System

Abstract

A system is provided.SOLUTION: A system comprising: input means for inputting an educational policy and a scope; collecting means for collecting relevant information from a knowledge base based on the educational policy and the scope; generating means for generating an optimized educational curriculum based on the collected information; delivering means for delivering the generated curriculum to a user terminal; tracking means for tracking a user's learning progress and comprehension; and feedback means for providing feedback to an educator based on data collected by the tracking means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's business environment, the mobility of human resources is increasing, creating a need to quickly and effectively integrate newly hired personnel into the workforce. However, the current training system relies on the experience and knowledge of the person in charge to determine the quality of training, and there is no system in place to systematically develop new employees. As a result, new employees often struggle with their initial training, reducing the efficiency of the entire organization. To solve these problems, a system is needed that can effectively and efficiently generate and deliver OJT curricula optimized for each job and track progress. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including: an input means for inputting a training policy and scope; a collection means for collecting related information from a knowledge base based on the training policy and scope; a generation means for generating an optimized training curriculum based on the collected information; a distribution means for distributing the generated curriculum to a user terminal; a tracking means for tracking the user's learning progress and level of understanding; and a feedback means for providing feedback to training personnel based on the data collected by the tracking means. This system enables newcomers to efficiently acquire necessary knowledge and allows training personnel to grasp the newcomers' progress and level of understanding in real time. Furthermore, feedback based on the acquired data makes it possible to review the training content and optimize individual instruction, promoting stable human resource development.

[0006] The "input means" is an interface device for allowing the user to input information such as the educational policy and scope.

[0007] A "collection means" is a device or function for acquiring relevant information from a knowledge base based on the input educational policy and scope.

[0008] The "generation means" is a device or function for automatically creating an optimized educational curriculum based on collected information.

[0009] The "distribution means" is a device or function that transmits the generated educational curriculum to a user terminal, allowing the user to use the curriculum.

[0010] A "tracking means" is a device or function for monitoring a user's learning progress and comprehension and collecting data.

[0011] A "feedback means" is a device or function that analyzes the data collected by the tracking means and provides the results to the educator.

[0012] A "knowledge base" is a database that stores information related to education, such as texts, videos, confirmation tests, technical materials, and competitive information.

[0013] "Training policy" refers to the overall guidance policy for the skills, knowledge, and education that new employees should acquire.

[0014] "Scope" refers to the details of the educational plan, including the scope and duration of the education, specific goals, etc.

[0015] A "user terminal" is a device such as a computer or smartphone that a new employee uses to learn.

[0016] An "education officer" is a person in charge of educating new employees, such as a superior or senior employee.

[0017] A "newcomer" is someone who has just joined an organization and is receiving training. [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] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking the user's learning progress and level of understanding, and a feedback means for providing feedback to the training personnel.

[0040] System programs and their processing

[0041] Collecting User Input

[0042] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[0043] Preparing and gathering a knowledge base

[0044] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[0045] Generative AI-based curriculum generation

[0046] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[0047] Curriculum Delivery

[0048] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[0049] Tracking learning progress and understanding

[0050] The user (newcomer) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server.

[0051] The server uses the transmitted data to track the newcomer's progress and level of understanding in real time, allowing the training manager to understand the newcomer's current learning status.

[0052] Providing feedback

[0053] The server analyzes the collected tracking data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individualized instruction and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[0054] Specific examples

[0055] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is then distributed to the new employee's device, and the new employee begins learning. Learning progress and comprehension test results are sent from the device to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the trainer.

[0056] The present invention allows new employees to acquire knowledge efficiently, and allows training personnel to grasp the progress in real time and provide appropriate guidance, thereby enabling stable human resource development.

[0057] The processing flow will be explained below.

[0058] Program processing steps

[0059] Step 1:

[0060] User (Educator):

[0061] A dedicated form for entering educational policy and scope opens on the terminal.

[0062] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[0063] Device:

[0064] The entered data is sent to the server.

[0065] Step 2:

[0066] server:

[0067] Save the received educational policy and scope.

[0068] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[0069] Preprocess the collected data and prepare it for passing to the generative AI.

[0070] Step 3:

[0071] server:

[0072] Provide preprocessed information to the generation AI.

[0073] Generation AI:

[0074] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[0075] Example: Design a curriculum like the following:

[0076] Basic programming text

[0077] Beginner's exercises

[0078] Code review explanation and examples

[0079] Test to check your understanding

[0080] Step 4:

[0081] server:

[0082] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[0083] Device:

[0084] Display the delivered curriculum and make it accessible to users (newcomers).

[0085] For example, click a link to view text, video, or download a file.

[0086] Step 5:

[0087] User (newcomer):

[0088] Study the distributed materials.

[0089] Example: Reading a programming textbook and solving the exercises.

[0090] Device:

[0091] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[0092] The recorded data is sent to the server.

[0093] Step 6:

[0094] server:

[0095] Analyze submitted learning progress data and assessment results.

[0096] Track and assess new hires' progress and understanding in real time.

[0097] Step 7:

[0098] server:

[0099] Generate feedback based on tracking data.

[0100] Organize the results in leaderboard format or progress reports and provide them to educators.

[0101] Educators (users):

[0102] Review the generated feedback to understand the progress and understanding of new employees.

[0103] Plan for additional tutoring and supplemental materials.

[0104] Through these steps, the system of this invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on progress in real time, enabling stable human resource development.

[0105] Example 1

[0106] 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."

[0107] Conventional OJT (On-the-Job Training) systems only provide uniform training programs, making it difficult to efficiently generate curricula optimized for each job. Furthermore, it is difficult to grasp training progress and comprehension levels in real time and provide appropriate feedback. This leads to issues such as inefficient training for new employees and an increased burden on training personnel.

[0108] 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.

[0109] In this invention, the server includes an input means for inputting an educational policy and a scope, a search means for collecting related information from a knowledge base based on the educational policy and scope, a generation means for generating an optimized educational curriculum based on the collected information, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking a user's learning progress and level of understanding, and a feedback means for providing feedback to an educator based on the data collected by the monitoring means. This makes it possible to automatically generate a curriculum optimized for each task, monitor a user's learning progress and level of understanding in real time, and provide appropriate feedback to an educator.

[0110] "Educational policy" refers to the goals and direction of education, and indicates what skills and knowledge new employees should acquire.

[0111] "Scope" refers to the range and duration of the education, and clarifies the specific fields and content included.

[0112] "Input means" refers to an interface for users to input educational policies and scopes, and may be a dedicated form or device.

[0113] "Searching means" refers to the function of finding relevant information from the knowledge base based on educational policy and scope.

[0114] A "knowledge base" refers to a database that stores information and materials necessary for education, including text data, video content, confirmation tests, technical materials, and external information.

[0115] The "generation means" refers to a function that automatically creates an optimized educational curriculum based on the information collected by the search means.

[0116] The "distribution means" refers to the function of transferring the generated educational curriculum to the user's terminal, and corresponds to the information transmission means via a network.

[0117] "Monitoring means" refers to a mechanism for tracking a user's learning progress and level of understanding in real time and collecting data.

[0118] "Feedback means" refers to a function that analyzes the data collected by the monitoring means and provides information to the training personnel about the learning status of newcomers.

[0119] This invention relates to a system that automatically generates an OJT curriculum optimized for each job and systematically and efficiently trains new employees. This system includes an input means for inputting the training policy and scope, a search means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking learning progress and comprehension, and a feedback means for providing feedback to training personnel.

[0120] The user (educator) uses a dedicated form on the terminal to input the training policy and scope. For example, this may include "understanding basic program structure" or "three months of initial training." This input data is sent from the terminal to the server, which then stores it in a database.

[0121] The server searches for relevant information from a knowledge base based on the stored educational policy and scope, and collects text data, video content, confirmation tests, technical documents, and external information. This knowledge base stores various educational materials. The server uses collection tools to select appropriate materials and performs preprocessing such as standardizing data formats and filtering unnecessary information.

[0122] The server then passes the preprocessed information to a generative AI model to begin generating an optimized educational curriculum. The generative AI model utilizes knowledge base information to effectively structure learning content based on the educational policy and scope. For example, a curriculum is designed that includes programming textbooks for beginners, code review examples for training, and test questions to confirm understanding. This generated curriculum is stored in a database managed by the server.

[0123] The generated curriculum is distributed from the server to the newcomer's device. The device displays the curriculum, and the user (newcomer) begins learning using the provided links and files. The user (newcomer) studies the distributed learning materials through the device and takes a comprehension test.

[0124] The device sends the learning progress (e.g., videos watched, test questions answered) and the results of the comprehension test to the server, and the server uses this data to track the newcomer's progress and comprehension in real time, allowing the educator to understand the user's (newcomer's) current learning status.

[0125] The server analyzes the collected tracking data and provides feedback to trainers in the form of leaderboards and detailed progress reports. Trainers can use this feedback to revise training plans and plan additional tutoring and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[0126] Examples of concrete examples and prompts

[0127] For example, when training newly hired engineers, the training manager inputs the training policy of "understanding basic program structure" into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the learning progress and comprehension test results to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the training manager.

[0128] Example prompt sentence:

[0129] The prompt to generate the optimal curriculum for a three-month initial training course for new engineers is as follows:

[0130] Create a three-month initial training curriculum for newly hired engineers. The training content should include "understanding basic program structure." Include relevant textbooks, videos, and confirmation tests in the curriculum.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1: Collecting User Input

[0133] Specific behavior:

[0134] 1. The user (educator) opens a dedicated form on the device.

[0135] 2. The user enters the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training") into the form.

[0136] 3. The terminal sends the entered data to the server.

[0137] 4. The server stores the received data in the educational policy database.

[0138] Input: Educational Policy and Scope

[0139] Output: Educational policy and scope stored in the database

[0140] Step 2: Prepare and gather your knowledge base

[0141] Specific behavior:

[0142] 1. The server searches for relevant information from the knowledge base based on the educational policy and scope.

[0143] 2. The knowledge base includes text data, video content, confirmation tests, technical materials, and external information.

[0144] 3. The server selects data from the search results that matches the educational policy, and as preprocessing, standardizes the data format and filters out unnecessary information.

[0145] Input: Educational policy and scope, knowledge base

[0146] Output: Preprocessed educational resources

[0147] Step 3: Generative AI generates curriculum

[0148] Specific behavior:

[0149] 1. The server inputs the preprocessed information into the generative AI model.

[0150] 2. The generative AI model generates the optimal educational curriculum based on the educational policy and scope.

[0151] 3. The generated curriculum includes programming textbooks, code review examples, comprehension tests, etc.

[0152] 4. The server stores the generated curriculum in the curriculum database.

[0153] Input: Preprocessed educational resources

[0154] Output: Generated educational curriculum

[0155] Step 4: Deliver the curriculum

[0156] Specific behavior:

[0157] 1. The server distributes the generated curriculum to the new employee's device.

[0158] 2. The terminal receives the delivered curriculum and displays it on the display screen.

[0159] 3. The user (newcomer) begins learning by using each curriculum content (links and files) on the device screen.

[0160] Input: Generated educational curriculum

[0161] Output: Curriculum displayed on the new employee's device

[0162] Step 5: Track your progress and understanding

[0163] Specific behavior:

[0164] 1. The user (newcomer) uses the device to study the learning material and take a comprehension test.

[0165] 2. The device collects the user's learning progress (videos watched, test questions answered) and the results of the comprehension test.

[0166] 3. The device sends the collected data to the server.

[0167] 4. The server analyzes the received data in real time and tracks the newcomer's progress and understanding.

[0168] Input: Learning progress and comprehension test results

[0169] Output: Tracked progress and understanding data

[0170] Step 6: Provide feedback

[0171] Specific behavior:

[0172] 1. The server analyzes the tracking data and generates leaderboards and detailed progress reports.

[0173] 2. The server sends the generated feedback to the teacher's device.

[0174] 3. The person in charge of training (supervisor or senior) will review the training plan based on the feedback.

[0175] 4. Educators will provide new recruits with tutoring and additional materials as needed.

[0176] Input: Tracked progress and understanding data

[0177] Output: Feedback and progress reports provided to educators

[0178] (Application example 1)

[0179] 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."

[0180] In conventional training systems, creating a curriculum and managing progress to effectively provide on-the-job training (OJT) for new staff was time-consuming, and training quality was prone to vary depending on the person in charge. There was also a lack of efficient means to teach specialized knowledge about robot operation and maintenance in factories. Furthermore, real-time progress tracking and feedback were insufficient, making it difficult to accurately grasp the level of understanding of new employees.

[0181] 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.

[0182] In this invention, the server includes an input means for inputting training content, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training curriculum based on the collected information. This enables automatic generation and distribution of an optimal curriculum based on the training policy and scope. Furthermore, the distributed curriculum is displayed on an augmented reality-compatible display, making it easier for new staff to learn factory robot operation and maintenance procedures in real time. The system also includes a tracking means for tracking learning progress and comprehension and a feedback means for providing feedback to training personnel based on the collected data, allowing for accurate understanding of new staff's learning progress and appropriate guidance as needed.

[0183] An "education policy" is a policy that defines the specific knowledge and skills that training personnel should provide to new staff members.

[0184] "Scope" is a term that refers to the content or range covered in a particular educational activity.

[0185] "Input means" refers to a device or method for inputting educational policy and scope into the system.

[0186] "Collection means" refers to a device or method for collecting relevant information from a knowledge base.

[0187] A "knowledge base" is a database that stores information such as texts, videos, confirmation tests, and technical materials for use in education.

[0188] "Generation means" refers to a device or method for generating an optimized educational curriculum based on collected information.

[0189] "Distribution means" refers to a device or method for distributing the generated curriculum to a user terminal.

[0190] "User terminal" refers to electronic devices used by new staff undergoing training.

[0191] "Display means" refers to a device or method for displaying educational content via an augmented reality-enabled display.

[0192] "Tracking means" refers to a device or method for tracking a user's learning progress and comprehension.

[0193] "Feedback means" refers to a device or method for providing feedback to educators based on data collected by the tracking means.

[0194] An "augmented reality display" is a display device that displays digital information superimposed on visual information from the real world.

[0195] The present invention provides a system for efficiently training new staff members. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum based on the collected information, a distribution means for distributing the generated curriculum to a user terminal, a tracking means for tracking the user's learning progress and level of understanding, a feedback means for providing feedback to a trainer based on data collected by the tracking means, and a display means for displaying the training content on an augmented reality-compatible display.

[0196] The server collects relevant information from a knowledge base based on the educational policy and scope entered by the educator. Using the collection means, the server obtains texts, videos, confirmation tests, technical documents, etc., and passes them to the generative AI model. The generative AI model generates an optimized educational curriculum based on the input information. This generated curriculum is then distributed to the user's device.

[0197] By wearing an augmented reality display, users (new staff) can visually check the training content in the actual workplace. For example, videos and text of robot maintenance procedures are superimposed on real-world visual information through the display means. Furthermore, as the user progresses through the learning content, their progress and comprehension test results are tracked in real time and sent to the server. The server uses this data to grasp the user's progress in real time and provide feedback to the training staff.

[0198] As a specific example, a trainer inputs training guidelines into the system for newly hired technical staff, such as "understanding basic robot maintenance procedures." The server collects maintenance procedure manuals and operation videos from the knowledge base, and the generative AI model creates an optimal curriculum. The curriculum is then distributed to the user's device, and the technical staff begins learning via an augmented reality display.

[0199] An example of this prompt is as follows:

[0200] "Generate a training curriculum for new technical staff to gain an understanding of basic robot maintenance procedures. The knowledge base includes:

[0201] 1. Title: Basic Robot Maintenance

[0202] Type:Video

[0203] URL:https: / / example.com / video1

[0204] 2. Title: Basic Robot Maintenance Procedures

[0205] Type:Text

[0206] URL:https: / / example.com / text1

[0207] Selected educational scope: 1 month

[0208] In this way, new staff can learn work procedures effectively, and training personnel can grasp the progress and level of understanding of new staff in real time and provide appropriate guidance.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The user (trainer) enters the training policy and scope using a dedicated form on the terminal. Specific input examples include "understanding basic robot maintenance procedures" and "one month of initial training." The entered data is sent from the terminal to the server and saved. The input data includes information such as the training content and duration.

[0212] Step 2:

[0213] The server collects relevant information from the knowledge base based on the stored educational policy and scope. Using the collection means, the server obtains educationally relevant information such as texts, videos, confirmation tests, and technical materials from the knowledge base. The obtained data undergoes preprocessing before being input into the generative AI model.

[0214] Step 3:

[0215] The server passes the preprocessed knowledge base information to a generative AI model to generate an optimized educational curriculum. The generative AI model uses the knowledge base information to effectively structure learning content based on the input educational policy and scope. As an example of output, a curriculum is generated that includes learning sequences, learning material links, and test questions.

[0216] Step 4:

[0217] The generated curriculum is delivered from the server to the user's device. The device displays the delivered curriculum, and the user (new staff member) begins learning using the provided links and files. An augmented reality-enabled display is used on the device to visually present the educational content.

[0218] Step 5:

[0219] The user (new staff member) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server. The sent data is stored on the server.

[0220] Step 6:

[0221] The server tracks the user's learning progress and comprehension level in real time based on the saved learning progress and comprehension test data. Using the tracking means, the user's progress (e.g., learning completion rate, test correct answer rate, etc.) is analyzed and fed back to the educator.

[0222] Step 7:

[0223] The training staff can then review the training plan based on the new employee's progress and understanding, which is provided in real time through feedback channels. Additional individualized instruction and training can be planned as needed. For example, if a new employee is struggling in a particular area, additional learning materials or explanations can be provided.

[0224] 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.

[0225] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking a user's learning progress and level of understanding, a feedback means for providing feedback to training personnel, and an emotion engine for recognizing the user's emotions.

[0226] System programs and their processing

[0227] Collecting User Input

[0228] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[0229] Preparing and gathering a knowledge base

[0230] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[0231] Generative AI-based curriculum generation

[0232] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[0233] Curriculum Delivery

[0234] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[0235] Tracking learning progress and understanding

[0236] The user (newcomer) uses a device to study the distributed learning materials and take a comprehension test. The device records the learning progress (e.g., browsing history, study time) and the results of the comprehension test. The recorded data is sent to a server, which uses this data to track the newcomer's progress and comprehension in real time.

[0237] Emotion recognition by emotion engine

[0238] While the user (newcomer) is learning, their facial expressions and voice are analyzed using the device's camera and microphone. Based on this data, the emotion engine recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). The emotion data is sent to the server along with learning progress data.

[0239] Providing feedback

[0240] The server analyzes the collected tracking and emotional data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individual instruction or supplementary materials. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations. Furthermore, based on the emotional data, they can analyze at what point the new employee experienced difficulty and further optimize the curriculum accordingly.

[0241] Specific examples

[0242] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and a generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[0243] The present invention allows new employees to acquire knowledge efficiently, and enables training personnel to grasp the progress and emotional state of the new employees in real time and provide appropriate guidance, thereby enabling stable human resource development.

[0244] The processing flow will be explained below.

[0245] Program processing steps

[0246] Step 1:

[0247] User (Educator):

[0248] A dedicated form for entering educational policy and scope opens on the terminal.

[0249] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[0250] Device:

[0251] The entered data is sent to the server.

[0252] Step 2:

[0253] server:

[0254] Save the received educational policy and scope.

[0255] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[0256] Preprocess the collected data and prepare it for passing to the generative AI.

[0257] Step 3:

[0258] server:

[0259] Provide preprocessed information to the generation AI.

[0260] Generation AI:

[0261] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[0262] Example: Design a curriculum like the following:

[0263] Basic programming text

[0264] Beginner's exercises

[0265] Code review explanation and examples

[0266] Test to check your understanding

[0267] Step 4:

[0268] server:

[0269] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[0270] Device:

[0271] Display the delivered curriculum and make it accessible to users (newcomers).

[0272] For example, click a link to view text, video, or download a file.

[0273] Step 5:

[0274] User (newcomer):

[0275] Study the provided materials, read the programming textbook, and solve the exercises.

[0276] Device:

[0277] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[0278] The recorded data is sent to the server.

[0279] Step 6:

[0280] server:

[0281] Analyze submitted learning progress data and assessment results.

[0282] Track and assess new hires' progress and understanding in real time.

[0283] Step 7:

[0284] User (newcomer):

[0285] During learning, the device's camera and microphone are used to record your facial expressions and voice, allowing your emotional state to be recognized.

[0286] Emotion Engine:

[0287] Identify the user's emotional state (e.g., understanding, excitement, fatigue) based on collected facial and voice data.

[0288] The emotion data is sent to the server.

[0289] Step 8:

[0290] server:

[0291] The emotional data recognized by the emotion engine is integrated with learning progress data and analyzed.

[0292] Create tracking data that takes into account the emotional state of new employees and optimize the content of feedback.

[0293] Step 9:

[0294] server:

[0295] The integrated data will be used to generate leaderboards and detailed progress reports, which will be provided to educators.

[0296] Educators (users):

[0297] Review the generated feedback to understand the new employee's progress, understanding, and emotional state.

[0298] Plan for additional tutoring and supplemental materials.

[0299] Through these steps, the system of the present invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on learning progress and emotional state in real time, enabling stable human resource development.

[0300] Example 2

[0301] 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."

[0302] In modern companies, training new employees requires a great deal of time and effort, creating a demand for efficient and systematic training programs. However, it is difficult to closely track each new employee's learning progress and level of understanding and optimize training plans based on that information. It is also difficult to grasp the new employee's emotional state during training and provide appropriate guidance accordingly. Traditional training systems are unable to effectively address these challenges.

[0303] 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.

[0304] In this invention, the server includes input means for inputting a training policy and scope, collection means for collecting related information from a knowledge base, generation means for generating an optimized training curriculum based on the collected information, distribution means for distributing the generated curriculum to a user terminal, tracking means for tracking the user's learning progress and level of understanding, an emotion engine for recognizing the user's emotional state, and feedback means for providing feedback to the training personnel based on the data collected by the tracking means and the emotion engine. This makes it possible to track the learning progress and level of understanding of newcomers in real time and to grasp their emotional state, thereby realizing individually optimized training plans and instruction.

[0305] An "educational policy" indicates the goals and direction of education.

[0306] "Scope" refers to the range and duration of the educational program.

[0307] "Input means" refers to an interface that allows a user to input educational principles and scope into the system.

[0308] "Collection means" refers to the function of extracting and collecting the necessary information from the knowledge base.

[0309] A "knowledge base" refers to a database that stores various materials and information related to education and work.

[0310] "Generation means" refers to the function of creating an optimized educational curriculum based on collected information.

[0311] "Distribution means" refers to the function for transmitting the generated educational curriculum to a user terminal.

[0312] "User terminal" refers to the device used by a user to receive the educational curriculum.

[0313] "Tracking means" refers to the functionality that records and monitors a user's learning progress and understanding.

[0314] "Emotion engine" refers to the function that analyzes the user's facial expressions and voice and identifies their emotional state.

[0315] "Feedback Measures" refers to the functionality that provides data collected by the Tracking Measures and Emotion Engine to the Educator.

[0316] The system of the present invention automatically generates an OJT (On-the-Job Training) curriculum optimized for each job, and systematically and efficiently educates new employees. This system has the following main functions:

[0317] First, the trainer (user) uses a dedicated form on the terminal to input the training policy and scope. For example, they input specific details such as "understanding basic program structure" and "three months of initial training." The terminal then sends the input information to the server, which then stores it in a database.

[0318] Next, the server collects relevant information from the company's knowledge base based on the stored training policy and scope. The knowledge base includes text, videos, confirmation tests, technical materials, and competitive information. The server collects this information and performs preprocessing to pass it to the generative AI model. Preprocessing includes text cleaning and video summarization.

[0319] Once the collection and preprocessing are complete, the server passes the information to a generative AI model to generate an optimal educational curriculum. The generative AI model effectively structures the learning content based on the educational policy and scope. For example, it designs a curriculum that includes programming textbooks, training code review examples, and test questions to confirm understanding. The generated curriculum is then returned to the server.

[0320] The server then distributes the generated curriculum to the user's (newcomer's) device, where the device displays the curriculum and the user can begin learning using the provided links and files.

[0321] As the learning progresses, the user (newcomer) studies the learning materials through the device and takes comprehension tests. The device records the learning progress and the results of the comprehension tests and sends them to the server. The server uses this data to track the user's learning progress and comprehension in real time.

[0322] Furthermore, an emotion engine is used to recognize the user's (newcomer's) emotional state during learning. The user uses the device's camera and microphone to collect their facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). This data is also sent to the server.

[0323] The server analyzes the collected learning progress and emotion data and provides feedback to the educator. The feedback is displayed as a leaderboard and detailed progress reports. Based on this, the educator can revise the learning plan and provide individualized instruction or additional materials as needed. For example, if a newcomer is struggling in a particular area, they can provide additional materials or explanations.

[0324] Specific examples

[0325] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[0326] Prompt Sentence Examples

[0327] "Generate a three-month training curriculum aimed at helping new engineers understand basic programming structure."

[0328] "Collect relevant information from your knowledge base and create an optimized training curriculum for new hires."

[0329] "Explain how you would provide feedback to educators based on progress data and emotional state."

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1:

[0332] Collecting User Input

[0333] Specific behavior:

[0334] 1.1: The user (educator) opens a dedicated form on the device.

[0335] 1.2: The user fills in the form with the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training").

[0336] 1.3: When the user clicks the submit button, the entered data is sent from the device to the server.

[0337] Input: Educational Policy and Scope

[0338] Output: Educational policy and scope saved on the server

[0339] Step 2:

[0340] Preparing and gathering a knowledge base

[0341] Specific behavior:

[0342] 2.1: The server reads the stored educational policy and scope.

[0343] 2.2: The server accesses the company's knowledge base and searches for relevant materials (e.g., texts, videos, quizzes, etc.).

[0344] 2.3: The server preprocesses the found material, cleaning the text and summarizing the video.

[0345] Input: Saved educational policy and scope

[0346] Output: Preprocessed material

[0347] Step 3:

[0348] Generative AI-based curriculum generation

[0349] Specific behavior:

[0350] 3.1: The server passes the preprocessed material to the generation AI.

[0351] 3.2: The generative AI generates the optimal curriculum based on the educational policy and scope.

[0352] 3.3: The generated curriculum is sent to the server.

[0353] Input: Preprocessed material

[0354] Output: Generated curriculum

[0355] Step 4:

[0356] Curriculum Delivery

[0357] Specific behavior:

[0358] 4.1: The server distributes the generated curriculum to the user's (newcomer's) terminal.

[0359] 4.2: The device displays the delivered curriculum and provides links and files for the user (newcomer) to begin learning.

[0360] Input: Generated curriculum

[0361] Output: Curriculum displayed on the user's device

[0362] Step 5:

[0363] Tracking learning progress and understanding

[0364] Specific behavior:

[0365] 5.1: The user (newcomer) uses the device to study the learning material.

[0366] 5.2: The device will record learning progress (e.g., browsing history, learning time, etc.).

[0367] 5.3: When a user takes the assessment, the results are recorded on the device.

[0368] 5.4: The device sends progress data and test results to the server.

[0369] Input: Learning progress, comprehension test results

[0370] Output: Progress data and test results sent to the server.

[0371] Step 6:

[0372] Emotion recognition by emotion engine

[0373] Specific behavior:

[0374] 6.1: During training, the user (newcomer)'s facial expressions and voice are collected using the device's camera and microphone.

[0375] 6.2: The device sends these data to the emotion engine.

[0376] 6.3: The emotion engine analyzes these data and recognizes the user's emotional state (e.g., excitement, fatigue, understanding).

[0377] 6.4: The recognized emotion data is sent from the device to the server.

[0378] Input: User's facial expressions and voice

[0379] Output: Recognized emotion data

[0380] Step 7:

[0381] Providing feedback

[0382] Specific behavior:

[0383] 7.1: The server performs analysis based on the collected learning progress data and emotion data.

[0384] 7.2: The server provides the analysis results to the educator in the form of leaderboards and detailed progress reports.

[0385] 7.3: Educators (users) will use the feedback to revise their educational plans and provide individualized instruction or additional materials as needed.

[0386] Input: Learning progress data and emotion data

[0387] Output: Leaderboard and detailed progress report

[0388] (Application example 2)

[0389] 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."

[0390] In conventional education systems, it was difficult to provide efficient and systematic training for new employees. Furthermore, there was a lack of means to grasp learning progress and comprehension in real time and provide feedback that took into account the employee's emotional state. This limited the effectiveness of new employee training and made it difficult to determine the appropriate timing for individual instruction.

[0391] 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 an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training plan based on the collected information. This makes it possible to collect optimal teaching materials from the knowledge base and automatically generate and distribute a training plan that takes into account the progress and emotional state of the new employee. In addition, by using an emotion recognition means, it is possible to grasp the emotional state of the user during learning in real time and adjust the training plan based on that. This achieves improved efficiency and quality of new employee training.

[0392] "Educational strategy" refers to the overall plan, goals, and approach to imparting specific knowledge and skills to an educational audience.

[0393] "Scope" refers to the specific definition of the content, scope, and period covered by the educational curriculum.

[0394] "Input means" refers to a device or interface for inputting educational principles and scope into the system.

[0395] "Information Base" means a database of educationally relevant texts, videos, tests, technical materials, and other materials.

[0396] "Collection means" refers to a device or program for searching and collecting required information from an information base.

[0397] "Generation means" refers to a device or program for automatically generating an optimized educational plan based on collected information.

[0398] "User Device" means a device for individual learner access, including smartphones, tablets, smart glasses, and head-mounted displays.

[0399] The term "distribution means" refers to a device or program for transmitting the generated educational plan to a user device and displaying it.

[0400] "Tracking means" refers to a device or program that records and tracks a user's learning progress and understanding in real time.

[0401] "Emotion recognition means" refers to a device or program that analyzes a user's facial expressions and voice and identifies their emotional state.

[0402] "Feedback means" refers to a device or program that provides appropriate instructional improvement information to educators based on data obtained from the tracking means and emotion recognition means.

[0403] This invention provides a system for efficiently and systematically training factory robot operators and maintenance staff. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training plan, a distribution means for distributing the generated training plan, a tracking means for tracking a user's learning progress and level of understanding, an emotion recognition means, and a feedback means for providing feedback to a trainer.

[0404] Input Method

[0405] On the server side, the person in charge of training (for example, a factory training manager) uses a smartphone or tablet to input the training policy and scope. A dedicated input form is provided, allowing training goals to be set, such as "understanding basic robot operation" or "mastering safety protocols." This clarifies the scope of the training.

[0406] Collection Method

[0407] Based on the input educational policy and scope, the server collects relevant information from the company's internal knowledge base, which includes texts, videos, confirmation tests, technical materials, etc. It then preprocesses the collected information before passing it to a generative AI model (e.g., OpenAI's GPT-4).

[0408] generation means

[0409] Based on the preprocessed information, the server uses a generative AI model to generate an optimized training plan. Based on the input training policy and scope, this generative AI model uses knowledge-based information to create an effective learning curriculum. For example, the learning sequence may include basic textbooks on robot operation, videos on safety protocols, and test questions to confirm understanding.

[0410] Delivery Method

[0411] The generated training plan is then distributed from the server to the user's device, which can be a smart eyeglass or a head-mounted display. This allows the student to access the training plan in the actual workplace and begin learning immediately.

[0412] Tracking Methods

[0413] The tracking means records the user's learning progress and level of understanding in real time. For example, which learning materials were viewed and how much, and the results of comprehension tests. This data is sent to the server, allowing the user's progress and level of understanding to be grasped in real time.

[0414] emotion recognition means

[0415] The emotion recognition means uses the camera and microphone installed in the smart glasses or head-mounted display to analyze the user's facial expressions and voice to recognize their emotional state. This data is also sent to the server and integrated with the tracking data for analysis.

[0416] Feedback Methods

[0417] The server provides feedback to the trainer (e.g., factory manager) based on the collected tracking and emotion data. The feedback includes specific progress reports and areas for improvement displayed in a leaderboard format. This allows the trainer to review the training plan and prepare additional individualized instruction or supplementary materials as needed.

[0418] Specific examples

[0419] For example, if a newly hired robot operator is to undergo training under the educational policy of "basic understanding of robot operation," the trainer inputs the policy and scope using a smart device. The server collects relevant information from the knowledge base and creates an optimized training plan using generative AI. This plan is distributed to the user's device, and the trainee begins learning immediately. Learning progress and level of understanding are monitored in real time, and emotional states during learning are also grasped using emotion recognition means. Trainers can obtain feedback from this data and revise the training plan as appropriate.

[0420] Prompt Sentence Examples

[0421] Automatically generate an educational curriculum. Consider the following information:

[0422] Educational policy: Basic understanding of robot operation

[0423] Duration: 3 months of initial training

[0424] For reference, please refer to the following information:

[0425] Basic textbook for robot operation

[0426] Safety Protocol Video

[0427] Sample comprehension check test

[0428] The generated curriculum should include a learning sequence and detailed explanations for each step.

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] The instructor uses a smart device to input the training policy and scope. The input information includes training goals such as "basic understanding of robot operation" and "mastery of safety protocols." This clarifies the training scope and saves it on the server.

[0432] Step 2:

[0433] The server collects relevant information from the information base based on the input educational policy and scope, including texts, videos, confirmation tests, technical documents, etc. The server preprocesses the collected data before passing it to the generative AI model.

[0434] Step 3:

[0435] The server passes the preprocessed information to a generative AI model, which then automatically generates an educational plan. Based on the input educational policy and scope, the generative AI model creates an optimized learning curriculum using relevant information. Specifically, a curriculum is generated that includes the learning order and details of each step.

[0436] Step 4:

[0437] The generated training plan is sent from the server to the user's device (smart glasses or head-mounted display), allowing the student to access the training plan and continue learning at the actual workplace.

[0438] Step 5:

[0439] The user's device records learning progress and level of understanding in real time and sends it to the server. This includes which learning materials were viewed to what extent, test results, etc. The server uses this data to grasp the user's progress and level of understanding.

[0440] Step 6:

[0441] The user's facial expressions and voice are analyzed using a camera and microphone installed on the user device to determine the user's emotional state. Emotional data such as the user's level of fatigue and excitement is collected in real time by the emotion recognition means and transmitted to the server.

[0442] Step 7:

[0443] The server integrates and analyzes the tracking and emotional data and provides feedback to educators in the form of progress reports and leaderboards, allowing educators to revise their teaching plans and prepare additional individualized instruction or supplementary materials as needed.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Second embodiment]

[0448] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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."

[0460] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking the user's learning progress and level of understanding, and a feedback means for providing feedback to the training personnel.

[0461] System programs and their processing

[0462] Collecting User Input

[0463] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[0464] Preparing and gathering a knowledge base

[0465] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[0466] Generative AI-based curriculum generation

[0467] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[0468] Curriculum Delivery

[0469] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[0470] Tracking learning progress and understanding

[0471] The user (newcomer) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server.

[0472] The server uses the transmitted data to track the newcomer's progress and level of understanding in real time, allowing the training manager to understand the newcomer's current learning status.

[0473] Providing feedback

[0474] The server analyzes the collected tracking data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individualized instruction and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[0475] Specific examples

[0476] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is then distributed to the new employee's device, and the new employee begins learning. Learning progress and comprehension test results are sent from the device to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the trainer.

[0477] The present invention allows new employees to acquire knowledge efficiently, and allows training personnel to grasp the progress in real time and provide appropriate guidance, thereby enabling stable human resource development.

[0478] The processing flow will be explained below.

[0479] Program processing steps

[0480] Step 1:

[0481] User (Educator):

[0482] A dedicated form for entering educational policy and scope opens on the terminal.

[0483] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[0484] Device:

[0485] The entered data is sent to the server.

[0486] Step 2:

[0487] server:

[0488] Save the received educational policy and scope.

[0489] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[0490] Preprocess the collected data and prepare it for passing to the generative AI.

[0491] Step 3:

[0492] server:

[0493] Provide preprocessed information to the generation AI.

[0494] Generation AI:

[0495] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[0496] Example: Design a curriculum like the following:

[0497] Basic programming text

[0498] Beginner's exercises

[0499] Code review explanation and examples

[0500] Test to check your understanding

[0501] Step 4:

[0502] server:

[0503] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[0504] Device:

[0505] Display the delivered curriculum and make it accessible to users (newcomers).

[0506] For example, click a link to view text, video, or download a file.

[0507] Step 5:

[0508] User (newcomer):

[0509] Study the distributed materials.

[0510] Example: Reading a programming textbook and solving the exercises.

[0511] Device:

[0512] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[0513] The recorded data is sent to the server.

[0514] Step 6:

[0515] server:

[0516] Analyze submitted learning progress data and assessment results.

[0517] Track and assess new hires' progress and understanding in real time.

[0518] Step 7:

[0519] server:

[0520] Generate feedback based on tracking data.

[0521] Organize the results in leaderboard format or progress reports and provide them to educators.

[0522] Educators (users):

[0523] Review the generated feedback to understand the progress and understanding of new employees.

[0524] Plan for additional tutoring and supplemental materials.

[0525] Through these steps, the system of this invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on progress in real time, enabling stable human resource development.

[0526] Example 1

[0527] 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."

[0528] Conventional OJT (On-the-Job Training) systems only provide uniform training programs, making it difficult to efficiently generate curricula optimized for each job. Furthermore, it is difficult to grasp training progress and comprehension levels in real time and provide appropriate feedback. This leads to issues such as inefficient training for new employees and an increased burden on training personnel.

[0529] 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.

[0530] In this invention, the server includes an input means for inputting an educational policy and a scope, a search means for collecting related information from a knowledge base based on the educational policy and scope, a generation means for generating an optimized educational curriculum based on the collected information, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking a user's learning progress and level of understanding, and a feedback means for providing feedback to an educator based on the data collected by the monitoring means. This makes it possible to automatically generate a curriculum optimized for each task, monitor a user's learning progress and level of understanding in real time, and provide appropriate feedback to an educator.

[0531] "Educational policy" refers to the goals and direction of education, and indicates what skills and knowledge new employees should acquire.

[0532] "Scope" refers to the range and duration of the education, and clarifies the specific fields and content included.

[0533] "Input means" refers to an interface for users to input educational policies and scopes, and may be a dedicated form or device.

[0534] "Searching means" refers to the function of finding relevant information from the knowledge base based on educational policy and scope.

[0535] A "knowledge base" refers to a database that stores information and materials necessary for education, including text data, video content, confirmation tests, technical materials, and external information.

[0536] The "generation means" refers to a function that automatically creates an optimized educational curriculum based on the information collected by the search means.

[0537] The "distribution means" refers to the function of transferring the generated educational curriculum to the user's terminal, and corresponds to the information transmission means via a network.

[0538] "Monitoring means" refers to a mechanism for tracking a user's learning progress and level of understanding in real time and collecting data.

[0539] "Feedback means" refers to a function that analyzes the data collected by the monitoring means and provides information to the training personnel about the learning status of newcomers.

[0540] This invention relates to a system that automatically generates an OJT curriculum optimized for each job and systematically and efficiently trains new employees. This system includes an input means for inputting the training policy and scope, a search means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking learning progress and comprehension, and a feedback means for providing feedback to training personnel.

[0541] The user (educator) uses a dedicated form on the terminal to input the training policy and scope. For example, this may include "understanding basic program structure" or "three months of initial training." This input data is sent from the terminal to the server, which then stores it in a database.

[0542] The server searches for relevant information from a knowledge base based on the stored educational policy and scope, and collects text data, video content, confirmation tests, technical documents, and external information. This knowledge base stores various educational materials. The server uses collection tools to select appropriate materials and performs preprocessing such as standardizing data formats and filtering unnecessary information.

[0543] The server then passes the preprocessed information to a generative AI model to begin generating an optimized educational curriculum. The generative AI model utilizes knowledge base information to effectively structure learning content based on the educational policy and scope. For example, a curriculum is designed that includes programming textbooks for beginners, code review examples for training, and test questions to confirm understanding. This generated curriculum is stored in a database managed by the server.

[0544] The generated curriculum is distributed from the server to the newcomer's device. The device displays the curriculum, and the user (newcomer) begins learning using the provided links and files. The user (newcomer) studies the distributed learning materials through the device and takes a comprehension test.

[0545] The device sends the learning progress (e.g., videos watched, test questions answered) and the results of the comprehension test to the server, and the server uses this data to track the newcomer's progress and comprehension in real time, allowing the educator to understand the user's (newcomer's) current learning status.

[0546] The server analyzes the collected tracking data and provides feedback to trainers in the form of leaderboards and detailed progress reports. Trainers can use this feedback to revise training plans and plan additional tutoring and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[0547] Examples of concrete examples and prompts

[0548] For example, when training newly hired engineers, the training manager inputs the training policy of "understanding basic program structure" into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the learning progress and comprehension test results to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the training manager.

[0549] Example prompt sentence:

[0550] The prompt to generate the optimal curriculum for a three-month initial training course for new engineers is as follows:

[0551] Create a three-month initial training curriculum for newly hired engineers. The training content should include "understanding basic program structure." Include relevant textbooks, videos, and confirmation tests in the curriculum.

[0552] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0553] Step 1: Collecting User Input

[0554] Specific behavior:

[0555] 1. The user (educator) opens a dedicated form on the device.

[0556] 2. The user enters the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training") into the form.

[0557] 3. The terminal sends the entered data to the server.

[0558] 4. The server stores the received data in the educational policy database.

[0559] Input: Educational Policy and Scope

[0560] Output: Educational policy and scope stored in the database

[0561] Step 2: Prepare and gather your knowledge base

[0562] Specific behavior:

[0563] 1. The server searches for relevant information from the knowledge base based on the educational policy and scope.

[0564] 2. The knowledge base includes text data, video content, confirmation tests, technical materials, and external information.

[0565] 3. The server selects data from the search results that matches the educational policy, and as preprocessing, standardizes the data format and filters out unnecessary information.

[0566] Input: Educational policy and scope, knowledge base

[0567] Output: Preprocessed educational resources

[0568] Step 3: Generative AI generates curriculum

[0569] Specific behavior:

[0570] 1. The server inputs the preprocessed information into the generative AI model.

[0571] 2. The generative AI model generates the optimal educational curriculum based on the educational policy and scope.

[0572] 3. The generated curriculum includes programming textbooks, code review examples, comprehension tests, etc.

[0573] 4. The server stores the generated curriculum in the curriculum database.

[0574] Input: Preprocessed educational resources

[0575] Output: Generated educational curriculum

[0576] Step 4: Deliver the curriculum

[0577] Specific behavior:

[0578] 1. The server distributes the generated curriculum to the new employee's device.

[0579] 2. The terminal receives the delivered curriculum and displays it on the display screen.

[0580] 3. The user (newcomer) begins learning by using each curriculum content (links and files) on the device screen.

[0581] Input: Generated educational curriculum

[0582] Output: Curriculum displayed on the new employee's device

[0583] Step 5: Track your progress and understanding

[0584] Specific behavior:

[0585] 1. The user (newcomer) uses the device to study the learning material and take a comprehension test.

[0586] 2. The device collects the user's learning progress (videos watched, test questions answered) and the results of the comprehension test.

[0587] 3. The device sends the collected data to the server.

[0588] 4. The server analyzes the received data in real time and tracks the newcomer's progress and understanding.

[0589] Input: Learning progress and comprehension test results

[0590] Output: Tracked progress and understanding data

[0591] Step 6: Provide feedback

[0592] Specific behavior:

[0593] 1. The server analyzes the tracking data and generates leaderboards and detailed progress reports.

[0594] 2. The server sends the generated feedback to the teacher's device.

[0595] 3. The person in charge of training (supervisor or senior) will review the training plan based on the feedback.

[0596] 4. Educators will provide new recruits with tutoring and additional materials as needed.

[0597] Input: Tracked progress and understanding data

[0598] Output: Feedback and progress reports provided to educators

[0599] (Application example 1)

[0600] 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."

[0601] In conventional training systems, creating a curriculum and managing progress to effectively provide on-the-job training (OJT) for new staff was time-consuming, and training quality was prone to vary depending on the person in charge. There was also a lack of efficient means to teach specialized knowledge about robot operation and maintenance in factories. Furthermore, real-time progress tracking and feedback were insufficient, making it difficult to accurately grasp the level of understanding of new employees.

[0602] 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.

[0603] In this invention, the server includes an input means for inputting training content, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training curriculum based on the collected information. This enables automatic generation and distribution of an optimal curriculum based on the training policy and scope. Furthermore, the distributed curriculum is displayed on an augmented reality-compatible display, making it easier for new staff to learn factory robot operation and maintenance procedures in real time. The system also includes a tracking means for tracking learning progress and comprehension and a feedback means for providing feedback to training personnel based on the collected data, allowing for accurate understanding of new staff's learning progress and appropriate guidance as needed.

[0604] An "education policy" is a policy that defines the specific knowledge and skills that training personnel should provide to new staff members.

[0605] "Scope" is a term that refers to the content or range covered in a particular educational activity.

[0606] "Input means" refers to a device or method for inputting educational policy and scope into the system.

[0607] "Collection means" refers to a device or method for collecting relevant information from a knowledge base.

[0608] A "knowledge base" is a database that stores information such as texts, videos, confirmation tests, and technical materials for use in education.

[0609] "Generation means" refers to a device or method for generating an optimized educational curriculum based on collected information.

[0610] "Distribution means" refers to a device or method for distributing the generated curriculum to a user terminal.

[0611] "User terminal" refers to electronic devices used by new staff undergoing training.

[0612] "Display means" refers to a device or method for displaying educational content via an augmented reality-enabled display.

[0613] "Tracking means" refers to a device or method for tracking a user's learning progress and comprehension.

[0614] "Feedback means" refers to a device or method for providing feedback to educators based on data collected by the tracking means.

[0615] An "augmented reality display" is a display device that displays digital information superimposed on visual information from the real world.

[0616] The present invention provides a system for efficiently training new staff members. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum based on the collected information, a distribution means for distributing the generated curriculum to a user terminal, a tracking means for tracking the user's learning progress and level of understanding, a feedback means for providing feedback to a trainer based on data collected by the tracking means, and a display means for displaying the training content on an augmented reality-compatible display.

[0617] The server collects relevant information from a knowledge base based on the educational policy and scope entered by the educator. Using the collection means, the server obtains texts, videos, confirmation tests, technical documents, etc., and passes them to the generative AI model. The generative AI model generates an optimized educational curriculum based on the input information. This generated curriculum is then distributed to the user's device.

[0618] By wearing an augmented reality display, users (new staff) can visually check the training content in the actual workplace. For example, videos and text of robot maintenance procedures are superimposed on real-world visual information through the display means. Furthermore, as the user progresses through the learning content, their progress and comprehension test results are tracked in real time and sent to the server. The server uses this data to grasp the user's progress in real time and provide feedback to the training staff.

[0619] As a specific example, a trainer inputs training guidelines into the system for newly hired technical staff, such as "understanding basic robot maintenance procedures." The server collects maintenance procedure manuals and operation videos from the knowledge base, and the generative AI model creates an optimal curriculum. The curriculum is then distributed to the user's device, and the technical staff begins learning via an augmented reality display.

[0620] An example of this prompt is as follows:

[0621] "Generate a training curriculum for new technical staff to gain an understanding of basic robot maintenance procedures. The knowledge base includes:

[0622] 1. Title: Basic Robot Maintenance

[0623] Type:Video

[0624] URL:https: / / example.com / video1

[0625] 2. Title: Basic Robot Maintenance Procedures

[0626] Type:Text

[0627] URL:https: / / example.com / text1

[0628] Selected educational scope: 1 month

[0629] In this way, new staff can learn work procedures effectively, and training personnel can grasp the progress and level of understanding of new staff in real time and provide appropriate guidance.

[0630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0631] Step 1:

[0632] The user (trainer) enters the training policy and scope using a dedicated form on the terminal. Specific input examples include "understanding basic robot maintenance procedures" and "one month of initial training." The entered data is sent from the terminal to the server and saved. The input data includes information such as the training content and duration.

[0633] Step 2:

[0634] The server collects relevant information from the knowledge base based on the stored educational policy and scope. Using the collection means, the server obtains educationally relevant information such as texts, videos, confirmation tests, and technical materials from the knowledge base. The obtained data undergoes preprocessing before being input into the generative AI model.

[0635] Step 3:

[0636] The server passes the preprocessed knowledge base information to a generative AI model to generate an optimized educational curriculum. The generative AI model uses the knowledge base information to effectively structure learning content based on the input educational policy and scope. As an example of output, a curriculum is generated that includes learning sequences, learning material links, and test questions.

[0637] Step 4:

[0638] The generated curriculum is delivered from the server to the user's device. The device displays the delivered curriculum, and the user (new staff member) begins learning using the provided links and files. An augmented reality-enabled display is used on the device to visually present the educational content.

[0639] Step 5:

[0640] The user (new staff member) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server. The sent data is stored on the server.

[0641] Step 6:

[0642] The server tracks the user's learning progress and comprehension level in real time based on the saved learning progress and comprehension test data. Using the tracking means, the user's progress (e.g., learning completion rate, test correct answer rate, etc.) is analyzed and fed back to the educator.

[0643] Step 7:

[0644] The training staff can then review the training plan based on the new employee's progress and understanding, which is provided in real time through feedback channels. Additional individualized instruction and training can be planned as needed. For example, if a new employee is struggling in a particular area, additional learning materials or explanations can be provided.

[0645] 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.

[0646] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking a user's learning progress and level of understanding, a feedback means for providing feedback to training personnel, and an emotion engine for recognizing the user's emotions.

[0647] System programs and their processing

[0648] Collecting User Input

[0649] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[0650] Preparing and gathering a knowledge base

[0651] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[0652] Generative AI-based curriculum generation

[0653] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[0654] Curriculum Delivery

[0655] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[0656] Tracking learning progress and understanding

[0657] The user (newcomer) uses a device to study the distributed learning materials and take a comprehension test. The device records the learning progress (e.g., browsing history, study time) and the results of the comprehension test. The recorded data is sent to a server, which uses this data to track the newcomer's progress and comprehension in real time.

[0658] Emotion recognition by emotion engine

[0659] While the user (newcomer) is learning, their facial expressions and voice are analyzed using the device's camera and microphone. Based on this data, the emotion engine recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). The emotion data is sent to the server along with learning progress data.

[0660] Providing feedback

[0661] The server analyzes the collected tracking and emotional data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individual instruction or supplementary materials. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations. Furthermore, based on the emotional data, they can analyze at what point the new employee experienced difficulty and further optimize the curriculum accordingly.

[0662] Specific examples

[0663] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and a generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[0664] The present invention allows new employees to acquire knowledge efficiently, and enables training personnel to grasp the progress and emotional state of the new employees in real time and provide appropriate guidance, thereby enabling stable human resource development.

[0665] The processing flow will be explained below.

[0666] Program processing steps

[0667] Step 1:

[0668] User (Educator):

[0669] A dedicated form for entering educational policy and scope opens on the terminal.

[0670] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[0671] Device:

[0672] The entered data is sent to the server.

[0673] Step 2:

[0674] server:

[0675] Save the received educational policy and scope.

[0676] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[0677] Preprocess the collected data and prepare it for passing to the generative AI.

[0678] Step 3:

[0679] server:

[0680] Provide preprocessed information to the generation AI.

[0681] Generation AI:

[0682] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[0683] Example: Design a curriculum like the following:

[0684] Basic programming text

[0685] Beginner's exercises

[0686] Code review explanation and examples

[0687] Test to check your understanding

[0688] Step 4:

[0689] server:

[0690] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[0691] Device:

[0692] Display the delivered curriculum and make it accessible to users (newcomers).

[0693] For example, click a link to view text, video, or download a file.

[0694] Step 5:

[0695] User (newcomer):

[0696] Study the provided materials, read the programming textbook, and solve the exercises.

[0697] Device:

[0698] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[0699] The recorded data is sent to the server.

[0700] Step 6:

[0701] server:

[0702] Analyze submitted learning progress data and assessment results.

[0703] Track and assess new hires' progress and understanding in real time.

[0704] Step 7:

[0705] User (newcomer):

[0706] During learning, the device's camera and microphone are used to record your facial expressions and voice, allowing your emotional state to be recognized.

[0707] Emotion Engine:

[0708] Identify the user's emotional state (e.g., understanding, excitement, fatigue) based on collected facial and voice data.

[0709] The emotion data is sent to the server.

[0710] Step 8:

[0711] server:

[0712] The emotional data recognized by the emotion engine is integrated with learning progress data and analyzed.

[0713] Create tracking data that takes into account the emotional state of new employees and optimize the content of feedback.

[0714] Step 9:

[0715] server:

[0716] The integrated data will be used to generate leaderboards and detailed progress reports, which will be provided to educators.

[0717] Educators (users):

[0718] Review the generated feedback to understand the new employee's progress, understanding, and emotional state.

[0719] Plan for additional tutoring and supplemental materials.

[0720] Through these steps, the system of the present invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on learning progress and emotional state in real time, enabling stable human resource development.

[0721] Example 2

[0722] 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."

[0723] In modern companies, training new employees requires a great deal of time and effort, creating a demand for efficient and systematic training programs. However, it is difficult to closely track each new employee's learning progress and level of understanding and optimize training plans based on that information. It is also difficult to grasp the new employee's emotional state during training and provide appropriate guidance accordingly. Traditional training systems are unable to effectively address these challenges.

[0724] 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.

[0725] In this invention, the server includes input means for inputting a training policy and scope, collection means for collecting related information from a knowledge base, generation means for generating an optimized training curriculum based on the collected information, distribution means for distributing the generated curriculum to a user terminal, tracking means for tracking the user's learning progress and level of understanding, an emotion engine for recognizing the user's emotional state, and feedback means for providing feedback to the training personnel based on the data collected by the tracking means and the emotion engine. This makes it possible to track the learning progress and level of understanding of newcomers in real time and to grasp their emotional state, thereby realizing individually optimized training plans and instruction.

[0726] An "educational policy" indicates the goals and direction of education.

[0727] "Scope" refers to the range and duration of the educational program.

[0728] "Input means" refers to an interface that allows a user to input educational principles and scope into the system.

[0729] "Collection means" refers to the function of extracting and collecting the necessary information from the knowledge base.

[0730] A "knowledge base" refers to a database that stores various materials and information related to education and work.

[0731] "Generation means" refers to the function of creating an optimized educational curriculum based on collected information.

[0732] "Distribution means" refers to the function for transmitting the generated educational curriculum to a user terminal.

[0733] "User terminal" refers to the device used by a user to receive the educational curriculum.

[0734] "Tracking means" refers to the functionality that records and monitors a user's learning progress and understanding.

[0735] "Emotion engine" refers to the function that analyzes the user's facial expressions and voice and identifies their emotional state.

[0736] "Feedback Measures" refers to the functionality that provides data collected by the Tracking Measures and Emotion Engine to the Educator.

[0737] The system of the present invention automatically generates an OJT (On-the-Job Training) curriculum optimized for each job, and systematically and efficiently educates new employees. This system has the following main functions:

[0738] First, the trainer (user) uses a dedicated form on the terminal to input the training policy and scope. For example, they input specific details such as "understanding basic program structure" and "three months of initial training." The terminal then sends the input information to the server, which then stores it in a database.

[0739] Next, the server collects relevant information from the company's knowledge base based on the stored training policy and scope. The knowledge base includes text, videos, confirmation tests, technical materials, and competitive information. The server collects this information and performs preprocessing to pass it to the generative AI model. Preprocessing includes text cleaning and video summarization.

[0740] Once the collection and preprocessing are complete, the server passes the information to a generative AI model to generate an optimal educational curriculum. The generative AI model effectively structures the learning content based on the educational policy and scope. For example, it designs a curriculum that includes programming textbooks, training code review examples, and test questions to confirm understanding. The generated curriculum is then returned to the server.

[0741] The server then distributes the generated curriculum to the user's (newcomer's) device, where the device displays the curriculum and the user can begin learning using the provided links and files.

[0742] As the learning progresses, the user (newcomer) studies the learning materials through the device and takes comprehension tests. The device records the learning progress and the results of the comprehension tests and sends them to the server. The server uses this data to track the user's learning progress and comprehension in real time.

[0743] Furthermore, an emotion engine is used to recognize the user's (newcomer's) emotional state during learning. The user uses the device's camera and microphone to collect their facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). This data is also sent to the server.

[0744] The server analyzes the collected learning progress and emotion data and provides feedback to the educator. The feedback is displayed as a leaderboard and detailed progress reports. Based on this, the educator can revise the learning plan and provide individualized instruction or additional materials as needed. For example, if a newcomer is struggling in a particular area, they can provide additional materials or explanations.

[0745] Specific examples

[0746] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[0747] Prompt Sentence Examples

[0748] "Generate a three-month training curriculum aimed at helping new engineers understand basic programming structure."

[0749] "Collect relevant information from your knowledge base and create an optimized training curriculum for new hires."

[0750] "Explain how you would provide feedback to educators based on progress data and emotional state."

[0751] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0752] Step 1:

[0753] Collecting User Input

[0754] Specific behavior:

[0755] 1.1: The user (educator) opens a dedicated form on the device.

[0756] 1.2: The user fills in the form with the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training").

[0757] 1.3: When the user clicks the submit button, the entered data is sent from the device to the server.

[0758] Input: Educational Policy and Scope

[0759] Output: Educational policy and scope saved on the server

[0760] Step 2:

[0761] Preparing and gathering a knowledge base

[0762] Specific behavior:

[0763] 2.1: The server reads the stored educational policy and scope.

[0764] 2.2: The server accesses the company's knowledge base and searches for relevant materials (e.g., texts, videos, quizzes, etc.).

[0765] 2.3: The server preprocesses the found material, cleaning the text and summarizing the video.

[0766] Input: Saved educational policy and scope

[0767] Output: Preprocessed material

[0768] Step 3:

[0769] Generative AI-based curriculum generation

[0770] Specific behavior:

[0771] 3.1: The server passes the preprocessed material to the generation AI.

[0772] 3.2: The generative AI generates the optimal curriculum based on the educational policy and scope.

[0773] 3.3: The generated curriculum is sent to the server.

[0774] Input: Preprocessed material

[0775] Output: Generated curriculum

[0776] Step 4:

[0777] Curriculum Delivery

[0778] Specific behavior:

[0779] 4.1: The server distributes the generated curriculum to the user's (newcomer's) terminal.

[0780] 4.2: The device displays the delivered curriculum and provides links and files for the user (newcomer) to begin learning.

[0781] Input: Generated curriculum

[0782] Output: Curriculum displayed on the user's device

[0783] Step 5:

[0784] Tracking learning progress and understanding

[0785] Specific behavior:

[0786] 5.1: The user (newcomer) uses the device to study the learning material.

[0787] 5.2: The device will record learning progress (e.g., browsing history, learning time, etc.).

[0788] 5.3: When a user takes the assessment, the results are recorded on the device.

[0789] 5.4: The device sends progress data and test results to the server.

[0790] Input: Learning progress, comprehension test results

[0791] Output: Progress data and test results sent to the server.

[0792] Step 6:

[0793] Emotion recognition by emotion engine

[0794] Specific behavior:

[0795] 6.1: During training, the user (newcomer)'s facial expressions and voice are collected using the device's camera and microphone.

[0796] 6.2: The device sends these data to the emotion engine.

[0797] 6.3: The emotion engine analyzes these data and recognizes the user's emotional state (e.g., excitement, fatigue, understanding).

[0798] 6.4: The recognized emotion data is sent from the device to the server.

[0799] Input: User's facial expressions and voice

[0800] Output: Recognized emotion data

[0801] Step 7:

[0802] Providing feedback

[0803] Specific behavior:

[0804] 7.1: The server performs analysis based on the collected learning progress data and emotion data.

[0805] 7.2: The server provides the analysis results to the educator in the form of leaderboards and detailed progress reports.

[0806] 7.3: Educators (users) will use the feedback to revise their educational plans and provide individualized instruction or additional materials as needed.

[0807] Input: Learning progress data and emotion data

[0808] Output: Leaderboard and detailed progress report

[0809] (Application example 2)

[0810] 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."

[0811] In conventional education systems, it was difficult to provide efficient and systematic training for new employees. Furthermore, there was a lack of means to grasp learning progress and comprehension in real time and provide feedback that took into account the employee's emotional state. This limited the effectiveness of new employee training and made it difficult to determine the appropriate timing for individual instruction.

[0812] 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 an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training plan based on the collected information. This makes it possible to collect optimal teaching materials from the knowledge base and automatically generate and distribute a training plan that takes into account the progress and emotional state of the new employee. In addition, by using an emotion recognition means, it is possible to grasp the emotional state of the user during learning in real time and adjust the training plan based on that. This achieves improved efficiency and quality of new employee training.

[0813] "Educational strategy" refers to the overall plan, goals, and approach to imparting specific knowledge and skills to an educational audience.

[0814] "Scope" refers to the specific definition of the content, scope, and period covered by the educational curriculum.

[0815] "Input means" refers to a device or interface for inputting educational principles and scope into the system.

[0816] "Information Base" means a database of educationally relevant texts, videos, tests, technical materials, and other materials.

[0817] "Collection means" refers to a device or program for searching and collecting required information from an information base.

[0818] "Generation means" refers to a device or program for automatically generating an optimized educational plan based on collected information.

[0819] "User Device" means a device for individual learner access, including smartphones, tablets, smart glasses, and head-mounted displays.

[0820] The term "distribution means" refers to a device or program for transmitting the generated educational plan to a user device and displaying it.

[0821] "Tracking means" refers to a device or program that records and tracks a user's learning progress and understanding in real time.

[0822] "Emotion recognition means" refers to a device or program that analyzes a user's facial expressions and voice and identifies their emotional state.

[0823] "Feedback means" refers to a device or program that provides appropriate instructional improvement information to educators based on data obtained from the tracking means and emotion recognition means.

[0824] This invention provides a system for efficiently and systematically training factory robot operators and maintenance staff. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training plan, a distribution means for distributing the generated training plan, a tracking means for tracking a user's learning progress and level of understanding, an emotion recognition means, and a feedback means for providing feedback to a trainer.

[0825] Input Method

[0826] On the server side, the person in charge of training (for example, a factory training manager) uses a smartphone or tablet to input the training policy and scope. A dedicated input form is provided, allowing training goals to be set, such as "understanding basic robot operation" or "mastering safety protocols." This clarifies the scope of the training.

[0827] Collection Method

[0828] Based on the input educational policy and scope, the server collects relevant information from the company's internal knowledge base, which includes texts, videos, confirmation tests, technical materials, etc. It then preprocesses the collected information before passing it to a generative AI model (e.g., OpenAI's GPT-4).

[0829] generation means

[0830] Based on the preprocessed information, the server uses a generative AI model to generate an optimized training plan. Based on the input training policy and scope, this generative AI model uses knowledge-based information to create an effective learning curriculum. For example, the learning sequence may include basic textbooks on robot operation, videos on safety protocols, and test questions to confirm understanding.

[0831] Delivery Method

[0832] The generated training plan is then distributed from the server to the user's device, which can be a smart eyeglass or a head-mounted display. This allows the student to access the training plan in the actual workplace and begin learning immediately.

[0833] Tracking Methods

[0834] The tracking means records the user's learning progress and level of understanding in real time. For example, which learning materials were viewed and how much, and the results of comprehension tests. This data is sent to the server, allowing the user's progress and level of understanding to be grasped in real time.

[0835] emotion recognition means

[0836] The emotion recognition means uses the camera and microphone installed in the smart glasses or head-mounted display to analyze the user's facial expressions and voice to recognize their emotional state. This data is also sent to the server and integrated with the tracking data for analysis.

[0837] Feedback Methods

[0838] The server provides feedback to the trainer (e.g., factory manager) based on the collected tracking and emotion data. The feedback includes specific progress reports and areas for improvement displayed in a leaderboard format. This allows the trainer to review the training plan and prepare additional individualized instruction or supplementary materials as needed.

[0839] Specific examples

[0840] For example, if a newly hired robot operator is to undergo training under the educational policy of "basic understanding of robot operation," the trainer inputs the policy and scope using a smart device. The server collects relevant information from the knowledge base and creates an optimized training plan using generative AI. This plan is distributed to the user's device, and the trainee begins learning immediately. Learning progress and level of understanding are monitored in real time, and emotional states during learning are also grasped using emotion recognition means. Trainers can obtain feedback from this data and revise the training plan as appropriate.

[0841] Prompt Sentence Examples

[0842] Automatically generate an educational curriculum. Consider the following information:

[0843] Educational policy: Basic understanding of robot operation

[0844] Duration: 3 months of initial training

[0845] For reference, please refer to the following information:

[0846] Basic textbook for robot operation

[0847] Safety Protocol Video

[0848] Sample comprehension check test

[0849] The generated curriculum should include a learning sequence and detailed explanations for each step.

[0850] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0851] Step 1:

[0852] The instructor uses a smart device to input the training policy and scope. The input information includes training goals such as "basic understanding of robot operation" and "mastery of safety protocols." This clarifies the training scope and saves it on the server.

[0853] Step 2:

[0854] The server collects relevant information from the information base based on the input educational policy and scope, including texts, videos, confirmation tests, technical documents, etc. The server preprocesses the collected data before passing it to the generative AI model.

[0855] Step 3:

[0856] The server passes the preprocessed information to a generative AI model, which then automatically generates an educational plan. Based on the input educational policy and scope, the generative AI model creates an optimized learning curriculum using relevant information. Specifically, a curriculum is generated that includes the learning order and details of each step.

[0857] Step 4:

[0858] The generated training plan is sent from the server to the user's device (smart glasses or head-mounted display), allowing the student to access the training plan and continue learning at the actual workplace.

[0859] Step 5:

[0860] The user's device records learning progress and level of understanding in real time and sends it to the server. This includes which learning materials were viewed to what extent, test results, etc. The server uses this data to grasp the user's progress and level of understanding.

[0861] Step 6:

[0862] The user's facial expressions and voice are analyzed using a camera and microphone installed on the user device to determine the user's emotional state. Emotional data such as the user's level of fatigue and excitement is collected in real time by the emotion recognition means and transmitted to the server.

[0863] Step 7:

[0864] The server integrates and analyzes the tracking and emotional data and provides feedback to educators in the form of progress reports and leaderboards, allowing educators to revise their teaching plans and prepare additional individualized instruction or supplementary materials as needed.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] [Third embodiment]

[0869] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0870] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0871] 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).

[0872] 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.

[0873] 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.

[0874] 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).

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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."

[0881] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking the user's learning progress and level of understanding, and a feedback means for providing feedback to the training personnel.

[0882] System programs and their processing

[0883] Collecting User Input

[0884] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[0885] Preparing and gathering a knowledge base

[0886] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[0887] Generative AI-based curriculum generation

[0888] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[0889] Curriculum Delivery

[0890] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[0891] Tracking learning progress and understanding

[0892] The user (newcomer) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server.

[0893] The server uses the transmitted data to track the newcomer's progress and level of understanding in real time, allowing the training manager to understand the newcomer's current learning status.

[0894] Providing feedback

[0895] The server analyzes the collected tracking data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individualized instruction and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[0896] Specific examples

[0897] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is then distributed to the new employee's device, and the new employee begins learning. Learning progress and comprehension test results are sent from the device to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the trainer.

[0898] The present invention allows new employees to acquire knowledge efficiently, and allows training personnel to grasp the progress in real time and provide appropriate guidance, thereby enabling stable human resource development.

[0899] The processing flow will be explained below.

[0900] Program processing steps

[0901] Step 1:

[0902] User (Educator):

[0903] A dedicated form for entering educational policy and scope opens on the terminal.

[0904] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[0905] Device:

[0906] The entered data is sent to the server.

[0907] Step 2:

[0908] server:

[0909] Save the received educational policy and scope.

[0910] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[0911] Preprocess the collected data and prepare it for passing to the generative AI.

[0912] Step 3:

[0913] server:

[0914] Provide preprocessed information to the generation AI.

[0915] Generation AI:

[0916] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[0917] Example: Design a curriculum like the following:

[0918] Basic programming text

[0919] Beginner's exercises

[0920] Code review explanation and examples

[0921] Test to check your understanding

[0922] Step 4:

[0923] server:

[0924] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[0925] Device:

[0926] Display the delivered curriculum and make it accessible to users (newcomers).

[0927] For example, click a link to view text, video, or download a file.

[0928] Step 5:

[0929] User (newcomer):

[0930] Study the distributed materials.

[0931] Example: Reading a programming textbook and solving the exercises.

[0932] Device:

[0933] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[0934] The recorded data is sent to the server.

[0935] Step 6:

[0936] server:

[0937] Analyze submitted learning progress data and assessment results.

[0938] Track and assess new hires' progress and understanding in real time.

[0939] Step 7:

[0940] server:

[0941] Generate feedback based on tracking data.

[0942] Organize the results in leaderboard format or progress reports and provide them to educators.

[0943] Educators (users):

[0944] Review the generated feedback to understand the progress and understanding of new employees.

[0945] Plan for additional tutoring and supplemental materials.

[0946] Through these steps, the system of this invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on progress in real time, enabling stable human resource development.

[0947] Example 1

[0948] 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."

[0949] Conventional OJT (On-the-Job Training) systems only provide uniform training programs, making it difficult to efficiently generate curricula optimized for each job. Furthermore, it is difficult to grasp training progress and comprehension levels in real time and provide appropriate feedback. This leads to issues such as inefficient training for new employees and an increased burden on training personnel.

[0950] 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.

[0951] In this invention, the server includes an input means for inputting an educational policy and a scope, a search means for collecting related information from a knowledge base based on the educational policy and scope, a generation means for generating an optimized educational curriculum based on the collected information, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking a user's learning progress and level of understanding, and a feedback means for providing feedback to an educator based on the data collected by the monitoring means. This makes it possible to automatically generate a curriculum optimized for each task, monitor a user's learning progress and level of understanding in real time, and provide appropriate feedback to an educator.

[0952] "Educational policy" refers to the goals and direction of education, and indicates what skills and knowledge new employees should acquire.

[0953] "Scope" refers to the range and duration of the education, and clarifies the specific fields and content included.

[0954] "Input means" refers to an interface for users to input educational policies and scopes, and may be a dedicated form or device.

[0955] "Searching means" refers to the function of finding relevant information from the knowledge base based on educational policy and scope.

[0956] A "knowledge base" refers to a database that stores information and materials necessary for education, including text data, video content, confirmation tests, technical materials, and external information.

[0957] The "generation means" refers to a function that automatically creates an optimized educational curriculum based on the information collected by the search means.

[0958] The "distribution means" refers to the function of transferring the generated educational curriculum to the user's terminal, and corresponds to the information transmission means via a network.

[0959] "Monitoring means" refers to a mechanism for tracking a user's learning progress and level of understanding in real time and collecting data.

[0960] "Feedback means" refers to a function that analyzes the data collected by the monitoring means and provides information to the training personnel about the learning status of newcomers.

[0961] This invention relates to a system that automatically generates an OJT curriculum optimized for each job and systematically and efficiently trains new employees. This system includes an input means for inputting the training policy and scope, a search means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking learning progress and comprehension, and a feedback means for providing feedback to training personnel.

[0962] The user (educator) uses a dedicated form on the terminal to input the training policy and scope. For example, this may include "understanding basic program structure" or "three months of initial training." This input data is sent from the terminal to the server, which then stores it in a database.

[0963] The server searches for relevant information from a knowledge base based on the stored educational policy and scope, and collects text data, video content, confirmation tests, technical documents, and external information. This knowledge base stores various educational materials. The server uses collection tools to select appropriate materials and performs preprocessing such as standardizing data formats and filtering unnecessary information.

[0964] The server then passes the preprocessed information to a generative AI model to begin generating an optimized educational curriculum. The generative AI model utilizes knowledge base information to effectively structure learning content based on the educational policy and scope. For example, a curriculum is designed that includes programming textbooks for beginners, code review examples for training, and test questions to confirm understanding. This generated curriculum is stored in a database managed by the server.

[0965] The generated curriculum is distributed from the server to the newcomer's device. The device displays the curriculum, and the user (newcomer) begins learning using the provided links and files. The user (newcomer) studies the distributed learning materials through the device and takes a comprehension test.

[0966] The device sends the learning progress (e.g., videos watched, test questions answered) and the results of the comprehension test to the server, and the server uses this data to track the newcomer's progress and comprehension in real time, allowing the educator to understand the user's (newcomer's) current learning status.

[0967] The server analyzes the collected tracking data and provides feedback to trainers in the form of leaderboards and detailed progress reports. Trainers can use this feedback to revise training plans and plan additional tutoring and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[0968] Examples of concrete examples and prompts

[0969] For example, when training newly hired engineers, the training manager inputs the training policy of "understanding basic program structure" into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the learning progress and comprehension test results to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the training manager.

[0970] Example prompt sentence:

[0971] The prompt to generate the optimal curriculum for a three-month initial training course for new engineers is as follows:

[0972] Create a three-month initial training curriculum for newly hired engineers. The training content should include "understanding basic program structure." Include relevant textbooks, videos, and confirmation tests in the curriculum.

[0973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0974] Step 1: Collecting User Input

[0975] Specific behavior:

[0976] 1. The user (educator) opens a dedicated form on the device.

[0977] 2. The user enters the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training") into the form.

[0978] 3. The terminal sends the entered data to the server.

[0979] 4. The server stores the received data in the educational policy database.

[0980] Input: Educational Policy and Scope

[0981] Output: Educational policy and scope stored in the database

[0982] Step 2: Prepare and gather your knowledge base

[0983] Specific behavior:

[0984] 1. The server searches for relevant information from the knowledge base based on the educational policy and scope.

[0985] 2. The knowledge base includes text data, video content, confirmation tests, technical materials, and external information.

[0986] 3. The server selects data from the search results that matches the educational policy, and as preprocessing, standardizes the data format and filters out unnecessary information.

[0987] Input: Educational policy and scope, knowledge base

[0988] Output: Preprocessed educational resources

[0989] Step 3: Generative AI generates curriculum

[0990] Specific behavior:

[0991] 1. The server inputs the preprocessed information into the generative AI model.

[0992] 2. The generative AI model generates the optimal educational curriculum based on the educational policy and scope.

[0993] 3. The generated curriculum includes programming textbooks, code review examples, comprehension tests, etc.

[0994] 4. The server stores the generated curriculum in the curriculum database.

[0995] Input: Preprocessed educational resources

[0996] Output: Generated educational curriculum

[0997] Step 4: Deliver the curriculum

[0998] Specific behavior:

[0999] 1. The server distributes the generated curriculum to the new employee's device.

[1000] 2. The terminal receives the delivered curriculum and displays it on the display screen.

[1001] 3. The user (newcomer) begins learning by using each curriculum content (links and files) on the device screen.

[1002] Input: Generated educational curriculum

[1003] Output: Curriculum displayed on the new employee's device

[1004] Step 5: Track your progress and understanding

[1005] Specific behavior:

[1006] 1. The user (newcomer) uses the device to study the learning material and take a comprehension test.

[1007] 2. The device collects the user's learning progress (videos watched, test questions answered) and the results of the comprehension test.

[1008] 3. The device sends the collected data to the server.

[1009] 4. The server analyzes the received data in real time and tracks the newcomer's progress and understanding.

[1010] Input: Learning progress and comprehension test results

[1011] Output: Tracked progress and understanding data

[1012] Step 6: Provide feedback

[1013] Specific behavior:

[1014] 1. The server analyzes the tracking data and generates leaderboards and detailed progress reports.

[1015] 2. The server sends the generated feedback to the teacher's device.

[1016] 3. The person in charge of training (supervisor or senior) will review the training plan based on the feedback.

[1017] 4. Educators will provide new recruits with tutoring and additional materials as needed.

[1018] Input: Tracked progress and understanding data

[1019] Output: Feedback and progress reports provided to educators

[1020] (Application example 1)

[1021] 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."

[1022] In conventional training systems, creating a curriculum and managing progress to effectively provide on-the-job training (OJT) for new staff was time-consuming, and training quality was prone to vary depending on the person in charge. There was also a lack of efficient means to teach specialized knowledge about robot operation and maintenance in factories. Furthermore, real-time progress tracking and feedback were insufficient, making it difficult to accurately grasp the level of understanding of new employees.

[1023] 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.

[1024] In this invention, the server includes an input means for inputting training content, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training curriculum based on the collected information. This enables automatic generation and distribution of an optimal curriculum based on the training policy and scope. Furthermore, the distributed curriculum is displayed on an augmented reality-compatible display, making it easier for new staff to learn factory robot operation and maintenance procedures in real time. The system also includes a tracking means for tracking learning progress and comprehension and a feedback means for providing feedback to training personnel based on the collected data, allowing for accurate understanding of new staff's learning progress and appropriate guidance as needed.

[1025] An "education policy" is a policy that defines the specific knowledge and skills that training personnel should provide to new staff members.

[1026] "Scope" is a term that refers to the content or range covered in a particular educational activity.

[1027] "Input means" refers to a device or method for inputting educational policy and scope into the system.

[1028] "Collection means" refers to a device or method for collecting relevant information from a knowledge base.

[1029] A "knowledge base" is a database that stores information such as texts, videos, confirmation tests, and technical materials for use in education.

[1030] "Generation means" refers to a device or method for generating an optimized educational curriculum based on collected information.

[1031] "Distribution means" refers to a device or method for distributing the generated curriculum to a user terminal.

[1032] "User terminal" refers to electronic devices used by new staff undergoing training.

[1033] "Display means" refers to a device or method for displaying educational content via an augmented reality-enabled display.

[1034] "Tracking means" refers to a device or method for tracking a user's learning progress and comprehension.

[1035] "Feedback means" refers to a device or method for providing feedback to educators based on data collected by the tracking means.

[1036] An "augmented reality display" is a display device that displays digital information superimposed on visual information from the real world.

[1037] The present invention provides a system for efficiently training new staff members. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum based on the collected information, a distribution means for distributing the generated curriculum to a user terminal, a tracking means for tracking the user's learning progress and level of understanding, a feedback means for providing feedback to a trainer based on data collected by the tracking means, and a display means for displaying the training content on an augmented reality-compatible display.

[1038] The server collects relevant information from a knowledge base based on the educational policy and scope entered by the educator. Using the collection means, the server obtains texts, videos, confirmation tests, technical documents, etc., and passes them to the generative AI model. The generative AI model generates an optimized educational curriculum based on the input information. This generated curriculum is then distributed to the user's device.

[1039] By wearing an augmented reality display, users (new staff) can visually check the training content in the actual workplace. For example, videos and text of robot maintenance procedures are superimposed on real-world visual information through the display means. Furthermore, as the user progresses through the learning content, their progress and comprehension test results are tracked in real time and sent to the server. The server uses this data to grasp the user's progress in real time and provide feedback to the training staff.

[1040] As a specific example, a trainer inputs training guidelines into the system for newly hired technical staff, such as "understanding basic robot maintenance procedures." The server collects maintenance procedure manuals and operation videos from the knowledge base, and the generative AI model creates an optimal curriculum. The curriculum is then distributed to the user's device, and the technical staff begins learning via an augmented reality display.

[1041] An example of this prompt is as follows:

[1042] "Generate a training curriculum for new technical staff to gain an understanding of basic robot maintenance procedures. The knowledge base includes:

[1043] 1. Title: Basic Robot Maintenance

[1044] Type:Video

[1045] URL:https: / / example.com / video1

[1046] 2. Title: Basic Robot Maintenance Procedures

[1047] Type:Text

[1048] URL:https: / / example.com / text1

[1049] Selected educational scope: 1 month

[1050] In this way, new staff can learn work procedures effectively, and training personnel can grasp the progress and level of understanding of new staff in real time and provide appropriate guidance.

[1051] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1052] Step 1:

[1053] The user (trainer) enters the training policy and scope using a dedicated form on the terminal. Specific input examples include "understanding basic robot maintenance procedures" and "one month of initial training." The entered data is sent from the terminal to the server and saved. The input data includes information such as the training content and duration.

[1054] Step 2:

[1055] The server collects relevant information from the knowledge base based on the stored educational policy and scope. Using the collection means, the server obtains educationally relevant information such as texts, videos, confirmation tests, and technical materials from the knowledge base. The obtained data undergoes preprocessing before being input into the generative AI model.

[1056] Step 3:

[1057] The server passes the preprocessed knowledge base information to a generative AI model to generate an optimized educational curriculum. The generative AI model uses the knowledge base information to effectively structure learning content based on the input educational policy and scope. As an example of output, a curriculum is generated that includes learning sequences, learning material links, and test questions.

[1058] Step 4:

[1059] The generated curriculum is delivered from the server to the user's device. The device displays the delivered curriculum, and the user (new staff member) begins learning using the provided links and files. An augmented reality-enabled display is used on the device to visually present the educational content.

[1060] Step 5:

[1061] The user (new staff member) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server. The sent data is stored on the server.

[1062] Step 6:

[1063] The server tracks the user's learning progress and comprehension level in real time based on the saved learning progress and comprehension test data. Using the tracking means, the user's progress (e.g., learning completion rate, test correct answer rate, etc.) is analyzed and fed back to the educator.

[1064] Step 7:

[1065] The training staff can then review the training plan based on the new employee's progress and understanding, which is provided in real time through feedback channels. Additional individualized instruction and training can be planned as needed. For example, if a new employee is struggling in a particular area, additional learning materials or explanations can be provided.

[1066] 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.

[1067] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking a user's learning progress and level of understanding, a feedback means for providing feedback to training personnel, and an emotion engine for recognizing the user's emotions.

[1068] System programs and their processing

[1069] Collecting User Input

[1070] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[1071] Preparing and gathering a knowledge base

[1072] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[1073] Generative AI-based curriculum generation

[1074] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[1075] Curriculum Delivery

[1076] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[1077] Tracking learning progress and understanding

[1078] The user (newcomer) uses a device to study the distributed learning materials and take a comprehension test. The device records the learning progress (e.g., browsing history, study time) and the results of the comprehension test. The recorded data is sent to a server, which uses this data to track the newcomer's progress and comprehension in real time.

[1079] Emotion recognition by emotion engine

[1080] While the user (newcomer) is learning, their facial expressions and voice are analyzed using the device's camera and microphone. Based on this data, the emotion engine recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). The emotion data is sent to the server along with learning progress data.

[1081] Providing feedback

[1082] The server analyzes the collected tracking and emotional data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individual instruction or supplementary materials. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations. Furthermore, based on the emotional data, they can analyze at what point the new employee experienced difficulty and further optimize the curriculum accordingly.

[1083] Specific examples

[1084] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and a generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[1085] The present invention allows new employees to acquire knowledge efficiently, and enables training personnel to grasp the progress and emotional state of the new employees in real time and provide appropriate guidance, thereby enabling stable human resource development.

[1086] The processing flow will be explained below.

[1087] Program processing steps

[1088] Step 1:

[1089] User (Educator):

[1090] A dedicated form for entering educational policy and scope opens on the terminal.

[1091] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[1092] Device:

[1093] The entered data is sent to the server.

[1094] Step 2:

[1095] server:

[1096] Save the received educational policy and scope.

[1097] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[1098] Preprocess the collected data and prepare it for passing to the generative AI.

[1099] Step 3:

[1100] server:

[1101] Provide preprocessed information to the generation AI.

[1102] Generation AI:

[1103] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[1104] Example: Design a curriculum like the following:

[1105] Basic programming text

[1106] Beginner's exercises

[1107] Code review explanation and examples

[1108] Test to check your understanding

[1109] Step 4:

[1110] server:

[1111] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[1112] Device:

[1113] Display the delivered curriculum and make it accessible to users (newcomers).

[1114] For example, click a link to view text, video, or download a file.

[1115] Step 5:

[1116] User (newcomer):

[1117] Study the provided materials, read the programming textbook, and solve the exercises.

[1118] Device:

[1119] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[1120] The recorded data is sent to the server.

[1121] Step 6:

[1122] server:

[1123] Analyze submitted learning progress data and assessment results.

[1124] Track and assess new hires' progress and understanding in real time.

[1125] Step 7:

[1126] User (newcomer):

[1127] During learning, the device's camera and microphone are used to record your facial expressions and voice, allowing your emotional state to be recognized.

[1128] Emotion Engine:

[1129] Identify the user's emotional state (e.g., understanding, excitement, fatigue) based on collected facial and voice data.

[1130] The emotion data is sent to the server.

[1131] Step 8:

[1132] server:

[1133] The emotional data recognized by the emotion engine is integrated with learning progress data and analyzed.

[1134] Create tracking data that takes into account the emotional state of new employees and optimize the content of feedback.

[1135] Step 9:

[1136] server:

[1137] The integrated data will be used to generate leaderboards and detailed progress reports, which will be provided to educators.

[1138] Educators (users):

[1139] Review the generated feedback to understand the new employee's progress, understanding, and emotional state.

[1140] Plan for additional tutoring and supplemental materials.

[1141] Through these steps, the system of the present invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on learning progress and emotional state in real time, enabling stable human resource development.

[1142] Example 2

[1143] 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."

[1144] In modern companies, training new employees requires a great deal of time and effort, creating a demand for efficient and systematic training programs. However, it is difficult to closely track each new employee's learning progress and level of understanding and optimize training plans based on that information. It is also difficult to grasp the new employee's emotional state during training and provide appropriate guidance accordingly. Traditional training systems are unable to effectively address these challenges.

[1145] 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.

[1146] In this invention, the server includes input means for inputting a training policy and scope, collection means for collecting related information from a knowledge base, generation means for generating an optimized training curriculum based on the collected information, distribution means for distributing the generated curriculum to a user terminal, tracking means for tracking the user's learning progress and level of understanding, an emotion engine for recognizing the user's emotional state, and feedback means for providing feedback to the training personnel based on the data collected by the tracking means and the emotion engine. This makes it possible to track the learning progress and level of understanding of newcomers in real time and to grasp their emotional state, thereby realizing individually optimized training plans and instruction.

[1147] An "educational policy" indicates the goals and direction of education.

[1148] "Scope" refers to the range and duration of the educational program.

[1149] "Input means" refers to an interface that allows a user to input educational principles and scope into the system.

[1150] "Collection means" refers to the function of extracting and collecting the necessary information from the knowledge base.

[1151] A "knowledge base" refers to a database that stores various materials and information related to education and work.

[1152] "Generation means" refers to the function of creating an optimized educational curriculum based on collected information.

[1153] "Distribution means" refers to the function for transmitting the generated educational curriculum to a user terminal.

[1154] "User terminal" refers to the device used by a user to receive the educational curriculum.

[1155] "Tracking means" refers to the functionality that records and monitors a user's learning progress and understanding.

[1156] "Emotion engine" refers to the function that analyzes the user's facial expressions and voice and identifies their emotional state.

[1157] "Feedback Measures" refers to the functionality that provides data collected by the Tracking Measures and Emotion Engine to the Educator.

[1158] The system of the present invention automatically generates an OJT (On-the-Job Training) curriculum optimized for each job, and systematically and efficiently educates new employees. This system has the following main functions:

[1159] First, the trainer (user) uses a dedicated form on the terminal to input the training policy and scope. For example, they input specific details such as "understanding basic program structure" and "three months of initial training." The terminal then sends the input information to the server, which then stores it in a database.

[1160] Next, the server collects relevant information from the company's knowledge base based on the stored training policy and scope. The knowledge base includes text, videos, confirmation tests, technical materials, and competitive information. The server collects this information and performs preprocessing to pass it to the generative AI model. Preprocessing includes text cleaning and video summarization.

[1161] Once the collection and preprocessing are complete, the server passes the information to a generative AI model to generate an optimal educational curriculum. The generative AI model effectively structures the learning content based on the educational policy and scope. For example, it designs a curriculum that includes programming textbooks, training code review examples, and test questions to confirm understanding. The generated curriculum is then returned to the server.

[1162] The server then distributes the generated curriculum to the user's (newcomer's) device, where the device displays the curriculum and the user can begin learning using the provided links and files.

[1163] As the learning progresses, the user (newcomer) studies the learning materials through the device and takes comprehension tests. The device records the learning progress and the results of the comprehension tests and sends them to the server. The server uses this data to track the user's learning progress and comprehension in real time.

[1164] Furthermore, an emotion engine is used to recognize the user's (newcomer's) emotional state during learning. The user uses the device's camera and microphone to collect their facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). This data is also sent to the server.

[1165] The server analyzes the collected learning progress and emotion data and provides feedback to the educator. The feedback is displayed as a leaderboard and detailed progress reports. Based on this, the educator can revise the learning plan and provide individualized instruction or additional materials as needed. For example, if a newcomer is struggling in a particular area, they can provide additional materials or explanations.

[1166] Specific examples

[1167] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[1168] Prompt Sentence Examples

[1169] "Generate a three-month training curriculum aimed at helping new engineers understand basic programming structure."

[1170] "Collect relevant information from your knowledge base and create an optimized training curriculum for new hires."

[1171] "Explain how you would provide feedback to educators based on progress data and emotional state."

[1172] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1173] Step 1:

[1174] Collecting User Input

[1175] Specific behavior:

[1176] 1.1: The user (educator) opens a dedicated form on the device.

[1177] 1.2: The user fills in the form with the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training").

[1178] 1.3: When the user clicks the submit button, the entered data is sent from the device to the server.

[1179] Input: Educational Policy and Scope

[1180] Output: Educational policy and scope saved on the server

[1181] Step 2:

[1182] Preparing and gathering a knowledge base

[1183] Specific behavior:

[1184] 2.1: The server reads the stored educational policy and scope.

[1185] 2.2: The server accesses the company's knowledge base and searches for relevant materials (e.g., texts, videos, quizzes, etc.).

[1186] 2.3: The server preprocesses the found material, cleaning the text and summarizing the video.

[1187] Input: Saved educational policy and scope

[1188] Output: Preprocessed material

[1189] Step 3:

[1190] Generative AI-based curriculum generation

[1191] Specific behavior:

[1192] 3.1: The server passes the preprocessed material to the generation AI.

[1193] 3.2: The generative AI generates the optimal curriculum based on the educational policy and scope.

[1194] 3.3: The generated curriculum is sent to the server.

[1195] Input: Preprocessed material

[1196] Output: Generated curriculum

[1197] Step 4:

[1198] Curriculum Delivery

[1199] Specific behavior:

[1200] 4.1: The server distributes the generated curriculum to the user's (newcomer's) terminal.

[1201] 4.2: The device displays the delivered curriculum and provides links and files for the user (newcomer) to begin learning.

[1202] Input: Generated curriculum

[1203] Output: Curriculum displayed on the user's device

[1204] Step 5:

[1205] Tracking learning progress and understanding

[1206] Specific behavior:

[1207] 5.1: The user (newcomer) uses the device to study the learning material.

[1208] 5.2: The device will record learning progress (e.g., browsing history, learning time, etc.).

[1209] 5.3: When a user takes the assessment, the results are recorded on the device.

[1210] 5.4: The device sends progress data and test results to the server.

[1211] Input: Learning progress, comprehension test results

[1212] Output: Progress data and test results sent to the server.

[1213] Step 6:

[1214] Emotion recognition by emotion engine

[1215] Specific behavior:

[1216] 6.1: During training, the user (newcomer)'s facial expressions and voice are collected using the device's camera and microphone.

[1217] 6.2: The device sends these data to the emotion engine.

[1218] 6.3: The emotion engine analyzes these data and recognizes the user's emotional state (e.g., excitement, fatigue, understanding).

[1219] 6.4: The recognized emotion data is sent from the device to the server.

[1220] Input: User's facial expressions and voice

[1221] Output: Recognized emotion data

[1222] Step 7:

[1223] Providing feedback

[1224] Specific behavior:

[1225] 7.1: The server performs analysis based on the collected learning progress data and emotion data.

[1226] 7.2: The server provides the analysis results to the educator in the form of leaderboards and detailed progress reports.

[1227] 7.3: Educators (users) will use the feedback to revise their educational plans and provide individualized instruction or additional materials as needed.

[1228] Input: Learning progress data and emotion data

[1229] Output: Leaderboard and detailed progress report

[1230] (Application example 2)

[1231] 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."

[1232] In conventional education systems, it was difficult to provide efficient and systematic training for new employees. Furthermore, there was a lack of means to grasp learning progress and comprehension in real time and provide feedback that took into account the employee's emotional state. This limited the effectiveness of new employee training and made it difficult to determine the appropriate timing for individual instruction.

[1233] 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 an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training plan based on the collected information. This makes it possible to collect optimal teaching materials from the knowledge base and automatically generate and distribute a training plan that takes into account the progress and emotional state of the new employee. In addition, by using an emotion recognition means, it is possible to grasp the emotional state of the user during learning in real time and adjust the training plan based on that. This achieves improved efficiency and quality of new employee training.

[1234] "Educational strategy" refers to the overall plan, goals, and approach to imparting specific knowledge and skills to an educational audience.

[1235] "Scope" refers to the specific definition of the content, scope, and period covered by the educational curriculum.

[1236] "Input means" refers to a device or interface for inputting educational principles and scope into the system.

[1237] "Information Base" means a database of educationally relevant texts, videos, tests, technical materials, and other materials.

[1238] "Collection means" refers to a device or program for searching and collecting required information from an information base.

[1239] "Generation means" refers to a device or program for automatically generating an optimized educational plan based on collected information.

[1240] "User Device" means a device for individual learner access, including smartphones, tablets, smart glasses, and head-mounted displays.

[1241] The term "distribution means" refers to a device or program for transmitting the generated educational plan to a user device and displaying it.

[1242] "Tracking means" refers to a device or program that records and tracks a user's learning progress and understanding in real time.

[1243] "Emotion recognition means" refers to a device or program that analyzes a user's facial expressions and voice and identifies their emotional state.

[1244] "Feedback means" refers to a device or program that provides appropriate instructional improvement information to educators based on data obtained from the tracking means and emotion recognition means.

[1245] This invention provides a system for efficiently and systematically training factory robot operators and maintenance staff. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training plan, a distribution means for distributing the generated training plan, a tracking means for tracking a user's learning progress and level of understanding, an emotion recognition means, and a feedback means for providing feedback to a trainer.

[1246] Input Method

[1247] On the server side, the person in charge of training (for example, a factory training manager) uses a smartphone or tablet to input the training policy and scope. A dedicated input form is provided, allowing training goals to be set, such as "understanding basic robot operation" or "mastering safety protocols." This clarifies the scope of the training.

[1248] Collection Method

[1249] Based on the input educational policy and scope, the server collects relevant information from the company's internal knowledge base, which includes texts, videos, confirmation tests, technical materials, etc. It then preprocesses the collected information before passing it to a generative AI model (e.g., OpenAI's GPT-4).

[1250] generation means

[1251] Based on the preprocessed information, the server uses a generative AI model to generate an optimized training plan. Based on the input training policy and scope, this generative AI model uses knowledge-based information to create an effective learning curriculum. For example, the learning sequence may include basic textbooks on robot operation, videos on safety protocols, and test questions to confirm understanding.

[1252] Delivery Method

[1253] The generated training plan is then distributed from the server to the user's device, which can be a smart eyeglass or a head-mounted display. This allows the student to access the training plan in the actual workplace and begin learning immediately.

[1254] Tracking Methods

[1255] The tracking means records the user's learning progress and level of understanding in real time. For example, which learning materials were viewed and how much, and the results of comprehension tests. This data is sent to the server, allowing the user's progress and level of understanding to be grasped in real time.

[1256] emotion recognition means

[1257] The emotion recognition means uses the camera and microphone installed in the smart glasses or head-mounted display to analyze the user's facial expressions and voice to recognize their emotional state. This data is also sent to the server and integrated with the tracking data for analysis.

[1258] Feedback Methods

[1259] The server provides feedback to the trainer (e.g., factory manager) based on the collected tracking and emotion data. The feedback includes specific progress reports and areas for improvement displayed in a leaderboard format. This allows the trainer to review the training plan and prepare additional individualized instruction or supplementary materials as needed.

[1260] Specific examples

[1261] For example, if a newly hired robot operator is to undergo training under the educational policy of "basic understanding of robot operation," the trainer inputs the policy and scope using a smart device. The server collects relevant information from the knowledge base and creates an optimized training plan using generative AI. This plan is distributed to the user's device, and the trainee begins learning immediately. Learning progress and level of understanding are monitored in real time, and emotional states during learning are also grasped using emotion recognition means. Trainers can obtain feedback from this data and revise the training plan as appropriate.

[1262] Prompt Sentence Examples

[1263] Automatically generate an educational curriculum. Consider the following information:

[1264] Educational policy: Basic understanding of robot operation

[1265] Duration: 3 months of initial training

[1266] For reference, please refer to the following information:

[1267] Basic textbook for robot operation

[1268] Safety Protocol Video

[1269] Sample comprehension check test

[1270] The generated curriculum should include a learning sequence and detailed explanations for each step.

[1271] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1272] Step 1:

[1273] The instructor uses a smart device to input the training policy and scope. The input information includes training goals such as "basic understanding of robot operation" and "mastery of safety protocols." This clarifies the training scope and saves it on the server.

[1274] Step 2:

[1275] The server collects relevant information from the information base based on the input educational policy and scope, including texts, videos, confirmation tests, technical documents, etc. The server preprocesses the collected data before passing it to the generative AI model.

[1276] Step 3:

[1277] The server passes the preprocessed information to a generative AI model, which then automatically generates an educational plan. Based on the input educational policy and scope, the generative AI model creates an optimized learning curriculum using relevant information. Specifically, a curriculum is generated that includes the learning order and details of each step.

[1278] Step 4:

[1279] The generated training plan is sent from the server to the user's device (smart glasses or head-mounted display), allowing the student to access the training plan and continue learning at the actual workplace.

[1280] Step 5:

[1281] The user's device records learning progress and level of understanding in real time and sends it to the server. This includes which learning materials were viewed to what extent, test results, etc. The server uses this data to grasp the user's progress and level of understanding.

[1282] Step 6:

[1283] The user's facial expressions and voice are analyzed using a camera and microphone installed on the user device to determine the user's emotional state. Emotional data such as the user's level of fatigue and excitement is collected in real time by the emotion recognition means and transmitted to the server.

[1284] Step 7:

[1285] The server integrates and analyzes the tracking and emotional data and provides feedback to educators in the form of progress reports and leaderboards, allowing educators to revise their teaching plans and prepare additional individualized instruction or supplementary materials as needed.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] [Fourth embodiment]

[1290] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1291] 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.

[1292] 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).

[1293] 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.

[1294] 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.

[1295] 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).

[1296] 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.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] 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.

[1302] 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."

[1303] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking the user's learning progress and level of understanding, and a feedback means for providing feedback to the training personnel.

[1304] System programs and their processing

[1305] Collecting User Input

[1306] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[1307] Preparing and gathering a knowledge base

[1308] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[1309] Generative AI-based curriculum generation

[1310] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[1311] Curriculum Delivery

[1312] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[1313] Tracking learning progress and understanding

[1314] The user (newcomer) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server.

[1315] The server uses the transmitted data to track the newcomer's progress and level of understanding in real time, allowing the training manager to understand the newcomer's current learning status.

[1316] Providing feedback

[1317] The server analyzes the collected tracking data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individualized instruction and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[1318] Specific examples

[1319] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is then distributed to the new employee's device, and the new employee begins learning. Learning progress and comprehension test results are sent from the device to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the trainer.

[1320] The present invention allows new employees to acquire knowledge efficiently, and allows training personnel to grasp the progress in real time and provide appropriate guidance, thereby enabling stable human resource development.

[1321] The processing flow will be explained below.

[1322] Program processing steps

[1323] Step 1:

[1324] User (Educator):

[1325] A dedicated form for entering educational policy and scope opens on the terminal.

[1326] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[1327] Device:

[1328] The entered data is sent to the server.

[1329] Step 2:

[1330] server:

[1331] Save the received educational policy and scope.

[1332] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[1333] Preprocess the collected data and prepare it for passing to the generative AI.

[1334] Step 3:

[1335] server:

[1336] Provide preprocessed information to the generation AI.

[1337] Generation AI:

[1338] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[1339] Example: Design a curriculum like the following:

[1340] Basic programming text

[1341] Beginner's exercises

[1342] Code review explanation and examples

[1343] Test to check your understanding

[1344] Step 4:

[1345] server:

[1346] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[1347] Device:

[1348] Display the delivered curriculum and make it accessible to users (newcomers).

[1349] For example, click a link to view text, video, or download a file.

[1350] Step 5:

[1351] User (newcomer):

[1352] Study the distributed materials.

[1353] Example: Reading a programming textbook and solving the exercises.

[1354] Device:

[1355] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[1356] The recorded data is sent to the server.

[1357] Step 6:

[1358] server:

[1359] Analyze submitted learning progress data and assessment results.

[1360] Track and assess new hires' progress and understanding in real time.

[1361] Step 7:

[1362] server:

[1363] Generate feedback based on tracking data.

[1364] Organize the results in leaderboard format or progress reports and provide them to educators.

[1365] Educators (users):

[1366] Review the generated feedback to understand the progress and understanding of new employees.

[1367] Plan for additional tutoring and supplemental materials.

[1368] Through these steps, the system of this invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on progress in real time, enabling stable human resource development.

[1369] Example 1

[1370] 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."

[1371] Conventional OJT (On-the-Job Training) systems only provide uniform training programs, making it difficult to efficiently generate curricula optimized for each job. Furthermore, it is difficult to grasp training progress and comprehension levels in real time and provide appropriate feedback. This leads to issues such as inefficient training for new employees and an increased burden on training personnel.

[1372] 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.

[1373] In this invention, the server includes an input means for inputting an educational policy and a scope, a search means for collecting related information from a knowledge base based on the educational policy and scope, a generation means for generating an optimized educational curriculum based on the collected information, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking a user's learning progress and level of understanding, and a feedback means for providing feedback to an educator based on the data collected by the monitoring means. This makes it possible to automatically generate a curriculum optimized for each task, monitor a user's learning progress and level of understanding in real time, and provide appropriate feedback to an educator.

[1374] "Educational policy" refers to the goals and direction of education, and indicates what skills and knowledge new employees should acquire.

[1375] "Scope" refers to the range and duration of the education, and clarifies the specific fields and content included.

[1376] "Input means" refers to an interface for users to input educational policies and scopes, and may be a dedicated form or device.

[1377] "Searching means" refers to the function of finding relevant information from the knowledge base based on educational policy and scope.

[1378] A "knowledge base" refers to a database that stores information and materials necessary for education, including text data, video content, confirmation tests, technical materials, and external information.

[1379] The "generation means" refers to a function that automatically creates an optimized educational curriculum based on the information collected by the search means.

[1380] The "distribution means" refers to the function of transferring the generated educational curriculum to the user's terminal, and corresponds to the information transmission means via a network.

[1381] "Monitoring means" refers to a mechanism for tracking a user's learning progress and level of understanding in real time and collecting data.

[1382] "Feedback means" refers to a function that analyzes the data collected by the monitoring means and provides information to the training personnel about the learning status of newcomers.

[1383] This invention relates to a system that automatically generates an OJT curriculum optimized for each job and systematically and efficiently trains new employees. This system includes an input means for inputting the training policy and scope, a search means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum to terminals, a monitoring means for tracking learning progress and comprehension, and a feedback means for providing feedback to training personnel.

[1384] The user (educator) uses a dedicated form on the terminal to input the training policy and scope. For example, this may include "understanding basic program structure" or "three months of initial training." This input data is sent from the terminal to the server, which then stores it in a database.

[1385] The server searches for relevant information from a knowledge base based on the stored educational policy and scope, and collects text data, video content, confirmation tests, technical documents, and external information. This knowledge base stores various educational materials. The server uses collection tools to select appropriate materials and performs preprocessing such as standardizing data formats and filtering unnecessary information.

[1386] The server then passes the preprocessed information to a generative AI model to begin generating an optimized educational curriculum. The generative AI model utilizes knowledge base information to effectively structure learning content based on the educational policy and scope. For example, a curriculum is designed that includes programming textbooks for beginners, code review examples for training, and test questions to confirm understanding. This generated curriculum is stored in a database managed by the server.

[1387] The generated curriculum is distributed from the server to the newcomer's device. The device displays the curriculum, and the user (newcomer) begins learning using the provided links and files. The user (newcomer) studies the distributed learning materials through the device and takes a comprehension test.

[1388] The device sends the learning progress (e.g., videos watched, test questions answered) and the results of the comprehension test to the server, and the server uses this data to track the newcomer's progress and comprehension in real time, allowing the educator to understand the user's (newcomer's) current learning status.

[1389] The server analyzes the collected tracking data and provides feedback to trainers in the form of leaderboards and detailed progress reports. Trainers can use this feedback to revise training plans and plan additional tutoring and training. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations.

[1390] Examples of concrete examples and prompts

[1391] For example, when training newly hired engineers, the training manager inputs the training policy of "understanding basic program structure" into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the learning progress and comprehension test results to the server, and the server uses this data to provide real-time feedback on the new employee's progress to the training manager.

[1392] Example prompt sentence:

[1393] The prompt to generate the optimal curriculum for a three-month initial training course for new engineers is as follows:

[1394] Create a three-month initial training curriculum for newly hired engineers. The training content should include "understanding basic program structure." Include relevant textbooks, videos, and confirmation tests in the curriculum.

[1395] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1396] Step 1: Collecting User Input

[1397] Specific behavior:

[1398] 1. The user (educator) opens a dedicated form on the device.

[1399] 2. The user enters the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training") into the form.

[1400] 3. The terminal sends the entered data to the server.

[1401] 4. The server stores the received data in the educational policy database.

[1402] Input: Educational Policy and Scope

[1403] Output: Educational policy and scope stored in the database

[1404] Step 2: Prepare and gather your knowledge base

[1405] Specific behavior:

[1406] 1. The server searches for relevant information from the knowledge base based on the educational policy and scope.

[1407] 2. The knowledge base includes text data, video content, confirmation tests, technical materials, and external information.

[1408] 3. The server selects data from the search results that matches the educational policy, and as preprocessing, standardizes the data format and filters out unnecessary information.

[1409] Input: Educational policy and scope, knowledge base

[1410] Output: Preprocessed educational resources

[1411] Step 3: Generative AI generates curriculum

[1412] Specific behavior:

[1413] 1. The server inputs the preprocessed information into the generative AI model.

[1414] 2. The generative AI model generates the optimal educational curriculum based on the educational policy and scope.

[1415] 3. The generated curriculum includes programming textbooks, code review examples, comprehension tests, etc.

[1416] 4. The server stores the generated curriculum in the curriculum database.

[1417] Input: Preprocessed educational resources

[1418] Output: Generated educational curriculum

[1419] Step 4: Deliver the curriculum

[1420] Specific behavior:

[1421] 1. The server distributes the generated curriculum to the new employee's device.

[1422] 2. The terminal receives the delivered curriculum and displays it on the display screen.

[1423] 3. The user (newcomer) begins learning by using each curriculum content (links and files) on the device screen.

[1424] Input: Generated educational curriculum

[1425] Output: Curriculum displayed on the new employee's device

[1426] Step 5: Track your progress and understanding

[1427] Specific behavior:

[1428] 1. The user (newcomer) uses the device to study the learning material and take a comprehension test.

[1429] 2. The device collects the user's learning progress (videos watched, test questions answered) and the results of the comprehension test.

[1430] 3. The device sends the collected data to the server.

[1431] 4. The server analyzes the received data in real time and tracks the newcomer's progress and understanding.

[1432] Input: Learning progress and comprehension test results

[1433] Output: Tracked progress and understanding data

[1434] Step 6: Provide feedback

[1435] Specific behavior:

[1436] 1. The server analyzes the tracking data and generates leaderboards and detailed progress reports.

[1437] 2. The server sends the generated feedback to the teacher's device.

[1438] 3. The person in charge of training (supervisor or senior) will review the training plan based on the feedback.

[1439] 4. Educators will provide new recruits with tutoring and additional materials as needed.

[1440] Input: Tracked progress and understanding data

[1441] Output: Feedback and progress reports provided to educators

[1442] (Application example 1)

[1443] 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."

[1444] In conventional training systems, creating a curriculum and managing progress to effectively provide on-the-job training (OJT) for new staff was time-consuming, and training quality was prone to vary depending on the person in charge. There was also a lack of efficient means to teach specialized knowledge about robot operation and maintenance in factories. Furthermore, real-time progress tracking and feedback were insufficient, making it difficult to accurately grasp the level of understanding of new employees.

[1445] 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.

[1446] In this invention, the server includes an input means for inputting training content, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training curriculum based on the collected information. This enables automatic generation and distribution of an optimal curriculum based on the training policy and scope. Furthermore, the distributed curriculum is displayed on an augmented reality-compatible display, making it easier for new staff to learn factory robot operation and maintenance procedures in real time. The system also includes a tracking means for tracking learning progress and comprehension and a feedback means for providing feedback to training personnel based on the collected data, allowing for accurate understanding of new staff's learning progress and appropriate guidance as needed.

[1447] An "education policy" is a policy that defines the specific knowledge and skills that training personnel should provide to new staff members.

[1448] "Scope" is a term that refers to the content or range covered in a particular educational activity.

[1449] "Input means" refers to a device or method for inputting educational policy and scope into the system.

[1450] "Collection means" refers to a device or method for collecting relevant information from a knowledge base.

[1451] A "knowledge base" is a database that stores information such as texts, videos, confirmation tests, and technical materials for use in education.

[1452] "Generation means" refers to a device or method for generating an optimized educational curriculum based on collected information.

[1453] "Distribution means" refers to a device or method for distributing the generated curriculum to a user terminal.

[1454] "User terminal" refers to electronic devices used by new staff undergoing training.

[1455] "Display means" refers to a device or method for displaying educational content via an augmented reality-enabled display.

[1456] "Tracking means" refers to a device or method for tracking a user's learning progress and comprehension.

[1457] "Feedback means" refers to a device or method for providing feedback to educators based on data collected by the tracking means.

[1458] An "augmented reality display" is a display device that displays digital information superimposed on visual information from the real world.

[1459] The present invention provides a system for efficiently training new staff members. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum based on the collected information, a distribution means for distributing the generated curriculum to a user terminal, a tracking means for tracking the user's learning progress and level of understanding, a feedback means for providing feedback to a trainer based on data collected by the tracking means, and a display means for displaying the training content on an augmented reality-compatible display.

[1460] The server collects relevant information from a knowledge base based on the educational policy and scope entered by the educator. Using the collection means, the server obtains texts, videos, confirmation tests, technical documents, etc., and passes them to the generative AI model. The generative AI model generates an optimized educational curriculum based on the input information. This generated curriculum is then distributed to the user's device.

[1461] By wearing an augmented reality display, users (new staff) can visually check the training content in the actual workplace. For example, videos and text of robot maintenance procedures are superimposed on real-world visual information through the display means. Furthermore, as the user progresses through the learning content, their progress and comprehension test results are tracked in real time and sent to the server. The server uses this data to grasp the user's progress in real time and provide feedback to the training staff.

[1462] As a specific example, a trainer inputs training guidelines into the system for newly hired technical staff, such as "understanding basic robot maintenance procedures." The server collects maintenance procedure manuals and operation videos from the knowledge base, and the generative AI model creates an optimal curriculum. The curriculum is then distributed to the user's device, and the technical staff begins learning via an augmented reality display.

[1463] An example of this prompt is as follows:

[1464] "Generate a training curriculum for new technical staff to gain an understanding of basic robot maintenance procedures. The knowledge base includes:

[1465] 1. Title: Basic Robot Maintenance

[1466] Type:Video

[1467] URL:https: / / example.com / video1

[1468] 2. Title: Basic Robot Maintenance Procedures

[1469] Type:Text

[1470] URL:https: / / example.com / text1

[1471] Selected educational scope: 1 month

[1472] In this way, new staff can learn work procedures effectively, and training personnel can grasp the progress and level of understanding of new staff in real time and provide appropriate guidance.

[1473] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1474] Step 1:

[1475] The user (trainer) enters the training policy and scope using a dedicated form on the terminal. Specific input examples include "understanding basic robot maintenance procedures" and "one month of initial training." The entered data is sent from the terminal to the server and saved. The input data includes information such as the training content and duration.

[1476] Step 2:

[1477] The server collects relevant information from the knowledge base based on the stored educational policy and scope. Using the collection means, the server obtains educationally relevant information such as texts, videos, confirmation tests, and technical materials from the knowledge base. The obtained data undergoes preprocessing before being input into the generative AI model.

[1478] Step 3:

[1479] The server passes the preprocessed knowledge base information to a generative AI model to generate an optimized educational curriculum. The generative AI model uses the knowledge base information to effectively structure learning content based on the input educational policy and scope. As an example of output, a curriculum is generated that includes learning sequences, learning material links, and test questions.

[1480] Step 4:

[1481] The generated curriculum is delivered from the server to the user's device. The device displays the delivered curriculum, and the user (new staff member) begins learning using the provided links and files. An augmented reality-enabled display is used on the device to visually present the educational content.

[1482] Step 5:

[1483] The user (new staff member) uses the terminal to study the distributed learning materials and take a comprehension test. The terminal sends the learning progress (e.g., videos watched, test questions answered, etc.) and the results of the comprehension test to the server. The sent data is stored on the server.

[1484] Step 6:

[1485] The server tracks the user's learning progress and comprehension level in real time based on the saved learning progress and comprehension test data. Using the tracking means, the user's progress (e.g., learning completion rate, test correct answer rate, etc.) is analyzed and fed back to the educator.

[1486] Step 7:

[1487] The training staff can then review the training plan based on the new employee's progress and understanding, which is provided in real time through feedback channels. Additional individualized instruction and training can be planned as needed. For example, if a new employee is struggling in a particular area, additional learning materials or explanations can be provided.

[1488] 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.

[1489] The present invention relates to a system for automatically generating an OJT curriculum optimized for each job and systematically and efficiently training new employees. This system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training curriculum, a distribution means for distributing the generated curriculum, a tracking means for tracking a user's learning progress and level of understanding, a feedback means for providing feedback to training personnel, and an emotion engine for recognizing the user's emotions.

[1490] System programs and their processing

[1491] Collecting User Input

[1492] The user (trainer) uses a dedicated form on the terminal to input the training policy and scope. This information includes, for example, the training content, such as "understanding basic program structure," and the duration, such as "3 months of initial training." The input data is sent to the server and saved.

[1493] Preparing and gathering a knowledge base

[1494] Based on the saved educational policy and scope, the server collects relevant information from the company's knowledge base. The knowledge base stores various educational materials, such as texts, videos, confirmation tests, technical documents, and competitive information. Using the collection method, the server selects appropriate materials and performs preprocessing to pass them to the generation AI.

[1495] Generative AI-based curriculum generation

[1496] The server passes the preprocessed information to the generative AI, which then begins the process of generating an optimized educational curriculum. Based on the input educational policy and scope, the generative AI uses knowledge base information to effectively structure the learning content. For example, it designs a curriculum that includes a learning sequence, such as a programming textbook for beginners, code review examples for training, and test questions to confirm understanding.

[1497] Curriculum Delivery

[1498] The generated curriculum is delivered from the server to the newcomer's device. The device displays the delivered curriculum, and the user (newcomer) begins learning using the provided links and files.

[1499] Tracking learning progress and understanding

[1500] The user (newcomer) uses a device to study the distributed learning materials and take a comprehension test. The device records the learning progress (e.g., browsing history, study time) and the results of the comprehension test. The recorded data is sent to a server, which uses this data to track the newcomer's progress and comprehension in real time.

[1501] Emotion recognition by emotion engine

[1502] While the user (newcomer) is learning, their facial expressions and voice are analyzed using the device's camera and microphone. Based on this data, the emotion engine recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). The emotion data is sent to the server along with learning progress data.

[1503] Providing feedback

[1504] The server analyzes the collected tracking and emotional data and provides it to trainers in the form of leaderboards and detailed progress reports. Trainers (supervisors and senior employees) can use this feedback to revise the training plan and plan additional individual instruction or supplementary materials. For example, if a new employee is struggling in a particular area, they can provide additional learning materials or explanations. Furthermore, based on the emotional data, they can analyze at what point the new employee experienced difficulty and further optimize the curriculum accordingly.

[1505] Specific examples

[1506] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and a generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[1507] The present invention allows new employees to acquire knowledge efficiently, and enables training personnel to grasp the progress and emotional state of the new employees in real time and provide appropriate guidance, thereby enabling stable human resource development.

[1508] The processing flow will be explained below.

[1509] Program processing steps

[1510] Step 1:

[1511] User (Educator):

[1512] A dedicated form for entering educational policy and scope opens on the terminal.

[1513] Example: Enter "Understanding basic program structure" as the educational policy and "3 months of initial education" as the scope.

[1514] Device:

[1515] The entered data is sent to the server.

[1516] Step 2:

[1517] server:

[1518] Save the received educational policy and scope.

[1519] Access your company's knowledge base and gather relevant texts, videos, quizzes, technical documentation, competitive intelligence, and more.

[1520] Preprocess the collected data and prepare it for passing to the generative AI.

[1521] Step 3:

[1522] server:

[1523] Provide preprocessed information to the generation AI.

[1524] Generation AI:

[1525] Based on the information provided, an optimized educational curriculum is generated according to the input educational policy and scope.

[1526] Example: Design a curriculum like the following:

[1527] Basic programming text

[1528] Beginner's exercises

[1529] Code review explanation and examples

[1530] Test to check your understanding

[1531] Step 4:

[1532] server:

[1533] The generated educational curriculum is distributed to the user's (new employee's) terminal.

[1534] Device:

[1535] Display the delivered curriculum and make it accessible to users (newcomers).

[1536] For example, click a link to view text, video, or download a file.

[1537] Step 5:

[1538] User (newcomer):

[1539] Study the provided materials, read the programming textbook, and solve the exercises.

[1540] Device:

[1541] Record your learning progress (e.g., browsing history, study time) and comprehension test results.

[1542] The recorded data is sent to the server.

[1543] Step 6:

[1544] server:

[1545] Analyze submitted learning progress data and assessment results.

[1546] Track and assess new hires' progress and understanding in real time.

[1547] Step 7:

[1548] User (newcomer):

[1549] During learning, the device's camera and microphone are used to record your facial expressions and voice, allowing your emotional state to be recognized.

[1550] Emotion Engine:

[1551] Identify the user's emotional state (e.g., understanding, excitement, fatigue) based on collected facial and voice data.

[1552] The emotion data is sent to the server.

[1553] Step 8:

[1554] server:

[1555] The emotional data recognized by the emotion engine is integrated with learning progress data and analyzed.

[1556] Create tracking data that takes into account the emotional state of new employees and optimize the content of feedback.

[1557] Step 9:

[1558] server:

[1559] The integrated data will be used to generate leaderboards and detailed progress reports, which will be provided to educators.

[1560] Educators (users):

[1561] Review the generated feedback to understand the new employee's progress, understanding, and emotional state.

[1562] Plan for additional tutoring and supplemental materials.

[1563] Through these steps, the system of the present invention realizes efficient training for new employees. Based on the training policy and scope, it automatically generates a curriculum optimized for each job, and monitors and provides feedback on learning progress and emotional state in real time, enabling stable human resource development.

[1564] Example 2

[1565] 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."

[1566] In modern companies, training new employees requires a great deal of time and effort, creating a demand for efficient and systematic training programs. However, it is difficult to closely track each new employee's learning progress and level of understanding and optimize training plans based on that information. It is also difficult to grasp the new employee's emotional state during training and provide appropriate guidance accordingly. Traditional training systems are unable to effectively address these challenges.

[1567] 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.

[1568] In this invention, the server includes input means for inputting a training policy and scope, collection means for collecting related information from a knowledge base, generation means for generating an optimized training curriculum based on the collected information, distribution means for distributing the generated curriculum to a user terminal, tracking means for tracking the user's learning progress and level of understanding, an emotion engine for recognizing the user's emotional state, and feedback means for providing feedback to the training personnel based on the data collected by the tracking means and the emotion engine. This makes it possible to track the learning progress and level of understanding of newcomers in real time and to grasp their emotional state, thereby realizing individually optimized training plans and instruction.

[1569] An "educational policy" indicates the goals and direction of education.

[1570] "Scope" refers to the range and duration of the educational program.

[1571] "Input means" refers to an interface that allows a user to input educational principles and scope into the system.

[1572] "Collection means" refers to the function of extracting and collecting the necessary information from the knowledge base.

[1573] A "knowledge base" refers to a database that stores various materials and information related to education and work.

[1574] "Generation means" refers to the function of creating an optimized educational curriculum based on collected information.

[1575] "Distribution means" refers to the function for transmitting the generated educational curriculum to a user terminal.

[1576] "User terminal" refers to the device used by a user to receive the educational curriculum.

[1577] "Tracking means" refers to the functionality that records and monitors a user's learning progress and understanding.

[1578] "Emotion engine" refers to the function that analyzes the user's facial expressions and voice and identifies their emotional state.

[1579] "Feedback Measures" refers to the functionality that provides data collected by the Tracking Measures and Emotion Engine to the Educator.

[1580] The system of the present invention automatically generates an OJT (On-the-Job Training) curriculum optimized for each job, and systematically and efficiently educates new employees. This system has the following main functions:

[1581] First, the trainer (user) uses a dedicated form on the terminal to input the training policy and scope. For example, they input specific details such as "understanding basic program structure" and "three months of initial training." The terminal then sends the input information to the server, which then stores it in a database.

[1582] Next, the server collects relevant information from the company's knowledge base based on the stored training policy and scope. The knowledge base includes text, videos, confirmation tests, technical materials, and competitive information. The server collects this information and performs preprocessing to pass it to the generative AI model. Preprocessing includes text cleaning and video summarization.

[1583] Once the collection and preprocessing are complete, the server passes the information to a generative AI model to generate an optimal educational curriculum. The generative AI model effectively structures the learning content based on the educational policy and scope. For example, it designs a curriculum that includes programming textbooks, training code review examples, and test questions to confirm understanding. The generated curriculum is then returned to the server.

[1584] The server then distributes the generated curriculum to the user's (newcomer's) device, where the device displays the curriculum and the user can begin learning using the provided links and files.

[1585] As the learning progresses, the user (newcomer) studies the learning materials through the device and takes comprehension tests. The device records the learning progress and the results of the comprehension tests and sends them to the server. The server uses this data to track the user's learning progress and comprehension in real time.

[1586] Furthermore, an emotion engine is used to recognize the user's (newcomer's) emotional state during learning. The user uses the device's camera and microphone to collect their facial expressions and voice. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., level of understanding, excitement, fatigue, etc.). This data is also sent to the server.

[1587] The server analyzes the collected learning progress and emotion data and provides feedback to the educator. The feedback is displayed as a leaderboard and detailed progress reports. Based on this, the educator can revise the learning plan and provide individualized instruction or additional materials as needed. For example, if a newcomer is struggling in a particular area, they can provide additional materials or explanations.

[1588] Specific examples

[1589] For example, when training a newly hired engineer, the trainer first inputs a training policy, such as "understanding basic program structure," into the system. The server collects text and videos related to the basic structure of programming from the knowledge base, and the generative AI uses these to create a curriculum. The curriculum is distributed to the new employee's device, and the new employee begins learning. The device sends the results of the learning progress and comprehension test to the server, and an emotion engine analyzes the user's emotional data and provides information about their emotional state to the server. The server uses this data to provide feedback on the new employee's progress to the trainer, who can then use the report to create an individualized teaching plan.

[1590] Prompt Sentence Examples

[1591] "Generate a three-month training curriculum aimed at helping new engineers understand basic programming structure."

[1592] "Collect relevant information from your knowledge base and create an optimized training curriculum for new hires."

[1593] "Explain how you would provide feedback to educators based on progress data and emotional state."

[1594] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1595] Step 1:

[1596] Collecting User Input

[1597] Specific behavior:

[1598] 1.1: The user (educator) opens a dedicated form on the device.

[1599] 1.2: The user fills in the form with the training objectives (e.g., "Understanding basic program structure") and scope (e.g., "3 months of initial training").

[1600] 1.3: When the user clicks the submit button, the entered data is sent from the device to the server.

[1601] Input: Educational Policy and Scope

[1602] Output: Educational policy and scope saved on the server

[1603] Step 2:

[1604] Preparing and gathering a knowledge base

[1605] Specific behavior:

[1606] 2.1: The server reads the stored educational policy and scope.

[1607] 2.2: The server accesses the company's knowledge base and searches for relevant materials (e.g., texts, videos, quizzes, etc.).

[1608] 2.3: The server preprocesses the found material, cleaning the text and summarizing the video.

[1609] Input: Saved educational policy and scope

[1610] Output: Preprocessed material

[1611] Step 3:

[1612] Generative AI-based curriculum generation

[1613] Specific behavior:

[1614] 3.1: The server passes the preprocessed material to the generation AI.

[1615] 3.2: The generative AI generates the optimal curriculum based on the educational policy and scope.

[1616] 3.3: The generated curriculum is sent to the server.

[1617] Input: Preprocessed material

[1618] Output: Generated curriculum

[1619] Step 4:

[1620] Curriculum Delivery

[1621] Specific behavior:

[1622] 4.1: The server distributes the generated curriculum to the user's (newcomer's) terminal.

[1623] 4.2: The device displays the delivered curriculum and provides links and files for the user (newcomer) to begin learning.

[1624] Input: Generated curriculum

[1625] Output: Curriculum displayed on the user's device

[1626] Step 5:

[1627] Tracking learning progress and understanding

[1628] Specific behavior:

[1629] 5.1: The user (newcomer) uses the device to study the learning material.

[1630] 5.2: The device will record learning progress (e.g., browsing history, learning time, etc.).

[1631] 5.3: When a user takes the assessment, the results are recorded on the device.

[1632] 5.4: The device sends progress data and test results to the server.

[1633] Input: Learning progress, comprehension test results

[1634] Output: Progress data and test results sent to the server.

[1635] Step 6:

[1636] Emotion recognition by emotion engine

[1637] Specific behavior:

[1638] 6.1: During training, the user (newcomer)'s facial expressions and voice are collected using the device's camera and microphone.

[1639] 6.2: The device sends these data to the emotion engine.

[1640] 6.3: The emotion engine analyzes these data and recognizes the user's emotional state (e.g., excitement, fatigue, understanding).

[1641] 6.4: The recognized emotion data is sent from the device to the server.

[1642] Input: User's facial expressions and voice

[1643] Output: Recognized emotion data

[1644] Step 7:

[1645] Providing feedback

[1646] Specific behavior:

[1647] 7.1: The server performs analysis based on the collected learning progress data and emotion data.

[1648] 7.2: The server provides the analysis results to the educator in the form of leaderboards and detailed progress reports.

[1649] 7.3: Educators (users) will use the feedback to revise their educational plans and provide individualized instruction or additional materials as needed.

[1650] Input: Learning progress data and emotion data

[1651] Output: Leaderboard and detailed progress report

[1652] (Application example 2)

[1653] 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."

[1654] In conventional education systems, it was difficult to provide efficient and systematic training for new employees. Furthermore, there was a lack of means to grasp learning progress and comprehension in real time and provide feedback that took into account the employee's emotional state. This limited the effectiveness of new employee training and made it difficult to determine the appropriate timing for individual instruction.

[1655] 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 an input means for inputting the training policy and scope, a collection means for collecting related information from a knowledge base, and a generation means for generating an optimized training plan based on the collected information. This makes it possible to collect optimal teaching materials from the knowledge base and automatically generate and distribute a training plan that takes into account the progress and emotional state of the new employee. In addition, by using an emotion recognition means, it is possible to grasp the emotional state of the user during learning in real time and adjust the training plan based on that. This achieves improved efficiency and quality of new employee training.

[1656] "Educational strategy" refers to the overall plan, goals, and approach to imparting specific knowledge and skills to an educational audience.

[1657] "Scope" refers to the specific definition of the content, scope, and period covered by the educational curriculum.

[1658] "Input means" refers to a device or interface for inputting educational principles and scope into the system.

[1659] "Information Base" means a database of educationally relevant texts, videos, tests, technical materials, and other materials.

[1660] "Collection means" refers to a device or program for searching and collecting required information from an information base.

[1661] "Generation means" refers to a device or program for automatically generating an optimized educational plan based on collected information.

[1662] "User Device" means a device for individual learner access, including smartphones, tablets, smart glasses, and head-mounted displays.

[1663] The term "distribution means" refers to a device or program for transmitting the generated educational plan to a user device and displaying it.

[1664] "Tracking means" refers to a device or program that records and tracks a user's learning progress and understanding in real time.

[1665] "Emotion recognition means" refers to a device or program that analyzes a user's facial expressions and voice and identifies their emotional state.

[1666] "Feedback means" refers to a device or program that provides appropriate instructional improvement information to educators based on data obtained from the tracking means and emotion recognition means.

[1667] This invention provides a system for efficiently and systematically training factory robot operators and maintenance staff. The system includes an input means for inputting a training policy and scope, a collection means for collecting related information from a knowledge base, a generation means for generating an optimized training plan, a distribution means for distributing the generated training plan, a tracking means for tracking a user's learning progress and level of understanding, an emotion recognition means, and a feedback means for providing feedback to a trainer.

[1668] Input Method

[1669] On the server side, the person in charge of training (for example, a factory training manager) uses a smartphone or tablet to input the training policy and scope. A dedicated input form is provided, allowing training goals to be set, such as "understanding basic robot operation" or "mastering safety protocols." This clarifies the scope of the training.

[1670] Collection Method

[1671] Based on the input educational policy and scope, the server collects relevant information from the company's internal knowledge base, which includes texts, videos, confirmation tests, technical materials, etc. It then preprocesses the collected information before passing it to a generative AI model (e.g., OpenAI's GPT-4).

[1672] generation means

[1673] Based on the preprocessed information, the server uses a generative AI model to generate an optimized training plan. Based on the input training policy and scope, this generative AI model uses knowledge-based information to create an effective learning curriculum. For example, the learning sequence may include basic textbooks on robot operation, videos on safety protocols, and test questions to confirm understanding.

[1674] Delivery Method

[1675] The generated training plan is then distributed from the server to the user's device, which can be a smart eyeglass or a head-mounted display. This allows the student to access the training plan in the actual workplace and begin learning immediately.

[1676] Tracking Methods

[1677] The tracking means records the user's learning progress and level of understanding in real time. For example, which learning materials were viewed and how much, and the results of comprehension tests. This data is sent to the server, allowing the user's progress and level of understanding to be grasped in real time.

[1678] emotion recognition means

[1679] The emotion recognition means uses the camera and microphone installed in the smart glasses or head-mounted display to analyze the user's facial expressions and voice to recognize their emotional state. This data is also sent to the server and integrated with the tracking data for analysis.

[1680] Feedback Methods

[1681] The server provides feedback to the trainer (e.g., factory manager) based on the collected tracking and emotion data. The feedback includes specific progress reports and areas for improvement displayed in a leaderboard format. This allows the trainer to review the training plan and prepare additional individualized instruction or supplementary materials as needed.

[1682] Specific examples

[1683] For example, if a newly hired robot operator is to undergo training under the educational policy of "basic understanding of robot operation," the trainer inputs the policy and scope using a smart device. The server collects relevant information from the knowledge base and creates an optimized training plan using generative AI. This plan is distributed to the user's device, and the trainee begins learning immediately. Learning progress and level of understanding are monitored in real time, and emotional states during learning are also grasped using emotion recognition means. Trainers can obtain feedback from this data and revise the training plan as appropriate.

[1684] Prompt Sentence Examples

[1685] Automatically generate an educational curriculum. Consider the following information:

[1686] Educational policy: Basic understanding of robot operation

[1687] Duration: 3 months of initial training

[1688] For reference, please refer to the following information:

[1689] Basic textbook for robot operation

[1690] Safety Protocol Video

[1691] Sample comprehension check test

[1692] The generated curriculum should include a learning sequence and detailed explanations for each step.

[1693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1694] Step 1:

[1695] The instructor uses a smart device to input the training policy and scope. The input information includes training goals such as "basic understanding of robot operation" and "mastery of safety protocols." This clarifies the training scope and saves it on the server.

[1696] Step 2:

[1697] The server collects relevant information from the information base based on the input educational policy and scope, including texts, videos, confirmation tests, technical documents, etc. The server preprocesses the collected data before passing it to the generative AI model.

[1698] Step 3:

[1699] The server passes the preprocessed information to a generative AI model, which then automatically generates an educational plan. Based on the input educational policy and scope, the generative AI model creates an optimized learning curriculum using relevant information. Specifically, a curriculum is generated that includes the learning order and details of each step.

[1700] Step 4:

[1701] The generated training plan is sent from the server to the user's device (smart glasses or head-mounted display), allowing the student to access the training plan and continue learning at the actual workplace.

[1702] Step 5:

[1703] The user's device records learning progress and level of understanding in real time and sends it to the server. This includes which learning materials were viewed to what extent, test results, etc. The server uses this data to grasp the user's progress and level of understanding.

[1704] Step 6:

[1705] The user's facial expressions and voice are analyzed using a camera and microphone installed on the user device to determine the user's emotional state. Emotional data such as the user's level of fatigue and excitement is collected in real time by the emotion recognition means and transmitted to the server.

[1706] Step 7:

[1707] The server integrates and analyzes the tracking and emotional data and provides feedback to educators in the form of progress reports and leaderboards, allowing educators to revise their teaching plans and prepare additional individualized instruction or supplementary materials as needed.

[1708] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1709] 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.

[1710] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1711] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1712] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1713] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1714] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1715] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1716] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1717] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1718] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1719] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1720] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1721] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1722] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1723] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1724] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1725] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1726] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1727] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1728] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1729] The following is further disclosed regarding the above embodiment.

[1730] (Claim 1)

[1731] an input means for inputting an educational policy and scope;

[1732] a collection means for collecting relevant information from a knowledge base based on the educational policy and scope;

[1733] a generating means for generating an optimized educational curriculum based on the collected information;

[1734] a distribution means for distributing the generated curriculum to a user terminal;

[1735] tracking means for tracking a user's learning progress and comprehension;

[1736] a feedback means for providing feedback to an educator based on the data collected by the tracking means;

[1737] A system including:

[1738] (Claim 2)

[1739] 10. The system of claim 1, wherein the information collected from the knowledge base includes text, videos, validation tests, technical documents, and competitive information.

[1740] (Claim 3)

[1741] 2. The system according to claim 1, wherein a training person can review a training plan for a new employee based on the feedback.

[1742] "Example 1"

[1743] (Claim 1)

[1744] an input means for inputting an educational policy and scope;

[1745] a search means for collecting relevant information from a knowledge base based on the educational policy and scope;

[1746] a generating means for generating an optimized educational curriculum based on the collected information;

[1747] a distribution means for distributing the generated curriculum to a terminal;

[1748] monitoring means for tracking a user's learning progress and comprehension;

[1749] a feedback means for providing feedback to an educator based on the data collected by the monitoring means;

[1750] A system including:

[1751] (Claim 2)

[1752] 2. The system of claim 1, wherein the information collected from the knowledge base includes text data, video content, verification tests, technical documents, and external information.

[1753] (Claim 3)

[1754] 2. The system according to claim 1, wherein a training person can review a training plan for a new employee based on the feedback.

[1755] "Application Example 1"

[1756] (Claim 1)

[1757] an input means for inputting an educational policy and scope;

[1758] a collection means for collecting relevant information from a knowledge base based on the educational policy and scope;

[1759] a generating means for generating an optimized educational curriculum based on the collected information;

[1760] a distribution means for distributing the generated curriculum to a user terminal;

[1761] tracking means for tracking a user's learning progress and comprehension;

[1762] a feedback means for providing feedback to an educator based on the data collected by the tracking means;

[1763] a display means for displaying the educational content via an augmented reality enabled display;

[1764] A system including:

[1765] (Claim 2)

[1766] 10. The system of claim 1, wherein the information collected from the knowledge base includes text, videos, validation tests, technical documents, and competitive information.

[1767] (Claim 3)

[1768] 2. The system according to claim 1, wherein a training person can review a training plan for a new employee based on the feedback.

[1769] "Example 2: Combining Emotion Engines"

[1770] (Claim 1)

[1771] an input means for inputting an educational policy and scope;

[1772] a collection means for collecting relevant information from a knowledge base based on the educational policy and scope;

[1773] a generating means for generating an optimized educational curriculum based on the collected information;

[1774] a distribution means for distributing the generated curriculum to a user terminal;

[1775] tracking means for tracking a user's learning progress and comprehension;

[1776] an emotion engine for recognizing the emotional state of a user;

[1777] feedback means for providing feedback to an educator based on the data collected by the tracking means and the emotion engine;

[1778] A system including:

[1779] (Claim 2)

[1780] 10. The system of claim 1, wherein the information collected from the knowledge base includes text, videos, validation tests, technical documents, and competitive information.

[1781] (Claim 3)

[1782] 2. The system according to claim 1, wherein a training person can review a training plan for a new employee based on the feedback.

[1783] "Application example 2 when combining emotion engines"

[1784] Claims (rewritten)

[1785] (Claim 1)

[1786] an input means for inputting an educational policy and scope;

[1787] a collection means for collecting relevant information from an information base based on the educational policy and scope;

[1788] a generating means for generating an optimized training plan based on the collected information;

[1789] a distribution means for distributing the generated educational plan to a user device;

[1790] a tracking means for tracking a user's learning progress and comprehension;

[1791] emotion recognition means for recognizing an emotional state of a user;

[1792] a feedback means for providing feedback to an educator based on the data collected by the tracking means and the emotion recognition means;

[1793] A system including:

[1794] (Claim 2)

[1795] 10. The system of claim 1, wherein the information collected from the information base includes text, videos, tests, materials, and related information.

[1796] (Claim 3)

[1797] 10. The system of claim 1, wherein the training staff can review the training plan for the new employee based on the feedback, and further, the training plan can be adjusted based on the user's emotional state. [Explanation of symbols]

[1798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting an educational policy and scope; a collection means for collecting relevant information from a knowledge base based on the educational policy and scope; a generating means for generating an optimized educational curriculum based on the collected information; a distribution means for distributing the generated curriculum to a user terminal; tracking means for tracking a user's learning progress and comprehension; a feedback means for providing feedback to an educator based on the data collected by the tracking means; A system including:

2. 10. The system of claim 1, wherein the information collected from the knowledge base includes text, videos, validation tests, technical documents, and competitive information.

3. 2. The system according to claim 1, wherein a training person can review a training plan for a new employee based on the feedback.

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