System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving academic ability information, questionnaire information, and personalized information; generative model means for generating a personalized educational curriculum based on the received information; means for providing optimal webinar and learning materials based on the generated educational curriculum; and means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional uniform education systems make it difficult to efficiently provide education tailored to individual learning styles and goals, making it particularly difficult for working adults to access appropriate information and educational opportunities when reviewing their careers or relearning. This presents a challenge in that individuals are unable to efficiently progress toward their goals. The objective of this invention is to solve this problem and support users in learning in the most optimal way. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal webinars and learning materials based on the generated educational curriculum, and a means for continuously collecting a user's learning progress and re-inputting it into the generative model to update the curriculum, thereby providing an optimal educational curriculum suited to the user's life stage and supporting efficient learning.

[0006] "Academic achievement information" means test results or other assessment data used to assess a user's current level of knowledge or skills.

[0007] "Survey information" is information provided by users regarding their motivation to learn, areas of interest, future goals, etc.

[0008] "Personal Information" refers to basic profile information such as a user's age, gender, and area of ​​residence.

[0009] A "means for receiving" is a software and hardware component for collecting and storing information from a user.

[0010] A "generative model means" is an algorithm or AI model that analyzes received information and generates an optimal educational curriculum.

[0011] An "educational curriculum" is a set of learning content, materials, and activity plans designed around the user's learning goals.

[0012] A "webinar" is a lecture or seminar delivered online in real time or in a recorded format.

[0013] "Learning materials" means materials or content provided to support learning, including books, videos, slides, etc.

[0014] The "means of delivery" refers to the mechanism for distributing the generated curriculum, teaching materials, and webinars to users.

[0015] "Study progress" is data used to evaluate how far a user has progressed in their studies according to the curriculum.

[0016] A "means for continuous collection" is a system component that monitors a user's learning activities and collects data periodically.

[0017] The "means for re-inputting the generative model and updating the curriculum" refers to the processes and algorithms used to analyze the collected learning progress data and dynamically change and update the educational curriculum as needed. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] User registration and data collection

[0040] server

[0041] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, location, and learning goals), the server stores this information in a database and generates a unique ID for each user based on the stored information.

[0042] User

[0043] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[0044] server

[0045] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[0046] Generation of individually optimized curriculum

[0047] server

[0048] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[0049] server

[0050] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[0051] Curriculum delivery and webinar distribution

[0052] User

[0053] Users can log in to the platform and view the curriculum content that is tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[0054] server

[0055] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[0056] User

[0057] Users can join the webinar using the provided link and learn in real time or on-demand.

[0058] Assessment of learning progress and curriculum updates

[0059] server

[0060] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0061] server

[0062] The collected data is fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and if necessary, update the educational curriculum and apply new learning content and recommended learning methods.

[0063] server

[0064] Notifying users of updated curriculum and making new content available on the platform.

[0065] Specific examples

[0066] When user "Tanaka" registers

[0067] server

[0068] A new user named "Tanaka" accesses the platform, enters the necessary information, and sets his / her learning goal as "I want to learn digital marketing." The server registers this in the database and automatically generates an academic ability test.

[0069] User

[0070] Tanaka takes an academic ability test and sends the results to the server.

[0071] server

[0072] The server analyzes the test results and survey information and generates a curriculum called "Marketing Fundamentals," "Market Analysis," and "Digital Marketing Practice." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[0073] User

[0074] John logs into the platform and joins the "Marketing Fundamentals" webinar using the link provided. He also downloads the provided materials to review later.

[0075] server

[0076] Monitor Tanaka's progress and update the curriculum as needed, for example, by suggesting additional learning resources or different learning methods if she is performing poorly in a particular area.

[0077] In this way, a system can be created that continues to provide an educational curriculum optimized for the user's life stage and goals.

[0078] The processing flow will be explained below.

[0079] User registration and data collection

[0080] Step 1:

[0081] Server: Displays the user registration form on the web browser or mobile app.

[0082] Step 2:

[0083] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[0084] Step 3:

[0085] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[0086] Step 4:

[0087] Server: Automatically generates individual academic ability tests based on collected profile information.

[0088] Step 5:

[0089] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[0090] Step 6:

[0091] User: Takes an online academic achievement test and sends the test results to the server.

[0092] Generation of individually optimized curriculum

[0093] Step 7:

[0094] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[0095] Step 8:

[0096] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[0097] Step 9:

[0098] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[0099] Step 10:

[0100] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[0101] Curriculum delivery and webinar distribution

[0102] Step 11:

[0103] Server: Displays individually optimized curriculum content to users who log in to the platform.

[0104] Step 12:

[0105] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[0106] Step 13:

[0107] Users: Join the webinar using the provided link and learn in real time or on demand.

[0108] Step 14:

[0109] Users: Download the webinar materials and videos for future reference.

[0110] Assessment of learning progress and curriculum updates

[0111] Step 15:

[0112] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0113] Step 16:

[0114] Server: Re-inputs the collected learning progress data into the generative AI model to evaluate the user's progress and learning effectiveness.

[0115] Step 17:

[0116] Server: Based on the evaluation results, update the educational curriculum as necessary.

[0117] Step 18:

[0118] Server: Notifies users of updated curriculum content and makes new learning content available on the platform.

[0119] Specific examples

[0120] For new user "Yamada"

[0121] Step 1:

[0122] Server: Displays the user registration form on the web browser.

[0123] Step 2:

[0124] User (Yamada): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn the basics of marketing") and submit.

[0125] Step 3:

[0126] Server: Saves Yamada's information in the database and generates a unique user ID.

[0127] Step 4:

[0128] Server: Automatically generate an individual academic ability test based on Yamada's profile.

[0129] Step 5:

[0130] Server: Send the generated academic ability test to Yamada's registered email address.

[0131] Step 6:

[0132] User (Yamada): Takes an online academic achievement test and sends the results to the server.

[0133] Step 7:

[0134] Server: Receives Yamada's academic test results, survey information, and personal information and inputs them into the generative AI model.

[0135] Step 8:

[0136] Server: The generative AI model generates the optimal educational curriculum for Yamada.

[0137] Step 9:

[0138] Server: Stores the generated curriculum ("Fundamentals of Marketing," "Market Analysis," "Fundamentals of Digital Marketing") in a database.

[0139] Step 10:

[0140] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Yamada's curriculum.

[0141] Step 11:

[0142] Server: When Yamada logs in to the platform, the contents of the individually optimized curriculum are displayed.

[0143] Step 12:

[0144] Server: Notifies Yamada of the distribution schedule and live webinar schedule.

[0145] Step 13:

[0146] User (Yamada): Attends the "Marketing Basics" webinar using the link provided.

[0147] Step 14:

[0148] User (Yamada): Download the webinar materials and videos and use them for review later.

[0149] Step 15:

[0150] Server: Continuously collects Yamada's learning progress (test results, webinar participation records, viewing history).

[0151] Step 16:

[0152] Server: Re-inputs the collected data into the generative AI model to evaluate Yamada's progress and the effectiveness of his learning.

[0153] Step 17:

[0154] Server: Update Yamada's curriculum to include new learning content and recommended learning methods.

[0155] Step 18:

[0156] Server: Notifies Yamada of updated curriculum and displays new learning content on the platform.

[0157] Example 1

[0158] 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."

[0159] Conventional educational systems have struggled to efficiently provide individually optimized educational curricula for each user. As a result, users are unable to receive effective educational programs tailored to their learning goals, leaving them seeking further improvement in their learning outcomes. Furthermore, there is a lack of mechanisms for continuously evaluating learning progress and updating the curriculum, preventing users from maximizing their learning effectiveness.

[0160] 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.

[0161] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generating AI model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal teaching materials and webinars based on the generated educational curriculum, a means for continuously collecting the user's learning progress and re-inputting it into the generating AI model to update the curriculum, and a means for notifying the user of the updated curriculum and making the new content available on the platform. This makes it possible to provide an individually optimized educational curriculum for each user and to continuously update the curriculum based on the user's learning progress.

[0162] "Academic ability information" is information that indicates the user's academic ability and learning ability.

[0163] "Survey information" is survey information used to collect information about users' interests, concerns, learning goals, etc.

[0164] "Personal Information" refers to personal data such as a user's name, age, gender, and area of ​​residence.

[0165] A "generative AI model" is an artificial intelligence model used to generate individually optimized educational curricula based on input data.

[0166] A "curriculum" is the content of an educational program designed to meet specific learning objectives.

[0167] "Instructional materials" are educational resources such as books, videos, and documents used for learning.

[0168] A "webinar" is a lecture or seminar delivered online.

[0169] "Study progress" refers to data and records that indicate how far a user has progressed in their studies.

[0170] "Platform" means a website or application through which users log in and access learning resources and curriculum.

[0171] "Notifications" are messages or alerts that inform users of new or updated information.

[0172] The present invention relates to a system for providing an individually optimized educational curriculum. How the present invention is implemented will be described below in detail.

[0173] User registration and data collection

[0174] server

[0175] The server provides a user registration form on the web browser or mobile app for new user registration. This form is displayed when the user accesses the site. The user enters required information such as name, age, gender, residential area, and learning goals, and submits the form to provide the information to the server. The server stores this received information in a database and generates a unique ID for each user, which is also stored in the database.

[0176] User

[0177] The user enters the necessary information into the provided registration form and submits it, then takes an online academic ability test provided by the server and submits the results of the academic ability test to the server.

[0178] server

[0179] The server stores the academic test results and questionnaire information received from the user in a database.

[0180] Generation of individually optimized curriculum

[0181] server

[0182] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum. An example of the input prompt is as follows:

[0183] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[0184] teaching materials

[0185] Study Schedule

[0186] Recommended learning methods

[0187] The server receives the optimal educational curriculum output from the generative AI model and stores it in a database, including learning materials, study schedules, and recommended learning methods.

[0188] server

[0189] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[0190] Curriculum delivery and webinar distribution

[0191] User

[0192] Users log in to the platform and view the curriculum content tailored to them, and the server notifies them of the schedule of pre-recorded webinar videos and live webinars.

[0193] server

[0194] The server provides users with webinar links and materials, making it easier for them to learn through webinars. After watching the webinar, users can download the materials and videos.

[0195] User

[0196] Users can join the webinar using the provided link and learn in real time or on-demand.

[0197] Assessment of learning progress and curriculum updates

[0198] server

[0199] The server continuously collects user learning progress data (e.g., test results, webinar participation records, viewing history, etc.).

[0200] server

[0201] The server re-inputs the collected data into the generative AI model to evaluate the user's progress and learning effectiveness, and based on this, updates the educational curriculum as needed.

[0202] server

[0203] The server notifies users of updated curriculum and makes new content available on the platform.

[0204] Specific examples

[0205] For example, if a user named "Tanaka" accesses the platform saying that he wants to learn digital marketing, the server stores input data, including Tanaka's name and other basic information, in a database. Tanaka takes an online academic ability test provided by the server and sends the results to the server. The server analyzes the results and automatically generates a curriculum, such as "Marketing Basics," "Market Analysis," and "Digital Marketing Practice," based on a generative AI model. Tanaka can log in to check the curriculum content and progress through webinars and learning materials. The server also monitors Tanaka's learning progress and updates the curriculum content accordingly.

[0206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0207] Step 1:

[0208] User registration and data collection

[0209] server

[0210] The server provides a user registration form on the web browser or mobile app that the user accesses.

[0211] Input: User-entered name, age, gender, location, and learning goal

[0212] Output: User information stored in database, unique user ID generated

[0213] What happens: The server receives the information the user entered into the form and saves it to a database. When saving, it generates a unique user ID and adds it to the database.

[0214] Step 2:

[0215] Academic achievement tests

[0216] User

[0217] Users take online academic achievement tests provided by the server.

[0218] Input: Test questions and user answers

[0219] Output: User's academic achievement test results

[0220] How it works: Users take online academic tests and send their answers to the server, which then calculates test results based on the received answers.

[0221] Step 3:

[0222] Collecting survey information

[0223] User

[0224] The user enters additional survey information and submits it to the server.

[0225] Input: User's survey response

[0226] Output: Survey information stored in a database

[0227] What it does: Users fill out a survey about their interests and learning goals and submit it to the server, which stores this information in a database.

[0228] Step 4:

[0229] Generation of individually optimized curriculum

[0230] server

[0231] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum.

[0232] Input: User's academic test results, survey information, personal information

[0233] Output: Individually optimized educational curriculum

[0234] What happens: The server inputs the following prompt into the generative AI model:

[0235] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[0236] teaching materials

[0237] Study Schedule

[0238] Recommended learning methods

[0239] The optimal curriculum content (teaching materials, study schedule, recommended study methods) output by the generative AI model is stored in a database.

[0240] Step 5:

[0241] Providing the best educational materials and webinars

[0242] server

[0243] Based on the generated curriculum, the server selects the most suitable teaching materials and webinar links from a related teaching material database and adds them to the curriculum.

[0244] Input: Generated educational curriculum

[0245] Output: A curriculum with the best learning materials and webinar links

[0246] Specific operation: Based on the curriculum content, the server searches for the most suitable teaching materials from the related teaching material database and adds them to the user's curriculum.

[0247] Step 6:

[0248] Provision and notification of curriculum content

[0249] User

[0250] Users log in to the platform and view the curriculum content tailored to them, and the server also notifies them of the webinar schedule.

[0251] Input: Platform login information

[0252] Output: Optimized curriculum content and webinar schedule

[0253] Specific behavior: When a user accesses the platform, the server displays a personalized curriculum and webinar schedule.

[0254] Step 7:

[0255] Webinar link and materials provided

[0256] server

[0257] The server provides the webinar link and materials to the user.

[0258] Input: Webinar link and materials

[0259] Output: Webinar link and materials provided to users

[0260] What it does: The server provides users with the webinar link and necessary materials electronically, facilitating their learning.

[0261] Step 8:

[0262] Assessment of learning progress and curriculum updates

[0263] server

[0264] The server continuously collects user learning progress data and re-inputs it into the generative AI model to update the curriculum.

[0265] Input: Learning progress data (test results, webinar participation records, viewing history, etc.)

[0266] Output: Updated educational curriculum

[0267] What it does: The server re-inputs the collected progress data into the generative AI model to evaluate the user's progress and learning effectiveness, update the curriculum as needed, and notify the user of the new content.

[0268] (Application example 1)

[0269] 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."

[0270] There is a need to provide efficient and effective technical education to robot operators and engineers working in factories. However, there is currently a lack of means to provide a curriculum tailored to the abilities and learning progress of each engineer, and standardized education methods have their limitations. Another challenge is providing an environment where engineers can learn at any time without interfering with on-site work.

[0271] 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.

[0272] In this invention, the server further includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal webinars and learning materials based on the generated educational curriculum, a means for continuously collecting user learning progress and re-inputting it into the generative model to update the curriculum, a means for generating and providing an individually optimized technical educational curriculum for factory robot operators and engineers, and a distribution means for providing webinars and technical materials. This allows learning content tailored to individual engineers to enable efficient and effective technical acquisition.

[0273] "Academic ability information" is data that indicates the current academic level and learning ability of the educational recipient.

[0274] "Survey information" is survey response data related to the learning goals and learning strategies of the education recipients.

[0275] "Personal information" refers to data that can individually identify an educational recipient, such as the name, age, gender, and area of ​​residence.

[0276] The "generative model means" refers to an artificial intelligence model and its execution mechanism for generating an individually optimized educational curriculum by utilizing the received academic ability information, questionnaire information, and personal information.

[0277] A "webinar" is an online lecture or seminar delivered over the Internet in real time or on demand.

[0278] "Learning materials" refers to educational content such as books, videos, and documents provided based on the educational curriculum.

[0279] "Learning progress" is data that indicates how much learning outcomes an educational subject has achieved through the educational curriculum.

[0280] A "factory robot operator" is an engineer responsible for operating and maintaining robots within a factory.

[0281] "Engineers" are professionals who have specific specialized skills and use them to carry out their work.

[0282] "Delivery medium" refers to the communications technology and its implementation for delivering educational curriculum, webinars, and technical materials to educational audiences via the Internet.

[0283] In the following, a specific method for implementing the present invention will be described, in which a system is realized in which factory robot operators and engineers receive an individually optimized technical training curriculum.

[0284] First, the server provides a user registration function. Factory robot operators and technicians (hereafter referred to as users) use a smartphone or head-mounted display to access a user registration form provided in a web browser or mobile app, and enter the required personal information (such as name, age, gender, residential area, and learning goals). This information is sent to the server and stored in a database. The server then generates a unique ID for each user based on the stored information.

[0285] The server then administers an online academic ability test to the user. The user takes the test and sends the results to the server. Based on the test results and the questionnaire information entered at the time of registration, the server generates an individually optimized educational curriculum tailored to each user's academic ability level and learning goals. The server automatically generates the curriculum using a generative AI model, and stores its contents (teaching materials, study schedule, recommended study methods, etc.) in a database.

[0286] Based on the generated curriculum, the server selects the most appropriate learning materials from a database of related learning materials and adds them to the user's curriculum. The server also provides the user with a webinar schedule and video links based on the curriculum. The user can join the webinar using the provided link and progress with their learning in real time or on-demand. After viewing the webinar, the user can download the materials and videos for future reference.

[0287] The server continuously collects user learning progress data (such as academic test results, webinar participation records, and viewing history). This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness. If necessary, the educational curriculum is updated and new learning content and recommended learning methods are applied. This process provides an optimal learning environment for factory robot operators and technicians, enabling them to acquire skills efficiently and effectively.

[0288] As a concrete example, consider the case where a user named "Yamada" registers with the system with the goal of learning "robot programming." Yamada enters his personal information and learning goals and passes an academic ability test. The server analyzes Yamada's academic ability test results and questionnaire information and generates an individually optimized "robot programming" curriculum. The curriculum includes chapters such as "Robot Basics," "Programming Applications," and "Robot Maintenance," and provides corresponding webinars and learning materials for each. Yamada participates in webinars using a smartphone or head-mounted display, downloads the provided materials, and advances his studies. Yamada's learning progress is also monitored by the server, and the curriculum content is updated and improved as necessary.

[0289] This invention provides learning content tailored to individual factory robot operators and engineers, enabling them to acquire skills efficiently and effectively without interfering with on-site work.

[0290] An example of an input prompt for a generative AI model is:

[0291] "Generate an educational curriculum with the learning objective 'Robot Programming'."

[0292] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0293] Step 1:

[0294] A user accesses a web browser or mobile app using a smartphone or head-mounted display, enters the required personal information (such as name, age, gender, residential area, learning goals, etc.) in the user registration form, and submits it. Based on this input, the server stores the information in a database and generates a unique ID. The output is the stored personal information and the generated user ID.

[0295] Step 2:

[0296] The server provides online achievement tests to users. The users take the achievement tests and send their results to the server. Based on this input, the server stores the achievement test results in a database. The output is the stored achievement test results.

[0297] Step 3:

[0298] Based on the above academic ability information, questionnaire information, and personal information, the server uses a generative AI model to generate an individually optimized educational curriculum. The prompt sentence input to the generative AI model is, "Please generate an educational curriculum whose learning goal is 'robot programming'." The server saves the generated curriculum in a database. The output is the generated individually optimized curriculum.

[0299] Step 4:

[0300] The server selects the most suitable learning materials and webinar links based on the generated curriculum. These learning materials and links are added to the curriculum and notified to the user. The output is the notified learning materials and webinar links.

[0301] Step 5:

[0302] Users join a webinar using a link provided by the server. The server delivers the webinar in real-time or on-demand format, and users send their participation records and viewing histories to the server. Based on this input, the server stores the webinar participation records and viewing histories in a database. The output is the stored participation records and viewing histories.

[0303] Step 6:

[0304] The server continuously collects the user's learning progress data (such as academic test results, webinar participation records, and viewing history) and re-inputs it into the generative AI model. The generative AI model evaluates the user's progress and learning effectiveness and generates a new curriculum. The server saves the updated curriculum in a database and notifies the user again. The output is an updated, individually optimized curriculum.

[0305] Step 7:

[0306] The user continues learning based on new curriculum provided by the server. This process is cyclical until the user has fully mastered the target skill. The final output is the user's degree of mastery of the skill.

[0307] 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.

[0308] User registration and data collection

[0309] server

[0310] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, place of residence, and learning goals), the server stores this information in a database. A unique ID is generated for each user based on the stored information.

[0311] User

[0312] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[0313] server

[0314] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[0315] Generation of individually optimized curriculum

[0316] server

[0317] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[0318] server

[0319] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[0320] Introducing the Emotion Engine

[0321] Emotion Engine

[0322] The emotion engine recognizes emotions in real time by analyzing the user's facial expressions, tone of voice, and other biometric signals.

[0323] server

[0324] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[0325] Curriculum delivery and webinar distribution

[0326] User

[0327] Users can log in to the platform and view the curriculum tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[0328] server

[0329] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[0330] User

[0331] Users can join the webinar using the provided link and learn in real time or on-demand.

[0332] Emotion Engine

[0333] During a webinar or while learning, the emotion engine analyzes the user's emotions in real time and sends the results to the server.

[0334] Assessment of learning progress and curriculum updates

[0335] server

[0336] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0337] server

[0338] The collected data and emotional data are fed back into the generative AI model to assess the user's progress and the effectiveness of their learning.

[0339] server

[0340] Based on the evaluation results, the educational curriculum is updated as needed. For example, if users feel "confused" or "stressed" about a particular topic, additional resources or different teaching methods will be introduced to reinforce that section.

[0341] server

[0342] Notifying users of updated curriculum and making new content available on the platform.

[0343] Specific examples

[0344] For new user "Sato"

[0345] server

[0346] A new user named "Sato" accesses the platform, enters the necessary information, and sets his or her learning goal as "I want to learn data science." The server then registers this information in the database and automatically generates an academic achievement test.

[0347] User

[0348] Sato takes an academic ability test and sends the results to the server.

[0349] server

[0350] The server analyzes the test results and survey information and generates a curriculum called "Fundamental Concepts of Data Science," "Data Analysis," and "Machine Learning." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[0351] User

[0352] Sato logs in to the platform and joins the "Fundamental Concepts of Data Science" webinar using the provided link. He also downloads the provided materials for later review.

[0353] Emotion Engine

[0354] During the webinar, the emotion engine analyzes Sato's facial expressions and tone of voice, recognizing emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[0355] server

[0356] Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[0357] server

[0358] Based on the analysis, if Sato feels "uneasy" about a particular section, we will provide him with additional resources to reinforce that section and update the curriculum.

[0359] server

[0360] The updated curriculum will be notified to Sato and new learning content will be displayed on the platform.

[0361] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[0362] The processing flow will be explained below.

[0363] User registration and data collection

[0364] Step 1:

[0365] Server: Displays the user registration form on the web browser or mobile app.

[0366] Step 2:

[0367] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[0368] Step 3:

[0369] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[0370] Step 4:

[0371] Server: Automatically generates individual academic ability tests based on collected profile information.

[0372] Step 5:

[0373] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[0374] Step 6:

[0375] User: Takes an online academic achievement test and sends the test results to the server.

[0376] Generation of individually optimized curriculum

[0377] Step 7:

[0378] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[0379] Step 8:

[0380] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[0381] Step 9:

[0382] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[0383] Step 10:

[0384] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[0385] Introducing the Emotion Engine

[0386] Step 11:

[0387] Emotion Engine: Recognizes the user's emotions in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[0388] Step 12:

[0389] Server: Receives user emotion data sent from the emotion engine and inputs it into the generative AI model.

[0390] Curriculum delivery and webinar distribution

[0391] Step 13:

[0392] Server: Displays individually optimized curriculum content to users who log in to the platform.

[0393] Step 14:

[0394] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[0395] Step 15:

[0396] Users: Join the webinar using the provided link and learn in real time or on demand.

[0397] Step 16:

[0398] Users: Download the webinar materials and videos for future reference.

[0399] Step 17:

[0400] Emotion Engine: Analyzes user emotions in real time during webinars and learning sessions and sends the results to the server.

[0401] Assessment of learning progress and curriculum updates

[0402] Step 18:

[0403] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0404] Step 19:

[0405] Server: Collected learning progress data and emotional data are re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[0406] Step 20:

[0407] Server: Based on the assessment results, update the educational curriculum as needed. For example, if users are "confident" or "stressed" about a particular topic, introduce additional resources or different teaching methods to reinforce that section.

[0408] Step 21:

[0409] Server: Notifies users of updated curriculum and makes new learning content visible on the platform.

[0410] Specific examples

[0411] For new user "Sato"

[0412] Step 1:

[0413] Server: Displays the user registration form on the web browser.

[0414] Step 2:

[0415] User (Sato): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn data science") and submit.

[0416] Step 3:

[0417] Server: Saves Sato's information in a database and generates a unique user ID.

[0418] Step 4:

[0419] Server: Automatically generate an individual academic ability test based on Sato's profile.

[0420] Step 5:

[0421] Server: Send the generated academic ability test to Sato's registered email address.

[0422] Step 6:

[0423] User (Sato): Takes an online academic ability test and sends the results to the server.

[0424] Step 7:

[0425] Server: Receives Sato's academic test results, survey information, and personal information and inputs them into the generative AI model.

[0426] Step 8:

[0427] Server: The generative AI model generates the optimal educational curriculum for Sato.

[0428] Step 9:

[0429] Server: Stores the generated curriculum ("Fundamentals of Data Science," "Data Analysis," and "Machine Learning") in a database.

[0430] Step 10:

[0431] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Sato's curriculum.

[0432] Step 11:

[0433] Server: When Sato logs in to the platform, the contents of the individually optimized curriculum are displayed.

[0434] Step 12:

[0435] Server: Notifies Sato of the distribution schedule and live webinar schedule.

[0436] Step 13:

[0437] User (Sato): Join the "Fundamental Concepts of Data Science" webinar using the provided link.

[0438] Step 14:

[0439] User (Sato): Download the webinar materials and videos and use them for review later.

[0440] Step 15:

[0441] Emotion engine: During the webinar, Sato's facial expressions and tone of voice are analyzed to recognize emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[0442] Step 16:

[0443] Server: Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[0444] Step 17:

[0445] Server: Based on the analysis, if Sato feels "uneasy" about a particular section, provide him with additional resources to reinforce that section and update the curriculum.

[0446] Step 18:

[0447] Server: Notifies Sato of updated curriculum and displays new learning content on the platform.

[0448] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[0449] Example 2

[0450] 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."

[0451] Conventional educational systems have difficulty providing individually optimized educational programs tailored to the user's academic ability and learning goals. Furthermore, they lack a means to grasp the user's emotional state in real time while learning, making it difficult to update educational programs accordingly. Furthermore, there are limitations to providing an environment where users can learn anytime, anywhere.

[0452] 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.

[0453] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational program based on the received information, a means for providing optimal online lectures and learning materials based on the generated educational program, a means for analyzing the user's biosignals and collecting emotional data in real time, a means for inputting the emotional data into the generative model and updating the educational program taking the user's emotional state into account, and a means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum. This allows an individually optimized educational program to be provided in real time, taking the user's emotional state into consideration, enabling the user to study efficiently anytime, anywhere.

[0454] "Academic ability information" is information that indicates the user's current academic ability level, including test results and past learning history.

[0455] "Survey Information" is subjective information provided by users, including information about their learning goals, interests, and experiences.

[0456] "Personal information" refers to basic information such as a user's name, age, gender, and area of ​​residence.

[0457] A "generative model means" is a means including a generative AI model for automatically generating an optimal educational program based on input data.

[0458] An "educational program" is an individually optimized curriculum tailored to a user's learning goals and academic level, and includes teaching materials, study schedules, recommended study methods, etc.

[0459] "Online lectures" refer to classes provided over the Internet, either in real time or via recorded video lectures.

[0460] "Learning Materials" are resources such as textbooks, videos, and exercises provided as part of an educational program.

[0461] "Biological signals" are signals emitted from the user's body, including facial expressions, tone of voice, heart rate, etc.

[0462] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing biosignals.

[0463] "Study progress" is data that shows how far a user has progressed in their studies, and includes test results, viewing history, participation records, etc.

[0464] "Generative AI" is a type of artificial intelligence that refers to a system that generates optimal educational programs and curricula based on large amounts of data.

[0465] "Delivery medium" means a means, including internet-based technologies and services, for providing online courses and educational materials to users.

[0466] The present invention is a system for providing users with individually optimized educational programs. A specific implementation method thereof will be described below.

[0467] User registration and data collection

[0468] server

[0469] The server displays a user registration form on the web browser or mobile app. The form includes fields for entering information such as name, age, gender, location, and learning goals. After the user enters and submits the required information, the server stores the entered data in a database. At the same time, it generates a unique ID for each user. For example, if a user enters "I want to study data science," that information is stored.

[0470] User

[0471] The user fills in the required information in the provided form and submits it to the server. The user also clicks on the link for the academic achievement test sent from the server and takes the test online. For example, a user named "Sato" fills in the form with his / her goal of "I want to study data science," submits it, and then takes the academic achievement test.

[0472] server

[0473] The server receives the results of the academic ability test sent by the user, stores them in a database, and then analyzes the test results and questionnaire information to generate an individual educational program based on the user's academic level and goals.

[0474] Generation of individually optimized curriculum

[0475] server

[0476] The server inputs the received data into the generative AI model. At that time, the data is sent to the generative AI model as a prompt. By sending a prompt in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" to the generative AI model, the model generates an optimal curriculum.

[0477] server

[0478] The server receives the curriculum content (teaching materials, study schedule, recommended learning methods) returned by the generative AI model and stores them in a database. At the same time, it picks up appropriate teaching materials from the related teaching materials database and adds them to this curriculum.

[0479] Introducing the Emotion Engine

[0480] Emotion Engine

[0481] The emotion engine generates emotion data by analyzing the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) in real time. For example, while a user is attending an online lecture, it analyzes camera footage and audio data to generate emotion data such as "interested" or "confused."

[0482] server

[0483] The server receives the user's emotional data sent from the emotion engine and inputs it into the generative AI model. This emotional data is analyzed as the user progresses and is used to update the educational program accordingly.

[0484] Curriculum delivery and webinar distribution

[0485] User

[0486] Users log in to the platform and check their curriculum. The server notifies them of the links and materials for the online lectures they provide. For example, "Sato" logs in to the platform and obtains a link to participate in an online lecture called "Basics of Data Science."

[0487] server

[0488] The server provides links to online lectures and materials for users to access anytime, anywhere, and also provides recorded videos and materials for users to review later.

[0489] User

[0490] When a user clicks on an online lecture link, they can take the lecture in real time or on-demand. For example, "Sato" can participate in the "Basics of Data Science" course in real time and download the provided materials for review.

[0491] Emotion Engine

[0492] While users are taking online classes, the emotion engine analyzes their facial expressions and tone of voice and sends the data to a server, which collects it and feeds it into a generative AI model to update the curriculum.

[0493] Assessment of learning progress and curriculum updates

[0494] server

[0495] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.), which is then re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[0496] server

[0497] Based on the assessment, the user's educational program is updated as needed, for example, if a user feels "stuck" in a particular learning section, additional resources or different teaching methods are provided to reinforce that section.

[0498] server

[0499] Users are notified of updated curriculum and can view the new content on the platform. For example, if "Sato" receives an email notification of an updated educational program, the new learning content will be displayed when he logs in to the platform.

[0500] Through this system, it is possible to optimize the user's learning experience and provide an efficient learning process.

[0501] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0502] Step 1:

[0503] User registration and data collection

[0504] server

[0505] The server displays a user registration form on a web browser or mobile app. As input, the user enters their name, age, gender, location, learning goals, etc. When the user submits the input, the server receives it and stores it in a database. At the same time, it generates and stores a unique ID for each user. The output includes the stored user information and the unique user ID.

[0506] User

[0507] The user fills in the required information in the form and submits it. For example, a user named "Sato" enters his / her learning goal of "I want to learn data science" and submits it. The server then receives an automatically generated academic achievement test link. The user clicks on the link and takes the test online. The results of the academic achievement test are then sent as input to the server.

[0508] server

[0509] The server receives the academic ability test results sent by the user and stores them in a database. The input includes the academic ability test results and the user ID. It then analyzes the results and questionnaire information and generates an individual educational program based on the user's academic ability level and goals as output.

[0510] Step 2:

[0511] Generation of individually optimized curriculum

[0512] server

[0513] The server inputs the user information stored in the database and the academic achievement test results into the generative AI model. As input, it generates a prompt sentence in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" and sends it to the generative AI model. The model analyzes the data and generates an optimal educational curriculum. The output is the curriculum content returned by the generative AI model.

[0514] server

[0515] The generated curriculum content (teaching materials, study schedule, recommended study methods) is saved in a database. The most suitable teaching materials are selected from the related teaching materials database and added to the curriculum. This completes the educational program provided to the user.

[0516] Step 3:

[0517] Introducing the Emotion Engine

[0518] Emotion Engine

[0519] The emotion engine analyzes the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) and generates emotional data. Inputs include real-time camera footage and audio data. Based on this, it generates emotional data such as "User ID: XXXX, facial expression: smiling, tone of voice: optimistic." The output is the emotional data resulting from the analysis.

[0520] server

[0521] The server receives the emotion data sent from the emotion engine and inputs it into the generative AI model. The emotion data is included as input. The model updates the generated curriculum based on the emotion data as appropriate. An individualized educational program is generated that takes the emotion data into consideration.

[0522] Step 4:

[0523] Curriculum delivery and webinar distribution

[0524] User

[0525] The user logs in to the platform and checks the optimized curriculum. The server notifies them of the online lecture link and materials. For example, Sato logs in to the platform and checks the link to join the "Basic Data Science Course." The user clicks and receives the lecture in real time. The online lecture link and materials are provided as input.

[0526] server

[0527] The server provides links to online lectures and materials, allowing users to access them anytime, anywhere. As an output, it collects user participation records and viewing histories. It also provides recorded videos and materials for offline use.

[0528] Emotion Engine

[0529] While a user is taking an online lecture, the emotion engine analyzes their facial expressions and tone of voice to generate emotion data, which is then sent to the server. The input includes real-time video and audio data, and the output is the emotion data resulting from the analysis.

[0530] Step 5:

[0531] Assessment of learning progress and curriculum updates

[0532] server

[0533] The server continuously collects users' learning progress data (such as test results, webinar participation records, and viewing history). This data is included as input. This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and update the educational program as needed. The output is the evaluation results and an updated curriculum.

[0534] server

[0535] Based on the evaluation results, the educational program is updated accordingly and notified to the user. For example, if a user is struggling with a particular learning section, additional resources or different teaching methods can be provided to reinforce that section. The updated curriculum is then notified to the user.

[0536] User

[0537] Users can check the updated curriculum on the platform and engage in new learning content. For example, Sato revisits the updated "Basic Data Science Course" and continues his learning using new materials and resources. New learning resources are provided as input.

[0538] In this way, the entire system is designed to collect and analyze data in real time to optimize the user's learning experience.

[0539] (Application example 2)

[0540] 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."

[0541] Conventional education and work instruction systems have had difficulty providing individually optimized curricula that reflect the user's (worker's) learning progress and emotional state in real time. In addition, there has been a lack of means to improve learning and work efficiency by capturing the user's emotional state and providing appropriate feedback.

[0542] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving academic ability information, questionnaire information, and personal information; a generative model means for generating an individually optimized educational curriculum based on the received information; means for providing optimal webinars and learning materials based on the generated educational curriculum; means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum; means for analyzing the worker's emotional state in real time using a camera and microphone and inputting the data into the generative model; and means for dynamically updating the worker's operation method and supplementary materials based on the analyzed emotional data. This makes it possible to provide an individually optimized curriculum and real-time feedback that improves the user's learning efficiency and work efficiency.

[0543] "Academic achievement information" is data that indicates an individual user's educational level and academic achievement test results.

[0544] "Survey information" is data collected to understand user attributes, learning objectives, interests, etc.

[0545] "Personal information" is information that can be used to identify an individual user, such as the user's name, age, gender, and area of ​​residence.

[0546] "Means" is a broad concept that includes methods and devices for achieving a specific purpose.

[0547] A "generative model" is an algorithm or software used to create an individually optimized educational curriculum based on collected data.

[0548] A "webinar" is a form of online seminar or lecture held over the web.

[0549] "Learning Materials" are educational resources and materials provided to achieve specific learning objectives.

[0550] "Curriculum updating" is the process of adjusting and improving existing educational plans based on users' learning progress and emotional data.

[0551] A "camera" is a device for capturing images and storing them as digital data.

[0552] A "microphone" is a device that collects sound and stores it as digital data.

[0553] An "emotional state" is a state that indicates a user's psychological and emotional response.

[0554] "Real-time analysis" is the process of processing collected data immediately and reflecting and utilizing the results immediately.

[0555] "Dynamic updates" means flexibly changing the system and curriculum in response to changes in the user's status and environment.

[0556] The embodiment of this invention is a system that provides individually optimized educational curricula using a generative AI model based on user registration information and academic achievement test results. This system utilizes a server, user terminals, and an emotion engine to educate and train workers.

[0557] User registration and data collection

[0558] server

[0559] The server receives the user's registration information (academic achievement information, questionnaire information, and personal information).

[0560] This information is entered using a web browser or mobile app and stored in a database.

[0561] User

[0562] The user uses a terminal to enter the necessary information and send it to the server. For example, a factory worker uses a tablet to take an academic achievement test and then sends the results to the server.

[0563] Generation of individually optimized curriculum

[0564] server

[0565] The server uses a generative AI model based on the received data to generate an optimal educational curriculum, which includes learning materials, a study schedule, and recommended study methods.

[0566] The generated curriculum is stored in a database.

[0567] Introducing the Emotion Engine

[0568] Emotion Engine

[0569] The emotion engine uses hardware such as a camera and microphone to recognize a user's emotional state in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[0570] server

[0571] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[0572] Curriculum delivery and monitoring

[0573] User

[0574] Users view educational videos and instructional materials on holographic displays and tablets based on the provided curriculum.

[0575] server

[0576] The server provides optimal webinars and learning materials based on the generated curriculum, such as safety training videos and instructional curriculum for operating procedures for factory workers.

[0577] The server monitors the user's emotional state in real time and dynamically updates the curriculum based on this.

[0578] Assessment of learning progress and curriculum updates

[0579] server

[0580] The server re-inputs the user's learning progress data (test results, webinar participation records, viewing history) and emotional data into the generative AI model to evaluate the user's progress and the effectiveness of their learning.

[0581] Based on the evaluation results, the curriculum will be updated as needed. For example, if users experience "stress" or "anxiety" during a particular operation, additional resources will be provided to reinforce instruction on how to perform that operation.

[0582] Specific examples

[0583] New worker "Tanaka"

[0584] Example prompt: "Generate the optimal training curriculum for new worker Tanaka in the field where he will be working."

[0585] Specific examples of processing

[0586] The server receives the user information entered by Tanaka and the results of the operation skill test and stores them in a database.

[0587] The server inputs this data into a generative AI model to generate basic safety training videos and operating instructions.

[0588] As Tanaka begins working, the robot's camera and microphone collect emotional data and send it to the server. For example, if the emotion engine determines that Tanaka's face is tense, it inputs that data into the generative AI model.

[0589] The server updates the curriculum in real time based on Tanaka's progress and emotional data, and provides the necessary guidance.

[0590] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0591] Step 1:

[0592] The server receives the user's registration information (academic achievement information, survey information, and personal information). The user enters the information using a terminal and sends it to the server. The entered information is stored in a database. In this step, the user's profile data is received as input, and this is processed and stored in the database.

[0593] Step 2:

[0594] The user takes the academic ability test using a terminal and sends the results to the server. The server stores the sent academic ability test results in a database. In this step, the server receives the user's test results as input, processes the data, and stores it in the database.

[0595] Step 3:

[0596] The server uses a generative AI model to generate an individually optimized educational curriculum based on the received registration information and academic achievement test results. The generated curriculum includes learning materials, a study schedule, and recommended study methods and is stored in a database. It receives user information and test results as input and outputs the generated curriculum.

[0597] Step 4:

[0598] The user logs in to the server using a terminal and checks the individually optimized educational curriculum. In this step, the server sends the curriculum generated in the previous step to the terminal, and the user views it. The input is the user's login information, and the output is the curriculum information.

[0599] Step 5:

[0600] The server provides optimal webinars and learning materials based on the generated curriculum. The webinar links and learning materials are notified to the user. The server receives curriculum information as input and generates webinar links and learning material information as output.

[0601] Step 6:

[0602] Users join the webinar using the provided link and progress through the learning in real time or on-demand format. The learning progress is sent to the server. The input is the user's webinar participation information, and the output is the learning progress data.

[0603] Step 7:

[0604] The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotion data, which is then sent to a server. The input is biometric signals from the camera and microphone, and the output is analyzed emotion data.

[0605] Step 8:

[0606] The server inputs the emotion data obtained from the emotion engine into the generative AI model to analyze the user's emotional state. It dynamically updates the curriculum as needed. The input is emotion data, and the output is the updated curriculum.

[0607] Step 9:

[0608] The server evaluates and readjusts the curriculum using a generative AI model based on the user's learning progress and emotion data. The input is the progress and emotion data, and the output is an optimized updated curriculum.

[0609] Step 10:

[0610] The server notifies the user of the updated curriculum and allows the new learning content to be displayed on the platform. Here, the server sends the updated curriculum information to the user's terminal, and the user confirms it. The input is the updated curriculum, and the output is the notification and displayed learning content.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Second embodiment]

[0615] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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."

[0627] User registration and data collection

[0628] server

[0629] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, location, and learning goals), the server stores this information in a database and generates a unique ID for each user based on the stored information.

[0630] User

[0631] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[0632] server

[0633] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[0634] Generation of individually optimized curriculum

[0635] server

[0636] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[0637] server

[0638] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[0639] Curriculum delivery and webinar distribution

[0640] User

[0641] Users can log in to the platform and view the curriculum content that is tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[0642] server

[0643] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[0644] User

[0645] Users can join the webinar using the provided link and learn in real time or on-demand.

[0646] Assessment of learning progress and curriculum updates

[0647] server

[0648] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0649] server

[0650] The collected data is fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and if necessary, update the educational curriculum and apply new learning content and recommended learning methods.

[0651] server

[0652] Notifying users of updated curriculum and making new content available on the platform.

[0653] Specific examples

[0654] When user "Tanaka" registers

[0655] server

[0656] A new user named "Tanaka" accesses the platform, enters the necessary information, and sets his / her learning goal as "I want to learn digital marketing." The server registers this in the database and automatically generates an academic ability test.

[0657] User

[0658] Tanaka takes an academic ability test and sends the results to the server.

[0659] server

[0660] The server analyzes the test results and survey information and generates a curriculum called "Marketing Fundamentals," "Market Analysis," and "Digital Marketing Practice." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[0661] User

[0662] John logs into the platform and joins the "Marketing Fundamentals" webinar using the link provided. He also downloads the provided materials to review later.

[0663] server

[0664] Monitor Tanaka's progress and update the curriculum as needed, for example, by suggesting additional learning resources or different learning methods if she is performing poorly in a particular area.

[0665] In this way, a system can be created that continues to provide an educational curriculum optimized for the user's life stage and goals.

[0666] The processing flow will be explained below.

[0667] User registration and data collection

[0668] Step 1:

[0669] Server: Displays the user registration form on the web browser or mobile app.

[0670] Step 2:

[0671] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[0672] Step 3:

[0673] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[0674] Step 4:

[0675] Server: Automatically generates individual academic ability tests based on collected profile information.

[0676] Step 5:

[0677] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[0678] Step 6:

[0679] User: Takes an online academic achievement test and sends the test results to the server.

[0680] Generation of individually optimized curriculum

[0681] Step 7:

[0682] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[0683] Step 8:

[0684] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[0685] Step 9:

[0686] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[0687] Step 10:

[0688] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[0689] Curriculum delivery and webinar distribution

[0690] Step 11:

[0691] Server: Displays individually optimized curriculum content to users who log in to the platform.

[0692] Step 12:

[0693] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[0694] Step 13:

[0695] Users: Join the webinar using the provided link and learn in real time or on demand.

[0696] Step 14:

[0697] Users: Download the webinar materials and videos for future reference.

[0698] Assessment of learning progress and curriculum updates

[0699] Step 15:

[0700] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0701] Step 16:

[0702] Server: Re-inputs the collected learning progress data into the generative AI model to evaluate the user's progress and learning effectiveness.

[0703] Step 17:

[0704] Server: Based on the evaluation results, update the educational curriculum as necessary.

[0705] Step 18:

[0706] Server: Notifies users of updated curriculum content and makes new learning content available on the platform.

[0707] Specific examples

[0708] For new user "Yamada"

[0709] Step 1:

[0710] Server: Displays the user registration form on the web browser.

[0711] Step 2:

[0712] User (Yamada): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn the basics of marketing") and submit.

[0713] Step 3:

[0714] Server: Saves Yamada's information in the database and generates a unique user ID.

[0715] Step 4:

[0716] Server: Automatically generate an individual academic ability test based on Yamada's profile.

[0717] Step 5:

[0718] Server: Send the generated academic ability test to Yamada's registered email address.

[0719] Step 6:

[0720] User (Yamada): Takes an online academic achievement test and sends the results to the server.

[0721] Step 7:

[0722] Server: Receives Yamada's academic test results, survey information, and personal information and inputs them into the generative AI model.

[0723] Step 8:

[0724] Server: The generative AI model generates the optimal educational curriculum for Yamada.

[0725] Step 9:

[0726] Server: Stores the generated curriculum ("Fundamentals of Marketing," "Market Analysis," "Fundamentals of Digital Marketing") in a database.

[0727] Step 10:

[0728] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Yamada's curriculum.

[0729] Step 11:

[0730] Server: When Yamada logs in to the platform, the contents of the individually optimized curriculum are displayed.

[0731] Step 12:

[0732] Server: Notifies Yamada of the distribution schedule and live webinar schedule.

[0733] Step 13:

[0734] User (Yamada): Attends the "Marketing Basics" webinar using the link provided.

[0735] Step 14:

[0736] User (Yamada): Download the webinar materials and videos and use them for review later.

[0737] Step 15:

[0738] Server: Continuously collects Yamada's learning progress (test results, webinar participation records, viewing history).

[0739] Step 16:

[0740] Server: Re-inputs the collected data into the generative AI model to evaluate Yamada's progress and the effectiveness of his learning.

[0741] Step 17:

[0742] Server: Update Yamada's curriculum to include new learning content and recommended learning methods.

[0743] Step 18:

[0744] Server: Notifies Yamada of updated curriculum and displays new learning content on the platform.

[0745] Example 1

[0746] 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."

[0747] Conventional educational systems have struggled to efficiently provide individually optimized educational curricula for each user. As a result, users are unable to receive effective educational programs tailored to their learning goals, leaving them seeking further improvement in their learning outcomes. Furthermore, there is a lack of mechanisms for continuously evaluating learning progress and updating the curriculum, preventing users from maximizing their learning effectiveness.

[0748] 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.

[0749] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generating AI model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal teaching materials and webinars based on the generated educational curriculum, a means for continuously collecting the user's learning progress and re-inputting it into the generating AI model to update the curriculum, and a means for notifying the user of the updated curriculum and making the new content available on the platform. This makes it possible to provide an individually optimized educational curriculum for each user and to continuously update the curriculum based on the user's learning progress.

[0750] "Academic ability information" is information that indicates the user's academic ability and learning ability.

[0751] "Survey information" is survey information used to collect information about users' interests, concerns, learning goals, etc.

[0752] "Personal Information" refers to personal data such as a user's name, age, gender, and area of ​​residence.

[0753] A "generative AI model" is an artificial intelligence model used to generate individually optimized educational curricula based on input data.

[0754] A "curriculum" is the content of an educational program designed to meet specific learning objectives.

[0755] "Instructional materials" are educational resources such as books, videos, and documents used for learning.

[0756] A "webinar" is a lecture or seminar delivered online.

[0757] "Study progress" refers to data and records that indicate how far a user has progressed in their studies.

[0758] "Platform" means a website or application through which users log in and access learning resources and curriculum.

[0759] "Notifications" are messages or alerts that inform users of new or updated information.

[0760] The present invention relates to a system for providing an individually optimized educational curriculum. How the present invention is implemented will be described below in detail.

[0761] User registration and data collection

[0762] server

[0763] The server provides a user registration form on the web browser or mobile app for new user registration. This form is displayed when the user accesses the site. The user enters required information such as name, age, gender, residential area, and learning goals, and submits the form to provide the information to the server. The server stores this received information in a database and generates a unique ID for each user, which is also stored in the database.

[0764] User

[0765] The user enters the necessary information into the provided registration form and submits it, then takes an online academic ability test provided by the server and submits the results of the academic ability test to the server.

[0766] server

[0767] The server stores the academic test results and questionnaire information received from the user in a database.

[0768] Generation of individually optimized curriculum

[0769] server

[0770] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum. An example of the input prompt is as follows:

[0771] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[0772] teaching materials

[0773] Study Schedule

[0774] Recommended learning methods

[0775] The server receives the optimal educational curriculum output from the generative AI model and stores it in a database, including learning materials, study schedules, and recommended learning methods.

[0776] server

[0777] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[0778] Curriculum delivery and webinar distribution

[0779] User

[0780] Users log in to the platform and view the curriculum content tailored to them, and the server notifies them of the schedule of pre-recorded webinar videos and live webinars.

[0781] server

[0782] The server provides users with webinar links and materials, making it easier for them to learn through webinars. After watching the webinar, users can download the materials and videos.

[0783] User

[0784] Users can join the webinar using the provided link and learn in real time or on-demand.

[0785] Assessment of learning progress and curriculum updates

[0786] server

[0787] The server continuously collects user learning progress data (e.g., test results, webinar participation records, viewing history, etc.).

[0788] server

[0789] The server re-inputs the collected data into the generative AI model to evaluate the user's progress and learning effectiveness, and based on this, updates the educational curriculum as needed.

[0790] server

[0791] The server notifies users of updated curriculum and makes new content available on the platform.

[0792] Specific examples

[0793] For example, if a user named "Tanaka" accesses the platform saying that he wants to learn digital marketing, the server stores input data, including Tanaka's name and other basic information, in a database. Tanaka takes an online academic ability test provided by the server and sends the results to the server. The server analyzes the results and automatically generates a curriculum, such as "Marketing Basics," "Market Analysis," and "Digital Marketing Practice," based on a generative AI model. Tanaka can log in to check the curriculum content and progress through webinars and learning materials. The server also monitors Tanaka's learning progress and updates the curriculum content accordingly.

[0794] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0795] Step 1:

[0796] User registration and data collection

[0797] server

[0798] The server provides a user registration form on the web browser or mobile app that the user accesses.

[0799] Input: User-entered name, age, gender, location, and learning goal

[0800] Output: User information stored in database, unique user ID generated

[0801] What happens: The server receives the information the user entered into the form and saves it to a database. When saving, it generates a unique user ID and adds it to the database.

[0802] Step 2:

[0803] Academic achievement tests

[0804] User

[0805] Users take online academic achievement tests provided by the server.

[0806] Input: Test questions and user answers

[0807] Output: User's academic achievement test results

[0808] How it works: Users take online academic tests and send their answers to the server, which then calculates test results based on the received answers.

[0809] Step 3:

[0810] Collecting survey information

[0811] User

[0812] The user enters additional survey information and submits it to the server.

[0813] Input: User's survey response

[0814] Output: Survey information stored in a database

[0815] What it does: Users fill out a survey about their interests and learning goals and submit it to the server, which stores this information in a database.

[0816] Step 4:

[0817] Generation of individually optimized curriculum

[0818] server

[0819] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum.

[0820] Input: User's academic test results, survey information, personal information

[0821] Output: Individually optimized educational curriculum

[0822] What happens: The server inputs the following prompt into the generative AI model:

[0823] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[0824] teaching materials

[0825] Study Schedule

[0826] Recommended learning methods

[0827] The optimal curriculum content (teaching materials, study schedule, recommended study methods) output by the generative AI model is stored in a database.

[0828] Step 5:

[0829] Providing the best educational materials and webinars

[0830] server

[0831] Based on the generated curriculum, the server selects the most suitable teaching materials and webinar links from a related teaching material database and adds them to the curriculum.

[0832] Input: Generated educational curriculum

[0833] Output: A curriculum with the best learning materials and webinar links

[0834] Specific operation: Based on the curriculum content, the server searches for the most suitable teaching materials from the related teaching material database and adds them to the user's curriculum.

[0835] Step 6:

[0836] Provision and notification of curriculum content

[0837] User

[0838] Users log in to the platform and view the curriculum content tailored to them, and the server also notifies them of the webinar schedule.

[0839] Input: Platform login information

[0840] Output: Optimized curriculum content and webinar schedule

[0841] Specific behavior: When a user accesses the platform, the server displays a personalized curriculum and webinar schedule.

[0842] Step 7:

[0843] Webinar link and materials provided

[0844] server

[0845] The server provides the webinar link and materials to the user.

[0846] Input: Webinar link and materials

[0847] Output: Webinar link and materials provided to users

[0848] What it does: The server provides users with the webinar link and necessary materials electronically, facilitating their learning.

[0849] Step 8:

[0850] Assessment of learning progress and curriculum updates

[0851] server

[0852] The server continuously collects user learning progress data and re-inputs it into the generative AI model to update the curriculum.

[0853] Input: Learning progress data (test results, webinar participation records, viewing history, etc.)

[0854] Output: Updated educational curriculum

[0855] What it does: The server re-inputs the collected progress data into the generative AI model to evaluate the user's progress and learning effectiveness, update the curriculum as needed, and notify the user of the new content.

[0856] (Application example 1)

[0857] 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."

[0858] There is a need to provide efficient and effective technical education to robot operators and engineers working in factories. However, there is currently a lack of means to provide a curriculum tailored to the abilities and learning progress of each engineer, and standardized education methods have their limitations. Another challenge is providing an environment where engineers can learn at any time without interfering with on-site work.

[0859] 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.

[0860] In this invention, the server further includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal webinars and learning materials based on the generated educational curriculum, a means for continuously collecting user learning progress and re-inputting it into the generative model to update the curriculum, a means for generating and providing an individually optimized technical educational curriculum for factory robot operators and engineers, and a distribution means for providing webinars and technical materials. This allows learning content tailored to individual engineers to enable efficient and effective technical acquisition.

[0861] "Academic ability information" is data that indicates the current academic level and learning ability of the educational recipient.

[0862] "Survey information" is survey response data related to the learning goals and learning strategies of the education recipients.

[0863] "Personal information" refers to data that can individually identify an educational recipient, such as the name, age, gender, and area of ​​residence.

[0864] The "generative model means" refers to an artificial intelligence model and its execution mechanism for generating an individually optimized educational curriculum by utilizing the received academic ability information, questionnaire information, and personal information.

[0865] A "webinar" is an online lecture or seminar delivered over the Internet in real time or on demand.

[0866] "Learning materials" refers to educational content such as books, videos, and documents provided based on the educational curriculum.

[0867] "Learning progress" is data that indicates how much learning outcomes an educational subject has achieved through the educational curriculum.

[0868] A "factory robot operator" is an engineer responsible for operating and maintaining robots within a factory.

[0869] "Engineers" are professionals who have specific specialized skills and use them to carry out their work.

[0870] "Delivery medium" refers to the communications technology and its implementation for delivering educational curriculum, webinars, and technical materials to educational audiences via the Internet.

[0871] In the following, a specific method for implementing the present invention will be described, in which a system is realized in which factory robot operators and engineers receive an individually optimized technical training curriculum.

[0872] First, the server provides a user registration function. Factory robot operators and technicians (hereafter referred to as users) use a smartphone or head-mounted display to access a user registration form provided in a web browser or mobile app, and enter the required personal information (such as name, age, gender, residential area, and learning goals). This information is sent to the server and stored in a database. The server then generates a unique ID for each user based on the stored information.

[0873] The server then administers an online academic ability test to the user. The user takes the test and sends the results to the server. Based on the test results and the questionnaire information entered at the time of registration, the server generates an individually optimized educational curriculum tailored to each user's academic ability level and learning goals. The server automatically generates the curriculum using a generative AI model, and stores its contents (teaching materials, study schedule, recommended study methods, etc.) in a database.

[0874] Based on the generated curriculum, the server selects the most appropriate learning materials from a database of related learning materials and adds them to the user's curriculum. The server also provides the user with a webinar schedule and video links based on the curriculum. The user can join the webinar using the provided link and progress with their learning in real time or on-demand. After viewing the webinar, the user can download the materials and videos for future reference.

[0875] The server continuously collects user learning progress data (such as academic test results, webinar participation records, and viewing history). This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness. If necessary, the educational curriculum is updated and new learning content and recommended learning methods are applied. This process provides an optimal learning environment for factory robot operators and technicians, enabling them to acquire skills efficiently and effectively.

[0876] As a concrete example, consider the case where a user named "Yamada" registers with the system with the goal of learning "robot programming." Yamada enters his personal information and learning goals and passes an academic ability test. The server analyzes Yamada's academic ability test results and questionnaire information and generates an individually optimized "robot programming" curriculum. The curriculum includes chapters such as "Robot Basics," "Programming Applications," and "Robot Maintenance," and provides corresponding webinars and learning materials for each. Yamada participates in webinars using a smartphone or head-mounted display, downloads the provided materials, and advances his studies. Yamada's learning progress is also monitored by the server, and the curriculum content is updated and improved as necessary.

[0877] This invention provides learning content tailored to individual factory robot operators and engineers, enabling them to acquire skills efficiently and effectively without interfering with on-site work.

[0878] An example of an input prompt for a generative AI model is:

[0879] "Generate an educational curriculum with the learning objective 'Robot Programming'."

[0880] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0881] Step 1:

[0882] A user accesses a web browser or mobile app using a smartphone or head-mounted display, enters the required personal information (such as name, age, gender, residential area, learning goals, etc.) in the user registration form, and submits it. Based on this input, the server stores the information in a database and generates a unique ID. The output is the stored personal information and the generated user ID.

[0883] Step 2:

[0884] The server provides online achievement tests to users. The users take the achievement tests and send their results to the server. Based on this input, the server stores the achievement test results in a database. The output is the stored achievement test results.

[0885] Step 3:

[0886] Based on the above academic ability information, questionnaire information, and personal information, the server uses a generative AI model to generate an individually optimized educational curriculum. The prompt sentence input to the generative AI model is, "Please generate an educational curriculum whose learning goal is 'robot programming'." The server saves the generated curriculum in a database. The output is the generated individually optimized curriculum.

[0887] Step 4:

[0888] The server selects the most suitable learning materials and webinar links based on the generated curriculum. These learning materials and links are added to the curriculum and notified to the user. The output is the notified learning materials and webinar links.

[0889] Step 5:

[0890] Users join a webinar using a link provided by the server. The server delivers the webinar in real-time or on-demand format, and users send their participation records and viewing histories to the server. Based on this input, the server stores the webinar participation records and viewing histories in a database. The output is the stored participation records and viewing histories.

[0891] Step 6:

[0892] The server continuously collects the user's learning progress data (such as academic test results, webinar participation records, and viewing history) and re-inputs it into the generative AI model. The generative AI model evaluates the user's progress and learning effectiveness and generates a new curriculum. The server saves the updated curriculum in a database and notifies the user again. The output is an updated, individually optimized curriculum.

[0893] Step 7:

[0894] The user continues learning based on new curriculum provided by the server. This process is cyclical until the user has fully mastered the target skill. The final output is the user's degree of mastery of the skill.

[0895] 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.

[0896] User registration and data collection

[0897] server

[0898] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, place of residence, and learning goals), the server stores this information in a database. A unique ID is generated for each user based on the stored information.

[0899] User

[0900] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[0901] server

[0902] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[0903] Generation of individually optimized curriculum

[0904] server

[0905] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[0906] server

[0907] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[0908] Introducing the Emotion Engine

[0909] Emotion Engine

[0910] The emotion engine recognizes emotions in real time by analyzing the user's facial expressions, tone of voice, and other biometric signals.

[0911] server

[0912] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[0913] Curriculum delivery and webinar distribution

[0914] User

[0915] Users can log in to the platform and view the curriculum tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[0916] server

[0917] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[0918] User

[0919] Users can join the webinar using the provided link and learn in real time or on-demand.

[0920] Emotion Engine

[0921] During a webinar or while learning, the emotion engine analyzes the user's emotions in real time and sends the results to the server.

[0922] Assessment of learning progress and curriculum updates

[0923] server

[0924] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0925] server

[0926] The collected data and emotional data are fed back into the generative AI model to assess the user's progress and the effectiveness of their learning.

[0927] server

[0928] Based on the evaluation results, the educational curriculum is updated as needed. For example, if users feel "confused" or "stressed" about a particular topic, additional resources or different teaching methods will be introduced to reinforce that section.

[0929] server

[0930] Notifying users of updated curriculum and making new content available on the platform.

[0931] Specific examples

[0932] For new user "Sato"

[0933] server

[0934] A new user named "Sato" accesses the platform, enters the necessary information, and sets his or her learning goal as "I want to learn data science." The server then registers this information in the database and automatically generates an academic achievement test.

[0935] User

[0936] Sato takes an academic ability test and sends the results to the server.

[0937] server

[0938] The server analyzes the test results and survey information and generates a curriculum called "Fundamental Concepts of Data Science," "Data Analysis," and "Machine Learning." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[0939] User

[0940] Sato logs in to the platform and joins the "Fundamental Concepts of Data Science" webinar using the provided link. He also downloads the provided materials for later review.

[0941] Emotion Engine

[0942] During the webinar, the emotion engine analyzes Sato's facial expressions and tone of voice, recognizing emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[0943] server

[0944] Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[0945] server

[0946] Based on the analysis, if Sato feels "uneasy" about a particular section, we will provide him with additional resources to reinforce that section and update the curriculum.

[0947] server

[0948] The updated curriculum will be notified to Sato and new learning content will be displayed on the platform.

[0949] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[0950] The processing flow will be explained below.

[0951] User registration and data collection

[0952] Step 1:

[0953] Server: Displays the user registration form on the web browser or mobile app.

[0954] Step 2:

[0955] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[0956] Step 3:

[0957] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[0958] Step 4:

[0959] Server: Automatically generates individual academic ability tests based on collected profile information.

[0960] Step 5:

[0961] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[0962] Step 6:

[0963] User: Takes an online academic achievement test and sends the test results to the server.

[0964] Generation of individually optimized curriculum

[0965] Step 7:

[0966] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[0967] Step 8:

[0968] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[0969] Step 9:

[0970] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[0971] Step 10:

[0972] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[0973] Introducing the Emotion Engine

[0974] Step 11:

[0975] Emotion Engine: Recognizes the user's emotions in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[0976] Step 12:

[0977] Server: Receives user emotion data sent from the emotion engine and inputs it into the generative AI model.

[0978] Curriculum delivery and webinar distribution

[0979] Step 13:

[0980] Server: Displays individually optimized curriculum content to users who log in to the platform.

[0981] Step 14:

[0982] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[0983] Step 15:

[0984] Users: Join the webinar using the provided link and learn in real time or on demand.

[0985] Step 16:

[0986] Users: Download the webinar materials and videos for future reference.

[0987] Step 17:

[0988] Emotion Engine: Analyzes user emotions in real time during webinars and learning sessions and sends the results to the server.

[0989] Assessment of learning progress and curriculum updates

[0990] Step 18:

[0991] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[0992] Step 19:

[0993] Server: Collected learning progress data and emotional data are re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[0994] Step 20:

[0995] Server: Based on the assessment results, update the educational curriculum as needed. For example, if users are "confident" or "stressed" about a particular topic, introduce additional resources or different teaching methods to reinforce that section.

[0996] Step 21:

[0997] Server: Notifies users of updated curriculum and makes new learning content visible on the platform.

[0998] Specific examples

[0999] For new user "Sato"

[1000] Step 1:

[1001] Server: Displays the user registration form on the web browser.

[1002] Step 2:

[1003] User (Sato): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn data science") and submit.

[1004] Step 3:

[1005] Server: Saves Sato's information in a database and generates a unique user ID.

[1006] Step 4:

[1007] Server: Automatically generate an individual academic ability test based on Sato's profile.

[1008] Step 5:

[1009] Server: Send the generated academic ability test to Sato's registered email address.

[1010] Step 6:

[1011] User (Sato): Takes an online academic ability test and sends the results to the server.

[1012] Step 7:

[1013] Server: Receives Sato's academic test results, survey information, and personal information and inputs them into the generative AI model.

[1014] Step 8:

[1015] Server: The generative AI model generates the optimal educational curriculum for Sato.

[1016] Step 9:

[1017] Server: Stores the generated curriculum ("Fundamentals of Data Science," "Data Analysis," and "Machine Learning") in a database.

[1018] Step 10:

[1019] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Sato's curriculum.

[1020] Step 11:

[1021] Server: When Sato logs in to the platform, the contents of the individually optimized curriculum are displayed.

[1022] Step 12:

[1023] Server: Notifies Sato of the distribution schedule and live webinar schedule.

[1024] Step 13:

[1025] User (Sato): Join the "Fundamental Concepts of Data Science" webinar using the provided link.

[1026] Step 14:

[1027] User (Sato): Download the webinar materials and videos and use them for review later.

[1028] Step 15:

[1029] Emotion engine: During the webinar, Sato's facial expressions and tone of voice are analyzed to recognize emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[1030] Step 16:

[1031] Server: Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[1032] Step 17:

[1033] Server: Based on the analysis, if Sato feels "uneasy" about a particular section, provide him with additional resources to reinforce that section and update the curriculum.

[1034] Step 18:

[1035] Server: Notifies Sato of updated curriculum and displays new learning content on the platform.

[1036] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[1037] Example 2

[1038] 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."

[1039] Conventional educational systems have difficulty providing individually optimized educational programs tailored to the user's academic ability and learning goals. Furthermore, they lack a means to grasp the user's emotional state in real time while learning, making it difficult to update educational programs accordingly. Furthermore, there are limitations to providing an environment where users can learn anytime, anywhere.

[1040] 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.

[1041] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational program based on the received information, a means for providing optimal online lectures and learning materials based on the generated educational program, a means for analyzing the user's biosignals and collecting emotional data in real time, a means for inputting the emotional data into the generative model and updating the educational program taking the user's emotional state into account, and a means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum. This allows an individually optimized educational program to be provided in real time, taking the user's emotional state into consideration, enabling the user to study efficiently anytime, anywhere.

[1042] "Academic ability information" is information that indicates the user's current academic ability level, including test results and past learning history.

[1043] "Survey Information" is subjective information provided by users, including information about their learning goals, interests, and experiences.

[1044] "Personal information" refers to basic information such as a user's name, age, gender, and area of ​​residence.

[1045] A "generative model means" is a means including a generative AI model for automatically generating an optimal educational program based on input data.

[1046] An "educational program" is an individually optimized curriculum tailored to a user's learning goals and academic level, and includes teaching materials, study schedules, recommended study methods, etc.

[1047] "Online lectures" refer to classes provided over the Internet, either in real time or via recorded video lectures.

[1048] "Learning Materials" are resources such as textbooks, videos, and exercises provided as part of an educational program.

[1049] "Biological signals" are signals emitted from the user's body, including facial expressions, tone of voice, heart rate, etc.

[1050] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing biosignals.

[1051] "Study progress" is data that shows how far a user has progressed in their studies, and includes test results, viewing history, participation records, etc.

[1052] "Generative AI" is a type of artificial intelligence that refers to a system that generates optimal educational programs and curricula based on large amounts of data.

[1053] "Delivery medium" means a means, including internet-based technologies and services, for providing online courses and educational materials to users.

[1054] The present invention is a system for providing users with individually optimized educational programs. A specific implementation method thereof will be described below.

[1055] User registration and data collection

[1056] server

[1057] The server displays a user registration form on the web browser or mobile app. The form includes fields for entering information such as name, age, gender, location, and learning goals. After the user enters and submits the required information, the server stores the entered data in a database. At the same time, it generates a unique ID for each user. For example, if a user enters "I want to study data science," that information is stored.

[1058] User

[1059] The user fills in the required information in the provided form and submits it to the server. The user also clicks on the link for the academic achievement test sent from the server and takes the test online. For example, a user named "Sato" fills in the form with his / her goal of "I want to study data science," submits it, and then takes the academic achievement test.

[1060] server

[1061] The server receives the results of the academic ability test sent by the user, stores them in a database, and then analyzes the test results and questionnaire information to generate an individual educational program based on the user's academic level and goals.

[1062] Generation of individually optimized curriculum

[1063] server

[1064] The server inputs the received data into the generative AI model. At that time, the data is sent to the generative AI model as a prompt. By sending a prompt in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" to the generative AI model, the model generates an optimal curriculum.

[1065] server

[1066] The server receives the curriculum content (teaching materials, study schedule, recommended learning methods) returned by the generative AI model and stores them in a database. At the same time, it picks up appropriate teaching materials from the related teaching materials database and adds them to this curriculum.

[1067] Introducing the Emotion Engine

[1068] Emotion Engine

[1069] The emotion engine generates emotion data by analyzing the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) in real time. For example, while a user is attending an online lecture, it analyzes camera footage and audio data to generate emotion data such as "interested" or "confused."

[1070] server

[1071] The server receives the user's emotional data sent from the emotion engine and inputs it into the generative AI model. This emotional data is analyzed as the user progresses and is used to update the educational program accordingly.

[1072] Curriculum delivery and webinar distribution

[1073] User

[1074] Users log in to the platform and check their curriculum. The server notifies them of the links and materials for the online lectures they provide. For example, "Sato" logs in to the platform and obtains a link to participate in an online lecture called "Basics of Data Science."

[1075] server

[1076] The server provides links to online lectures and materials for users to access anytime, anywhere, and also provides recorded videos and materials for users to review later.

[1077] User

[1078] When a user clicks on an online lecture link, they can take the lecture in real time or on-demand. For example, "Sato" can participate in the "Basics of Data Science" course in real time and download the provided materials for review.

[1079] Emotion Engine

[1080] While users are taking online classes, the emotion engine analyzes their facial expressions and tone of voice and sends the data to a server, which collects it and feeds it into a generative AI model to update the curriculum.

[1081] Assessment of learning progress and curriculum updates

[1082] server

[1083] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.), which is then re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[1084] server

[1085] Based on the assessment, the user's educational program is updated as needed, for example, if a user feels "stuck" in a particular learning section, additional resources or different teaching methods are provided to reinforce that section.

[1086] server

[1087] Users are notified of updated curriculum and can view the new content on the platform. For example, if "Sato" receives an email notification of an updated educational program, the new learning content will be displayed when he logs in to the platform.

[1088] Through this system, it is possible to optimize the user's learning experience and provide an efficient learning process.

[1089] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] User registration and data collection

[1092] server

[1093] The server displays a user registration form on a web browser or mobile app. As input, the user enters their name, age, gender, location, learning goals, etc. When the user submits the input, the server receives it and stores it in a database. At the same time, it generates and stores a unique ID for each user. The output includes the stored user information and the unique user ID.

[1094] User

[1095] The user fills in the required information in the form and submits it. For example, a user named "Sato" enters his / her learning goal of "I want to learn data science" and submits it. The server then receives an automatically generated academic achievement test link. The user clicks on the link and takes the test online. The results of the academic achievement test are then sent as input to the server.

[1096] server

[1097] The server receives the academic ability test results sent by the user and stores them in a database. The input includes the academic ability test results and the user ID. It then analyzes the results and questionnaire information and generates an individual educational program based on the user's academic ability level and goals as output.

[1098] Step 2:

[1099] Generation of individually optimized curriculum

[1100] server

[1101] The server inputs the user information stored in the database and the academic achievement test results into the generative AI model. As input, it generates a prompt sentence in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" and sends it to the generative AI model. The model analyzes the data and generates an optimal educational curriculum. The output is the curriculum content returned by the generative AI model.

[1102] server

[1103] The generated curriculum content (teaching materials, study schedule, recommended study methods) is saved in a database. The most suitable teaching materials are selected from the related teaching materials database and added to the curriculum. This completes the educational program provided to the user.

[1104] Step 3:

[1105] Introducing the Emotion Engine

[1106] Emotion Engine

[1107] The emotion engine analyzes the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) and generates emotional data. Inputs include real-time camera footage and audio data. Based on this, it generates emotional data such as "User ID: XXXX, facial expression: smiling, tone of voice: optimistic." The output is the emotional data resulting from the analysis.

[1108] server

[1109] The server receives the emotion data sent from the emotion engine and inputs it into the generative AI model. The emotion data is included as input. The model updates the generated curriculum based on the emotion data as appropriate. An individualized educational program is generated that takes the emotion data into consideration.

[1110] Step 4:

[1111] Curriculum delivery and webinar distribution

[1112] User

[1113] The user logs in to the platform and checks the optimized curriculum. The server notifies them of the online lecture link and materials. For example, Sato logs in to the platform and checks the link to join the "Basic Data Science Course." The user clicks and receives the lecture in real time. The online lecture link and materials are provided as input.

[1114] server

[1115] The server provides links to online lectures and materials, allowing users to access them anytime, anywhere. As an output, it collects user participation records and viewing histories. It also provides recorded videos and materials for offline use.

[1116] Emotion Engine

[1117] While a user is taking an online lecture, the emotion engine analyzes their facial expressions and tone of voice to generate emotion data, which is then sent to the server. The input includes real-time video and audio data, and the output is the emotion data resulting from the analysis.

[1118] Step 5:

[1119] Assessment of learning progress and curriculum updates

[1120] server

[1121] The server continuously collects users' learning progress data (such as test results, webinar participation records, and viewing history). This data is included as input. This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and update the educational program as needed. The output is the evaluation results and an updated curriculum.

[1122] server

[1123] Based on the evaluation results, the educational program is updated accordingly and notified to the user. For example, if a user is struggling with a particular learning section, additional resources or different teaching methods can be provided to reinforce that section. The updated curriculum is then notified to the user.

[1124] User

[1125] Users can check the updated curriculum on the platform and engage in new learning content. For example, Sato revisits the updated "Basic Data Science Course" and continues his learning using new materials and resources. New learning resources are provided as input.

[1126] In this way, the entire system is designed to collect and analyze data in real time to optimize the user's learning experience.

[1127] (Application example 2)

[1128] 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."

[1129] Conventional education and work instruction systems have had difficulty providing individually optimized curricula that reflect the user's (worker's) learning progress and emotional state in real time. In addition, there has been a lack of means to improve learning and work efficiency by capturing the user's emotional state and providing appropriate feedback.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving academic ability information, questionnaire information, and personal information; a generative model means for generating an individually optimized educational curriculum based on the received information; means for providing optimal webinars and learning materials based on the generated educational curriculum; means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum; means for analyzing the worker's emotional state in real time using a camera and microphone and inputting the data into the generative model; and means for dynamically updating the worker's operation method and supplementary materials based on the analyzed emotional data. This makes it possible to provide an individually optimized curriculum and real-time feedback that improves the user's learning efficiency and work efficiency.

[1131] "Academic achievement information" is data that indicates an individual user's educational level and academic achievement test results.

[1132] "Survey information" is data collected to understand user attributes, learning objectives, interests, etc.

[1133] "Personal information" is information that can be used to identify an individual user, such as the user's name, age, gender, and area of ​​residence.

[1134] "Means" is a broad concept that includes methods and devices for achieving a specific purpose.

[1135] A "generative model" is an algorithm or software used to create an individually optimized educational curriculum based on collected data.

[1136] A "webinar" is a form of online seminar or lecture held over the web.

[1137] "Learning Materials" are educational resources and materials provided to achieve specific learning objectives.

[1138] "Curriculum updating" is the process of adjusting and improving existing educational plans based on users' learning progress and emotional data.

[1139] A "camera" is a device for capturing images and storing them as digital data.

[1140] A "microphone" is a device that collects sound and stores it as digital data.

[1141] An "emotional state" is a state that indicates a user's psychological and emotional response.

[1142] "Real-time analysis" is the process of processing collected data immediately and reflecting and utilizing the results immediately.

[1143] "Dynamic updates" means flexibly changing the system and curriculum in response to changes in the user's status and environment.

[1144] The embodiment of this invention is a system that provides individually optimized educational curricula using a generative AI model based on user registration information and academic achievement test results. This system utilizes a server, user terminals, and an emotion engine to educate and train workers.

[1145] User registration and data collection

[1146] server

[1147] The server receives the user's registration information (academic achievement information, questionnaire information, and personal information).

[1148] This information is entered using a web browser or mobile app and stored in a database.

[1149] User

[1150] The user uses a terminal to enter the necessary information and send it to the server. For example, a factory worker uses a tablet to take an academic achievement test and then sends the results to the server.

[1151] Generation of individually optimized curriculum

[1152] server

[1153] The server uses a generative AI model based on the received data to generate an optimal educational curriculum, which includes learning materials, a study schedule, and recommended study methods.

[1154] The generated curriculum is stored in a database.

[1155] Introducing the Emotion Engine

[1156] Emotion Engine

[1157] The emotion engine uses hardware such as a camera and microphone to recognize a user's emotional state in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[1158] server

[1159] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[1160] Curriculum delivery and monitoring

[1161] User

[1162] Users view educational videos and instructional materials on holographic displays and tablets based on the provided curriculum.

[1163] server

[1164] The server provides optimal webinars and learning materials based on the generated curriculum, such as safety training videos and instructional curriculum for operating procedures for factory workers.

[1165] The server monitors the user's emotional state in real time and dynamically updates the curriculum based on this.

[1166] Assessment of learning progress and curriculum updates

[1167] server

[1168] The server re-inputs the user's learning progress data (test results, webinar participation records, viewing history) and emotional data into the generative AI model to evaluate the user's progress and the effectiveness of their learning.

[1169] Based on the evaluation results, the curriculum will be updated as needed. For example, if users experience "stress" or "anxiety" during a particular operation, additional resources will be provided to reinforce instruction on how to perform that operation.

[1170] Specific examples

[1171] New worker "Tanaka"

[1172] Example prompt: "Generate the optimal training curriculum for new worker Tanaka in the field where he will be working."

[1173] Specific examples of processing

[1174] The server receives the user information entered by Tanaka and the results of the operation skill test and stores them in a database.

[1175] The server inputs this data into a generative AI model to generate basic safety training videos and operating instructions.

[1176] As Tanaka begins working, the robot's camera and microphone collect emotional data and send it to the server. For example, if the emotion engine determines that Tanaka's face is tense, it inputs that data into the generative AI model.

[1177] The server updates the curriculum in real time based on Tanaka's progress and emotional data, and provides the necessary guidance.

[1178] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1179] Step 1:

[1180] The server receives the user's registration information (academic achievement information, survey information, and personal information). The user enters the information using a terminal and sends it to the server. The entered information is stored in a database. In this step, the user's profile data is received as input, and this is processed and stored in the database.

[1181] Step 2:

[1182] The user takes the academic ability test using a terminal and sends the results to the server. The server stores the sent academic ability test results in a database. In this step, the server receives the user's test results as input, processes the data, and stores it in the database.

[1183] Step 3:

[1184] The server uses a generative AI model to generate an individually optimized educational curriculum based on the received registration information and academic achievement test results. The generated curriculum includes learning materials, a study schedule, and recommended study methods and is stored in a database. It receives user information and test results as input and outputs the generated curriculum.

[1185] Step 4:

[1186] The user logs in to the server using a terminal and checks the individually optimized educational curriculum. In this step, the server sends the curriculum generated in the previous step to the terminal, and the user views it. The input is the user's login information, and the output is the curriculum information.

[1187] Step 5:

[1188] The server provides optimal webinars and learning materials based on the generated curriculum. The webinar links and learning materials are notified to the user. The server receives curriculum information as input and generates webinar links and learning material information as output.

[1189] Step 6:

[1190] Users join the webinar using the provided link and progress through the learning in real time or on-demand format. The learning progress is sent to the server. The input is the user's webinar participation information, and the output is the learning progress data.

[1191] Step 7:

[1192] The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotion data, which is then sent to a server. The input is biometric signals from the camera and microphone, and the output is analyzed emotion data.

[1193] Step 8:

[1194] The server inputs the emotion data obtained from the emotion engine into the generative AI model to analyze the user's emotional state. It dynamically updates the curriculum as needed. The input is emotion data, and the output is the updated curriculum.

[1195] Step 9:

[1196] The server evaluates and readjusts the curriculum using a generative AI model based on the user's learning progress and emotion data. The input is the progress and emotion data, and the output is an optimized updated curriculum.

[1197] Step 10:

[1198] The server notifies the user of the updated curriculum and allows the new learning content to be displayed on the platform. Here, the server sends the updated curriculum information to the user's terminal, and the user confirms it. The input is the updated curriculum, and the output is the notification and displayed learning content.

[1199] 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.

[1200] 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.

[1201] 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.

[1202] [Third embodiment]

[1203] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1204] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1205] 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).

[1206] 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.

[1207] 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.

[1208] 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).

[1209] 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.

[1210] 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.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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."

[1215] User registration and data collection

[1216] server

[1217] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, location, and learning goals), the server stores this information in a database and generates a unique ID for each user based on the stored information.

[1218] User

[1219] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[1220] server

[1221] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[1222] Generation of individually optimized curriculum

[1223] server

[1224] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[1225] server

[1226] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[1227] Curriculum delivery and webinar distribution

[1228] User

[1229] Users can log in to the platform and view the curriculum content that is tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[1230] server

[1231] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[1232] User

[1233] Users can join the webinar using the provided link and learn in real time or on-demand.

[1234] Assessment of learning progress and curriculum updates

[1235] server

[1236] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[1237] server

[1238] The collected data is fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and if necessary, update the educational curriculum and apply new learning content and recommended learning methods.

[1239] server

[1240] Notifying users of updated curriculum and making new content available on the platform.

[1241] Specific examples

[1242] When user "Tanaka" registers

[1243] server

[1244] A new user named "Tanaka" accesses the platform, enters the necessary information, and sets his / her learning goal as "I want to learn digital marketing." The server registers this in the database and automatically generates an academic ability test.

[1245] User

[1246] Tanaka takes an academic ability test and sends the results to the server.

[1247] server

[1248] The server analyzes the test results and survey information and generates a curriculum called "Marketing Fundamentals," "Market Analysis," and "Digital Marketing Practice." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[1249] User

[1250] John logs into the platform and joins the "Marketing Fundamentals" webinar using the link provided. He also downloads the provided materials to review later.

[1251] server

[1252] Monitor Tanaka's progress and update the curriculum as needed, for example, by suggesting additional learning resources or different learning methods if she is performing poorly in a particular area.

[1253] In this way, a system can be created that continues to provide an educational curriculum optimized for the user's life stage and goals.

[1254] The processing flow will be explained below.

[1255] User registration and data collection

[1256] Step 1:

[1257] Server: Displays the user registration form on the web browser or mobile app.

[1258] Step 2:

[1259] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[1260] Step 3:

[1261] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[1262] Step 4:

[1263] Server: Automatically generates individual academic ability tests based on collected profile information.

[1264] Step 5:

[1265] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[1266] Step 6:

[1267] User: Takes an online academic achievement test and sends the test results to the server.

[1268] Generation of individually optimized curriculum

[1269] Step 7:

[1270] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[1271] Step 8:

[1272] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[1273] Step 9:

[1274] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[1275] Step 10:

[1276] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[1277] Curriculum delivery and webinar distribution

[1278] Step 11:

[1279] Server: Displays individually optimized curriculum content to users who log in to the platform.

[1280] Step 12:

[1281] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[1282] Step 13:

[1283] Users: Join the webinar using the provided link and learn in real time or on demand.

[1284] Step 14:

[1285] Users: Download the webinar materials and videos for future reference.

[1286] Assessment of learning progress and curriculum updates

[1287] Step 15:

[1288] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[1289] Step 16:

[1290] Server: Re-inputs the collected learning progress data into the generative AI model to evaluate the user's progress and learning effectiveness.

[1291] Step 17:

[1292] Server: Based on the evaluation results, update the educational curriculum as necessary.

[1293] Step 18:

[1294] Server: Notifies users of updated curriculum content and makes new learning content available on the platform.

[1295] Specific examples

[1296] For new user "Yamada"

[1297] Step 1:

[1298] Server: Displays the user registration form on the web browser.

[1299] Step 2:

[1300] User (Yamada): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn the basics of marketing") and submit.

[1301] Step 3:

[1302] Server: Saves Yamada's information in the database and generates a unique user ID.

[1303] Step 4:

[1304] Server: Automatically generate an individual academic ability test based on Yamada's profile.

[1305] Step 5:

[1306] Server: Send the generated academic ability test to Yamada's registered email address.

[1307] Step 6:

[1308] User (Yamada): Takes an online academic achievement test and sends the results to the server.

[1309] Step 7:

[1310] Server: Receives Yamada's academic test results, survey information, and personal information and inputs them into the generative AI model.

[1311] Step 8:

[1312] Server: The generative AI model generates the optimal educational curriculum for Yamada.

[1313] Step 9:

[1314] Server: Stores the generated curriculum ("Fundamentals of Marketing," "Market Analysis," "Fundamentals of Digital Marketing") in a database.

[1315] Step 10:

[1316] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Yamada's curriculum.

[1317] Step 11:

[1318] Server: When Yamada logs in to the platform, the contents of the individually optimized curriculum are displayed.

[1319] Step 12:

[1320] Server: Notifies Yamada of the distribution schedule and live webinar schedule.

[1321] Step 13:

[1322] User (Yamada): Attends the "Marketing Basics" webinar using the link provided.

[1323] Step 14:

[1324] User (Yamada): Download the webinar materials and videos and use them for review later.

[1325] Step 15:

[1326] Server: Continuously collects Yamada's learning progress (test results, webinar participation records, viewing history).

[1327] Step 16:

[1328] Server: Re-inputs the collected data into the generative AI model to evaluate Yamada's progress and the effectiveness of his learning.

[1329] Step 17:

[1330] Server: Update Yamada's curriculum to include new learning content and recommended learning methods.

[1331] Step 18:

[1332] Server: Notifies Yamada of updated curriculum and displays new learning content on the platform.

[1333] Example 1

[1334] 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."

[1335] Conventional educational systems have struggled to efficiently provide individually optimized educational curricula for each user. As a result, users are unable to receive effective educational programs tailored to their learning goals, leaving them seeking further improvement in their learning outcomes. Furthermore, there is a lack of mechanisms for continuously evaluating learning progress and updating the curriculum, preventing users from maximizing their learning effectiveness.

[1336] 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.

[1337] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generating AI model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal teaching materials and webinars based on the generated educational curriculum, a means for continuously collecting the user's learning progress and re-inputting it into the generating AI model to update the curriculum, and a means for notifying the user of the updated curriculum and making the new content available on the platform. This makes it possible to provide an individually optimized educational curriculum for each user and to continuously update the curriculum based on the user's learning progress.

[1338] "Academic ability information" is information that indicates the user's academic ability and learning ability.

[1339] "Survey information" is survey information used to collect information about users' interests, concerns, learning goals, etc.

[1340] "Personal Information" refers to personal data such as a user's name, age, gender, and area of ​​residence.

[1341] A "generative AI model" is an artificial intelligence model used to generate individually optimized educational curricula based on input data.

[1342] A "curriculum" is the content of an educational program designed to meet specific learning objectives.

[1343] "Instructional materials" are educational resources such as books, videos, and documents used for learning.

[1344] A "webinar" is a lecture or seminar delivered online.

[1345] "Study progress" refers to data and records that indicate how far a user has progressed in their studies.

[1346] "Platform" means a website or application through which users log in and access learning resources and curriculum.

[1347] "Notifications" are messages or alerts that inform users of new or updated information.

[1348] The present invention relates to a system for providing an individually optimized educational curriculum. How the present invention is implemented will be described below in detail.

[1349] User registration and data collection

[1350] server

[1351] The server provides a user registration form on the web browser or mobile app for new user registration. This form is displayed when the user accesses the site. The user enters required information such as name, age, gender, residential area, and learning goals, and submits the form to provide the information to the server. The server stores this received information in a database and generates a unique ID for each user, which is also stored in the database.

[1352] User

[1353] The user enters the necessary information into the provided registration form and submits it, then takes an online academic ability test provided by the server and submits the results of the academic ability test to the server.

[1354] server

[1355] The server stores the academic test results and questionnaire information received from the user in a database.

[1356] Generation of individually optimized curriculum

[1357] server

[1358] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum. An example of the input prompt is as follows:

[1359] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[1360] teaching materials

[1361] Study Schedule

[1362] Recommended learning methods

[1363] The server receives the optimal educational curriculum output from the generative AI model and stores it in a database, including learning materials, study schedules, and recommended learning methods.

[1364] server

[1365] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[1366] Curriculum delivery and webinar distribution

[1367] User

[1368] Users log in to the platform and view the curriculum content tailored to them, and the server notifies them of the schedule of pre-recorded webinar videos and live webinars.

[1369] server

[1370] The server provides users with webinar links and materials, making it easier for them to learn through webinars. After watching the webinar, users can download the materials and videos.

[1371] User

[1372] Users can join the webinar using the provided link and learn in real time or on-demand.

[1373] Assessment of learning progress and curriculum updates

[1374] server

[1375] The server continuously collects user learning progress data (e.g., test results, webinar participation records, viewing history, etc.).

[1376] server

[1377] The server re-inputs the collected data into the generative AI model to evaluate the user's progress and learning effectiveness, and based on this, updates the educational curriculum as needed.

[1378] server

[1379] The server notifies users of updated curriculum and makes new content available on the platform.

[1380] Specific examples

[1381] For example, if a user named "Tanaka" accesses the platform saying that he wants to learn digital marketing, the server stores input data, including Tanaka's name and other basic information, in a database. Tanaka takes an online academic ability test provided by the server and sends the results to the server. The server analyzes the results and automatically generates a curriculum, such as "Marketing Basics," "Market Analysis," and "Digital Marketing Practice," based on a generative AI model. Tanaka can log in to check the curriculum content and progress through webinars and learning materials. The server also monitors Tanaka's learning progress and updates the curriculum content accordingly.

[1382] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1383] Step 1:

[1384] User registration and data collection

[1385] server

[1386] The server provides a user registration form on the web browser or mobile app that the user accesses.

[1387] Input: User-entered name, age, gender, location, and learning goal

[1388] Output: User information stored in database, unique user ID generated

[1389] What happens: The server receives the information the user entered into the form and saves it to a database. When saving, it generates a unique user ID and adds it to the database.

[1390] Step 2:

[1391] Academic achievement tests

[1392] User

[1393] Users take online academic achievement tests provided by the server.

[1394] Input: Test questions and user answers

[1395] Output: User's academic achievement test results

[1396] How it works: Users take online academic tests and send their answers to the server, which then calculates test results based on the received answers.

[1397] Step 3:

[1398] Collecting survey information

[1399] User

[1400] The user enters additional survey information and submits it to the server.

[1401] Input: User's survey response

[1402] Output: Survey information stored in a database

[1403] What it does: Users fill out a survey about their interests and learning goals and submit it to the server, which stores this information in a database.

[1404] Step 4:

[1405] Generation of individually optimized curriculum

[1406] server

[1407] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum.

[1408] Input: User's academic test results, survey information, personal information

[1409] Output: Individually optimized educational curriculum

[1410] What happens: The server inputs the following prompt into the generative AI model:

[1411] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[1412] teaching materials

[1413] Study Schedule

[1414] Recommended learning methods

[1415] The optimal curriculum content (teaching materials, study schedule, recommended study methods) output by the generative AI model is stored in a database.

[1416] Step 5:

[1417] Providing the best educational materials and webinars

[1418] server

[1419] Based on the generated curriculum, the server selects the most suitable teaching materials and webinar links from a related teaching material database and adds them to the curriculum.

[1420] Input: Generated educational curriculum

[1421] Output: A curriculum with the best learning materials and webinar links

[1422] Specific operation: Based on the curriculum content, the server searches for the most suitable teaching materials from the related teaching material database and adds them to the user's curriculum.

[1423] Step 6:

[1424] Provision and notification of curriculum content

[1425] User

[1426] Users log in to the platform and view the curriculum content tailored to them, and the server also notifies them of the webinar schedule.

[1427] Input: Platform login information

[1428] Output: Optimized curriculum content and webinar schedule

[1429] Specific behavior: When a user accesses the platform, the server displays a personalized curriculum and webinar schedule.

[1430] Step 7:

[1431] Webinar link and materials provided

[1432] server

[1433] The server provides the webinar link and materials to the user.

[1434] Input: Webinar link and materials

[1435] Output: Webinar link and materials provided to users

[1436] What it does: The server provides users with the webinar link and necessary materials electronically, facilitating their learning.

[1437] Step 8:

[1438] Assessment of learning progress and curriculum updates

[1439] server

[1440] The server continuously collects user learning progress data and re-inputs it into the generative AI model to update the curriculum.

[1441] Input: Learning progress data (test results, webinar participation records, viewing history, etc.)

[1442] Output: Updated educational curriculum

[1443] What it does: The server re-inputs the collected progress data into the generative AI model to evaluate the user's progress and learning effectiveness, update the curriculum as needed, and notify the user of the new content.

[1444] (Application example 1)

[1445] 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."

[1446] There is a need to provide efficient and effective technical education to robot operators and engineers working in factories. However, there is currently a lack of means to provide a curriculum tailored to the abilities and learning progress of each engineer, and standardized education methods have their limitations. Another challenge is providing an environment where engineers can learn at any time without interfering with on-site work.

[1447] 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.

[1448] In this invention, the server further includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal webinars and learning materials based on the generated educational curriculum, a means for continuously collecting user learning progress and re-inputting it into the generative model to update the curriculum, a means for generating and providing an individually optimized technical educational curriculum for factory robot operators and engineers, and a distribution means for providing webinars and technical materials. This allows learning content tailored to individual engineers to enable efficient and effective technical acquisition.

[1449] "Academic ability information" is data that indicates the current academic level and learning ability of the educational recipient.

[1450] "Survey information" is survey response data related to the learning goals and learning strategies of the education recipients.

[1451] "Personal information" refers to data that can individually identify an educational recipient, such as the name, age, gender, and area of ​​residence.

[1452] The "generative model means" refers to an artificial intelligence model and its execution mechanism for generating an individually optimized educational curriculum by utilizing the received academic ability information, questionnaire information, and personal information.

[1453] A "webinar" is an online lecture or seminar delivered over the Internet in real time or on demand.

[1454] "Learning materials" refers to educational content such as books, videos, and documents provided based on the educational curriculum.

[1455] "Learning progress" is data that indicates how much learning outcomes an educational subject has achieved through the educational curriculum.

[1456] A "factory robot operator" is an engineer responsible for operating and maintaining robots within a factory.

[1457] "Engineers" are professionals who have specific specialized skills and use them to carry out their work.

[1458] "Delivery medium" refers to the communications technology and its implementation for delivering educational curriculum, webinars, and technical materials to educational audiences via the Internet.

[1459] In the following, a specific method for implementing the present invention will be described, in which a system is realized in which factory robot operators and engineers receive an individually optimized technical training curriculum.

[1460] First, the server provides a user registration function. Factory robot operators and technicians (hereafter referred to as users) use a smartphone or head-mounted display to access a user registration form provided in a web browser or mobile app, and enter the required personal information (such as name, age, gender, residential area, and learning goals). This information is sent to the server and stored in a database. The server then generates a unique ID for each user based on the stored information.

[1461] The server then administers an online academic ability test to the user. The user takes the test and sends the results to the server. Based on the test results and the questionnaire information entered at the time of registration, the server generates an individually optimized educational curriculum tailored to each user's academic ability level and learning goals. The server automatically generates the curriculum using a generative AI model, and stores its contents (teaching materials, study schedule, recommended study methods, etc.) in a database.

[1462] Based on the generated curriculum, the server selects the most appropriate learning materials from a database of related learning materials and adds them to the user's curriculum. The server also provides the user with a webinar schedule and video links based on the curriculum. The user can join the webinar using the provided link and progress with their learning in real time or on-demand. After viewing the webinar, the user can download the materials and videos for future reference.

[1463] The server continuously collects user learning progress data (such as academic test results, webinar participation records, and viewing history). This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness. If necessary, the educational curriculum is updated and new learning content and recommended learning methods are applied. This process provides an optimal learning environment for factory robot operators and technicians, enabling them to acquire skills efficiently and effectively.

[1464] As a concrete example, consider the case where a user named "Yamada" registers with the system with the goal of learning "robot programming." Yamada enters his personal information and learning goals and passes an academic ability test. The server analyzes Yamada's academic ability test results and questionnaire information and generates an individually optimized "robot programming" curriculum. The curriculum includes chapters such as "Robot Basics," "Programming Applications," and "Robot Maintenance," and provides corresponding webinars and learning materials for each. Yamada participates in webinars using a smartphone or head-mounted display, downloads the provided materials, and advances his studies. Yamada's learning progress is also monitored by the server, and the curriculum content is updated and improved as necessary.

[1465] This invention provides learning content tailored to individual factory robot operators and engineers, enabling them to acquire skills efficiently and effectively without interfering with on-site work.

[1466] An example of an input prompt for a generative AI model is:

[1467] "Generate an educational curriculum with the learning objective 'Robot Programming'."

[1468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1469] Step 1:

[1470] A user accesses a web browser or mobile app using a smartphone or head-mounted display, enters the required personal information (such as name, age, gender, residential area, learning goals, etc.) in the user registration form, and submits it. Based on this input, the server stores the information in a database and generates a unique ID. The output is the stored personal information and the generated user ID.

[1471] Step 2:

[1472] The server provides online achievement tests to users. The users take the achievement tests and send their results to the server. Based on this input, the server stores the achievement test results in a database. The output is the stored achievement test results.

[1473] Step 3:

[1474] Based on the above academic ability information, questionnaire information, and personal information, the server uses a generative AI model to generate an individually optimized educational curriculum. The prompt sentence input to the generative AI model is, "Please generate an educational curriculum whose learning goal is 'robot programming'." The server saves the generated curriculum in a database. The output is the generated individually optimized curriculum.

[1475] Step 4:

[1476] The server selects the most suitable learning materials and webinar links based on the generated curriculum. These learning materials and links are added to the curriculum and notified to the user. The output is the notified learning materials and webinar links.

[1477] Step 5:

[1478] Users join a webinar using a link provided by the server. The server delivers the webinar in real-time or on-demand format, and users send their participation records and viewing histories to the server. Based on this input, the server stores the webinar participation records and viewing histories in a database. The output is the stored participation records and viewing histories.

[1479] Step 6:

[1480] The server continuously collects the user's learning progress data (such as academic test results, webinar participation records, and viewing history) and re-inputs it into the generative AI model. The generative AI model evaluates the user's progress and learning effectiveness and generates a new curriculum. The server saves the updated curriculum in a database and notifies the user again. The output is an updated, individually optimized curriculum.

[1481] Step 7:

[1482] The user continues learning based on new curriculum provided by the server. This process is cyclical until the user has fully mastered the target skill. The final output is the user's degree of mastery of the skill.

[1483] 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.

[1484] User registration and data collection

[1485] server

[1486] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, place of residence, and learning goals), the server stores this information in a database. A unique ID is generated for each user based on the stored information.

[1487] User

[1488] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[1489] server

[1490] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[1491] Generation of individually optimized curriculum

[1492] server

[1493] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[1494] server

[1495] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[1496] Introducing the Emotion Engine

[1497] Emotion Engine

[1498] The emotion engine recognizes emotions in real time by analyzing the user's facial expressions, tone of voice, and other biometric signals.

[1499] server

[1500] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[1501] Curriculum delivery and webinar distribution

[1502] User

[1503] Users can log in to the platform and view the curriculum tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[1504] server

[1505] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[1506] User

[1507] Users can join the webinar using the provided link and learn in real time or on-demand.

[1508] Emotion Engine

[1509] During a webinar or while learning, the emotion engine analyzes the user's emotions in real time and sends the results to the server.

[1510] Assessment of learning progress and curriculum updates

[1511] server

[1512] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[1513] server

[1514] The collected data and emotional data are fed back into the generative AI model to assess the user's progress and the effectiveness of their learning.

[1515] server

[1516] Based on the evaluation results, the educational curriculum is updated as needed. For example, if users feel "confused" or "stressed" about a particular topic, additional resources or different teaching methods will be introduced to reinforce that section.

[1517] server

[1518] Notifying users of updated curriculum and making new content available on the platform.

[1519] Specific examples

[1520] For new user "Sato"

[1521] server

[1522] A new user named "Sato" accesses the platform, enters the necessary information, and sets his or her learning goal as "I want to learn data science." The server then registers this information in the database and automatically generates an academic achievement test.

[1523] User

[1524] Sato takes an academic ability test and sends the results to the server.

[1525] server

[1526] The server analyzes the test results and survey information and generates a curriculum called "Fundamental Concepts of Data Science," "Data Analysis," and "Machine Learning." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[1527] User

[1528] Sato logs in to the platform and joins the "Fundamental Concepts of Data Science" webinar using the provided link. He also downloads the provided materials for later review.

[1529] Emotion Engine

[1530] During the webinar, the emotion engine analyzes Sato's facial expressions and tone of voice, recognizing emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[1531] server

[1532] Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[1533] server

[1534] Based on the analysis, if Sato feels "uneasy" about a particular section, we will provide him with additional resources to reinforce that section and update the curriculum.

[1535] server

[1536] The updated curriculum will be notified to Sato and new learning content will be displayed on the platform.

[1537] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[1538] The processing flow will be explained below.

[1539] User registration and data collection

[1540] Step 1:

[1541] Server: Displays the user registration form on the web browser or mobile app.

[1542] Step 2:

[1543] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[1544] Step 3:

[1545] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[1546] Step 4:

[1547] Server: Automatically generates individual academic ability tests based on collected profile information.

[1548] Step 5:

[1549] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[1550] Step 6:

[1551] User: Takes an online academic achievement test and sends the test results to the server.

[1552] Generation of individually optimized curriculum

[1553] Step 7:

[1554] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[1555] Step 8:

[1556] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[1557] Step 9:

[1558] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[1559] Step 10:

[1560] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[1561] Introducing the Emotion Engine

[1562] Step 11:

[1563] Emotion Engine: Recognizes the user's emotions in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[1564] Step 12:

[1565] Server: Receives user emotion data sent from the emotion engine and inputs it into the generative AI model.

[1566] Curriculum delivery and webinar distribution

[1567] Step 13:

[1568] Server: Displays individually optimized curriculum content to users who log in to the platform.

[1569] Step 14:

[1570] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[1571] Step 15:

[1572] Users: Join the webinar using the provided link and learn in real time or on demand.

[1573] Step 16:

[1574] Users: Download the webinar materials and videos for future reference.

[1575] Step 17:

[1576] Emotion Engine: Analyzes user emotions in real time during webinars and learning sessions and sends the results to the server.

[1577] Assessment of learning progress and curriculum updates

[1578] Step 18:

[1579] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[1580] Step 19:

[1581] Server: Collected learning progress data and emotional data are re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[1582] Step 20:

[1583] Server: Based on the assessment results, update the educational curriculum as needed. For example, if users are "confident" or "stressed" about a particular topic, introduce additional resources or different teaching methods to reinforce that section.

[1584] Step 21:

[1585] Server: Notifies users of updated curriculum and makes new learning content visible on the platform.

[1586] Specific examples

[1587] For new user "Sato"

[1588] Step 1:

[1589] Server: Displays the user registration form on the web browser.

[1590] Step 2:

[1591] User (Sato): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn data science") and submit.

[1592] Step 3:

[1593] Server: Saves Sato's information in a database and generates a unique user ID.

[1594] Step 4:

[1595] Server: Automatically generate an individual academic ability test based on Sato's profile.

[1596] Step 5:

[1597] Server: Send the generated academic ability test to Sato's registered email address.

[1598] Step 6:

[1599] User (Sato): Takes an online academic ability test and sends the results to the server.

[1600] Step 7:

[1601] Server: Receives Sato's academic test results, survey information, and personal information and inputs them into the generative AI model.

[1602] Step 8:

[1603] Server: The generative AI model generates the optimal educational curriculum for Sato.

[1604] Step 9:

[1605] Server: Stores the generated curriculum ("Fundamentals of Data Science," "Data Analysis," and "Machine Learning") in a database.

[1606] Step 10:

[1607] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Sato's curriculum.

[1608] Step 11:

[1609] Server: When Sato logs in to the platform, the contents of the individually optimized curriculum are displayed.

[1610] Step 12:

[1611] Server: Notifies Sato of the distribution schedule and live webinar schedule.

[1612] Step 13:

[1613] User (Sato): Join the "Fundamental Concepts of Data Science" webinar using the provided link.

[1614] Step 14:

[1615] User (Sato): Download the webinar materials and videos and use them for review later.

[1616] Step 15:

[1617] Emotion engine: During the webinar, Sato's facial expressions and tone of voice are analyzed to recognize emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[1618] Step 16:

[1619] Server: Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[1620] Step 17:

[1621] Server: Based on the analysis, if Sato feels "uneasy" about a particular section, provide him with additional resources to reinforce that section and update the curriculum.

[1622] Step 18:

[1623] Server: Notifies Sato of updated curriculum and displays new learning content on the platform.

[1624] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[1625] Example 2

[1626] 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."

[1627] Conventional educational systems have difficulty providing individually optimized educational programs tailored to the user's academic ability and learning goals. Furthermore, they lack a means to grasp the user's emotional state in real time while learning, making it difficult to update educational programs accordingly. Furthermore, there are limitations to providing an environment where users can learn anytime, anywhere.

[1628] 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.

[1629] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational program based on the received information, a means for providing optimal online lectures and learning materials based on the generated educational program, a means for analyzing the user's biosignals and collecting emotional data in real time, a means for inputting the emotional data into the generative model and updating the educational program taking the user's emotional state into account, and a means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum. This allows an individually optimized educational program to be provided in real time, taking the user's emotional state into consideration, enabling the user to study efficiently anytime, anywhere.

[1630] "Academic ability information" is information that indicates the user's current academic ability level, including test results and past learning history.

[1631] "Survey Information" is subjective information provided by users, including information about their learning goals, interests, and experiences.

[1632] "Personal information" refers to basic information such as a user's name, age, gender, and area of ​​residence.

[1633] A "generative model means" is a means including a generative AI model for automatically generating an optimal educational program based on input data.

[1634] An "educational program" is an individually optimized curriculum tailored to a user's learning goals and academic level, and includes teaching materials, study schedules, recommended study methods, etc.

[1635] "Online lectures" refer to classes provided over the Internet, either in real time or via recorded video lectures.

[1636] "Learning Materials" are resources such as textbooks, videos, and exercises provided as part of an educational program.

[1637] "Biological signals" are signals emitted from the user's body, including facial expressions, tone of voice, heart rate, etc.

[1638] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing biosignals.

[1639] "Study progress" is data that shows how far a user has progressed in their studies, and includes test results, viewing history, participation records, etc.

[1640] "Generative AI" is a type of artificial intelligence that refers to a system that generates optimal educational programs and curricula based on large amounts of data.

[1641] "Delivery medium" means a means, including internet-based technologies and services, for providing online courses and educational materials to users.

[1642] The present invention is a system for providing users with individually optimized educational programs. A specific implementation method thereof will be described below.

[1643] User registration and data collection

[1644] server

[1645] The server displays a user registration form on the web browser or mobile app. The form includes fields for entering information such as name, age, gender, location, and learning goals. After the user enters and submits the required information, the server stores the entered data in a database. At the same time, it generates a unique ID for each user. For example, if a user enters "I want to study data science," that information is stored.

[1646] User

[1647] The user fills in the required information in the provided form and submits it to the server. The user also clicks on the link for the academic achievement test sent from the server and takes the test online. For example, a user named "Sato" fills in the form with his / her goal of "I want to study data science," submits it, and then takes the academic achievement test.

[1648] server

[1649] The server receives the results of the academic ability test sent by the user, stores them in a database, and then analyzes the test results and questionnaire information to generate an individual educational program based on the user's academic level and goals.

[1650] Generation of individually optimized curriculum

[1651] server

[1652] The server inputs the received data into the generative AI model. At that time, the data is sent to the generative AI model as a prompt. By sending a prompt in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" to the generative AI model, the model generates an optimal curriculum.

[1653] server

[1654] The server receives the curriculum content (teaching materials, study schedule, recommended learning methods) returned by the generative AI model and stores them in a database. At the same time, it picks up appropriate teaching materials from the related teaching materials database and adds them to this curriculum.

[1655] Introducing the Emotion Engine

[1656] Emotion Engine

[1657] The emotion engine generates emotion data by analyzing the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) in real time. For example, while a user is attending an online lecture, it analyzes camera footage and audio data to generate emotion data such as "interested" or "confused."

[1658] server

[1659] The server receives the user's emotional data sent from the emotion engine and inputs it into the generative AI model. This emotional data is analyzed as the user progresses and is used to update the educational program accordingly.

[1660] Curriculum delivery and webinar distribution

[1661] User

[1662] Users log in to the platform and check their curriculum. The server notifies them of the links and materials for the online lectures they provide. For example, "Sato" logs in to the platform and obtains a link to participate in an online lecture called "Basics of Data Science."

[1663] server

[1664] The server provides links to online lectures and materials for users to access anytime, anywhere, and also provides recorded videos and materials for users to review later.

[1665] User

[1666] When a user clicks on an online lecture link, they can take the lecture in real time or on-demand. For example, "Sato" can participate in the "Basics of Data Science" course in real time and download the provided materials for review.

[1667] Emotion Engine

[1668] While users are taking online classes, the emotion engine analyzes their facial expressions and tone of voice and sends the data to a server, which collects it and feeds it into a generative AI model to update the curriculum.

[1669] Assessment of learning progress and curriculum updates

[1670] server

[1671] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.), which is then re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[1672] server

[1673] Based on the assessment, the user's educational program is updated as needed, for example, if a user feels "stuck" in a particular learning section, additional resources or different teaching methods are provided to reinforce that section.

[1674] server

[1675] Users are notified of updated curriculum and can view the new content on the platform. For example, if "Sato" receives an email notification of an updated educational program, the new learning content will be displayed when he logs in to the platform.

[1676] Through this system, it is possible to optimize the user's learning experience and provide an efficient learning process.

[1677] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1678] Step 1:

[1679] User registration and data collection

[1680] server

[1681] The server displays a user registration form on a web browser or mobile app. As input, the user enters their name, age, gender, location, learning goals, etc. When the user submits the input, the server receives it and stores it in a database. At the same time, it generates and stores a unique ID for each user. The output includes the stored user information and the unique user ID.

[1682] User

[1683] The user fills in the required information in the form and submits it. For example, a user named "Sato" enters his / her learning goal of "I want to learn data science" and submits it. The server then receives an automatically generated academic achievement test link. The user clicks on the link and takes the test online. The results of the academic achievement test are then sent as input to the server.

[1684] server

[1685] The server receives the academic ability test results sent by the user and stores them in a database. The input includes the academic ability test results and the user ID. It then analyzes the results and questionnaire information and generates an individual educational program based on the user's academic ability level and goals as output.

[1686] Step 2:

[1687] Generation of individually optimized curriculum

[1688] server

[1689] The server inputs the user information stored in the database and the academic achievement test results into the generative AI model. As input, it generates a prompt sentence in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" and sends it to the generative AI model. The model analyzes the data and generates an optimal educational curriculum. The output is the curriculum content returned by the generative AI model.

[1690] server

[1691] The generated curriculum content (teaching materials, study schedule, recommended study methods) is saved in a database. The most suitable teaching materials are selected from the related teaching materials database and added to the curriculum. This completes the educational program provided to the user.

[1692] Step 3:

[1693] Introducing the Emotion Engine

[1694] Emotion Engine

[1695] The emotion engine analyzes the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) and generates emotional data. Inputs include real-time camera footage and audio data. Based on this, it generates emotional data such as "User ID: XXXX, facial expression: smiling, tone of voice: optimistic." The output is the emotional data resulting from the analysis.

[1696] server

[1697] The server receives the emotion data sent from the emotion engine and inputs it into the generative AI model. The emotion data is included as input. The model updates the generated curriculum based on the emotion data as appropriate. An individualized educational program is generated that takes the emotion data into consideration.

[1698] Step 4:

[1699] Curriculum delivery and webinar distribution

[1700] User

[1701] The user logs in to the platform and checks the optimized curriculum. The server notifies them of the online lecture link and materials. For example, Sato logs in to the platform and checks the link to join the "Basic Data Science Course." The user clicks and receives the lecture in real time. The online lecture link and materials are provided as input.

[1702] server

[1703] The server provides links to online lectures and materials, allowing users to access them anytime, anywhere. As an output, it collects user participation records and viewing histories. It also provides recorded videos and materials for offline use.

[1704] Emotion Engine

[1705] While a user is taking an online lecture, the emotion engine analyzes their facial expressions and tone of voice to generate emotion data, which is then sent to the server. The input includes real-time video and audio data, and the output is the emotion data resulting from the analysis.

[1706] Step 5:

[1707] Assessment of learning progress and curriculum updates

[1708] server

[1709] The server continuously collects users' learning progress data (such as test results, webinar participation records, and viewing history). This data is included as input. This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and update the educational program as needed. The output is the evaluation results and an updated curriculum.

[1710] server

[1711] Based on the evaluation results, the educational program is updated accordingly and notified to the user. For example, if a user is struggling with a particular learning section, additional resources or different teaching methods can be provided to reinforce that section. The updated curriculum is then notified to the user.

[1712] User

[1713] Users can check the updated curriculum on the platform and engage in new learning content. For example, Sato revisits the updated "Basic Data Science Course" and continues his learning using new materials and resources. New learning resources are provided as input.

[1714] In this way, the entire system is designed to collect and analyze data in real time to optimize the user's learning experience.

[1715] (Application example 2)

[1716] 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."

[1717] Conventional education and work instruction systems have had difficulty providing individually optimized curricula that reflect the user's (worker's) learning progress and emotional state in real time. In addition, there has been a lack of means to improve learning and work efficiency by capturing the user's emotional state and providing appropriate feedback.

[1718] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving academic ability information, questionnaire information, and personal information; a generative model means for generating an individually optimized educational curriculum based on the received information; means for providing optimal webinars and learning materials based on the generated educational curriculum; means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum; means for analyzing the worker's emotional state in real time using a camera and microphone and inputting the data into the generative model; and means for dynamically updating the worker's operation method and supplementary materials based on the analyzed emotional data. This makes it possible to provide an individually optimized curriculum and real-time feedback that improves the user's learning efficiency and work efficiency.

[1719] "Academic achievement information" is data that indicates an individual user's educational level and academic achievement test results.

[1720] "Survey information" is data collected to understand user attributes, learning objectives, interests, etc.

[1721] "Personal information" is information that can be used to identify an individual user, such as the user's name, age, gender, and area of ​​residence.

[1722] "Means" is a broad concept that includes methods and devices for achieving a specific purpose.

[1723] A "generative model" is an algorithm or software used to create an individually optimized educational curriculum based on collected data.

[1724] A "webinar" is a form of online seminar or lecture held over the web.

[1725] "Learning Materials" are educational resources and materials provided to achieve specific learning objectives.

[1726] "Curriculum updating" is the process of adjusting and improving existing educational plans based on users' learning progress and emotional data.

[1727] A "camera" is a device for capturing images and storing them as digital data.

[1728] A "microphone" is a device that collects sound and stores it as digital data.

[1729] An "emotional state" is a state that indicates a user's psychological and emotional response.

[1730] "Real-time analysis" is the process of processing collected data immediately and reflecting and utilizing the results immediately.

[1731] "Dynamic updates" means flexibly changing the system and curriculum in response to changes in the user's status and environment.

[1732] The embodiment of this invention is a system that provides individually optimized educational curricula using a generative AI model based on user registration information and academic achievement test results. This system utilizes a server, user terminals, and an emotion engine to educate and train workers.

[1733] User registration and data collection

[1734] server

[1735] The server receives the user's registration information (academic achievement information, questionnaire information, and personal information).

[1736] This information is entered using a web browser or mobile app and stored in a database.

[1737] User

[1738] The user uses a terminal to enter the necessary information and send it to the server. For example, a factory worker uses a tablet to take an academic achievement test and then sends the results to the server.

[1739] Generation of individually optimized curriculum

[1740] server

[1741] The server uses a generative AI model based on the received data to generate an optimal educational curriculum, which includes learning materials, a study schedule, and recommended study methods.

[1742] The generated curriculum is stored in a database.

[1743] Introducing the Emotion Engine

[1744] Emotion Engine

[1745] The emotion engine uses hardware such as a camera and microphone to recognize a user's emotional state in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[1746] server

[1747] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[1748] Curriculum delivery and monitoring

[1749] User

[1750] Users view educational videos and instructional materials on holographic displays and tablets based on the provided curriculum.

[1751] server

[1752] The server provides optimal webinars and learning materials based on the generated curriculum, such as safety training videos and instructional curriculum for operating procedures for factory workers.

[1753] The server monitors the user's emotional state in real time and dynamically updates the curriculum based on this.

[1754] Assessment of learning progress and curriculum updates

[1755] server

[1756] The server re-inputs the user's learning progress data (test results, webinar participation records, viewing history) and emotional data into the generative AI model to evaluate the user's progress and the effectiveness of their learning.

[1757] Based on the evaluation results, the curriculum will be updated as needed. For example, if users experience "stress" or "anxiety" during a particular operation, additional resources will be provided to reinforce instruction on how to perform that operation.

[1758] Specific examples

[1759] New worker "Tanaka"

[1760] Example prompt: "Generate the optimal training curriculum for new worker Tanaka in the field where he will be working."

[1761] Specific examples of processing

[1762] The server receives the user information entered by Tanaka and the results of the operation skill test and stores them in a database.

[1763] The server inputs this data into a generative AI model to generate basic safety training videos and operating instructions.

[1764] As Tanaka begins working, the robot's camera and microphone collect emotional data and send it to the server. For example, if the emotion engine determines that Tanaka's face is tense, it inputs that data into the generative AI model.

[1765] The server updates the curriculum in real time based on Tanaka's progress and emotional data, and provides the necessary guidance.

[1766] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1767] Step 1:

[1768] The server receives the user's registration information (academic achievement information, survey information, and personal information). The user enters the information using a terminal and sends it to the server. The entered information is stored in a database. In this step, the user's profile data is received as input, and this is processed and stored in the database.

[1769] Step 2:

[1770] The user takes the academic ability test using a terminal and sends the results to the server. The server stores the sent academic ability test results in a database. In this step, the server receives the user's test results as input, processes the data, and stores it in the database.

[1771] Step 3:

[1772] The server uses a generative AI model to generate an individually optimized educational curriculum based on the received registration information and academic achievement test results. The generated curriculum includes learning materials, a study schedule, and recommended study methods and is stored in a database. It receives user information and test results as input and outputs the generated curriculum.

[1773] Step 4:

[1774] The user logs in to the server using a terminal and checks the individually optimized educational curriculum. In this step, the server sends the curriculum generated in the previous step to the terminal, and the user views it. The input is the user's login information, and the output is the curriculum information.

[1775] Step 5:

[1776] The server provides optimal webinars and learning materials based on the generated curriculum. The webinar links and learning materials are notified to the user. The server receives curriculum information as input and generates webinar links and learning material information as output.

[1777] Step 6:

[1778] Users join the webinar using the provided link and progress through the learning in real time or on-demand format. The learning progress is sent to the server. The input is the user's webinar participation information, and the output is the learning progress data.

[1779] Step 7:

[1780] The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to obtain emotion data, which is then sent to a server. The input is biometric signals from the camera and microphone, and the output is analyzed emotion data.

[1781] Step 8:

[1782] The server inputs the emotion data obtained from the emotion engine into the generative AI model to analyze the user's emotional state. It dynamically updates the curriculum as needed. The input is emotion data, and the output is the updated curriculum.

[1783] Step 9:

[1784] The server evaluates and readjusts the curriculum using a generative AI model based on the user's learning progress and emotion data. The input is the progress and emotion data, and the output is an optimized updated curriculum.

[1785] Step 10:

[1786] The server notifies the user of the updated curriculum and allows the new learning content to be displayed on the platform. Here, the server sends the updated curriculum information to the user's terminal, and the user confirms it. The input is the updated curriculum, and the output is the notification and displayed learning content.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] [Fourth embodiment]

[1791] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1792] 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.

[1793] 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).

[1794] 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.

[1795] 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.

[1796] 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).

[1797] 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.

[1798] 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.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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."

[1804] User registration and data collection

[1805] server

[1806] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, location, and learning goals), the server stores this information in a database and generates a unique ID for each user based on the stored information.

[1807] User

[1808] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[1809] server

[1810] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[1811] Generation of individually optimized curriculum

[1812] server

[1813] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[1814] server

[1815] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[1816] Curriculum delivery and webinar distribution

[1817] User

[1818] Users can log in to the platform and view the curriculum content that is tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[1819] server

[1820] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[1821] User

[1822] Users can join the webinar using the provided link and learn in real time or on-demand.

[1823] Assessment of learning progress and curriculum updates

[1824] server

[1825] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[1826] server

[1827] The collected data is fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and if necessary, update the educational curriculum and apply new learning content and recommended learning methods.

[1828] server

[1829] Notifying users of updated curriculum and making new content available on the platform.

[1830] Specific examples

[1831] When user "Tanaka" registers

[1832] server

[1833] A new user named "Tanaka" accesses the platform, enters the necessary information, and sets his / her learning goal as "I want to learn digital marketing." The server registers this in the database and automatically generates an academic ability test.

[1834] User

[1835] Tanaka takes an academic ability test and sends the results to the server.

[1836] server

[1837] The server analyzes the test results and survey information and generates a curriculum called "Marketing Fundamentals," "Market Analysis," and "Digital Marketing Practice." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[1838] User

[1839] John logs into the platform and joins the "Marketing Fundamentals" webinar using the link provided. He also downloads the provided materials to review later.

[1840] server

[1841] Monitor Tanaka's progress and update the curriculum as needed, for example, by suggesting additional learning resources or different learning methods if she is performing poorly in a particular area.

[1842] In this way, a system can be created that continues to provide an educational curriculum optimized for the user's life stage and goals.

[1843] The processing flow will be explained below.

[1844] User registration and data collection

[1845] Step 1:

[1846] Server: Displays the user registration form on the web browser or mobile app.

[1847] Step 2:

[1848] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[1849] Step 3:

[1850] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[1851] Step 4:

[1852] Server: Automatically generates individual academic ability tests based on collected profile information.

[1853] Step 5:

[1854] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[1855] Step 6:

[1856] User: Takes an online academic achievement test and sends the test results to the server.

[1857] Generation of individually optimized curriculum

[1858] Step 7:

[1859] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[1860] Step 8:

[1861] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[1862] Step 9:

[1863] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[1864] Step 10:

[1865] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[1866] Curriculum delivery and webinar distribution

[1867] Step 11:

[1868] Server: Displays individually optimized curriculum content to users who log in to the platform.

[1869] Step 12:

[1870] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[1871] Step 13:

[1872] Users: Join the webinar using the provided link and learn in real time or on demand.

[1873] Step 14:

[1874] Users: Download the webinar materials and videos for future reference.

[1875] Assessment of learning progress and curriculum updates

[1876] Step 15:

[1877] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[1878] Step 16:

[1879] Server: Re-inputs the collected learning progress data into the generative AI model to evaluate the user's progress and learning effectiveness.

[1880] Step 17:

[1881] Server: Based on the evaluation results, update the educational curriculum as necessary.

[1882] Step 18:

[1883] Server: Notifies users of updated curriculum content and makes new learning content available on the platform.

[1884] Specific examples

[1885] For new user "Yamada"

[1886] Step 1:

[1887] Server: Displays the user registration form on the web browser.

[1888] Step 2:

[1889] User (Yamada): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn the basics of marketing") and submit.

[1890] Step 3:

[1891] Server: Saves Yamada's information in the database and generates a unique user ID.

[1892] Step 4:

[1893] Server: Automatically generate an individual academic ability test based on Yamada's profile.

[1894] Step 5:

[1895] Server: Send the generated academic ability test to Yamada's registered email address.

[1896] Step 6:

[1897] User (Yamada): Takes an online academic achievement test and sends the results to the server.

[1898] Step 7:

[1899] Server: Receives Yamada's academic test results, survey information, and personal information and inputs them into the generative AI model.

[1900] Step 8:

[1901] Server: The generative AI model generates the optimal educational curriculum for Yamada.

[1902] Step 9:

[1903] Server: Stores the generated curriculum ("Fundamentals of Marketing," "Market Analysis," "Fundamentals of Digital Marketing") in a database.

[1904] Step 10:

[1905] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Yamada's curriculum.

[1906] Step 11:

[1907] Server: When Yamada logs in to the platform, the contents of the individually optimized curriculum are displayed.

[1908] Step 12:

[1909] Server: Notifies Yamada of the distribution schedule and live webinar schedule.

[1910] Step 13:

[1911] User (Yamada): Attends the "Marketing Basics" webinar using the link provided.

[1912] Step 14:

[1913] User (Yamada): Download the webinar materials and videos and use them for review later.

[1914] Step 15:

[1915] Server: Continuously collects Yamada's learning progress (test results, webinar participation records, viewing history).

[1916] Step 16:

[1917] Server: Re-inputs the collected data into the generative AI model to evaluate Yamada's progress and the effectiveness of his learning.

[1918] Step 17:

[1919] Server: Update Yamada's curriculum to include new learning content and recommended learning methods.

[1920] Step 18:

[1921] Server: Notifies Yamada of updated curriculum and displays new learning content on the platform.

[1922] Example 1

[1923] 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."

[1924] Conventional educational systems have struggled to efficiently provide individually optimized educational curricula for each user. As a result, users are unable to receive effective educational programs tailored to their learning goals, leaving them seeking further improvement in their learning outcomes. Furthermore, there is a lack of mechanisms for continuously evaluating learning progress and updating the curriculum, preventing users from maximizing their learning effectiveness.

[1925] 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.

[1926] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generating AI model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal teaching materials and webinars based on the generated educational curriculum, a means for continuously collecting the user's learning progress and re-inputting it into the generating AI model to update the curriculum, and a means for notifying the user of the updated curriculum and making the new content available on the platform. This makes it possible to provide an individually optimized educational curriculum for each user and to continuously update the curriculum based on the user's learning progress.

[1927] "Academic ability information" is information that indicates the user's academic ability and learning ability.

[1928] "Survey information" is survey information used to collect information about users' interests, concerns, learning goals, etc.

[1929] "Personal Information" refers to personal data such as a user's name, age, gender, and area of ​​residence.

[1930] A "generative AI model" is an artificial intelligence model used to generate individually optimized educational curricula based on input data.

[1931] A "curriculum" is the content of an educational program designed to meet specific learning objectives.

[1932] "Instructional materials" are educational resources such as books, videos, and documents used for learning.

[1933] A "webinar" is a lecture or seminar delivered online.

[1934] "Study progress" refers to data and records that indicate how far a user has progressed in their studies.

[1935] "Platform" means a website or application through which users log in and access learning resources and curriculum.

[1936] "Notifications" are messages or alerts that inform users of new or updated information.

[1937] The present invention relates to a system for providing an individually optimized educational curriculum. How the present invention is implemented will be described below in detail.

[1938] User registration and data collection

[1939] server

[1940] The server provides a user registration form on the web browser or mobile app for new user registration. This form is displayed when the user accesses the site. The user enters required information such as name, age, gender, residential area, and learning goals, and submits the form to provide the information to the server. The server stores this received information in a database and generates a unique ID for each user, which is also stored in the database.

[1941] User

[1942] The user enters the necessary information into the provided registration form and submits it, then takes an online academic ability test provided by the server and submits the results of the academic ability test to the server.

[1943] server

[1944] The server stores the academic test results and questionnaire information received from the user in a database.

[1945] Generation of individually optimized curriculum

[1946] server

[1947] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum. An example of the input prompt is as follows:

[1948] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[1949] teaching materials

[1950] Study Schedule

[1951] Recommended learning methods

[1952] The server receives the optimal educational curriculum output from the generative AI model and stores it in a database, including learning materials, study schedules, and recommended learning methods.

[1953] server

[1954] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[1955] Curriculum delivery and webinar distribution

[1956] User

[1957] Users log in to the platform and view the curriculum content tailored to them, and the server notifies them of the schedule of pre-recorded webinar videos and live webinars.

[1958] server

[1959] The server provides users with webinar links and materials, making it easier for them to learn through webinars. After watching the webinar, users can download the materials and videos.

[1960] User

[1961] Users can join the webinar using the provided link and learn in real time or on-demand.

[1962] Assessment of learning progress and curriculum updates

[1963] server

[1964] The server continuously collects user learning progress data (e.g., test results, webinar participation records, viewing history, etc.).

[1965] server

[1966] The server re-inputs the collected data into the generative AI model to evaluate the user's progress and learning effectiveness, and based on this, updates the educational curriculum as needed.

[1967] server

[1968] The server notifies users of updated curriculum and makes new content available on the platform.

[1969] Specific examples

[1970] For example, if a user named "Tanaka" accesses the platform saying that he wants to learn digital marketing, the server stores input data, including Tanaka's name and other basic information, in a database. Tanaka takes an online academic ability test provided by the server and sends the results to the server. The server analyzes the results and automatically generates a curriculum, such as "Marketing Basics," "Market Analysis," and "Digital Marketing Practice," based on a generative AI model. Tanaka can log in to check the curriculum content and progress through webinars and learning materials. The server also monitors Tanaka's learning progress and updates the curriculum content accordingly.

[1971] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1972] Step 1:

[1973] User registration and data collection

[1974] server

[1975] The server provides a user registration form on the web browser or mobile app that the user accesses.

[1976] Input: User-entered name, age, gender, location, and learning goal

[1977] Output: User information stored in database, unique user ID generated

[1978] What happens: The server receives the information the user entered into the form and saves it to a database. When saving, it generates a unique user ID and adds it to the database.

[1979] Step 2:

[1980] Academic achievement tests

[1981] User

[1982] Users take online academic achievement tests provided by the server.

[1983] Input: Test questions and user answers

[1984] Output: User's academic achievement test results

[1985] How it works: Users take online academic tests and send their answers to the server, which then calculates test results based on the received answers.

[1986] Step 3:

[1987] Collecting survey information

[1988] User

[1989] The user enters additional survey information and submits it to the server.

[1990] Input: User's survey response

[1991] Output: Survey information stored in a database

[1992] What it does: Users fill out a survey about their interests and learning goals and submit it to the server, which stores this information in a database.

[1993] Step 4:

[1994] Generation of individually optimized curriculum

[1995] server

[1996] The server inputs the collected data into a generative AI model to generate an individually optimized educational curriculum.

[1997] Input: User's academic test results, survey information, personal information

[1998] Output: Individually optimized educational curriculum

[1999] What happens: The server inputs the following prompt into the generative AI model:

[2000] Based on the user's academic test results and survey information, generate a personalized educational curriculum that includes:

[2001] teaching materials

[2002] Study Schedule

[2003] Recommended learning methods

[2004] The optimal curriculum content (teaching materials, study schedule, recommended study methods) output by the generative AI model is stored in a database.

[2005] Step 5:

[2006] Providing the best educational materials and webinars

[2007] server

[2008] Based on the generated curriculum, the server selects the most suitable teaching materials and webinar links from a related teaching material database and adds them to the curriculum.

[2009] Input: Generated educational curriculum

[2010] Output: A curriculum with the best learning materials and webinar links

[2011] Specific operation: Based on the curriculum content, the server searches for the most suitable teaching materials from the related teaching material database and adds them to the user's curriculum.

[2012] Step 6:

[2013] Provision and notification of curriculum content

[2014] User

[2015] Users log in to the platform and view the curriculum content tailored to them, and the server also notifies them of the webinar schedule.

[2016] Input: Platform login information

[2017] Output: Optimized curriculum content and webinar schedule

[2018] Specific behavior: When a user accesses the platform, the server displays a personalized curriculum and webinar schedule.

[2019] Step 7:

[2020] Webinar link and materials provided

[2021] server

[2022] The server provides the webinar link and materials to the user.

[2023] Input: Webinar link and materials

[2024] Output: Webinar link and materials provided to users

[2025] What it does: The server provides users with the webinar link and necessary materials electronically, facilitating their learning.

[2026] Step 8:

[2027] Assessment of learning progress and curriculum updates

[2028] server

[2029] The server continuously collects user learning progress data and re-inputs it into the generative AI model to update the curriculum.

[2030] Input: Learning progress data (test results, webinar participation records, viewing history, etc.)

[2031] Output: Updated educational curriculum

[2032] What it does: The server re-inputs the collected progress data into the generative AI model to evaluate the user's progress and learning effectiveness, update the curriculum as needed, and notify the user of the new content.

[2033] (Application example 1)

[2034] 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."

[2035] There is a need to provide efficient and effective technical education to robot operators and engineers working in factories. However, there is currently a lack of means to provide a curriculum tailored to the abilities and learning progress of each engineer, and standardized education methods have their limitations. Another challenge is providing an environment where engineers can learn at any time without interfering with on-site work.

[2036] 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.

[2037] In this invention, the server further includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational curriculum based on the received information, a means for providing optimal webinars and learning materials based on the generated educational curriculum, a means for continuously collecting user learning progress and re-inputting it into the generative model to update the curriculum, a means for generating and providing an individually optimized technical educational curriculum for factory robot operators and engineers, and a distribution means for providing webinars and technical materials. This allows learning content tailored to individual engineers to enable efficient and effective technical acquisition.

[2038] "Academic ability information" is data that indicates the current academic level and learning ability of the educational recipient.

[2039] "Survey information" is survey response data related to the learning goals and learning strategies of the education recipients.

[2040] "Personal information" refers to data that can individually identify an educational recipient, such as the name, age, gender, and area of ​​residence.

[2041] The "generative model means" refers to an artificial intelligence model and its execution mechanism for generating an individually optimized educational curriculum by utilizing the received academic ability information, questionnaire information, and personal information.

[2042] A "webinar" is an online lecture or seminar delivered over the Internet in real time or on demand.

[2043] "Learning materials" refers to educational content such as books, videos, and documents provided based on the educational curriculum.

[2044] "Learning progress" is data that indicates how much learning outcomes an educational subject has achieved through the educational curriculum.

[2045] A "factory robot operator" is an engineer responsible for operating and maintaining robots within a factory.

[2046] "Engineers" are professionals who have specific specialized skills and use them to carry out their work.

[2047] "Delivery medium" refers to the communications technology and its implementation for delivering educational curriculum, webinars, and technical materials to educational audiences via the Internet.

[2048] In the following, a specific method for implementing the present invention will be described, in which a system is realized in which factory robot operators and engineers receive an individually optimized technical training curriculum.

[2049] First, the server provides a user registration function. Factory robot operators and technicians (hereafter referred to as users) use a smartphone or head-mounted display to access a user registration form provided in a web browser or mobile app, and enter the required personal information (such as name, age, gender, residential area, and learning goals). This information is sent to the server and stored in a database. The server then generates a unique ID for each user based on the stored information.

[2050] The server then administers an online academic ability test to the user. The user takes the test and sends the results to the server. Based on the test results and the questionnaire information entered at the time of registration, the server generates an individually optimized educational curriculum tailored to each user's academic ability level and learning goals. The server automatically generates the curriculum using a generative AI model, and stores its contents (teaching materials, study schedule, recommended study methods, etc.) in a database.

[2051] Based on the generated curriculum, the server selects the most appropriate learning materials from a database of related learning materials and adds them to the user's curriculum. The server also provides the user with a webinar schedule and video links based on the curriculum. The user can join the webinar using the provided link and progress with their learning in real time or on-demand. After viewing the webinar, the user can download the materials and videos for future reference.

[2052] The server continuously collects user learning progress data (such as academic test results, webinar participation records, and viewing history). This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness. If necessary, the educational curriculum is updated and new learning content and recommended learning methods are applied. This process provides an optimal learning environment for factory robot operators and technicians, enabling them to acquire skills efficiently and effectively.

[2053] As a concrete example, consider the case where a user named "Yamada" registers with the system with the goal of learning "robot programming." Yamada enters his personal information and learning goals and passes an academic ability test. The server analyzes Yamada's academic ability test results and questionnaire information and generates an individually optimized "robot programming" curriculum. The curriculum includes chapters such as "Robot Basics," "Programming Applications," and "Robot Maintenance," and provides corresponding webinars and learning materials for each. Yamada participates in webinars using a smartphone or head-mounted display, downloads the provided materials, and advances his studies. Yamada's learning progress is also monitored by the server, and the curriculum content is updated and improved as necessary.

[2054] This invention provides learning content tailored to individual factory robot operators and engineers, enabling them to acquire skills efficiently and effectively without interfering with on-site work.

[2055] An example of an input prompt for a generative AI model is:

[2056] "Generate an educational curriculum with the learning objective 'Robot Programming'."

[2057] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2058] Step 1:

[2059] A user accesses a web browser or mobile app using a smartphone or head-mounted display, enters the required personal information (such as name, age, gender, residential area, learning goals, etc.) in the user registration form, and submits it. Based on this input, the server stores the information in a database and generates a unique ID. The output is the stored personal information and the generated user ID.

[2060] Step 2:

[2061] The server provides online achievement tests to users. The users take the achievement tests and send their results to the server. Based on this input, the server stores the achievement test results in a database. The output is the stored achievement test results.

[2062] Step 3:

[2063] Based on the above academic ability information, questionnaire information, and personal information, the server uses a generative AI model to generate an individually optimized educational curriculum. The prompt sentence input to the generative AI model is, "Please generate an educational curriculum whose learning goal is 'robot programming'." The server saves the generated curriculum in a database. The output is the generated individually optimized curriculum.

[2064] Step 4:

[2065] The server selects the most suitable learning materials and webinar links based on the generated curriculum. These learning materials and links are added to the curriculum and notified to the user. The output is the notified learning materials and webinar links.

[2066] Step 5:

[2067] Users join a webinar using a link provided by the server. The server delivers the webinar in real-time or on-demand format, and users send their participation records and viewing histories to the server. Based on this input, the server stores the webinar participation records and viewing histories in a database. The output is the stored participation records and viewing histories.

[2068] Step 6:

[2069] The server continuously collects the user's learning progress data (such as academic test results, webinar participation records, and viewing history) and re-inputs it into the generative AI model. The generative AI model evaluates the user's progress and learning effectiveness and generates a new curriculum. The server saves the updated curriculum in a database and notifies the user again. The output is an updated, individually optimized curriculum.

[2070] Step 7:

[2071] The user continues learning based on new curriculum provided by the server. This process is cyclical until the user has fully mastered the target skill. The final output is the user's degree of mastery of the skill.

[2072] 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.

[2073] User registration and data collection

[2074] server

[2075] The server provides a user registration form on a web browser or mobile app. When a user accesses the registration form and enters the required information (such as name, age, gender, place of residence, and learning goals), the server stores this information in a database. A unique ID is generated for each user based on the stored information.

[2076] User

[2077] The user fills in the required information in the provided form and sends it to the server. The user then takes the academic ability test sent from the server online and sends the results back to the server.

[2078] server

[2079] The server receives the results of the academic ability test and the questionnaire information sent by the user, and based on this information, automatically generates an individual educational curriculum according to the user's academic ability level and goals.

[2080] Generation of individually optimized curriculum

[2081] server

[2082] The server uses a generative AI model based on the received data to generate an optimal educational curriculum. The generated curriculum content (teaching materials, study schedule, recommended study methods) is stored in a database.

[2083] server

[2084] The server selects the most suitable teaching materials from a related teaching material database based on the generated curriculum and adds them to the user's curriculum.

[2085] Introducing the Emotion Engine

[2086] Emotion Engine

[2087] The emotion engine recognizes emotions in real time by analyzing the user's facial expressions, tone of voice, and other biometric signals.

[2088] server

[2089] The server receives the user's emotion data sent from the emotion engine and inputs it into the generative AI model.

[2090] Curriculum delivery and webinar distribution

[2091] User

[2092] Users can log in to the platform and view the curriculum tailored to them, and the server will notify them of the schedule of pre-recorded webinar videos and live webinars.

[2093] server

[2094] The server provides users with webinar links and materials to facilitate their learning through webinars, and also allows users to download the materials and videos after viewing the webinar for future reference.

[2095] User

[2096] Users can join the webinar using the provided link and learn in real time or on-demand.

[2097] Emotion Engine

[2098] During a webinar or while learning, the emotion engine analyzes the user's emotions in real time and sends the results to the server.

[2099] Assessment of learning progress and curriculum updates

[2100] server

[2101] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[2102] server

[2103] The collected data and emotional data are fed back into the generative AI model to assess the user's progress and the effectiveness of their learning.

[2104] server

[2105] Based on the evaluation results, the educational curriculum is updated as needed. For example, if users feel "confused" or "stressed" about a particular topic, additional resources or different teaching methods will be introduced to reinforce that section.

[2106] server

[2107] Notifying users of updated curriculum and making new content available on the platform.

[2108] Specific examples

[2109] For new user "Sato"

[2110] server

[2111] A new user named "Sato" accesses the platform, enters the necessary information, and sets his or her learning goal as "I want to learn data science." The server then registers this information in the database and automatically generates an academic achievement test.

[2112] User

[2113] Sato takes an academic ability test and sends the results to the server.

[2114] server

[2115] The server analyzes the test results and survey information and generates a curriculum called "Fundamental Concepts of Data Science," "Data Analysis," and "Machine Learning." Based on this, it selects relevant webinars and learning materials and adds them to the curriculum.

[2116] User

[2117] Sato logs in to the platform and joins the "Fundamental Concepts of Data Science" webinar using the provided link. He also downloads the provided materials for later review.

[2118] Emotion Engine

[2119] During the webinar, the emotion engine analyzes Sato's facial expressions and tone of voice, recognizing emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[2120] server

[2121] Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[2122] server

[2123] Based on the analysis, if Sato feels "uneasy" about a particular section, we will provide him with additional resources to reinforce that section and update the curriculum.

[2124] server

[2125] The updated curriculum will be notified to Sato and new learning content will be displayed on the platform.

[2126] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[2127] The processing flow will be explained below.

[2128] User registration and data collection

[2129] Step 1:

[2130] Server: Displays the user registration form on the web browser or mobile app.

[2131] Step 2:

[2132] User: Enters the required information such as name, age, gender, area of ​​residence, and learning goals into the form, and presses the submit button to send it to the server.

[2133] Step 3:

[2134] Server: Stores the information received from the user in a database and generates a unique ID for each user.

[2135] Step 4:

[2136] Server: Automatically generates individual academic ability tests based on collected profile information.

[2137] Step 5:

[2138] Server: Sends the generated academic achievement test to the user's registered email address or makes it available on the platform.

[2139] Step 6:

[2140] User: Takes an online academic achievement test and sends the test results to the server.

[2141] Generation of individually optimized curriculum

[2142] Step 7:

[2143] Server: Receives academic test results, survey information, and personal information sent by users and inputs them into the generative AI model.

[2144] Step 8:

[2145] Server: The generative AI model analyzes the input data and generates an individualized educational curriculum that is optimal for the user.

[2146] Step 9:

[2147] Server: Stores the generated educational curriculum contents (teaching materials, study schedule, recommended study methods) in a database.

[2148] Step 10:

[2149] Server: Picks up the teaching materials required for the curriculum from the relevant teaching material database and adds them to the user's curriculum.

[2150] Introducing the Emotion Engine

[2151] Step 11:

[2152] Emotion Engine: Recognizes the user's emotions in real time by analyzing their facial expressions, tone of voice, and other biometric signals.

[2153] Step 12:

[2154] Server: Receives user emotion data sent from the emotion engine and inputs it into the generative AI model.

[2155] Curriculum delivery and webinar distribution

[2156] Step 13:

[2157] Server: Displays individually optimized curriculum content to users who log in to the platform.

[2158] Step 14:

[2159] Server: Notifies users of scheduled pre-recorded webinar videos and live webinars.

[2160] Step 15:

[2161] Users: Join the webinar using the provided link and learn in real time or on demand.

[2162] Step 16:

[2163] Users: Download the webinar materials and videos for future reference.

[2164] Step 17:

[2165] Emotion Engine: Analyzes user emotions in real time during webinars and learning sessions and sends the results to the server.

[2166] Assessment of learning progress and curriculum updates

[2167] Step 18:

[2168] Server: Continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.).

[2169] Step 19:

[2170] Server: Collected learning progress data and emotional data are re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[2171] Step 20:

[2172] Server: Based on the assessment results, update the educational curriculum as needed. For example, if users are "confident" or "stressed" about a particular topic, introduce additional resources or different teaching methods to reinforce that section.

[2173] Step 21:

[2174] Server: Notifies users of updated curriculum and makes new learning content visible on the platform.

[2175] Specific examples

[2176] For new user "Sato"

[2177] Step 1:

[2178] Server: Displays the user registration form on the web browser.

[2179] Step 2:

[2180] User (Sato): Enter name, age, gender, residential area, and learning goal (e.g., "I want to learn data science") and submit.

[2181] Step 3:

[2182] Server: Saves Sato's information in a database and generates a unique user ID.

[2183] Step 4:

[2184] Server: Automatically generate an individual academic ability test based on Sato's profile.

[2185] Step 5:

[2186] Server: Send the generated academic ability test to Sato's registered email address.

[2187] Step 6:

[2188] User (Sato): Takes an online academic ability test and sends the results to the server.

[2189] Step 7:

[2190] Server: Receives Sato's academic test results, survey information, and personal information and inputs them into the generative AI model.

[2191] Step 8:

[2192] Server: The generative AI model generates the optimal educational curriculum for Sato.

[2193] Step 9:

[2194] Server: Stores the generated curriculum ("Fundamentals of Data Science," "Data Analysis," and "Machine Learning") in a database.

[2195] Step 10:

[2196] Server: Selects the most suitable teaching materials from the teaching materials database and adds them to Sato's curriculum.

[2197] Step 11:

[2198] Server: When Sato logs in to the platform, the contents of the individually optimized curriculum are displayed.

[2199] Step 12:

[2200] Server: Notifies Sato of the distribution schedule and live webinar schedule.

[2201] Step 13:

[2202] User (Sato): Join the "Fundamental Concepts of Data Science" webinar using the provided link.

[2203] Step 14:

[2204] User (Sato): Download the webinar materials and videos and use them for review later.

[2205] Step 15:

[2206] Emotion engine: During the webinar, Sato's facial expressions and tone of voice are analyzed to recognize emotions such as "interested" or "anxious" in real time. This data is sent to the server.

[2207] Step 16:

[2208] Server: Sato's learning progress (test results, webinar participation records, viewing history) and emotional data are input into the generative AI model for evaluation and analysis.

[2209] Step 17:

[2210] Server: Based on the analysis, if Sato feels "uneasy" about a particular section, provide him with additional resources to reinforce that section and update the curriculum.

[2211] Step 18:

[2212] Server: Notifies Sato of updated curriculum and displays new learning content on the platform.

[2213] In this way, a system can be realized that provides an individually optimized educational curriculum that takes into account the user's emotional data, improving the user's learning efficiency.

[2214] Example 2

[2215] 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."

[2216] Conventional educational systems have difficulty providing individually optimized educational programs tailored to the user's academic ability and learning goals. Furthermore, they lack a means to grasp the user's emotional state in real time while learning, making it difficult to update educational programs accordingly. Furthermore, there are limitations to providing an environment where users can learn anytime, anywhere.

[2217] 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.

[2218] In this invention, the server includes means for receiving academic ability information, questionnaire information, and personal information, a generative model means for generating an individually optimized educational program based on the received information, a means for providing optimal online lectures and learning materials based on the generated educational program, a means for analyzing the user's biosignals and collecting emotional data in real time, a means for inputting the emotional data into the generative model and updating the educational program taking the user's emotional state into account, and a means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum. This allows an individually optimized educational program to be provided in real time, taking the user's emotional state into consideration, enabling the user to study efficiently anytime, anywhere.

[2219] "Academic ability information" is information that indicates the user's current academic ability level, including test results and past learning history.

[2220] "Survey Information" is subjective information provided by users, including information about their learning goals, interests, and experiences.

[2221] "Personal information" refers to basic information such as a user's name, age, gender, and area of ​​residence.

[2222] A "generative model means" is a means including a generative AI model for automatically generating an optimal educational program based on input data.

[2223] An "educational program" is an individually optimized curriculum tailored to a user's learning goals and academic level, and includes teaching materials, study schedules, recommended study methods, etc.

[2224] "Online lectures" refer to classes provided over the Internet, either in real time or via recorded video lectures.

[2225] "Learning Materials" are resources such as textbooks, videos, and exercises provided as part of an educational program.

[2226] "Biological signals" are signals emitted from the user's body, including facial expressions, tone of voice, heart rate, etc.

[2227] "Emotion data" is data that indicates the emotional state of the user obtained as a result of analyzing biosignals.

[2228] "Study progress" is data that shows how far a user has progressed in their studies, and includes test results, viewing history, participation records, etc.

[2229] "Generative AI" is a type of artificial intelligence that refers to a system that generates optimal educational programs and curricula based on large amounts of data.

[2230] "Delivery medium" means a means, including internet-based technologies and services, for providing online courses and educational materials to users.

[2231] The present invention is a system for providing users with individually optimized educational programs. A specific implementation method thereof will be described below.

[2232] User registration and data collection

[2233] server

[2234] The server displays a user registration form on the web browser or mobile app. The form includes fields for entering information such as name, age, gender, location, and learning goals. After the user enters and submits the required information, the server stores the entered data in a database. At the same time, it generates a unique ID for each user. For example, if a user enters "I want to study data science," that information is stored.

[2235] User

[2236] The user fills in the required information in the provided form and submits it to the server. The user also clicks on the link for the academic achievement test sent from the server and takes the test online. For example, a user named "Sato" fills in the form with his / her goal of "I want to study data science," submits it, and then takes the academic achievement test.

[2237] server

[2238] The server receives the results of the academic ability test sent by the user, stores them in a database, and then analyzes the test results and questionnaire information to generate an individual educational program based on the user's academic level and goals.

[2239] Generation of individually optimized curriculum

[2240] server

[2241] The server inputs the received data into the generative AI model. At that time, the data is sent to the generative AI model as a prompt. By sending a prompt in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" to the generative AI model, the model generates an optimal curriculum.

[2242] server

[2243] The server receives the curriculum content (teaching materials, study schedule, recommended learning methods) returned by the generative AI model and stores them in a database. At the same time, it picks up appropriate teaching materials from the related teaching materials database and adds them to this curriculum.

[2244] Introducing the Emotion Engine

[2245] Emotion Engine

[2246] The emotion engine generates emotion data by analyzing the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) in real time. For example, while a user is attending an online lecture, it analyzes camera footage and audio data to generate emotion data such as "interested" or "confused."

[2247] server

[2248] The server receives the user's emotional data sent from the emotion engine and inputs it into the generative AI model. This emotional data is analyzed as the user progresses and is used to update the educational program accordingly.

[2249] Curriculum delivery and webinar distribution

[2250] User

[2251] Users log in to the platform and check their curriculum. The server notifies them of the links and materials for the online lectures they provide. For example, "Sato" logs in to the platform and obtains a link to participate in an online lecture called "Basics of Data Science."

[2252] server

[2253] The server provides links to online lectures and materials for users to access anytime, anywhere, and also provides recorded videos and materials for users to review later.

[2254] User

[2255] When a user clicks on an online lecture link, they can take the lecture in real time or on-demand. For example, "Sato" can participate in the "Basics of Data Science" course in real time and download the provided materials for review.

[2256] Emotion Engine

[2257] While users are taking online classes, the emotion engine analyzes their facial expressions and tone of voice and sends the data to a server, which collects it and feeds it into a generative AI model to update the curriculum.

[2258] Assessment of learning progress and curriculum updates

[2259] server

[2260] The server continuously collects user learning progress data (test results, webinar participation records, viewing history, etc.), which is then re-input into the generative AI model to evaluate the user's progress and learning effectiveness.

[2261] server

[2262] Based on the assessment, the user's educational program is updated as needed, for example, if a user feels "stuck" in a particular learning section, additional resources or different teaching methods are provided to reinforce that section.

[2263] server

[2264] Users are notified of updated curriculum and can view the new content on the platform. For example, if "Sato" receives an email notification of an updated educational program, the new learning content will be displayed when he logs in to the platform.

[2265] Through this system, it is possible to optimize the user's learning experience and provide an efficient learning process.

[2266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2267] Step 1:

[2268] User registration and data collection

[2269] server

[2270] The server displays a user registration form on a web browser or mobile app. As input, the user enters their name, age, gender, location, learning goals, etc. When the user submits the input, the server receives it and stores it in a database. At the same time, it generates and stores a unique ID for each user. The output includes the stored user information and the unique user ID.

[2271] User

[2272] The user fills in the required information in the form and submits it. For example, a user named "Sato" enters his / her learning goal of "I want to learn data science" and submits it. The server then receives an automatically generated academic achievement test link. The user clicks on the link and takes the test online. The results of the academic achievement test are then sent as input to the server.

[2273] server

[2274] The server receives the academic ability test results sent by the user and stores them in a database. The input includes the academic ability test results and the user ID. It then analyzes the results and questionnaire information and generates an individual educational program based on the user's academic ability level and goals as output.

[2275] Step 2:

[2276] Generation of individually optimized curriculum

[2277] server

[2278] The server inputs the user information stored in the database and the academic achievement test results into the generative AI model. As input, it generates a prompt sentence in the format "User ID: XXXX, Academic level: Basic, Goal: Intermediate data science" and sends it to the generative AI model. The model analyzes the data and generates an optimal educational curriculum. The output is the curriculum content returned by the generative AI model.

[2279] server

[2280] The generated curriculum content (teaching materials, study schedule, recommended study methods) is saved in a database. The most suitable teaching materials are selected from the related teaching materials database and added to the curriculum. This completes the educational program provided to the user.

[2281] Step 3:

[2282] Introducing the Emotion Engine

[2283] Emotion Engine

[2284] The emotion engine analyzes the user's biometric signals (facial expressions, tone of voice, heart rate, etc.) and generates emotional data. Inputs include real-time camera footage and audio data. Based on this, it generates emotional data such as "User ID: XXXX, facial expression: smiling, tone of voice: optimistic." The output is the emotional data resulting from the analysis.

[2285] server

[2286] The server receives the emotion data sent from the emotion engine and inputs it into the generative AI model. The emotion data is included as input. The model updates the generated curriculum based on the emotion data as appropriate. An individualized educational program is generated that takes the emotion data into consideration.

[2287] Step 4:

[2288] Curriculum delivery and webinar distribution

[2289] User

[2290] The user logs in to the platform and checks the optimized curriculum. The server notifies them of the online lecture link and materials. For example, Sato logs in to the platform and checks the link to join the "Basic Data Science Course." The user clicks and receives the lecture in real time. The online lecture link and materials are provided as input.

[2291] server

[2292] The server provides links to online lectures and materials, allowing users to access them anytime, anywhere. As an output, it collects user participation records and viewing histories. It also provides recorded videos and materials for offline use.

[2293] Emotion Engine

[2294] While a user is taking an online lecture, the emotion engine analyzes their facial expressions and tone of voice to generate emotion data, which is then sent to the server. The input includes real-time video and audio data, and the output is the emotion data resulting from the analysis.

[2295] Step 5:

[2296] Assessment of learning progress and curriculum updates

[2297] server

[2298] The server continuously collects users' learning progress data (such as test results, webinar participation records, and viewing history). This data is included as input. This data is then fed back into the generative AI model to evaluate the user's progress and learning effectiveness, and update the educational program as needed. The output is the evaluation results and an updated curriculum.

[2299] server

[2300] Based on the evaluation results, the educational program is updated accordingly and notified to the user. For example, if a user is struggling with a particular learning section, additional resources or different teaching methods can be provided to reinforce that section. The updated curriculum is then notified to the user.

[2301] User

[2302] Users can check the updated curriculum on the platform and engage in new learning content. For example, Sato revisits the updated "Basic Data Science Course" and continues his learning using new materials and resources. New learning resources are provided as input.

[2303] In this way, the entire system is designed to collect and analyze data in real time to optimize the user's learning experience.

[2304] (Application example 2)

[2305] 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."

[2306] Conventional education and work instruction systems have had difficulty providing individually optimized curricula that reflect the user's (worker's) learning progress and emotional state in real time. In addition, there has been a lack of means to improve learning and work efficiency by capturing the user's emotional state and providing appropriate feedback.

[2307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving academic ability information, questionnaire information, and personal information; a generative model means for generating an individually optimized educational curriculum based on the received information; means for providing optimal webinars and learning materials based on the generated educational curriculum; means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum; means for analyzing the worker's emotional state in real time using a camera and microphone and inputting the data into the generative model; and means for dynamically updating the worker's operation method and supplementary materials based on the analyzed emotional data. This makes it possible to provide an individually optimized curriculum and real-time feedback that improves the user's learning efficiency and work efficiency.

[2308] "Academic achievement information" is data that indicates an individual user's educational level and academic achievement test results.

[2309] "Survey information" is data collected to understand user attributes, learning objectives, interests, etc.

[2310] "Personal information" is information that can be used to identify an individual user, such as the user's name, age, gender, and area of ​​residence.

[2311] "Means" is a broad concept that includes methods and devices for achieving a specific purpose.

[2312] A "generative model" is an algorithm or software used to create an individually optimized educational curriculum based on collected data.

[2313] A "webinar" is a form of online seminar or lecture held over the web.

[2314] "Learning Materials" are educational resources and materials provided to achieve specific learning objectives.

[2315] "Curriculum updating" is the process of adjusting and improving existing educational plans based on users' learning progress and emotional data.

[2316] A "camera" is a device for capturing images and storing them as digital data.

[2317] A "microphone" is a device that collects sound and stores it as digital data.

[2318] An "emotional state" is a state that indicates a user's psychological and emotional response.

[2319] "Real-time analysis" is the process of processing collected data immediately and reflecting and utilizing the results immediately.

[2320] "Dynamic updates" means flexibly changing the system and curriculum in response to changes in the user's status and environment.

[2321] The embodiment of this invention is a system that provides individually optimized educational curricula us...

Claims

1. a means for receiving academic achievement information, survey information, and personal information; a generative model means for generating an individually optimized educational curriculum based on the received information; A means for providing optimal webinars and learning materials based on the generated educational curriculum; a means for continuously collecting the user's learning progress and re-inputting it into the generative model to update the curriculum; A system including:

2. The system of claim 1, wherein generative AI is used to optimize and update educational curricula.

3. 10. The system of claim 1, further comprising a distribution means for enabling a user to take a webinar-style lesson anytime and anywhere.

Images