, learners input test results or self-assessments, and factory operators input equipment data such as temperature and vibration. The input data is collected and sent to the server.

[1271] Step 3:

[1272] The terminal sends the collected data to the server. As input, it receives learning data and operational data entered by the user, and as output, it transfers the data to the server.

[1273] Step 4:

[1274] The server stores the received data in a database. As input, it receives data sent from the terminal and stores it in a database. As output, data is accumulated in a database.

[1275] Step 5:

[1276] The server inputs the data collected from the database into an artificial intelligence model for analysis. It uses the learning data and operational data obtained from the database as input, and generates optimal learning and operational route proposals as output. Specifically, it performs analysis by comparing the data with past data to find patterns.

[1277] Step 6:

[1278] The server designs optimal learning and maintenance routes based on the analysis results of the AI ​​model. As input, it formulates the necessary content and schedules based on the analysis results, and as output, it generates detailed learning plans and maintenance plans.

[1279] Step 7:

[1280] The server sends the designed learning route and maintenance route to the terminal. It uses the designed plan as input and sends data to the user's terminal as output.

[1281] Step 8:

[1282] The terminal displays the received learning route and maintenance route to the user. It receives the plan sent from the server as input and displays it on the screen as output. Specifically, it displays the learning content and schedule to the learner and the maintenance actions to the factory operator.

[1283] Step 9:

[1284] The user begins to act according to the learning plan and maintenance route provided by the server. As input, the user refers to the plan displayed on the terminal and performs the actual learning and maintenance work. As output, new learning progress data and operation data are generated.

[1285] Step 10:

[1286] The terminal again transmits new learning progress data and operation data input by the user to the server. It receives new learning data and operation data as input and transfers the data to the server as output.

[1287] Step 11:

[1288] The server then feeds the resubmitted data into its artificial intelligence model for additional analysis, using the newly submitted data as input and generating new analysis results as output, thereby identifying areas for improvement and suggesting the next best route.

[1289] Step 12:

[1290] The server sends the new proposal to the user's terminal, using the new analysis results as input and sending the new learning and maintenance routes as output to the terminal, where the user confirms it and prepares for the next cycle.

[1291] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1292] The present invention is a system for proposing and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[1293] Data collection

[1294] User

[1295] First, a user logs in to the learning platform. Then, they enter their learning content and test results for the day into a learning content input form. For example, if they take a math test on quadratic equations, they enter their accuracy rate and self-evaluation. Furthermore, the emotion engine uses a camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotion data.

[1296] Terminal

[1297] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[1298] Data analysis

[1299] server

[1300] The server receives the learning data and emotion data sent from the device and stores them in a database. Next, it inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current learning data and emotion data while referring to the data and emotion data of many other past learners.

[1301] Designing learning routes

[1302] server

[1303] The server then uses the analysis results from the AI ​​model to design the optimal learning path for the user. In this process, it not only determines the next topic to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each), but also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[1304] Providing learning routes

[1305] server

[1306] The server adds the designed learning route, recommended reference books and learning schedule to the user's profile and transmits them to the terminal.

[1307] Terminal

[1308] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[1309] Running training and collecting progress

[1310] User

[1311] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During study, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[1312] Terminal

[1313] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1314] Implementing the PDCA cycle

[1315] server

[1316] The server inputs the retransmitted learning progress data and emotional data into an AI model for tracking and analysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account.

[1317] In this way, by collecting and analyzing data and utilizing emotional data, learning support is provided that takes into account the user's psychological state, which makes it possible to maintain learner motivation and provide effective, high-quality education.

[1318] The processing flow will be explained below.

[1319] Step 1:

[1320] User

[1321] Users log in to the learning platform and enter their learning content and test results for the day into the learning content input form. For example, they enter their math test results for quadratic equations (60% correct answer rate) and their self-evaluation. At the same time, the emotion engine collects emotional data during the learning process.

[1322] Step 2:

[1323] Terminal

[1324] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[1325] Step 3:

[1326] server

[1327] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[1328] Step 4:

[1329] server

[1330] The server inputs the stored learning data and emotion data into the AI ​​model, which has been trained using a large amount of data, including data from other past learners and emotion data.

[1331] Step 5:

[1332] server

[1333] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. Specifically, it determines the next topic to learn (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (one hour of study three times a week). It also adjusts the difficulty and pace of the study based on the user's emotional data.

[1334] Step 6:

[1335] server

[1336] The server adds the designed learning route, recommended reference books, and learning schedule to the user profile and transmits it to the terminal.

[1337] Step 7:

[1338] Terminal

[1339] The device will then notify the user of the received learning route, recommended textbooks, and study schedule, and display it to them. For example, a notification might appear saying, "Next, learn the basics of factorization, use the recommended textbooks, and study for one hour three times a week."

[1340] Step 8:

[1341] User

[1342] Users study according to the study plan provided by their device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine analyzes the user's facial expressions and voice to collect emotional data in real time.

[1343] Step 9:

[1344] Terminal

[1345] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1346] Step 10:

[1347] server

[1348] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, for example, to evaluate whether the user's level of understanding has improved and whether the learning content was appropriate.

[1349] Step 11:

[1350] server

[1351] Based on the results of the reanalysis, the server determines what to learn next and what areas to improve, and also takes into account the user's emotional data to further optimize the pace and difficulty of the learning process.

[1352] Step 12:

[1353] server

[1354] The server adds the improved study plan to the user profile and transmits it again to the terminal.

[1355] Step 13:

[1356] User

[1357] Users start a new learning cycle based on their updated learning plan and periodically enter their progress into the platform. This provides continuous learning support as a PDCA cycle, thereby narrowing the educational gap among learners and enabling the provision of high-quality education.

[1358] Example 2

[1359] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1360] Conventional learning support systems only analyze learners' learning data and suggest optimal learning routes. However, because learners' emotions and psychological state also have a significant impact on learning effectiveness, appropriate support that takes this information into consideration is needed. In addition, there was a need for a system that could maintain learning motivation and design effective learning plans by linking the recollection of learning progress data with the analysis of emotional data.

[1361] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1362] In this invention, the server includes a means for collecting learning data from learners, a means for utilizing an AI model that analyzes the learning data and emotional data of learners, and a means for designing an optimal learning route based on the analysis results, thereby enabling effective learning support that takes into account the learner's psychological state.

[1363] Key Word Definitions

[1364] "Learning data" refers to data including test results and self-assessment data entered by learners.

[1365] "Emotion data" is data that includes facial expressions and voice data generated by the emotion engine.

[1366] An "artificial intelligence model" is an algorithm or system that analyzes and determines the optimal learning route by studying the learning data and emotional data of many other past learners.

[1367] A "learning route" is a learning plan designed based on the analysis results, including the next content to be learned, recommended reference books, and a learning schedule.

[1368] "Learning progress data" refers to data that indicates a learner's learning progress, including test results, self-assessments, and learning achievement levels.

[1369] An "emotion engine" is a system or software for collecting and analyzing learners' facial expressions and voice data via a camera or microphone.

[1370] The "server" is a central processing unit that receives learning data and emotion data, analyzes them using an artificial intelligence model, and designs learning routes.

[1371] A "terminal" is a device operated by a learner to input learning data, display learning routes, and collect emotional data.

[1372] MODE FOR CARRYING OUT THE INVENTION

[1373] The present invention is a system for suggesting and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[1374] Data collection

[1375] First, the user logs in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they take a test on quadratic equations in mathematics, they enter their accuracy rate and self-evaluation. The emotion engine then uses the necessary camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotional data. The device then sends the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice) to the server in real time. The emotion engine uses the PC's built-in camera and headset microphone.

[1376] Data analysis

[1377] The server receives the training data and emotion data sent from the device and stores them in a database. It then inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current training data and emotion data while referencing the data and emotion data of many other past learners. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis.

[1378] Designing learning routes

[1379] The server designs the optimal learning route for the user based on the analysis results obtained from the AI ​​model. Based on the analysis results, it determines the next learning content (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a learning schedule (three times a week, one hour each). It also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[1380] Providing learning routes

[1381] The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends it to the terminal. The terminal notifies the user of the received learning route, recommended reference books, and learning schedule and displays them.

[1382] Running training and collecting progress

[1383] The user studies according to the study plan notified by the device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine continues to analyze the user's facial expressions and voice to collect emotion data in real time. The device then transmits the newly entered study progress data and emotion data to the server in real time.

[1384] Implementing the PDCA cycle

[1385] The server inputs the retransmitted learning progress data and emotional data into the AI ​​model and performs a reanalysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account. This makes it possible to maintain learners' motivation and provide effective, high-quality education.

[1386] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1387] Processing flow

[1388] Step 1: Data collection

[1389] User

[1390] Users log in to the learning platform and enter their learning content and test results into a learning content input form. For example, they take a math test on quadratic equations and record their accuracy rate and self-evaluation. Furthermore, the platform collects facial and voice data using a camera and microphone, which are necessary for the emotion engine.

[1391] input

[1392] Training data (test results, accuracy rate, self-evaluation) and emotional data (facial expressions, voice).

[1393] output

[1394] Training data and emotion data are generated.

[1395] Specific actions

[1396] Users log in using their smartphone or PC and enter information into a data entry form. Emotional data is collected using the PC's built-in camera and headset microphone.

[1397] Step 2: Send data

[1398] Terminal

[1399] The terminal transmits the learning data and emotion data input by the user to the server in real time.

[1400] input

[1401] Training data and emotion data.

[1402] output

[1403] The data is sent to the server.

[1404] Specific actions

[1405] The terminal packetizes the input data and sends it to the server using a communication protocol (e.g., HTTP / HTTPS).

[1406] Step 3: Receiving and storing data

[1407] server

[1408] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[1409] input

[1410] Training data and sentiment data.

[1411] output

[1412] Data stored in a database.

[1413] Specific actions

[1414] The server listens on the incoming port and stores the data in a database (e.g. MongoDB or MySQL) in the appropriate format.

[1415] Step 4: Data analysis

[1416] server

[1417] The server inputs the data stored in the database into an artificial intelligence model and begins analysis, using machine learning libraries such as TensorFlow and PyTorch.

[1418] input

[1419] Saved training and sentiment data.

[1420] output

[1421] Analysis results.

[1422] Specific actions

[1423] The server periodically inputs new data into the model using a scheduled task, performs the analysis, and saves the analysis results in the appropriate format.

[1424] Step 5: Design your learning route

[1425] server

[1426] The server designs the optimal learning route for the user based on the analysis results of the artificial intelligence model.

[1427] input

[1428] Analysis results.

[1429] output

[1430] Designing your study route (what to study next, recommended reference books, study schedule).

[1431] Specific actions

[1432] The server references pre-registered teaching material data and schedule data and generates a learning path that reflects the analysis results.

[1433] Step 6: Providing a learning route

[1434] server

[1435] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[1436] input

[1437] Designed study route, recommended reference books, and study schedule.

[1438] output

[1439] Data sent to the device.

[1440] Specific actions

[1441] The server sends data to the terminal using communication methods such as RESTful API.

[1442] Step 7: View your learned route and schedule

[1443] Terminal

[1444] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[1445] input

[1446] Data received from the server.

[1447] output

[1448] The study route, recommended study materials, and study schedule displayed to the user.

[1449] Specific actions

[1450] The device displays the received data on the application screen and notifies the user via push notification.

[1451] Step 8: Run training and collect progress

[1452] User

[1453] Users study according to the study plan provided by their device, and periodically enter their progress and new test results into the platform. The emotion engine continues to analyze their facial expressions and voices while they study.

[1454] input

[1455] Informed learning plan.

[1456] output

[1457] Learning progress data and updated emotion data.

[1458] Specific actions

[1459] Users study on their PCs or smartphones, inputting their progress into the platform as needed, and keeping their cameras and microphones on while studying.

[1460] Step 9: Data retransmission

[1461] Terminal

[1462] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1463] input

[1464] New learning progress and emotion data.

[1465] output

[1466] Data resubmitted to the server.

[1467] Specific actions

[1468] At the end of each session, the device packets the progress data and emotion data and sends them to the server.

[1469] Step 10: Implement the PDCA cycle

[1470] server

[1471] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model for analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content and improvements.

[1472] input

[1473] Resubmitted learning progress and emotion data.

[1474] output

[1475] Suggestions for improvements and new learning content.

[1476] Specific actions

[1477] The server schedules periodic re-analysis tasks to re-analyze progress and emotion data, logs the results, and generates new learning paths and improvements.

[1478] (Application example 2)

[1479] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1480] Conventional learning support systems propose optimal learning routes based solely on learning data without considering the learner's emotional state, resulting in problems such as a decline in learner motivation and reduced learning efficiency. Furthermore, it was difficult to provide learning support in physical stores, and the provision of appropriate learning materials and services was insufficient. This resulted in the quality of the learning environment in physical stores not improving, and learners were not able to learn effectively.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1482] In this invention, the server includes a means for collecting learning data and emotional data of the learner, a means for utilizing an AI model for analyzing the learning data and emotional data, and a means for designing an optimal learning route based on the analysis results, thereby making it possible to propose an optimal learning route that also takes into account the learner's psychological state.

[1483] Furthermore, by utilizing smart devices in physical stores and providing appropriate learning materials and services, learners can receive effective learning support even in physical stores, which will help maintain learners' motivation and improve their learning efficiency.

[1484] "Learning data" refers to numerical and textual information that indicates the progress of learning, such as test results and self-assessment data entered by the learner.

[1485] "Emotional data" is information that indicates the learner's psychological state, such as the analysis results of the learner's facial expressions and voice.

[1486] An "artificial intelligence model" is an algorithm or system that learns from past data and analyzes new data to find patterns.

[1487] A "learning route" is a series of learning content and methods recommended for learners to progress effectively through their studies.

[1488] "Devices" refer to information and communication devices such as smartphones and tablets used by learners.

[1489] A "physical store" is a physical store or commercial establishment that learners actually visit.

[1490] A "smart device" is a device that can connect to the Internet and run various applications.

[1491] "Analysis results" are analytical information output by the artificial intelligence model based on collected data.

[1492] "Services" is a general term for teaching materials, educational support, technical support, etc. provided to learners.

[1493] "Data collection means" refers to a method or device for collecting learning data, emotional data, etc.

[1494] To implement this invention, it is necessary to build a system in which the server, terminal, and user elements interact with each other. The system of the present invention provides effective learning support by collecting learning data and emotional data from learners and proposing optimal learning routes through analysis using an artificial intelligence model.

[1495] Data collection

[1496] User

[1497] Users log in to the system using devices such as smartphones or tablets, enter their learning content and test results, and provide facial and voice data using the device's camera and microphone so that emotional data can be collected.

[1498] Terminal

[1499] The device transmits the learning data entered by the user (e.g., test results, self-evaluation) and the emotional data collected by the emotion engine (EmotionEngine) to the server in real time.

[1500] Data analysis

[1501] server

[1502] The server receives the learning data and emotion data sent from the device and stores them in a database. It then analyzes this data based on an artificial intelligence model (LearningAIModel) and designs the optimal learning path. Specifically, it analyzes the current learning data and emotion data while comparing it with the data of many past learners.

[1503] Designing and delivering learning routes

[1504] server

[1505] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. It determines the next content to study, recommended reference books, and study schedule, and makes adjustments based on the learner's psychological state. This information is sent to the device.

[1506] Terminal

[1507] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[1508] Running training and collecting progress

[1509] User

[1510] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During the study, the emotion engine continuously collects the user's emotional data.

[1511] Terminal

[1512] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1513] Implementing the PDCA cycle

[1514] server

[1515] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis. Based on the new analysis results, it identifies areas for improvement and optimizes the learning content and methods.

[1516] A concrete example of this system is its use in a study space in a bookstore. Users use devices in the bookstore's study area and provide the system with learning data and emotional data. Based on this, the bookstore will provide the most appropriate books and materials. Examples of prompts for the generative AI model that assists this process include:

[1517] Example prompt sentence:

[1518] "If a student's test score is 85, their self-assessment is good, and their sentiment data indicates 'Satisfied,' suggest the next learning content or material they should study."

[1519] Such a system can maintain learners' motivation and provide a high-quality learning environment.

[1520] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1521] Step 1:

[1522] The user inputs training data and emotion data.

[1523] Users log in to the learning platform using a smartphone or tablet, input their learning content and test results, and provide facial and voice data using a camera and microphone. This generates learning data (e.g., test results, self-evaluation) and emotional data.

[1524] Input: Training data, emotion data

[1525] Output: A set of training data and emotion data

[1526] Step 2:

[1527] The device sends the data to the server

[1528] The device sends the learning data entered by the user and the emotion data collected by the emotion engine to the server, which stores the data in real time.

[1529] Input: A set of training data and emotion data

[1530] Output: Training data and emotion data recorded on the server

[1531] Step 3:

[1532] The server analyzes the data

[1533] The server receives the learning data and emotion data sent from the device and stores them in a database. This data is then input into an artificial intelligence model (LearningAIModel) for analysis. The AI ​​model analyzes the current learning data and emotion data while comparing it with the data of many other learners in the past.

[1534] Input: A set of training data and emotion data, past learner data

[1535] Output: Analysis results

[1536] Step 4:

[1537] The server designs the optimal learning route

[1538] The server designs the optimal learning route for each user based on the analysis results of the AI ​​model. This route includes the next content to study, recommended reference books, and a study schedule. It also adjusts the difficulty and pace of the learning content based on the learner's emotional data.

[1539] Input: Analysis results

[1540] Output: Optimized learning route

[1541] Step 5:

[1542] The server sends the learned route to the device.

[1543] The server transmits the designed learning route to the user's terminal.

[1544] Input: Optimized learning route

[1545] Output: Learned routes sent to the device

[1546] Step 6:

[1547] The device displays and notifies you of the learned route.

[1548] The terminal notifies and displays the received learned route to the user, who can then confirm it and proceed to the next learning step.

[1549] Input: Received learned routes

[1550] Output: The learning route displayed to the user

[1551] Step 7:

[1552] Users conduct learning and update data

[1553] The user follows the provided learning plan and inputs their learning progress data and new test results into the system. The emotion engine continues to collect data on the user's facial expressions and voice.

[1554] Input: New learning progress data, emotion data

[1555] Output: Updated training data and emotion data set

[1556] Step 8:

[1557] The device sends new data to the server

[1558] The terminal again transmits the learning progress data and emotion data newly input by the user to the server.

[1559] Input: Updated training data and emotion data set

[1560] Output: New training data and emotion data recorded on the server

[1561] Step 9:

[1562] The server executes the PDCA cycle

[1563] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, thereby identifying new areas for improvement and optimizing the user's learning content and methods.

[1564] Input: A new set of training data and emotion data

[1565] Output: Improvements, optimized next learning route

[1566] This process ensures that learners always follow the learning path that best suits them, providing an effective and motivating learning environment.

[1567] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1568] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1569] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1570] [Fourth embodiment]

[1571] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1572] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1573] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1574] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1575] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1577] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1578] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1579] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1580] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1581] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1582] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1583] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1584] The present invention is a system for collecting and analyzing a learner's learning data to propose an optimal learning route and provide continuous learning support. Specific embodiments for carrying out the present invention will be described below.

[1585] Data collection

[1586] User

[1587] First, users log in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they took a math test on quadratic equations, they enter their accuracy rate and self-evaluation. This generates learning data.

[1588] Terminal

[1589] The device transmits the learning data entered by the user to the server in real time, including the learning content, test results, correct answer rate, and self-evaluation.

[1590] Data analysis

[1591] server

[1592] The server receives the learning data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the data of many other learners in the past, and by comparing it with data with similar patterns, it proposes the optimal learning route.

[1593] Designing learning routes

[1594] server

[1595] The server then designs the optimal learning path for the user based on the analysis results obtained from the AI ​​model. During this process, it determines the next content to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each).

[1596] Providing learning routes

[1597] server

[1598] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[1599] Terminal

[1600] The terminal displays the received learning route, reference materials, and learning schedule to the user so that the learner can check them at any time.

[1601] Running training and collecting progress

[1602] User

[1603] The user begins studying according to the study plan provided by the server, and periodically enters their study progress and new test results into the platform.

[1604] Terminal

[1605] The terminal again transmits the new learning progress data input by the user to the server.

[1606] Implementing the PDCA cycle

[1607] server

[1608] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. It then sends the user an optimized learning route again, preparing for the next learning cycle.

[1609] In this way, continuous learning improvement will be achieved through data collection and analysis, and learning based on the proposed learning pathways, making it possible to provide an environment in which even low-income families can receive effective, high-quality education.

[1610] The processing flow will be explained below.

[1611] Step 1:

[1612] User

[1613] The user logs in to the learning platform and enters the content of today's learning in the learning content input form, for example, the results of a math quadratic equation test (60% correct answer rate) or self-evaluation.

[1614] Step 2:

[1615] Terminal

[1616] The device transmits the learning data (test results, accuracy rate, self-evaluation) entered by the user to the server in real time.

[1617] Step 3:

[1618] server

[1619] The server receives the learning data sent from the terminal and stores it in a database.

[1620] Step 4:

[1621] server

[1622] The server inputs the saved learning data into an AI model, which analyzes the user's learning data while referencing the data of many other past learners.

[1623] Step 5:

[1624] server

[1625] The server then designs the optimal learning path for the user based on the analysis results of the AI ​​model. For example, it identifies the basics of factorization as the next thing to learn and identifies the necessary study materials and reference books.

[1626] Step 6:

[1627] server

[1628] The server adds the designed learning route, recommended reference books and learning schedule to the user profile and transmits it to the terminal.

[1629] Step 7:

[1630] Terminal

[1631] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[1632] Step 8:

[1633] User

[1634] Users study according to the study plan provided by their device and periodically enter their study progress into the platform.

[1635] Step 9:

[1636] Terminal

[1637] The terminal transmits the learning progress data newly input by the user to the server in real time.

[1638] Step 10:

[1639] server

[1640] The server inputs the retransmitted learning progress data into the AI ​​model for tracking and analysis. Based on the results of the reanalysis, it proposes new learning content and areas for improvement.

[1641] Step 11:

[1642] server

[1643] The server adds the improved study plan to the user profile and sends it to the terminal to prepare for the next study cycle.

[1644] Step 12:

[1645] User

[1646] The user starts a new learning cycle based on the updated learning plan and periodically enters their progress into the platform, and this process is repeated as a PDCA cycle.

[1647] Example 1

[1648] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1649] Conventional learning support systems have difficulty efficiently and effectively providing learners with appropriate learning routes based on their progress. Furthermore, learning data is often collected and analyzed manually, which can lead to delays in real-time feedback and improvement. This raises concerns about a decline in learner motivation and a decrease in learning effectiveness.

[1650] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1651] In this invention, the server includes means for collecting learner learning data, means for transmitting the learning data from the terminal to the server, means for storing the learning data in a database, means for utilizing a generative artificial intelligence model to analyze the learning data, means for designing an optimal learning route, recommended materials, and learning schedule based on the analysis results, means for transmitting the learning route, recommended materials, and learning schedule to the learner's terminal and displaying them, means for re-collecting the learner's learning progress data and analyzing areas for improvement, and means for providing an improved learning route based on the re-analysis results. This enables the collection, analysis, and feedback of learning data in real time, making it possible to continuously provide learners with an optimal learning route.

[1652] "Learning data" refers to data entered by a learner, including learning content, test results, and self-assessments.

[1653] A "terminal" is a digital device through which a user inputs learning data and receives instructions from a server.

[1654] "Server" means a central computer system for receiving and analyzing learning data, designing learning routes, and providing feedback to learners.

[1655] A "database" is a digital storage system in which the server stores learning data.

[1656] "Generative AI model" is a general term for machine learning algorithms and related technologies used to analyze training data and propose optimal learning routes.

[1657] A "learning route" is a guideline for the specific learning content and schedule that a learner should follow, proposed based on the analysis results.

[1658] "Recommended materials" are resources such as reference books and teaching materials that are provided to learners based on their learning route.

[1659] A "learning schedule" is a specific learning plan that a learner should follow based on their learning route.

[1660] "Study progress data" refers to data that indicates the progress of a learner's learning, which is generated in the process of the learner progressing with their studies according to the study plan.

[1661] "Reanalysis" refers to the process of re-analyzing data using learning progress data.

[1662] The present invention is a system that collects and analyzes a learner's learning data to propose an optimal learning route and provide continuous learning support.

[1663] This system begins when the user logs in to the learning platform. The user enters learning data, such as their learning content, test results, and self-evaluation, into a learning content input form. The device then transmits this learning data to the server in real time. This is achieved by using, for example, the HTTPS protocol to ensure secure communications.

[1664] The server stores the training data received from the device in a database. The server then uses a generative AI model to analyze the stored training data. This AI model is built using machine learning algorithms such as TensorFlow, and performs analysis by comparing it with past training data.

[1665] Based on the analyzed data, the server designs an optimal learning path for each learner, including what to study next (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a specific study schedule (three times a week, one hour each).

[1666] The designed learning route, recommended reference books, and learning schedule are sent from the server to the learner's device. The device displays this information to the user so that the learner can check it at any time. The user begins studying according to the learning plan provided by the server. Learning progress and new test results are periodically entered into the platform and sent back to the server via the device.

[1667] The server then inputs the retransmitted learning progress data into the AI ​​model for further analysis. Based on the analysis results, it identifies areas for improvement and suggests new learning content. The user is then provided with an optimized learning route again. In this way, continuous learning improvement is achieved.

[1668] As a concrete example, consider the case where a user takes a math test on "quadratic equations" and enters the results into the platform. The user enters data such as "Quadratic equations test result: 70%. Self-assessment: Medium level of understanding." This data is then sent to the server via the device, where an artificial intelligence model analyzes it based on past data, and designs study content such as "The Basics of Factorization," a recommended textbook, "High School Mathematics I: Quadratic Equations," and a schedule of "one hour of study three times a week."

[1669] An example of a prompt to input to a generative AI model is as follows:

[1670] "Please suggest the best learning route based on the user's learning data. The data is as follows: Learning content - Quadratic equations, Test result - 70%, Self-assessment - Medium."

[1671] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1672] Step 1:

[1673] A user logs into the learning platform.

[1674] Input: Username, Password

[1675] Output: Authentication token

[1676] Specific operation: The user enters their username and password into the login form and presses the submit button. The server receives this and performs authentication. If authentication is successful, it generates an authentication token and returns it to the user.

[1677] Step 2:

[1678] The user inputs the training data.

[1679] Input: Study content, test results, self-assessment

[1680] Output: Training data (e.g., math test result on quadratic equations: 75%, self-assessment: moderate)

[1681] Specific operation: The user enters the day's learning content, test results, and self-evaluation into the learning platform's input form, and presses the submit button. This data is saved on the device.

[1682] Step 3:

[1683] The device sends the learning data to the server.

[1684] Input: Training data

[1685] Output: Training data sent to the server

[1686] Specific operation: The device transmits the saved learning data to the server in real time using the HTTPS protocol to ensure data security.

[1687] Step 4:

[1688] The server stores the learning data in a database.

[1689] Input: Training data

[1690] Output: Training data stored in a database

[1691] Specific operation: The server stores the received training data in a database. This storage process uses SQL queries.

[1692] Step 5:

[1693] The server inputs the learning data into the artificial intelligence model and begins analysis.

[1694] Input: Training data stored in a database

[1695] Output: Analysis results (e.g., what to study next, recommended reference books, study schedule, etc.)

[1696] How it works: The server retrieves training data from the database and inputs it into an AI model. The AI ​​model (for example, a model built with TensorFlow) analyzes the data and proposes the optimal training route.

[1697] Step 6:

[1698] The server designs the optimal learning route.

[1699] Input: Analysis results

[1700] Output: Optimal study route, recommended reference books, study schedule

[1701] Specific operation: Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route for the learner. For example, it sets "Fundamentals of Factorization" as a learning item, recommends "High School Mathematics I: Quadratic Equations" as a reference book, and determines a learning schedule of "One hour of study three times a week."

[1702] Step 7:

[1703] The server sends the learned route to the user's terminal.

[1704] Input: optimal study route, recommended reference books, study schedule

[1705] Output: Study route, recommended study books, and study schedule sent to the device

[1706] Specific operation: The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends them to the terminal.

[1707] Step 8:

[1708] The device displays the learned route.

[1709] Input: Study route, recommended reference books, study schedule sent from the server

[1710] Output: User-confirmable learning route, recommended reference books, and study schedule

[1711] Specific operation: The device displays the received study route, recommended reference books, and study schedule to the user, who then confirms this and begins studying.

[1712] Step 9:

[1713] The user enters their learning progress.

[1714] Input: New learning progress data (progress, new test results, etc.)

[1715] Output: Learning progress data saved on the device

[1716] What it does: Users enter their learning progress and new test results into the learning platform, and this data is stored on their device.

[1717] Step 10:

[1718] The terminal again transmits the learning progress data to the server.

[1719] Input: Learning progress data

[1720] Output: Learning progress data sent to the server

[1721] Specific operation: The device transmits the saved learning progress data to the server in real time.

[1722] Step 11:

[1723] The server re-analyzes the learning progress data.

[1724] Input: Learning progress data

[1725] Output: Reanalysis results (further improved learning route)

[1726] Specific operation: The server inputs the retransmitted learning progress data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content.

[1727] Step 12:

[1728] The server provides the improved learning route to the user.

[1729] Input: Reanalysis result

[1730] Output: Improved study route, recommended reference books, study schedule

[1731] Specific operation: Based on the reanalysis results, the server designs an improved learning route and provides it to the user, who then proceeds to the next learning cycle.

[1732] (Application example 1)

[1733] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1734] Conventional learning support systems and factory equipment management systems did not adequately collect and analyze data, resulting in insufficient proposals for optimal learning or operation routes. Furthermore, it was difficult for learners and factory operators to take appropriate action individually, making it difficult to continuously improve performance.

[1735] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1736] In this invention, the server includes means for collecting learning data from learners, means for utilizing an AI model to analyze the learning data, means for designing an optimal learning route based on the analysis results, means for transmitting the learning route to the learner's terminal and displaying it, means for re-collecting the learner's learning progress data and analyzing areas for improvement, means for collecting operation data of factory equipment, means for utilizing an AI model to analyze the operation data, means for designing an optimal operation route or maintenance route based on the analysis results, and means for transmitting the operation route or maintenance route to an equipment operation terminal and displaying it. This enables continuous learning improvement for learners and efficient operation management of factory equipment.

[1737] A "learner" is a person receiving education or training.

[1738] "Learning data" refers to information entered by learners regarding test results, self-assessments, and learning content.

[1739] An "artificial intelligence model" is a computational system that uses machine learning and data analysis algorithms to analyze data and make predictions and classifications.

[1740] A "learning route" is a series of learning content and schedules suggested to help a learner optimally learn.

[1741] "Devices" are devices used by learners, such as computers, smartphones, and tablets.

[1742] "Study progress data" is information that indicates how far a learner has progressed in their studies.

[1743] "Factory equipment" refers to production facilities and machinery used in factories.

[1744] "Operational data" refers to information relating to the operating status and performance of factory equipment.

[1745] A "maintenance route" is a plan or procedure for the proper operation and maintenance of factory equipment.

[1746] An "equipment operation terminal" is a device for monitoring and operating factory equipment.

[1747] The present invention is a system for realizing efficient management of factory equipment and continuous learning support for learners. This system consists of three main elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[1748] Data collection

[1749] User

[1750] Users first log in to the system through a terminal, then enter their learning content and the operating status of factory equipment into a designated interface. For example, students enter math test results and self-evaluations, while factory equipment collects temperature and vibration data.

[1751] Terminal

[1752] The device sends the user-entered learning and operational data to the server in real time, including learning content, test results, accuracy rate, self-evaluation, and the operating status of factory equipment (e.g., temperature, vibration, and operating time).

[1753] Data analysis

[1754] server

[1755] The server receives the data sent from the device and stores it in a database. This data is then input into an AI model and analysis begins. The AI ​​model has learned from the operation data of many past learners and factory equipment, and by comparing it with data with similar patterns, it proposes optimal learning and operation routes.

[1756] Designing learning and maintenance routes

[1757] server

[1758] Based on the analysis results obtained from the AI ​​model, the server designs the optimal learning route and factory equipment maintenance route for the user. In this process, it determines the next learning content, reference books, learning schedule, factory equipment maintenance schedule, and necessary maintenance actions.

[1759] Providing learning and maintenance routes

[1760] server

[1761] The server adds the designed learning route and maintenance route to the user's profile and sends it to the terminal.

[1762] Terminal

[1763] The terminal displays the received learning route and maintenance route to the user, allowing the learner or factory operator to check them at any time.

[1764] Learning execution and operation management, and progress collection

[1765] User

[1766] The user begins to act according to the learning plan and maintenance route provided by the server, and periodically inputs learning progress, new test results, and the operating and maintenance status of factory equipment into the system via a terminal.

[1767] Terminal

[1768] The terminal again transmits the new learning progress data and operation data input by the user to the server.

[1769] Implementing the PDCA cycle

[1770] server

[1771] The server then inputs the retransmitted data into the AI ​​model for additional analysis. Based on the analysis results, it identifies areas for improvement in the learning and maintenance routes and makes new suggestions. It then sends the optimized route back to the user, preparing for the next cycle.

[1772] Hardware and software used

[1773] In this embodiment, the following hardware and software are used:

[1774] Hardware: Computers, smartphones, smart glasses, factory equipment.

[1775] Software: AI analysis libraries (TensorFlow and PyTorch), databases (MySQL and PostgreSQL), and cloud data processing services (AWS Lambda and Google Cloud Functions).

[1776] Specific examples

[1777] As a specific example, the temperature and vibration data of factory equipment can be analyzed, and if an abnormality is detected, a notification such as "The temperature of this robot is abnormal. Please perform maintenance immediately" can be displayed on the smart glasses.

[1778] Prompt sentence for generative AI model

[1779] Develop a model that detects anomalies and proposes optimal maintenance schedules based on operational data (temperature, vibration, timestamp) from factory equipment. See the following example:

[1780] Temperature: [65.0, 70.2, 75.1, 69.8, 72.3]

[1781] Vibration: [2.5, 2.7, 3.0, 2.8, 3.2]

[1782] Timestamps: ['2023-09-01T12:00:00', '2023-09-01T12:01:00', '2023-09-01T12:02:00', '2023-09-01T12:03:00', '2023-09-01T12:04:00']

[1783] This prompt enables the AI ​​model to analyze the operating data of factory equipment, detect abnormalities, and propose optimal maintenance schedules.

[1784] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1785] Step 1:

[1786] A user logs in to the system through a terminal. As input, the user ID and login information are entered and authentication is performed. As output, the user is able to access the system.

[1787] Step 2:

[1788] Users input learning data or factory equipment operation data. Specifically, learners input test results or self-assessments, and factory operators input equipment data such as temperature and vibration. The input data is collected and sent to the server.

[1789] Step 3:

[1790] The terminal sends the collected data to the server. As input, it receives learning data and operational data entered by the user, and as output, it transfers the data to the server.

[1791] Step 4:

[1792] The server stores the received data in a database. As input, it receives data sent from the terminal and stores it in a database. As output, data is accumulated in a database.

[1793] Step 5:

[1794] The server inputs the data collected from the database into an artificial intelligence model for analysis. It uses the learning data and operational data obtained from the database as input, and generates optimal learning and operational route proposals as output. Specifically, it performs analysis by comparing the data with past data to find patterns.

[1795] Step 6:

[1796] The server designs optimal learning and maintenance routes based on the analysis results of the AI ​​model. As input, it formulates the necessary content and schedules based on the analysis results, and as output, it generates detailed learning plans and maintenance plans.

[1797] Step 7:

[1798] The server sends the designed learning route and maintenance route to the terminal. It uses the designed plan as input and sends data to the user's terminal as output.

[1799] Step 8:

[1800] The terminal displays the received learning route and maintenance route to the user. It receives the plan sent from the server as input and displays it on the screen as output. Specifically, it displays the learning content and schedule to the learner and the maintenance actions to the factory operator.

[1801] Step 9:

[1802] The user begins to act according to the learning plan and maintenance route provided by the server. As input, the user refers to the plan displayed on the terminal and performs the actual learning and maintenance work. As output, new learning progress data and operation data are generated.

[1803] Step 10:

[1804] The terminal again transmits new learning progress data and operation data input by the user to the server. It receives new learning data and operation data as input and transfers the data to the server as output.

[1805] Step 11:

[1806] The server then feeds the resubmitted data into its artificial intelligence model for additional analysis, using the newly submitted data as input and generating new analysis results as output, thereby identifying areas for improvement and suggesting the next best route.

[1807] Step 12:

[1808] The server sends the new proposal to the user's terminal, using the new analysis results as input and sending the new learning and maintenance routes as output to the terminal, where the user confirms it and prepares for the next cycle.

[1809] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1810] The present invention is a system for proposing and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[1811] Data collection

[1812] User

[1813] First, a user logs in to the learning platform. Then, they enter their learning content and test results for the day into a learning content input form. For example, if they take a math test on quadratic equations, they enter their accuracy rate and self-evaluation. Furthermore, the emotion engine uses a camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotion data.

[1814] Terminal

[1815] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[1816] Data analysis

[1817] server

[1818] The server receives the learning data and emotion data sent from the device and stores them in a database. Next, it inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current learning data and emotion data while referring to the data and emotion data of many other past learners.

[1819] Designing learning routes

[1820] server

[1821] The server then uses the analysis results from the AI ​​model to design the optimal learning path for the user. In this process, it not only determines the next topic to study (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (three times a week, one hour each), but also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[1822] Providing learning routes

[1823] server

[1824] The server adds the designed learning route, recommended reference books and learning schedule to the user's profile and transmits them to the terminal.

[1825] Terminal

[1826] The terminal notifies and displays the received study route, recommended reference books, and study schedule to the user.

[1827] Running training and collecting progress

[1828] User

[1829] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During study, the emotion engine continuously analyzes the user's facial expressions and voice to collect emotional data in real time.

[1830] Terminal

[1831] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1832] Implementing the PDCA cycle

[1833] server

[1834] The server inputs the retransmitted learning progress data and emotional data into an AI model for tracking and analysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account.

[1835] In this way, by collecting and analyzing data and utilizing emotional data, learning support is provided that takes into account the user's psychological state, which makes it possible to maintain learner motivation and provide effective, high-quality education.

[1836] The processing flow will be explained below.

[1837] Step 1:

[1838] User

[1839] Users log in to the learning platform and enter their learning content and test results for the day into the learning content input form. For example, they enter their math test results for quadratic equations (60% correct answer rate) and their self-evaluation. At the same time, the emotion engine collects emotional data during the learning process.

[1840] Step 2:

[1841] Terminal

[1842] The device transmits the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice, behavior) to the server in real time.

[1843] Step 3:

[1844] server

[1845] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[1846] Step 4:

[1847] server

[1848] The server inputs the stored learning data and emotion data into the AI ​​model, which has been trained using a large amount of data, including data from other past learners and emotion data.

[1849] Step 5:

[1850] server

[1851] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. Specifically, it determines the next topic to learn (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a study schedule (one hour of study three times a week). It also adjusts the difficulty and pace of the study based on the user's emotional data.

[1852] Step 6:

[1853] server

[1854] The server adds the designed learning route, recommended reference books, and learning schedule to the user profile and transmits it to the terminal.

[1855] Step 7:

[1856] Terminal

[1857] The device will then notify the user of the received learning route, recommended textbooks, and study schedule, and display it to them. For example, a notification might appear saying, "Next, learn the basics of factorization, use the recommended textbooks, and study for one hour three times a week."

[1858] Step 8:

[1859] User

[1860] Users study according to the study plan provided by their device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine analyzes the user's facial expressions and voice to collect emotional data in real time.

[1861] Step 9:

[1862] Terminal

[1863] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1864] Step 10:

[1865] server

[1866] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, for example, to evaluate whether the user's level of understanding has improved and whether the learning content was appropriate.

[1867] Step 11:

[1868] server

[1869] Based on the results of the reanalysis, the server determines what to learn next and what areas to improve, and also takes into account the user's emotional data to further optimize the pace and difficulty of the learning process.

[1870] Step 12:

[1871] server

[1872] The server adds the improved study plan to the user profile and transmits it again to the terminal.

[1873] Step 13:

[1874] User

[1875] Users start a new learning cycle based on their updated learning plan and periodically enter their progress into the platform. This provides continuous learning support as a PDCA cycle, thereby narrowing the educational gap among learners and enabling the provision of high-quality education.

[1876] Example 2

[1877] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1878] Conventional learning support systems only analyze learners' learning data and suggest optimal learning routes. However, because learners' emotions and psychological state also have a significant impact on learning effectiveness, appropriate support that takes this information into consideration is needed. In addition, there was a need for a system that could maintain learning motivation and design effective learning plans by linking the recollection of learning progress data with the analysis of emotional data.

[1879] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1880] In this invention, the server includes a means for collecting learning data from learners, a means for utilizing an AI model that analyzes the learning data and emotional data of learners, and a means for designing an optimal learning route based on the analysis results, thereby enabling effective learning support that takes into account the learner's psychological state.

[1881] Key Word Definitions

[1882] "Learning data" refers to data including test results and self-assessment data entered by learners.

[1883] "Emotion data" is data that includes facial expressions and voice data generated by the emotion engine.

[1884] An "artificial intelligence model" is an algorithm or system that analyzes and determines the optimal learning route by studying the learning data and emotional data of many other past learners.

[1885] A "learning route" is a learning plan designed based on the analysis results, including the next content to be learned, recommended reference books, and a learning schedule.

[1886] "Learning progress data" refers to data that indicates a learner's learning progress, including test results, self-assessments, and learning achievement levels.

[1887] An "emotion engine" is a system or software for collecting and analyzing learners' facial expressions and voice data via a camera or microphone.

[1888] The "server" is a central processing unit that receives learning data and emotion data, analyzes them using an artificial intelligence model, and designs learning routes.

[1889] A "terminal" is a device operated by a learner to input learning data, display learning routes, and collect emotional data.

[1890] MODE FOR CARRYING OUT THE INVENTION

[1891] The present invention is a system for suggesting and providing an optimal learning route by collecting and analyzing learning data and emotional data of a learner. Specific embodiments for carrying out the present invention will be described below.

[1892] Data collection

[1893] First, the user logs in to the learning platform. Then, they enter today's learning content and test results into a learning content input form. For example, if they take a test on quadratic equations in mathematics, they enter their accuracy rate and self-evaluation. The emotion engine then uses the necessary camera and microphone to collect data on the user's facial expressions and voice. This generates learning data and emotional data. The device then sends the learning data entered by the user (test results, accuracy rate, self-evaluation) and the emotional data collected by the emotion engine (facial expressions, voice) to the server in real time. The emotion engine uses the PC's built-in camera and headset microphone.

[1894] Data analysis

[1895] The server receives the training data and emotion data sent from the device and stores them in a database. It then inputs this data into an AI model and begins analysis. The AI ​​model analyzes the current training data and emotion data while referencing the data and emotion data of many other past learners. Machine learning libraries such as TensorFlow and PyTorch are used for the analysis.

[1896] Designing learning routes

[1897] The server designs the optimal learning route for the user based on the analysis results obtained from the AI ​​model. Based on the analysis results, it determines the next learning content (e.g., the basics of factorization), recommended textbooks (e.g., "High School Mathematics I: Quadratic Equations"), and a learning schedule (three times a week, one hour each). It also adjusts the difficulty and pace of the learning content based on the user's psychological state.

[1898] Providing learning routes

[1899] The server adds the designed learning route, recommended reference books, and learning schedule to the user's profile and sends it to the terminal. The terminal notifies the user of the received learning route, recommended reference books, and learning schedule and displays them.

[1900] Running training and collecting progress

[1901] The user studies according to the study plan notified by the device. They periodically enter their study progress and new test results into the platform. While studying, the emotion engine continues to analyze the user's facial expressions and voice to collect emotion data in real time. The device then transmits the newly entered study progress data and emotion data to the server in real time.

[1902] Implementing the PDCA cycle

[1903] The server inputs the retransmitted learning progress data and emotional data into the AI ​​model and performs a reanalysis. Based on the results of the reanalysis, it identifies areas for improvement and proposes new learning content and improvements. It also optimizes learning methods and content by taking the user's psychological state into account. This makes it possible to maintain learners' motivation and provide effective, high-quality education.

[1904] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1905] Processing flow

[1906] Step 1: Data collection

[1907] User

[1908] Users log in to the learning platform and enter their learning content and test results into a learning content input form. For example, they take a math test on quadratic equations and record their accuracy rate and self-evaluation. Furthermore, the platform collects facial and voice data using a camera and microphone, which are necessary for the emotion engine.

[1909] input

[1910] Training data (test results, accuracy rate, self-evaluation) and emotional data (facial expressions, voice).

[1911] output

[1912] Training data and emotion data are generated.

[1913] Specific actions

[1914] Users log in using their smartphone or PC and enter information into a data entry form. Emotional data is collected using the PC's built-in camera and headset microphone.

[1915] Step 2: Send data

[1916] Terminal

[1917] The terminal transmits the learning data and emotion data input by the user to the server in real time.

[1918] input

[1919] Training data and emotion data.

[1920] output

[1921] The data is sent to the server.

[1922] Specific actions

[1923] The terminal packetizes the input data and sends it to the server using a communication protocol (e.g., HTTP / HTTPS).

[1924] Step 3: Receiving and storing data

[1925] server

[1926] The server receives the learning data and emotion data sent from the terminal and stores them in a database.

[1927] input

[1928] Training data and sentiment data.

[1929] output

[1930] Data stored in a database.

[1931] Specific actions

[1932] The server listens on the incoming port and stores the data in a database (e.g. MongoDB or MySQL) in the appropriate format.

[1933] Step 4: Data analysis

[1934] server

[1935] The server inputs the data stored in the database into an artificial intelligence model and begins analysis, using machine learning libraries such as TensorFlow and PyTorch.

[1936] input

[1937] Saved training and sentiment data.

[1938] output

[1939] Analysis results.

[1940] Specific actions

[1941] The server periodically inputs new data into the model using a scheduled task, performs the analysis, and saves the analysis results in the appropriate format.

[1942] Step 5: Design your learning route

[1943] server

[1944] The server designs the optimal learning route for the user based on the analysis results of the artificial intelligence model.

[1945] input

[1946] Analysis results.

[1947] output

[1948] Designing your study route (what to study next, recommended reference books, study schedule).

[1949] Specific actions

[1950] The server references pre-registered teaching material data and schedule data and generates a learning path that reflects the analysis results.

[1951] Step 6: Providing a learning route

[1952] server

[1953] The server adds the designed study route, recommended reference books, and study schedule to the user's profile and transmits them to the terminal.

[1954] input

[1955] Designed study route, recommended reference books, and study schedule.

[1956] output

[1957] Data sent to the device.

[1958] Specific actions

[1959] The server sends data to the terminal using communication methods such as RESTful API.

[1960] Step 7: View your learned route and schedule

[1961] Terminal

[1962] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[1963] input

[1964] Data received from the server.

[1965] output

[1966] The study route, recommended study materials, and study schedule displayed to the user.

[1967] Specific actions

[1968] The device displays the received data on the application screen and notifies the user via push notification.

[1969] Step 8: Run training and collect progress

[1970] User

[1971] Users study according to the study plan provided by their device, and periodically enter their progress and new test results into the platform. The emotion engine continues to analyze their facial expressions and voices while they study.

[1972] input

[1973] Informed learning plan.

[1974] output

[1975] Learning progress data and updated emotion data.

[1976] Specific actions

[1977] Users study on their PCs or smartphones, inputting their progress into the platform as needed, and keeping their cameras and microphones on while studying.

[1978] Step 9: Data retransmission

[1979] Terminal

[1980] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[1981] input

[1982] New learning progress and emotion data.

[1983] output

[1984] Data resubmitted to the server.

[1985] Specific actions

[1986] At the end of each session, the device packets the progress data and emotion data and sends them to the server.

[1987] Step 10: Implement the PDCA cycle

[1988] server

[1989] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model for analysis. Based on the analysis results, it identifies areas for improvement and proposes new learning content and improvements.

[1990] input

[1991] Resubmitted learning progress and emotion data.

[1992] output

[1993] Suggestions for improvements and new learning content.

[1994] Specific actions

[1995] The server schedules periodic re-analysis tasks to re-analyze progress and emotion data, logs the results, and generates new learning paths and improvements.

[1996] (Application example 2)

[1997] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1998] Conventional learning support systems propose optimal learning routes based solely on learning data without considering the learner's emotional state, resulting in problems such as a decline in learner motivation and reduced learning efficiency. Furthermore, it was difficult to provide learning support in physical stores, and the provision of appropriate learning materials and services was insufficient. This resulted in the quality of the learning environment in physical stores not improving, and learners were not able to learn effectively.

[1999] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2000] In this invention, the server includes a means for collecting learning data and emotional data of the learner, a means for utilizing an AI model for analyzing the learning data and emotional data, and a means for designing an optimal learning route based on the analysis results, thereby making it possible to propose an optimal learning route that also takes into account the learner's psychological state.

[2001] Furthermore, by utilizing smart devices in physical stores and providing appropriate learning materials and services, learners can receive effective learning support even in physical stores, which will help maintain learners' motivation and improve their learning efficiency.

[2002] "Learning data" refers to numerical and textual information that indicates the progress of learning, such as test results and self-assessment data entered by the learner.

[2003] "Emotional data" is information that indicates the learner's psychological state, such as the analysis results of the learner's facial expressions and voice.

[2004] An "artificial intelligence model" is an algorithm or system that learns from past data and analyzes new data to find patterns.

[2005] A "learning route" is a series of learning content and methods recommended for learners to progress effectively through their studies.

[2006] "Devices" refer to information and communication devices such as smartphones and tablets used by learners.

[2007] A "physical store" is a physical store or commercial establishment that learners actually visit.

[2008] A "smart device" is a device that can connect to the Internet and run various applications.

[2009] "Analysis results" are analytical information output by the artificial intelligence model based on collected data.

[2010] "Services" is a general term for teaching materials, educational support, technical support, etc. provided to learners.

[2011] "Data collection means" refers to a method or device for collecting learning data, emotional data, etc.

[2012] To implement this invention, it is necessary to build a system in which the server, terminal, and user elements interact with each other. The system of the present invention provides effective learning support by collecting learning data and emotional data from learners and proposing optimal learning routes through analysis using an artificial intelligence model.

[2013] Data collection

[2014] User

[2015] Users log in to the system using devices such as smartphones or tablets, enter their learning content and test results, and provide facial and voice data using the device's camera and microphone so that emotional data can be collected.

[2016] Terminal

[2017] The device transmits the learning data entered by the user (e.g., test results, self-evaluation) and the emotional data collected by the emotion engine (EmotionEngine) to the server in real time.

[2018] Data analysis

[2019] server

[2020] The server receives the learning data and emotion data sent from the device and stores them in a database. It then analyzes this data based on an artificial intelligence model (LearningAIModel) and designs the optimal learning path. Specifically, it analyzes the current learning data and emotion data while comparing it with the data of many past learners.

[2021] Designing and delivering learning routes

[2022] server

[2023] The server designs the optimal learning route for the user based on the analysis results of the AI ​​model. It determines the next content to study, recommended reference books, and study schedule, and makes adjustments based on the learner's psychological state. This information is sent to the device.

[2024] Terminal

[2025] The terminal notifies the user of the study route, recommended reference books, and study schedule received from the server and displays them.

[2026] Running training and collecting progress

[2027] User

[2028] Users study according to the study plan provided by their device, and periodically enter their study progress and new test results into the platform. During the study, the emotion engine continuously collects the user's emotional data.

[2029] Terminal

[2030] The terminal transmits the learning progress data and emotion data newly input by the user to the server in real time.

[2031] Implementing the PDCA cycle

[2032] server

[2033] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis. Based on the new analysis results, it identifies areas for improvement and optimizes the learning content and methods.

[2034] A concrete example of this system is its use in a study space in a bookstore. Users use devices in the bookstore's study area and provide the system with learning data and emotional data. Based on this, the bookstore will provide the most appropriate books and materials. Examples of prompts for the generative AI model that assists this process include:

[2035] Example prompt sentence:

[2036] "If a student's test score is 85, their self-assessment is good, and their sentiment data indicates 'Satisfied,' suggest the next learning content or material they should study."

[2037] Such a system can maintain learners' motivation and provide a high-quality learning environment.

[2038] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2039] Step 1:

[2040] The user inputs training data and emotion data.

[2041] Users log in to the learning platform using a smartphone or tablet, input their learning content and test results, and provide facial and voice data using a camera and microphone. This generates learning data (e.g., test results, self-evaluation) and emotional data.

[2042] Input: Training data, emotion data

[2043] Output: A set of training data and emotion data

[2044] Step 2:

[2045] The device sends the data to the server

[2046] The device sends the learning data entered by the user and the emotion data collected by the emotion engine to the server, which stores the data in real time.

[2047] Input: A set of training data and emotion data

[2048] Output: Training data and emotion data recorded on the server

[2049] Step 3:

[2050] The server analyzes the data

[2051] The server receives the learning data and emotion data sent from the device and stores them in a database. This data is then input into an artificial intelligence model (LearningAIModel) for analysis. The AI ​​model analyzes the current learning data and emotion data while comparing it with the data of many other learners in the past.

[2052] Input: A set of training data and emotion data, past learner data

[2053] Output: Analysis results

[2054] Step 4:

[2055] The server designs the optimal learning route

[2056] The server designs the optimal learning route for each user based on the analysis results of the AI ​​model. This route includes the next content to study, recommended reference books, and a study schedule. It also adjusts the difficulty and pace of the learning content based on the learner's emotional data.

[2057] Input: Analysis results

[2058] Output: Optimized learning route

[2059] Step 5:

[2060] The server sends the learned route to the device.

[2061] The server transmits the designed learning route to the user's terminal.

[2062] Input: Optimized learning route

[2063] Output: Learned routes sent to the device

[2064] Step 6:

[2065] The device displays and notifies you of the learned route.

[2066] The terminal notifies and displays the received learned route to the user, who can then confirm it and proceed to the next learning step.

[2067] Input: Received learned routes

[2068] Output: The learning route displayed to the user

[2069] Step 7:

[2070] Users conduct learning and update data

[2071] The user follows the provided learning plan and inputs their learning progress data and new test results into the system. The emotion engine continues to collect data on the user's facial expressions and voice.

[2072] Input: New learning progress data, emotion data

[2073] Output: Updated training data and emotion data set

[2074] Step 8:

[2075] The device sends new data to the server

[2076] The terminal again transmits the learning progress data and emotion data newly input by the user to the server.

[2077] Input: Updated training data and emotion data set

[2078] Output: New training data and emotion data recorded on the server

[2079] Step 9:

[2080] The server executes the PDCA cycle

[2081] The server inputs the retransmitted learning progress data and emotion data into the AI ​​model and performs a reanalysis, thereby identifying new areas for improvement and optimizing the user's learning content and methods.

[2082] Input: A new set of training data and emotion data

[2083] Output: Improvements, optimized next learning route

[2084] This process ensures that learners always follow the learning path that best suits them, providing an effective and motivating learning environment.

[2085] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2087] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2088] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2089] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2090] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2091] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2092] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2093] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2094] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2095] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2096] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2097] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2098] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2099] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2100] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2101] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2102] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2103] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2104] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2105] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2106] The following is further disclosed regarding the above embodiment.

[2107] (Claim 1)

[2108] A means for collecting learner learning data;

[2109] A means for utilizing an artificial intelligence model to analyze the training data;

[2110] A means to design the optimal learning route based on the analysis results,

[2111] means for transmitting the learning route to a learner's terminal and displaying it;

[2112] A means of collecting learner progress data again and analyzing areas for improvement;

[2113] A system including:

[2114] (Claim 2)

[2115] 10. The system of claim 1, wherein the learning data includes test results and self-assessment data entered by the learner.

[2116] (Claim 3)

[2117] 2. The system according to claim 1, wherein the artificial intelligence model determines the optimal learning route by studying the past learning data of many other learners.

[2118] "Example 1"

[2119] (Claim 1)

[2120] A means for collecting learner learning data;

[2121] means for transmitting the learning data from the terminal to a server;

[2122] means for storing the learning data in a database;

[2123] A means for utilizing a generative artificial intelligence model to analyze the training data;

[2124] A means to design the optimal learning route, recommended materials, and learning schedule based on the analysis results;

[2125] means for transmitting the learning route, recommended materials, and learning schedule to a learner's terminal and displaying them;

[2126] A means of collecting learner progress data again and analyzing areas for improvement;

[2127] a means of providing improved learning routes based on the results of the reanalysis;

[2128] A system including:

[2129] (Claim 2)

[2130] 10. The system of claim 1, wherein the learning data includes test results and self-assessment data entered by the learner.

[2131] (Claim 3)

[2132] The system according to claim 1, wherein the generative artificial intelligence model determines an optimal learning route by studying the past learning data of many other learners.

[2133] "Application Example 1"

[2134] (Claim 1)

[2135] A means for collecting learner learning data;

[2136] A means for utilizing an artificial intelligence model to analyze the training data;

[2137] A means to design the optimal learning route based on the analysis results,

[2138] means for transmitting the learning route to a learner's terminal and displaying it;

[2139] A means of collecting learner progress data again and analyzing areas for improvement;

[2140] A means for collecting operational data of factory equipment;

[2141] A means for utilizing an artificial intelligence model to analyze the operational data;

[2142] A means for designing an optimal operation route or maintenance route based on the analysis results;

[2143] means for transmitting the operation route or maintenance route to an equipment operation terminal and displaying the route;

[2144] A system including:

[2145] (Claim 2)

[2146] 10. The system of claim 1, wherein the learning data includes test results and self-assessment data entered by the learner.

[2147] (Claim 3)

[2148] 2. The system according to claim 1, wherein the artificial intelligence model determines the optimal learning route by studying the past learning data of many other learners.

[2149] "Example 2: Combining Emotion Engines"

[2150] Claims

[2151] (Claim 1)

[2152] A means for collecting learner learning data;

[2153] a means for utilizing an artificial intelligence model that analyzes the learning data and the learner's emotional data;

[2154] A means to design the optimal learning route based on the analysis results,

[2155] means for transmitting the learning route to a learner's terminal and displaying it;

[2156] A means for collecting the learner's learning progress data and emotion data again and analyzing areas for improvement;

[2157] A system including:

[2158] (Claim 2)

[2159] 2. The system of claim 1, wherein the learning data and emotion data include test results entered by the learner, self-assessment data, and facial and voice data generated by an emotion engine.

[2160] (Claim 3)

[2161] 2. The system according to claim 1, wherein the artificial intelligence model determines the optimal learning route by studying the learning data and emotional data of many other past learners.

[2162] "Application example 2 when combining emotion engines"

[2163] (Claim 1)

[2164] a means for collecting learning data and emotional data of learners;

[2165] a means for utilizing an artificial intelligence model to analyze the training data and emotion data;

[2166] A means to design the optimal learning route based on the analysis results,

[2167] means for transmitting the learning route to a learner's terminal and displaying it;

[2168] A means for collecting the learner's learning progress data and emotion data again and analyzing areas for improvement;

[2169] A means to provide appropriate educational materials and services using smart devices in physical stores,

[2170] A system including:

[2171] (Claim 2)

[2172] 2. The system of claim 1, wherein the learning data and emotional data include test results, self-assessment data, and emotional signals entered by the learner.

[2173] (Claim 3)

[2174] 2. The system according to claim 1, wherein the artificial intelligence model determines the optimal learning route by studying the learning data and emotional data of many other past learners. [Explanation of symbols]

[2175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting learner learning data; A means for utilizing an artificial intelligence model to analyze the training data; A means to design the optimal learning route based on the analysis results, means for transmitting the learning route to a learner's terminal and displaying it; A means of collecting learner progress data again and analyzing areas for improvement; A system including:

2. The system of claim 1 , wherein the learning data includes test results and self-assessment data entered by the learner.

3. 2. The system according to claim 1, wherein the artificial intelligence model determines an optimal learning route by studying past learning data of many other learners.

Images