system

Abstract

Provide a system. 【Solution means】 Means for acquiring information of the worker, Means for generating a learning plan based on the information of the worker, Means for providing the generated learning plan to the worker, Means for tracking and recording the learning progress of the worker, Means for adjusting the learning plan according to the learning progress, Means for providing practical training to the worker in a virtual reality environment, Means for visualizing the progress of the training, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional in-house training for skill improvement depends on collective training and individual self-study, so it cannot provide learning optimized for individual employees. As a result, companies feel it difficult to conduct efficient training. Also, employees themselves have difficulty grasping the skills they need, and there is a problem that it is difficult to achieve effective learning within limited time.

Means for Solving the Problems

[0005] This invention provides a system that acquires employee information and automatically generates individualized learning plans based on that information, thereby enabling learning optimized for each employee. Furthermore, it solves this problem by providing means to track and visualize employee learning progress in real time and adjust the learning plan as needed, thereby creating an efficient learning environment.

[0006] "Employee" refers to an individual worker who is employed by a company or organization and performs their duties.

[0007] "Means of acquiring information" refers to the system's function of collecting data such as employees' skills, work history, interests, and career goals.

[0008] "Means for generating learning plans" refers to a function that automatically constructs learning content and steps optimized for employees based on acquired information.

[0009] "Means of providing learning plans" refers to a function that notifies employees of the generated learning plans and allows them to access them.

[0010] "Means for tracking and recording learning progress" refers to a function that continuously tracks completed learning items and their achievement status as employees engage in learning activities, and accumulates this data.

[0011] "Means for adjusting learning plans" refers to a function that re-evaluates employees' learning plans in response to learning progress data and industry changes, and modifies them as needed.

[0012] "Means of visualizing learning progress" refers to functions that visually display employees' learning outcomes and progress using graphs, charts, and other methods.

[0013] "Means of selecting learning resources based on industry trends" refers to a function that selects and provides employees with learning content that reflects the skills and trends currently required in the industry. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system for supporting employee skill development, providing an optimized learning plan for each employee and creating an efficient learning environment. The system consists of three main elements: a server, terminals, and users.

[0036] The server connects to the company's database and retrieves detailed information about each employee, including their work history, skills, interests, and career goals. Based on this data, it analyzes the gap between the employee's current skill set and the skills the company requires. Based on this analysis, the server automatically generates a customized learning plan for each employee. The learning plan incorporates relevant learning resources such as videos, quizzes, and assignments, and is designed to reflect industry trends.

[0037] The device receives custom learning plans sent from the server and notifies the user. When the user begins learning activities on the device, the device tracks progress in real time and sends learning results and quiz results to the server. This allows learning progress to be monitored continuously, and alerts and reminders are sent to the user as needed.

[0038] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress and achievements on a dashboard. The learning plan is reviewed regularly and optimized according to business needs and the user's learning progress.

[0039] For example, if an employee wants to improve their "project management" skills, the server will create a learning plan based on the employee's job responsibilities and interests, including video tutorials and practical quizzes related to project management. By completing these tasks, the user can acquire new skills in a planned and efficient manner.

[0040] This allows for a more flexible and individually optimized learning experience than traditional training methods, supporting employee skill development and enhancing the company's competitiveness.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server connects to the company's employee database and retrieves information such as employees' work history, skills, interests, and career goals.

[0044] Step 2:

[0045] The server analyzes the acquired data to identify the gap between the current skill set and the skills required by the company. The results of the analysis are used in the next step.

[0046] Step 3:

[0047] The server automatically generates customized learning plans based on gap analysis for each employee. These learning plans include relevant videos, quizzes, and assignments.

[0048] Step 4:

[0049] The server sends the generated learning plan to each employee's terminal and notifies them of the plan details.

[0050] Step 5:

[0051] Users can review the learning plan received on their device and begin their learning activities. Learning can proceed at the user's own pace.

[0052] Step 6:

[0053] The device tracks the user's learning activities and sends progress data, such as the completion of each assignment and quiz results, to the server in real time.

[0054] Step 7:

[0055] The server updates the learning dashboard based on the received progress data, providing visual feedback to users and company administrators.

[0056] Step 8:

[0057] The server re-evaluates and adjusts the learning plan as needed, based on the user's learning progress and new industry trends. The revised plan is then notified to the user again.

[0058] By performing the above steps recursively, we can continuously provide an optimized learning environment for each individual employee.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] To efficiently improve employee skills, it is necessary to provide each employee with a learning plan optimized for their individual needs. However, traditional, uniform training methods have failed to adequately address individual needs, resulting in insufficient learning efficiency and effectiveness. Furthermore, there is a lack of appropriate feedback and adjustments regarding learning progress, making it difficult to optimize learning to suit each employee's pace.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring employee attributes, means for analyzing the acquired employee attributes and identifying skill gaps, and means for customizing learning content using a generation AI model based on the analysis results. This makes it possible to provide each employee with a customized learning plan and support efficient and effective skill acquisition.

[0064] "Employee attributes" refer to information that describes an employee's individual characteristics, such as their work history, skills, interests, and career goals.

[0065] A "skill gap" refers to the shortcomings or discrepancies between an employee's current skills and the skill set required by a company.

[0066] A "generative AI model" is a computer model that uses artificial intelligence technology to generate learning content and plans from data.

[0067] "Customizing learning content" refers to adjusting and optimizing learning plans and materials based on the individual attributes of each employee.

[0068] "Progress information" refers to data that shows the results and progress achieved by employees through their learning activities.

[0069] "Trend information" refers to data that reflects the latest changes and trends in an industry or market.

[0070] "Visualization" refers to representing information in an easily understandable form using visual means such as graphs and charts.

[0071] This invention is a system designed to support employee skill development, providing an efficient learning environment by offering an optimized learning plan for each individual employee. The system consists of three main elements: a server, terminals, and users.

[0072] The server has the capability to connect to the company's database and retrieve attribute information for each employee. This information includes the employee's work history, skills, interests, and career goals. The server uses data analysis software to analyze the retrieved information and identify gaps between current skills and required skills. Based on the analysis results, it automatically generates individually customized learning plans using a generative AI model. These plans include relevant videos, quizzes, and assignments.

[0073] The device receives custom learning plans sent from the server and notifies the user. Using a dedicated learning application, it displays the learning plan and tracks the user's progress in real time as they begin their learning activities. The device can also feed back the results to the server as the learning progresses.

[0074] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress on a dashboard on the terminal. In addition, the learning content and plan are dynamically reviewed and optimized by the server according to the user's progress and industry trends. For example, for an employee who wants to improve their project management skills by working backward, the server will create a learning plan that includes appropriate content based on the employee's current situation.

[0075] Examples of prompt messages are as follows:

[0076] "Create a learning plan to improve project management skills. Consider the user's current skill level, interests, and career goals, and include appropriate content."

[0077] This system allows for the provision of a learning experience optimized for each employee, compared to traditional, uniform training methods, and can support the improvement of a company's competitiveness.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server connects to the company's database and retrieves employee attribute information. Input includes employee IDs, work history, skills, interests, and career goals stored in the database. The server formats this information for analysis and generates an organized attribute dataset as output. This process involves data extraction using SQL queries.

[0081] Step 2:

[0082] The server analyzes the acquired attribute dataset to identify the gap between employees' current skills and the required skills. Inputs include employees' skill sets and the company's defined target skill sets. The server compares this information using data analysis algorithms and generates a skills gap report as output. This step primarily involves statistical analysis and variance analysis.

[0083] Step 3:

[0084] The server generates a customized learning plan using a generative AI model based on the skills gap report. Inputs include skills gap information and employee learning styles and interests. The server provides this information to the generative AI model as prompts, generating a custom learning plan incorporating appropriate learning content as output. This process involves content selection and optimization using AI technology.

[0085] Step 4:

[0086] The device receives a custom learning plan sent from the server and notifies the user. The input is learning plan data from the server. The device uses a dedicated learning application to visually present the learning plan to the user as output, prompting them to begin learning. This includes push notifications and displays in the user interface.

[0087] Step 5:

[0088] Users use their devices to engage in learning activities according to their learning plan. The input is the content of the learning plan displayed on the device. Users refer to this content while watching videos, taking quizzes, and answering assignments, and learning progress data is generated as output. The user's learning activities are recorded, and the learning results are sent from the device to the server.

[0089] Step 6:

[0090] The server analyzes learning progress data sent from the terminal and generates feedback. Input includes user progress data and learning outcomes. The server analyzes this data, adjusts the learning plan as needed, and generates a new, optimized learning plan as output. This step involves generating progress-based feedback and dynamically adjusting the plan.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] While there is a need for efficient skill development and enhanced expertise among workers in modern industrial settings, traditional education systems struggle to flexibly provide training tailored to individual needs. Furthermore, it is difficult to track workers' progress and provide optimal learning resources in real time. In particular, there is a lack of efficient means to integrate practical training programs utilizing virtual reality technology.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for acquiring worker information, means for providing practical training in a virtual reality environment, and means for visualizing the progress of the training. This enables workers to receive practical training tailored to their individual needs and to efficiently improve their skills.

[0096] The term "worker" refers to an individual engaged in a specific job or task, and is the target of improvement in their skills and knowledge.

[0097] "Information" refers to data related to the worker, such as their work history, skills, and current progress.

[0098] A "learning plan" refers to a collection of individually customized training programs and resources designed to improve the skills of workers.

[0099] "Providing" refers to the act of handing over the generated training plan to the workers and encouraging them to carry out the training.

[0100] "Tracking" is the process of recording training progress in real time and monitoring the progress of workers.

[0101] "Adjustment" is a step in updating and optimizing the learning plan according to the worker's progress.

[0102] A "virtual reality environment" refers to a computer-generated three-dimensional space where workers can realistically experience practical training.

[0103] "Visualization" is a technique for displaying data graphically so that workers can easily understand their own progress.

[0104] "Industry trends" refer to changes and advancements in technology and needs within an industry, and indicate the necessary skills and knowledge based on those changes.

[0105] The system that realizes this invention includes three main components: a server, a terminal, and a user. First, the server retrieves worker information from the company's database and analyzes skill gaps using an AI model. The analysis is performed using Python and TENSORFLOW® to generate individualized learning plans. These plans include training resources necessary for worker skill improvement and practical VR content using a virtual reality development environment such as Unity.

[0106] Next, the terminal uses smart glasses or a tablet to provide the worker with a generated learning plan in real time. As the worker views the VR content and progresses through the training, the terminal sends its progress to the server, which then adjusts the learning plan based on this data. The progress data is displayed on a graphical dashboard, allowing the worker to easily understand their own learning progress.

[0107] Through this system, users (workers) can train at their own pace. This enables efficient skill acquisition through immediate feedback and flexible learning programs.

[0108] For example, when conducting training on operating new machinery in a factory, the server distributes the relevant VR content and provides it to the workers via their terminals. The workers experience the actual operating procedures in a virtual space, and the server analyzes the results to determine whether further training is necessary.

[0109] An example of a prompt to input into a generative AI model is, "Please suggest VR training materials to help factory workers efficiently learn a new manufacturing process." This prompt is used to get the AI ​​to generate specific learning resources.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server retrieves worker information from the company's database. It receives data such as worker work history, skills, interests, and career goals as input, and supplies this information to an analysis program as output. Data retrieval is performed through database queries.

[0113] Step 2:

[0114] The server uses an AI model to analyze the skill gap based on the acquired worker information. The input for this step is the worker's current skill set, and the output is an analysis showing the difference between that skill set and the required skills. This analysis is performed using Python and TensorFlow.

[0115] Step 3:

[0116] The server generates a customized learning plan for each worker based on the analysis results. The input is the analysis results of the skill gap, and the output is a learning plan that includes relevant training resources and VR content. The generated plan is temporarily stored on the server.

[0117] Step 4:

[0118] The server sends the generated training plan to the terminal. The input is the generated training plan, which is then ready to be displayed on the terminal as output. The transmission takes place via network communication.

[0119] Step 5:

[0120] The terminal notifies the user of the received training plan and displays the training content. The input is the training plan sent from the server, and the output is visualized training information. Notification and visualization are performed via the terminal's display.

[0121] Step 6:

[0122] The user (worker) runs VR content through a terminal and begins training. The input is the displayed training content, and the output is data on the training progress. The actions performed during execution depend on the user's input.

[0123] Step 7:

[0124] The device tracks training progress in real time and sends progress data to the server. The input is the user's training progress information, and the output is the transmitted data. Tracking is performed via built-in sensors.

[0125] Step 8:

[0126] The server further adjusts the learning plan based on the received progress data. The input is real-time progress data, and the output is the updated learning plan. The adjustments are optimized by an algorithm.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention is a system designed to support employee skill development, enabling the analysis of emotions during the employee's learning process and the customization of learning plans accordingly. The system consists of four main components: a server, a terminal, a user, and an emotion engine.

[0129] The server connects with the company's database to retrieve employee personal information, current skills, work history, and career goals. Based on this data, it analyzes each employee's skill gaps and automatically generates custom learning plans. The learning content includes relevant videos, quizzes, and assignments, and is tailored based on industry trends and company needs.

[0130] The emotion engine uses the user's facial expressions and voice data to recognize their emotional state during learning in real time. The emotion data recognized by this engine is sent to the server and used to adjust the learning plan. For example, if the user is feeling stressed, the server will incorporate more relaxing content and tones into the learning plan.

[0131] The device serves as the learning environment for the user, receiving learning plans and sentiment-based adjustments from the server. On the device, the user completes assignments and records their progress. Progress and sentiment data are also sent to the server to ensure the learning experience is constantly optimized.

[0132] Users can improve their skills by following a learning plan on their device. The learning content provided on the device enables effective and efficient learning. Furthermore, the emotional engine provides a learning environment that takes the user's emotions into account, reducing stress and allowing for more focused learning.

[0133] As a concrete example, suppose an employee aims to improve their "presentation skills." In this case, the learning plan would include relevant videos and practical exercises. If the emotion engine detects the user's tension during learning, the server would recommend pre-prepared relaxation content to help the user relax and refocus on learning.

[0134] Thus, the present invention dynamically adjusts the learning plan according to the user's emotions, providing an optimized learning experience for each individual employee, and consequently promoting employee skill development and improved corporate competitiveness.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server connects to the company's database and retrieves information such as each employee's work history, skills, interests, and career goals.

[0138] Step 2:

[0139] The server analyzes skill gaps based on the acquired information and automatically generates individualized learning plans. These learning plans include videos, quizzes, and assignments.

[0140] Step 3:

[0141] The generated learning plan is sent from the server to the terminal, and the employee is notified that learning has begun.

[0142] Step 4:

[0143] The user progresses through the learning process according to the learning plan provided using the device. During the learning process, the user's facial expressions and voice data are collected by the device's sensors.

[0144] Step 5:

[0145] The device sends the collected sensor data to an emotion engine to analyze the user's emotional state.

[0146] Step 6:

[0147] Emotional data obtained from the emotion engine is sent to the server. The server adjusts the learning plan accordingly, reflecting the user's emotional state. For example, if a user is experiencing high stress, the server recommends relaxation content.

[0148] Step 7:

[0149] The server reflects learning progress and emotion-based adjustments on a dashboard, providing visual feedback to users and administrators.

[0150] Step 8:

[0151] Users can check their learning progress and receive feedback on their device, and adjust their learning pace and methods as needed.

[0152] Through this series of processes, the user's emotions are incorporated into the learning experience, and a learning environment optimized for each individual is provided.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In today's work environment, improving employee skills requires providing flexible training plans tailored to individual skills and career goals. However, traditional systems struggle to adapt dynamic training plans to learners' emotional states and learning progress, making it difficult to provide the optimal learning method for each employee. Furthermore, selecting learning materials that appropriately reflect industry trends and employee sentiments remains challenging.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for acquiring employee attribute information, means for automatically generating and dynamically adapting educational plans using a generative AI model, and means for acquiring and optimizing learners' emotional states using an emotion analysis device. This makes it possible to provide a flexible and efficient learning environment tailored to each individual employee.

[0158] "Employee attribute information" refers to information including employees' personal information, skills, work history, and career goals.

[0159] An "educational plan" refers to learning content and assignments designed to improve employees' skills, and includes materials such as videos, quizzes, and assignments.

[0160] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and automatically performs specific tasks or generates data.

[0161] An "emotion analysis device" refers to technology that analyzes data such as a user's facial expressions and voice to recognize their emotional state.

[0162] "Dynamic adaptation" refers to a process of adjusting and changing content in real time based on the learner's situation and feedback.

[0163] "Learner's emotional state" refers to an individual's emotional condition during learning (e.g., relaxed, stressed, excited, etc.).

[0164] "Optimization" refers to adjusting or improving conditions or states to be the most appropriate for a particular purpose.

[0165] The following system configurations are possible for carrying out this invention.

[0166] The server first accesses the company's database to retrieve employee attribute information. This information includes employee personal details, skills, work history, and career goals, and is collected using database management systems such as SQL queries. Next, it utilizes a generative AI model to automatically generate individual employee training plans. These training plans include relevant videos, quizzes, and assignments, which are tailored to the company's needs and industry trends.

[0167] The emotion analysis device is connected to the terminal and recognizes the user's emotional state in real time through facial expressions, voice, etc. This analysis device analyzes video and audio data displayed to the user during learning and identifies emotional states such as stress and excitement. The recognition results are sent to a server and used to optimize the educational plan.

[0168] The terminal is a device for users to engage in learning activities and has the function of presenting educational plans sent from the server. On the terminal, the user's progress as they work on assignments is recorded, and this progress information and emotional information are sent to the server to provide a more effective and personalized learning experience.

[0169] Through this system, users can efficiently progress through their learning based on a personalized educational plan. To help users relax and engage in learning, the server adjusts the content as needed based on emotional data obtained from an emotion analysis device.

[0170] For example, if a user wants to improve their "presentation skills," the server will suggest relevant educational videos and practical training exercises. Also, if the emotion analyzer detects signs of tension during learning, the server will provide pre-prepared relaxation content.

[0171] An example of a prompt for a generative AI model could be a text-based instruction such as, "The user is feeling stressed; please add relaxing content to the learning plan." In this way, the learning experience for learners is constantly optimized, and useful skill development becomes possible for companies as well.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The server accesses the company's database and retrieves employee attribute information. The employee's ID is provided as input, and SQL queries are executed based on this ID to search for the necessary data. The output is a dataset containing personal information, current skills, work history, and career goals.

[0175] Step 2:

[0176] The server automatically generates educational plans using a generative AI model based on collected attribute information. Employee attribute data is supplied to the AI ​​model as input, and the most appropriate educational content is selected based on that data. As output, a customized educational plan is generated for each employee. This plan includes relevant videos, quizzes, and assignments.

[0177] Step 3:

[0178] The device collects the user's facial expressions and voice data in real time through an emotion analysis device. The user's learning audio and video streams are provided as input, and the facial recognition algorithm analyzes this data. The output provides data about the user's current emotional state.

[0179] Step 4:

[0180] The server receives emotional state data sent from the terminal and dynamically adjusts the educational plan. The input consists of emotional data and an existing educational plan, which a generative AI model analyzes and modifies as needed. The output is an adjusted educational plan, sometimes with added relaxing content.

[0181] Step 5:

[0182] Users complete assignments according to the educational plan displayed on their devices. User inputs include data on the progress of learning content and quiz answers. Outputs include learning progress data, which is sent from the device to the server.

[0183] Step 6:

[0184] The server proposes the next steps and generates feedback based on the received learning progress data. Progress data is input to the server, where the AI ​​analyzes it. As output, a feedback report is generated, containing suggestions for the next learning content and areas for improvement, and presented to the user.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Providing a uniform learning plan to employees without considering their emotional state during the skill development process can reduce learning efficiency. Furthermore, if adjustments are not made based on each employee's emotions and stress levels, decreased motivation and learning interruptions may occur. Therefore, it is necessary to dynamically adjust learning plans based on employees' emotions to provide a more effective and customized learning experience.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for acquiring and analyzing emotional data, means for generating a learning plan based on employee information, and means for dynamically adjusting the learning plan based on the emotional data. This makes it possible to provide a learning plan adapted to the emotional state of each employee.

[0190] An "employee" is an individual hired to perform a specific task within an organization such as a company or factory.

[0191] "Information" refers to the collection of all data necessary for learning, including personal information, skill levels, work history, or sentiment data related to the employee.

[0192] A "learning plan" is a guideline for the process that includes the structure and progress management of learning materials and assignments developed to improve employees' skills.

[0193] "Emotional data" refers to information that indicates an employee's psychological state at a given moment, obtained from their facial expressions, voice, or behavior.

[0194] A "server" is a computer system that processes data centrally and transmits information to other terminals or devices.

[0195] "Dynamic adjustment" means modifying or optimizing the learning plan as needed, based on employee sentiment data and learning progress.

[0196] "Visualization" means representing data and information in the form of graphs, images, and other visual aids to make them easier to understand.

[0197] "Industry trends" refer to the current development and changes and trends in a particular field or industry as a whole.

[0198] The system for implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server interacts with the company's database to retrieve employee personal information, skill levels, and past work history, and generates individual learning plans based on this information. It also receives emotion data from the emotion engine and dynamically adjusts the learning plans. The software used employs an emotion recognition library (e.g., Microsoft® Azure® Emotion API) to analyze emotions from facial expressions and voice data.

[0199] The terminal serves as a learning environment for employees, accepting learning plans transmitted from the server and tracking and recording employee learning progress. Emotional data is also collected from the terminal and transmitted to the server. Hardware used includes emotion-recognizing cameras (e.g., Intel RealSense) and tablet devices (e.g., iPad®). This allows for real-time tracking of emotional changes during actual learning sessions and enables the presentation of relaxation content or tasks as needed.

[0200] Users can engage in tasks that involve mental activity and reactions through their devices, and monitor their own progress. For example, if an employee is undergoing training to improve their advanced presentation skills, the emotion engine can detect the employee's tension, and the server can provide relaxing music or simple break content to improve learning efficiency.

[0201] An example of a prompt in a generative AI model would be: "Explain how to use a neural network to perform real-time sentiment analysis of a work operator and generate a customized learning plan." This would make the learning plan more effective and personalized, and promote employee skill development.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server retrieves employee personal information, skill levels, and work history from the company's database as input. Based on this data, it analyzes the employee's skill gaps and generates appropriate learning plans. Utilizing a generative AI model, it selects the most suitable learning materials and assignments for each employee and creates a learning plan. The output is a customized learning plan.

[0205] Step 2:

[0206] The terminal receives the learning plan sent from the server and provides an interface for the user to begin learning. The user progresses through the terminal, recording their progress in real time as input data. This progress data is periodically sent to the server, and a learning log is generated as output.

[0207] Step 3:

[0208] The emotion engine acquires the user's facial expressions and voice as input data and analyzes them using an emotion recognition library. This emotion data is sent to the server as information representing the user's psychological state. Specifically, it processes the data to read stress and concentration levels from facial expressions and obtains the emotional state as output.

[0209] Step 4:

[0210] The server receives emotional data and learning progress data as input and dynamically adjusts the learning plan based on them. For example, if the server determines that the user is experiencing stress, it will add relaxation music or simple tasks to the learning plan. This adjusted learning plan is then output and sent to the terminal.

[0211] Step 5:

[0212] The user receives a customized learning plan via their device and then proceeds with their studies. This step involves the user utilizing provided relaxation content to learn in a relaxed state, resulting in a more efficient learning experience.

[0213] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0214] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0229] This invention relates to a system for supporting employee skill development, providing an optimized learning plan for each employee and creating an efficient learning environment. The system consists of three main elements: a server, terminals, and users.

[0230] The server connects to the company's database and retrieves detailed information about each employee, including their work history, skills, interests, and career goals. Based on this data, it analyzes the gap between the employee's current skill set and the skills the company requires. Based on this analysis, the server automatically generates a customized learning plan for each employee. The learning plan incorporates relevant learning resources such as videos, quizzes, and assignments, and is designed to reflect industry trends.

[0231] The device receives custom learning plans sent from the server and notifies the user. When the user begins learning activities on the device, the device tracks progress in real time and sends learning results and quiz results to the server. This allows learning progress to be monitored continuously, and alerts and reminders are sent to the user as needed.

[0232] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress and achievements on a dashboard. The learning plan is reviewed regularly and optimized according to business needs and the user's learning progress.

[0233] For example, if an employee wants to improve their "project management" skills, the server will create a learning plan based on the employee's job responsibilities and interests, including video tutorials and practical quizzes related to project management. By completing these tasks, the user can acquire new skills in a planned and efficient manner.

[0234] This allows for a more flexible and individually optimized learning experience than traditional training methods, supporting employee skill development and enhancing the company's competitiveness.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server connects to the company's employee database and retrieves information such as employees' work history, skills, interests, and career goals.

[0238] Step 2:

[0239] The server analyzes the acquired data to identify the gap between the current skill set and the skills required by the company. The results of the analysis are used in the next step.

[0240] Step 3:

[0241] The server automatically generates customized learning plans based on gap analysis for each employee. These learning plans include relevant videos, quizzes, and assignments.

[0242] Step 4:

[0243] The server sends the generated learning plan to each employee's terminal and notifies them of the plan details.

[0244] Step 5:

[0245] Users can review the learning plan received on their device and begin their learning activities. Learning can proceed at the user's own pace.

[0246] Step 6:

[0247] The device tracks the user's learning activities and sends progress data, such as the completion of each assignment and quiz results, to the server in real time.

[0248] Step 7:

[0249] The server updates the learning dashboard based on the received progress data, providing visual feedback to users and company administrators.

[0250] Step 8:

[0251] The server re-evaluates and adjusts the learning plan as needed, based on the user's learning progress and new industry trends. The revised plan is then notified to the user again.

[0252] By performing the above steps recursively, we can continuously provide an optimized learning environment for each individual employee.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] To efficiently improve employee skills, it is necessary to provide each employee with a learning plan optimized for their individual needs. However, traditional, uniform training methods have failed to adequately address individual needs, resulting in insufficient learning efficiency and effectiveness. Furthermore, there is a lack of appropriate feedback and adjustments regarding learning progress, making it difficult to optimize learning to suit each employee's pace.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for acquiring employee attributes, means for analyzing the acquired employee attributes and identifying skill gaps, and means for customizing learning content using a generation AI model based on the analysis results. This makes it possible to provide each employee with a customized learning plan and support efficient and effective skill acquisition.

[0258] "Employee attributes" refer to information that describes an employee's individual characteristics, such as their work history, skills, interests, and career goals.

[0259] A "skill gap" refers to the shortcomings or discrepancies between an employee's current skills and the skill set required by a company.

[0260] A "generative AI model" is a computer model that uses artificial intelligence technology to generate learning content and plans from data.

[0261] "Customizing learning content" refers to adjusting and optimizing learning plans and materials based on the individual attributes of each employee.

[0262] "Progress information" refers to data that shows the results and progress achieved by employees through their learning activities.

[0263] "Trend information" refers to data that reflects the latest changes and trends in an industry or market.

[0264] "Visualization" refers to representing information in an easily understandable form using visual means such as graphs and charts.

[0265] This invention is a system designed to support employee skill development, providing an efficient learning environment by offering an optimized learning plan for each individual employee. The system consists of three main elements: a server, terminals, and users.

[0266] The server has the capability to connect to the company's database and retrieve attribute information for each employee. This information includes the employee's work history, skills, interests, and career goals. The server uses data analysis software to analyze the retrieved information and identify gaps between current skills and required skills. Based on the analysis results, it automatically generates individually customized learning plans using a generative AI model. These plans include relevant videos, quizzes, and assignments.

[0267] The device receives custom learning plans sent from the server and notifies the user. Using a dedicated learning application, it displays the learning plan and tracks the user's progress in real time as they begin their learning activities. The device can also feed back the results to the server as the learning progresses.

[0268] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress on a dashboard on the terminal. In addition, the learning content and plan are dynamically reviewed and optimized by the server according to the user's progress and industry trends. For example, for an employee who wants to improve their project management skills by working backward, the server will create a learning plan that includes appropriate content based on the employee's current situation.

[0269] Examples of prompt messages are as follows:

[0270] "Create a learning plan to improve project management skills. Consider the user's current skill level, interests, and career goals, and include appropriate content."

[0271] This system allows for the provision of a learning experience optimized for each employee, compared to traditional, uniform training methods, and can support the improvement of a company's competitiveness.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The server connects to the company's database and retrieves employee attribute information. Input includes employee IDs, work history, skills, interests, and career goals stored in the database. The server formats this information for analysis and generates an organized attribute dataset as output. This process involves data extraction using SQL queries.

[0275] Step 2:

[0276] The server analyzes the acquired attribute dataset to identify the gap between employees' current skills and the required skills. Inputs include employees' skill sets and the company's defined target skill sets. The server compares this information using data analysis algorithms and generates a skills gap report as output. This step primarily involves statistical analysis and variance analysis.

[0277] Step 3:

[0278] The server generates a customized learning plan using a generative AI model based on the skills gap report. Inputs include skills gap information and employee learning styles and interests. The server provides this information to the generative AI model as prompts, generating a custom learning plan incorporating appropriate learning content as output. This process involves content selection and optimization using AI technology.

[0279] Step 4:

[0280] The device receives a custom learning plan sent from the server and notifies the user. The input is learning plan data from the server. The device uses a dedicated learning application to visually present the learning plan to the user as output, prompting them to begin learning. This includes push notifications and displays in the user interface.

[0281] Step 5:

[0282] The user uses the terminal to perform learning activities according to the learning plan. The input includes the content of the learning plan displayed on the terminal. While referring to this, the user proceeds with video viewing, quizzes, and answering questions, and learning progress data is generated as output. The user's learning activities are recorded, and the learning results are transmitted from the terminal to the server.

[0283] Step 6:

[0284] The server analyzes the learning progress data transmitted from the terminal and generates feedback. The inputs include the user's progress data and learning results. The server analyzes these data, adjusts the learning plan as necessary, and generates a new optimized learning plan as output. In this step, feedback corresponding to the progress is generated and the plan is dynamically adjusted.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] There is a demand for efficient skill improvement and specialization enhancement of workers in modern industrial sites, but it is difficult for conventional education systems to flexibly provide training tailored to individual needs. Also, it is difficult to grasp the progress of workers and provide optimal learning resources in real time. In particular, there is a lack of means to efficiently integrate practical training programs utilizing virtual reality technology.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0289] In this invention, the server includes means for acquiring worker information, means for providing practical training in a virtual reality environment, and means for visualizing the progress of the training. This enables workers to receive practical training tailored to their individual needs and to efficiently improve their skills.

[0290] The term "worker" refers to an individual engaged in a specific job or task, and is the target of improvement in their skills and knowledge.

[0291] "Information" refers to data related to the worker, such as their work history, skills, and current progress.

[0292] A "learning plan" refers to a collection of individually customized training programs and resources designed to improve the skills of workers.

[0293] "Providing" refers to the act of handing over the generated training plan to the workers and encouraging them to carry out the training.

[0294] "Tracking" is the process of recording training progress in real time and monitoring the progress of workers.

[0295] "Adjustment" is a step in updating and optimizing the learning plan according to the worker's progress.

[0296] A "virtual reality environment" refers to a computer-generated three-dimensional space where workers can realistically experience practical training.

[0297] "Visualization" is a technique for displaying data graphically so that workers can easily understand their own progress.

[0298] "Industry trends" refer to changes and advancements in technology and needs within an industry, and indicate the necessary skills and knowledge based on those changes.

[0299] The system that realizes this invention includes three main components: a server, a terminal, and a user. First, the server retrieves worker information from the company's database and analyzes skill gaps using an AI model. It performs the analysis using Python and TensorFlow and generates an individualized learning plan. This plan includes the training resources necessary for improving the workers' skills and includes practical VR content using a virtual reality development environment such as Unity.

[0300] Next, the terminal uses smart glasses or a tablet to provide the worker with a generated learning plan in real time. As the worker views the VR content and progresses through the training, the terminal sends its progress to the server, which then adjusts the learning plan based on this data. The progress data is displayed on a graphical dashboard, allowing the worker to easily understand their own learning progress.

[0301] Through this system, users (workers) can train at their own pace. This enables efficient skill acquisition through immediate feedback and flexible learning programs.

[0302] For example, when conducting training on operating new machinery in a factory, the server distributes the relevant VR content and provides it to the workers via their terminals. The workers experience the actual operating procedures in a virtual space, and the server analyzes the results to determine whether further training is necessary.

[0303] An example of a prompt to input into a generative AI model is, "Please suggest VR training materials to help factory workers efficiently learn a new manufacturing process." This prompt is used to get the AI ​​to generate specific learning resources.

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The server retrieves the information of employees from the enterprise database. It receives data such as employees' work history, skills, interests, career goals, etc. as input, and supplies this information to the analysis program as output. The acquisition of data is executed through database queries.

[0307] Step 2:

[0308] Based on the retrieved employee information, the server analyzes the skill gap using an AI model. The input for this step is the current skill set of the employee, and the output is the analysis result indicating the difference from the required skills. This analysis is calculated using Python and TensorFlow.

[0309] Step 3:

[0310] Based on the analysis result, the server generates a customized learning plan for each employee. The input is the analysis result of the skill gap, and the output is the learning plan including relevant training resources and VR content. The generated plan is temporarily saved in the server.

[0311] Step 4:

[0312] The server sends the created learning plan to the terminal. The input is the generated learning plan, and the output is the state being ready to be displayed on the terminal side. The sending is performed through network communication.

[0313] Step 5:

[0314] The terminal notifies the user of the received learning plan and displays the training content. The input is the learning plan sent from the server, and the output is the visualized training information. The notification and visualization are executed through the terminal's display.

[0315] Step 6:

[0316] The user (worker) runs VR content through a terminal and begins training. The input is the displayed training content, and the output is data on the training progress. The actions performed during execution depend on the user's input.

[0317] Step 7:

[0318] The device tracks training progress in real time and sends progress data to the server. The input is the user's training progress information, and the output is the transmitted data. Tracking is performed via built-in sensors.

[0319] Step 8:

[0320] The server further adjusts the learning plan based on the received progress data. The input is real-time progress data, and the output is the updated learning plan. The adjustments are optimized by an algorithm.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention is a system designed to support employee skill development, enabling the analysis of emotions during the employee's learning process and the customization of learning plans accordingly. The system consists of four main components: a server, a terminal, a user, and an emotion engine.

[0323] The server connects with the company's database to retrieve employee personal information, current skills, work history, and career goals. Based on this data, it analyzes each employee's skill gaps and automatically generates custom learning plans. The learning content includes relevant videos, quizzes, and assignments, and is tailored based on industry trends and company needs.

[0324] The emotion engine uses the user's facial expressions and voice data to recognize their emotional state during learning in real time. The emotion data recognized by this engine is sent to the server and used to adjust the learning plan. For example, if the user is feeling stressed, the server will incorporate more relaxing content and tones into the learning plan.

[0325] The device serves as the learning environment for the user, receiving learning plans and sentiment-based adjustments from the server. On the device, the user completes assignments and records their progress. Progress and sentiment data are also sent to the server to ensure the learning experience is constantly optimized.

[0326] Users can improve their skills by following a learning plan on their device. The learning content provided on the device enables effective and efficient learning. Furthermore, the emotional engine provides a learning environment that takes the user's emotions into account, reducing stress and allowing for more focused learning.

[0327] As a concrete example, suppose an employee aims to improve their "presentation skills." In this case, the learning plan would include relevant videos and practical exercises. If the emotion engine detects the user's tension during learning, the server would recommend pre-prepared relaxation content to help the user relax and refocus on learning.

[0328] Thus, the present invention dynamically adjusts the learning plan according to the user's emotions, providing an optimized learning experience for each individual employee, and consequently promoting employee skill development and improved corporate competitiveness.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server connects to the company's database and retrieves information such as each employee's work history, skills, interests, and career goals.

[0332] Step 2:

[0333] The server analyzes skill gaps based on the acquired information and automatically generates individualized learning plans. These learning plans include videos, quizzes, and assignments.

[0334] Step 3:

[0335] The generated learning plan is sent from the server to the terminal, and the employee is notified that learning has begun.

[0336] Step 4:

[0337] The user progresses through the learning process according to the learning plan provided using the device. During the learning process, the user's facial expressions and voice data are collected by the device's sensors.

[0338] Step 5:

[0339] The device sends the collected sensor data to an emotion engine to analyze the user's emotional state.

[0340] Step 6:

[0341] Emotional data obtained from the emotion engine is sent to the server. The server adjusts the learning plan accordingly, reflecting the user's emotional state. For example, if a user is experiencing high stress, the server recommends relaxation content.

[0342] Step 7:

[0343] The server reflects learning progress and emotion-based adjustments on a dashboard, providing visual feedback to users and administrators.

[0344] Step 8:

[0345] Users can check their learning progress and receive feedback on their device, and adjust their learning pace and methods as needed.

[0346] Through this series of processes, the user's emotions are incorporated into the learning experience, and a learning environment optimized for each individual is provided.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] In today's work environment, improving employee skills requires providing flexible training plans tailored to individual skills and career goals. However, traditional systems struggle to adapt dynamic training plans to learners' emotional states and learning progress, making it difficult to provide the optimal learning method for each employee. Furthermore, selecting learning materials that appropriately reflect industry trends and employee sentiments remains challenging.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes means for acquiring employee attribute information, means for automatically generating and dynamically adapting educational plans using a generative AI model, and means for acquiring and optimizing learners' emotional states using an emotion analysis device. This makes it possible to provide a flexible and efficient learning environment tailored to each individual employee.

[0352] "Employee attribute information" refers to information including employees' personal information, skills, work history, and career goals.

[0353] An "educational plan" refers to learning content and assignments designed to improve employees' skills, and includes materials such as videos, quizzes, and assignments.

[0354] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and automatically performs specific tasks or generates data.

[0355] An "emotion analysis device" refers to technology that analyzes data such as a user's facial expressions and voice to recognize their emotional state.

[0356] "Dynamic adaptation" refers to a process of adjusting and changing content in real time based on the learner's situation and feedback.

[0357] "Learner's emotional state" refers to an individual's emotional condition during learning (e.g., relaxed, stressed, excited, etc.).

[0358] "Optimization" refers to adjusting or improving conditions or states to be the most appropriate for a particular purpose.

[0359] The following system configurations are possible for carrying out this invention.

[0360] The server first accesses the company's database to retrieve employee attribute information. This information includes employee personal details, skills, work history, and career goals, and is collected using database management systems such as SQL queries. Next, it utilizes a generative AI model to automatically generate individual employee training plans. These training plans include relevant videos, quizzes, and assignments, which are tailored to the company's needs and industry trends.

[0361] The emotion analysis device is connected to the terminal and recognizes the user's emotional state in real time through facial expressions, voice, etc. This analysis device analyzes video and audio data displayed to the user during learning and identifies emotional states such as stress and excitement. The recognition results are sent to a server and used to optimize the educational plan.

[0362] The terminal is a device for users to engage in learning activities and has the function of presenting educational plans sent from the server. On the terminal, the user's progress as they work on assignments is recorded, and this progress information and emotional information are sent to the server to provide a more effective and personalized learning experience.

[0363] Through this system, users can efficiently progress through their learning based on a personalized educational plan. To help users relax and engage in learning, the server adjusts the content as needed based on emotional data obtained from an emotion analysis device.

[0364] For example, if a user wants to improve their "presentation skills," the server will suggest relevant educational videos and practical training exercises. Also, if the emotion analyzer detects signs of tension during learning, the server will provide pre-prepared relaxation content.

[0365] An example of a prompt for a generative AI model could be a text-based instruction such as, "The user is feeling stressed; please add relaxing content to the learning plan." In this way, the learning experience for learners is constantly optimized, and useful skill development becomes possible for companies as well.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The server accesses the company's database and retrieves employee attribute information. The employee's ID is provided as input, and SQL queries are executed based on this ID to search for the necessary data. The output is a dataset containing personal information, current skills, work history, and career goals.

[0369] Step 2:

[0370] The server automatically generates educational plans using a generative AI model based on collected attribute information. Employee attribute data is supplied to the AI ​​model as input, and the most appropriate educational content is selected based on that data. As output, a customized educational plan is generated for each employee. This plan includes relevant videos, quizzes, and assignments.

[0371] Step 3:

[0372] The device collects the user's facial expressions and voice data in real time through an emotion analysis device. The user's learning audio and video streams are provided as input, and the facial recognition algorithm analyzes this data. The output provides data about the user's current emotional state.

[0373] Step 4:

[0374] The server receives emotional state data sent from the terminal and dynamically adjusts the educational plan. The input consists of emotional data and an existing educational plan, which a generative AI model analyzes and modifies as needed. The output is an adjusted educational plan, sometimes with added relaxing content.

[0375] Step 5:

[0376] Users complete assignments according to the educational plan displayed on their devices. User inputs include data on the progress of learning content and quiz answers. Outputs include learning progress data, which is sent from the device to the server.

[0377] Step 6:

[0378] The server proposes the next steps and generates feedback based on the received learning progress data. Progress data is input to the server, where the AI ​​analyzes it. As output, a feedback report is generated, containing suggestions for the next learning content and areas for improvement, and presented to the user.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] Providing a uniform learning plan to employees without considering their emotional state during the skill development process can reduce learning efficiency. Furthermore, if adjustments are not made based on each employee's emotions and stress levels, decreased motivation and learning interruptions may occur. Therefore, it is necessary to dynamically adjust learning plans based on employees' emotions to provide a more effective and customized learning experience.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for acquiring and analyzing emotional data, means for generating a learning plan based on employee information, and means for dynamically adjusting the learning plan based on the emotional data. This makes it possible to provide a learning plan adapted to the emotional state of each employee.

[0384] An "employee" is an individual hired to perform a specific task within an organization such as a company or factory.

[0385] "Information" refers to the collection of all data necessary for learning, including personal information, skill levels, work history, or sentiment data related to the employee.

[0386] A "learning plan" is a guideline for the process that includes the structure and progress management of learning materials and assignments developed to improve employees' skills.

[0387] "Emotional data" refers to information that indicates an employee's psychological state at a given moment, obtained from their facial expressions, voice, or behavior.

[0388] A "server" is a computer system that processes data centrally and transmits information to other terminals or devices.

[0389] "Dynamic adjustment" means modifying or optimizing the learning plan as needed, based on employee sentiment data and learning progress.

[0390] "Visualization" means representing data and information in the form of graphs, images, and other visual aids to make them easier to understand.

[0391] "Industry trends" refer to the current development and changes and trends in a particular field or industry as a whole.

[0392] The system for implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server interacts with the company's database to retrieve employee personal information, skill levels, and past work history, and generates individual learning plans based on this information. It also receives emotion data from the emotion engine and dynamically adjusts the learning plans. The software used employs an emotion recognition library (e.g., Microsoft Azure Emotion API) to analyze emotions from facial expressions and voice data.

[0393] The terminal serves as a learning environment for employees, accepting learning plans sent from the server and tracking and recording their learning progress. Emotional data is also collected from the terminal and sent to the server. Hardware used includes emotion-recognizing cameras (e.g., Intel RealSense) and tablet devices (e.g., iPad). This allows for real-time tracking of emotional changes during actual learning sessions and enables the presentation of relaxation content or tasks as needed.

[0394] Users can engage in tasks that involve mental activity and reactions through their devices, and monitor their own progress. For example, if an employee is undergoing training to improve their advanced presentation skills, the emotion engine can detect the employee's tension, and the server can provide relaxing music or simple break content to improve learning efficiency.

[0395] An example of a prompt in a generative AI model would be: "Explain how to use a neural network to perform real-time sentiment analysis of a work operator and generate a customized learning plan." This would make the learning plan more effective and personalized, and promote employee skill development.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The server retrieves employee personal information, skill levels, and work history from the company's database as input. Based on this data, it analyzes the employee's skill gaps and generates appropriate learning plans. Utilizing a generative AI model, it selects the most suitable learning materials and assignments for each employee and creates a learning plan. The output is a customized learning plan.

[0399] Step 2:

[0400] The terminal receives the learning plan sent from the server and provides an interface for the user to begin learning. The user progresses through the terminal, recording their progress in real time as input data. This progress data is periodically sent to the server, and a learning log is generated as output.

[0401] Step 3:

[0402] The emotion engine acquires the user's facial expressions and voice as input data and analyzes them using an emotion recognition library. This emotion data is sent to the server as information representing the user's psychological state. Specifically, it processes the data to read stress and concentration levels from facial expressions and obtains the emotional state as output.

[0403] Step 4:

[0404] The server receives emotional data and learning progress data as input and dynamically adjusts the learning plan based on them. For example, if the server determines that the user is experiencing stress, it will add relaxation music or simple tasks to the learning plan. This adjusted learning plan is then output and sent to the terminal.

[0405] Step 5:

[0406] The user receives a customized learning plan via their device and then proceeds with their studies. This step involves the user utilizing provided relaxation content to learn in a relaxed state, resulting in a more efficient learning experience.

[0407] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0408] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0413] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0414] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0415] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0416] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0417] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0418] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0419] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0420] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0421] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0422] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0423] This invention relates to a system for supporting employee skill development, providing an optimized learning plan for each employee and creating an efficient learning environment. The system consists of three main elements: a server, terminals, and users.

[0424] The server connects to the company's database and retrieves detailed information about each employee, including their work history, skills, interests, and career goals. Based on this data, it analyzes the gap between the employee's current skill set and the skills the company requires. Based on this analysis, the server automatically generates a customized learning plan for each employee. The learning plan incorporates relevant learning resources such as videos, quizzes, and assignments, and is designed to reflect industry trends.

[0425] The device receives custom learning plans sent from the server and notifies the user. When the user begins learning activities on the device, the device tracks progress in real time and sends learning results and quiz results to the server. This allows learning progress to be monitored continuously, and alerts and reminders are sent to the user as needed.

[0426] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress and achievements on a dashboard. The learning plan is reviewed regularly and optimized according to business needs and the user's learning progress.

[0427] For example, if an employee wants to improve their "project management" skills, the server will create a learning plan based on the employee's job responsibilities and interests, including video tutorials and practical quizzes related to project management. By completing these tasks, the user can acquire new skills in a planned and efficient manner.

[0428] This allows for a more flexible and individually optimized learning experience than traditional training methods, supporting employee skill development and enhancing the company's competitiveness.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The server connects to the company's employee database and retrieves information such as employees' work history, skills, interests, and career goals.

[0432] Step 2:

[0433] The server analyzes the acquired data to identify the gap between the current skill set and the skills required by the company. The results of the analysis are used in the next step.

[0434] Step 3:

[0435] The server automatically generates customized learning plans based on gap analysis for each employee. These learning plans include relevant videos, quizzes, and assignments.

[0436] Step 4:

[0437] The server sends the generated learning plan to each employee's terminal and notifies them of the plan details.

[0438] Step 5:

[0439] Users can review the learning plan received on their device and begin their learning activities. Learning can proceed at the user's own pace.

[0440] Step 6:

[0441] The device tracks the user's learning activities and sends progress data, such as the completion of each assignment and quiz results, to the server in real time.

[0442] Step 7:

[0443] The server updates the learning dashboard based on the received progress data, providing visual feedback to users and company administrators.

[0444] Step 8:

[0445] The server re-evaluates and adjusts the learning plan as needed, based on the user's learning progress and new industry trends. The revised plan is then notified to the user again.

[0446] By performing the above steps recursively, we can continuously provide an optimized learning environment for each individual employee.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] To efficiently improve employee skills, it is necessary to provide each employee with a learning plan optimized for their individual needs. However, traditional, uniform training methods have failed to adequately address individual needs, resulting in insufficient learning efficiency and effectiveness. Furthermore, there is a lack of appropriate feedback and adjustments regarding learning progress, making it difficult to optimize learning to suit each employee's pace.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes means for acquiring employee attributes, means for analyzing the acquired employee attributes and identifying skill gaps, and means for customizing learning content using a generation AI model based on the analysis results. This makes it possible to provide each employee with a customized learning plan and support efficient and effective skill acquisition.

[0452] "Employee attributes" refer to information that describes an employee's individual characteristics, such as their work history, skills, interests, and career goals.

[0453] A "skill gap" refers to the shortcomings or discrepancies between an employee's current skills and the skill set required by a company.

[0454] A "generative AI model" is a computer model that uses artificial intelligence technology to generate learning content and plans from data.

[0455] "Customizing learning content" refers to adjusting and optimizing learning plans and materials based on the individual attributes of each employee.

[0456] "Progress information" refers to data that shows the results and progress achieved by employees through their learning activities.

[0457] "Trend information" refers to data that reflects the latest changes and trends in an industry or market.

[0458] "Visualization" refers to representing information in an easily understandable form using visual means such as graphs and charts.

[0459] This invention is a system designed to support employee skill development, providing an efficient learning environment by offering an optimized learning plan for each individual employee. The system consists of three main elements: a server, terminals, and users.

[0460] The server has the capability to connect to the company's database and retrieve attribute information for each employee. This information includes the employee's work history, skills, interests, and career goals. The server uses data analysis software to analyze the retrieved information and identify gaps between current skills and required skills. Based on the analysis results, it automatically generates individually customized learning plans using a generative AI model. These plans include relevant videos, quizzes, and assignments.

[0461] The device receives custom learning plans sent from the server and notifies the user. Using a dedicated learning application, it displays the learning plan and tracks the user's progress in real time as they begin their learning activities. The device can also feed back the results to the server as the learning progresses.

[0462] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress on a dashboard on the terminal. In addition, the learning content and plan are dynamically reviewed and optimized by the server according to the user's progress and industry trends. For example, for an employee who wants to improve their project management skills by working backward, the server will create a learning plan that includes appropriate content based on the employee's current situation.

[0463] Examples of prompt messages are as follows:

[0464] "Create a learning plan to improve project management skills. Consider the user's current skill level, interests, and career goals, and include appropriate content."

[0465] This system allows for the provision of a learning experience optimized for each employee, compared to traditional, uniform training methods, and can support the improvement of a company's competitiveness.

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The server connects to the company's database and retrieves employee attribute information. Input includes employee IDs, work history, skills, interests, and career goals stored in the database. The server formats this information for analysis and generates an organized attribute dataset as output. This process involves data extraction using SQL queries.

[0469] Step 2:

[0470] The server analyzes the acquired attribute dataset to identify the gap between employees' current skills and the required skills. Inputs include employees' skill sets and the company's defined target skill sets. The server compares this information using data analysis algorithms and generates a skills gap report as output. This step primarily involves statistical analysis and variance analysis.

[0471] Step 3:

[0472] The server generates a customized learning plan using a generative AI model based on the skills gap report. Inputs include skills gap information and employee learning styles and interests. The server provides this information to the generative AI model as prompts, generating a custom learning plan incorporating appropriate learning content as output. This process involves content selection and optimization using AI technology.

[0473] Step 4:

[0474] The device receives a custom learning plan sent from the server and notifies the user. The input is learning plan data from the server. The device uses a dedicated learning application to visually present the learning plan to the user as output, prompting them to begin learning. This includes push notifications and displays in the user interface.

[0475] Step 5:

[0476] Users use their devices to engage in learning activities according to their learning plan. The input is the content of the learning plan displayed on the device. Users refer to this content while watching videos, taking quizzes, and answering assignments, and learning progress data is generated as output. The user's learning activities are recorded, and the learning results are sent from the device to the server.

[0477] Step 6:

[0478] The server analyzes learning progress data sent from the terminal and generates feedback. Input includes user progress data and learning outcomes. The server analyzes this data, adjusts the learning plan as needed, and generates a new, optimized learning plan as output. This step involves generating progress-based feedback and dynamically adjusting the plan.

[0479] (Application Example 1)

[0480] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] While there is a need for efficient skill development and enhanced expertise among workers in modern industrial settings, traditional education systems struggle to flexibly provide training tailored to individual needs. Furthermore, it is difficult to track workers' progress and provide optimal learning resources in real time. In particular, there is a lack of efficient means to integrate practical training programs utilizing virtual reality technology.

[0482] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0483] In this invention, the server includes means for acquiring worker information, means for providing practical training in a virtual reality environment, and means for visualizing the progress of the training. This enables workers to receive practical training tailored to their individual needs and to efficiently improve their skills.

[0484] The term "worker" refers to an individual engaged in a specific job or task, and is the target of improvement in their skills and knowledge.

[0485] "Information" refers to data related to the worker, such as their work history, skills, and current progress.

[0486] A "learning plan" refers to a collection of individually customized training programs and resources designed to improve the skills of workers.

[0487] "Providing" refers to the act of handing over the generated training plan to the workers and encouraging them to carry out the training.

[0488] "Tracking" is the process of recording training progress in real time and monitoring the progress of workers.

[0489] "Adjustment" is a step in updating and optimizing the learning plan according to the worker's progress.

[0490] A "virtual reality environment" refers to a computer-generated three-dimensional space where workers can realistically experience practical training.

[0491] "Visualization" is a technique for displaying data graphically so that workers can easily understand their own progress.

[0492] "Industry trends" refer to changes and advancements in technology and needs within an industry, and indicate the necessary skills and knowledge based on those changes.

[0493] The system that realizes this invention includes three main components: a server, a terminal, and a user. First, the server retrieves worker information from the company's database and analyzes skill gaps using an AI model. It performs the analysis using Python and TensorFlow and generates an individualized learning plan. This plan includes the training resources necessary for improving the workers' skills and includes practical VR content using a virtual reality development environment such as Unity.

[0494] Next, the terminal uses smart glasses or a tablet to provide the worker with a generated learning plan in real time. As the worker views the VR content and progresses through the training, the terminal sends its progress to the server, which then adjusts the learning plan based on this data. The progress data is displayed on a graphical dashboard, allowing the worker to easily understand their own learning progress.

[0495] Through this system, users (workers) can train at their own pace. This enables efficient skill acquisition through immediate feedback and flexible learning programs.

[0496] For example, when conducting training on operating new machinery in a factory, the server distributes the relevant VR content and provides it to the workers via their terminals. The workers experience the actual operating procedures in a virtual space, and the server analyzes the results to determine whether further training is necessary.

[0497] An example of a prompt to input into a generative AI model is, "Please suggest VR training materials to help factory workers efficiently learn a new manufacturing process." This prompt is used to get the AI ​​to generate specific learning resources.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The server retrieves worker information from the company's database. It receives data such as worker work history, skills, interests, and career goals as input, and supplies this information to an analysis program as output. Data retrieval is performed through database queries.

[0501] Step 2:

[0502] The server uses an AI model to analyze the skill gap based on the acquired worker information. The input for this step is the worker's current skill set, and the output is an analysis showing the difference between that skill set and the required skills. This analysis is performed using Python and TensorFlow.

[0503] Step 3:

[0504] The server generates a customized learning plan for each worker based on the analysis results. The input is the analysis results of the skill gap, and the output is a learning plan that includes relevant training resources and VR content. The generated plan is temporarily stored on the server.

[0505] Step 4:

[0506] The server sends the generated training plan to the terminal. The input is the generated training plan, which is then ready to be displayed on the terminal as output. The transmission takes place via network communication.

[0507] Step 5:

[0508] The terminal notifies the user of the received training plan and displays the training content. The input is the training plan sent from the server, and the output is visualized training information. Notification and visualization are performed via the terminal's display.

[0509] Step 6:

[0510] The user (worker) runs VR content through a terminal and begins training. The input is the displayed training content, and the output is data on the training progress. The actions performed during execution depend on the user's input.

[0511] Step 7:

[0512] The device tracks training progress in real time and sends progress data to the server. The input is the user's training progress information, and the output is the transmitted data. Tracking is performed via built-in sensors.

[0513] Step 8:

[0514] The server further adjusts the learning plan based on the received progress data. The input is real-time progress data, and the output is the updated learning plan. The adjustments are optimized by an algorithm.

[0515] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0516] This invention is a system designed to support employee skill development, enabling the analysis of emotions during the employee's learning process and the customization of learning plans accordingly. The system consists of four main components: a server, a terminal, a user, and an emotion engine.

[0517] The server connects with the company's database to retrieve employee personal information, current skills, work history, and career goals. Based on this data, it analyzes each employee's skill gaps and automatically generates custom learning plans. The learning content includes relevant videos, quizzes, and assignments, and is tailored based on industry trends and company needs.

[0518] The emotion engine uses the user's facial expressions and voice data to recognize their emotional state during learning in real time. The emotion data recognized by this engine is sent to the server and used to adjust the learning plan. For example, if the user is feeling stressed, the server will incorporate more relaxing content and tones into the learning plan.

[0519] The device serves as the learning environment for the user, receiving learning plans and sentiment-based adjustments from the server. On the device, the user completes assignments and records their progress. Progress and sentiment data are also sent to the server to ensure the learning experience is constantly optimized.

[0520] Users can improve their skills by following a learning plan on their device. The learning content provided on the device enables effective and efficient learning. Furthermore, the emotional engine provides a learning environment that takes the user's emotions into account, reducing stress and allowing for more focused learning.

[0521] As a concrete example, suppose an employee aims to improve their "presentation skills." In this case, the learning plan would include relevant videos and practical exercises. If the emotion engine detects the user's tension during learning, the server would recommend pre-prepared relaxation content to help the user relax and refocus on learning.

[0522] Thus, the present invention dynamically adjusts the learning plan according to the user's emotions, providing an optimized learning experience for each individual employee, and consequently promoting employee skill development and improved corporate competitiveness.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The server connects to the company's database and retrieves information such as each employee's work history, skills, interests, and career goals.

[0526] Step 2:

[0527] The server analyzes skill gaps based on the acquired information and automatically generates individualized learning plans. These learning plans include videos, quizzes, and assignments.

[0528] Step 3:

[0529] The generated learning plan is sent from the server to the terminal, and the employee is notified that learning has begun.

[0530] Step 4:

[0531] The user progresses through the learning process according to the learning plan provided using the device. During the learning process, the user's facial expressions and voice data are collected by the device's sensors.

[0532] Step 5:

[0533] The device sends the collected sensor data to an emotion engine to analyze the user's emotional state.

[0534] Step 6:

[0535] Emotional data obtained from the emotion engine is sent to the server. The server adjusts the learning plan accordingly, reflecting the user's emotional state. For example, if a user is experiencing high stress, the server recommends relaxation content.

[0536] Step 7:

[0537] The server reflects learning progress and emotion-based adjustments on a dashboard, providing visual feedback to users and administrators.

[0538] Step 8:

[0539] Users can check their learning progress and receive feedback on their device, and adjust their learning pace and methods as needed.

[0540] Through this series of processes, the user's emotions are incorporated into the learning experience, and a learning environment optimized for each individual is provided.

[0541] (Example 2)

[0542] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] In today's work environment, improving employee skills requires providing flexible training plans tailored to individual skills and career goals. However, traditional systems struggle to adapt dynamic training plans to learners' emotional states and learning progress, making it difficult to provide the optimal learning method for each employee. Furthermore, selecting learning materials that appropriately reflect industry trends and employee sentiments remains challenging.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0545] In this invention, the server includes means for acquiring employee attribute information, means for automatically generating and dynamically adapting educational plans using a generative AI model, and means for acquiring and optimizing learners' emotional states using an emotion analysis device. This makes it possible to provide a flexible and efficient learning environment tailored to each individual employee.

[0546] "Employee attribute information" refers to information including employees' personal information, skills, work history, and career goals.

[0547] An "educational plan" refers to learning content and assignments designed to improve employees' skills, and includes materials such as videos, quizzes, and assignments.

[0548] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and automatically performs specific tasks or generates data.

[0549] An "emotion analysis device" refers to technology that analyzes data such as a user's facial expressions and voice to recognize their emotional state.

[0550] "Dynamic adaptation" refers to a process of adjusting and changing content in real time based on the learner's situation and feedback.

[0551] "Learner's emotional state" refers to an individual's emotional condition during learning (e.g., relaxed, stressed, excited, etc.).

[0552] "Optimization" refers to adjusting or improving conditions or states to be the most appropriate for a particular purpose.

[0553] The following system configurations are possible for carrying out this invention.

[0554] The server first accesses the company's database to retrieve employee attribute information. This information includes employee personal details, skills, work history, and career goals, and is collected using database management systems such as SQL queries. Next, it utilizes a generative AI model to automatically generate individual employee training plans. These training plans include relevant videos, quizzes, and assignments, which are tailored to the company's needs and industry trends.

[0555] The emotion analysis device is connected to the terminal and recognizes the user's emotional state in real time through facial expressions, voice, etc. This analysis device analyzes video and audio data displayed to the user during learning and identifies emotional states such as stress and excitement. The recognition results are sent to a server and used to optimize the educational plan.

[0556] The terminal is a device for users to engage in learning activities and has the function of presenting educational plans sent from the server. On the terminal, the user's progress as they work on assignments is recorded, and this progress information and emotional information are sent to the server to provide a more effective and personalized learning experience.

[0557] Through this system, users can efficiently progress through their learning based on a personalized educational plan. To help users relax and engage in learning, the server adjusts the content as needed based on emotional data obtained from an emotion analysis device.

[0558] For example, if a user wants to improve their "presentation skills," the server will suggest relevant educational videos and practical training exercises. Also, if the emotion analyzer detects signs of tension during learning, the server will provide pre-prepared relaxation content.

[0559] An example of a prompt for a generative AI model could be a text-based instruction such as, "The user is feeling stressed; please add relaxing content to the learning plan." In this way, the learning experience for learners is constantly optimized, and useful skill development becomes possible for companies as well.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] The server accesses the company's database and retrieves employee attribute information. The employee's ID is provided as input, and SQL queries are executed based on this ID to search for the necessary data. The output is a dataset containing personal information, current skills, work history, and career goals.

[0563] Step 2:

[0564] The server automatically generates educational plans using a generative AI model based on collected attribute information. Employee attribute data is supplied to the AI ​​model as input, and the most appropriate educational content is selected based on that data. As output, a customized educational plan is generated for each employee. This plan includes relevant videos, quizzes, and assignments.

[0565] Step 3:

[0566] The device collects the user's facial expressions and voice data in real time through an emotion analysis device. The user's learning audio and video streams are provided as input, and the facial recognition algorithm analyzes this data. The output provides data about the user's current emotional state.

[0567] Step 4:

[0568] The server receives emotional state data sent from the terminal and dynamically adjusts the educational plan. The input consists of emotional data and an existing educational plan, which a generative AI model analyzes and modifies as needed. The output is an adjusted educational plan, sometimes with added relaxing content.

[0569] Step 5:

[0570] Users complete assignments according to the educational plan displayed on their devices. User inputs include data on the progress of learning content and quiz answers. Outputs include learning progress data, which is sent from the device to the server.

[0571] Step 6:

[0572] The server proposes the next steps and generates feedback based on the received learning progress data. Progress data is input to the server, where the AI ​​analyzes it. As output, a feedback report is generated, containing suggestions for the next learning content and areas for improvement, and presented to the user.

[0573] (Application Example 2)

[0574] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0575] Providing a uniform learning plan to employees without considering their emotional state during the skill development process can reduce learning efficiency. Furthermore, if adjustments are not made based on each employee's emotions and stress levels, decreased motivation and learning interruptions may occur. Therefore, it is necessary to dynamically adjust learning plans based on employees' emotions to provide a more effective and customized learning experience.

[0576] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0577] In this invention, the server includes means for acquiring and analyzing emotional data, means for generating a learning plan based on employee information, and means for dynamically adjusting the learning plan based on the emotional data. This makes it possible to provide a learning plan adapted to the emotional state of each employee.

[0578] An "employee" is an individual hired to perform a specific task within an organization such as a company or factory.

[0579] "Information" refers to the collection of all data necessary for learning, including personal information, skill levels, work history, or sentiment data related to the employee.

[0580] A "learning plan" is a guideline for the process that includes the structure and progress management of learning materials and assignments developed to improve employees' skills.

[0581] "Emotional data" refers to information that indicates an employee's psychological state at a given moment, obtained from their facial expressions, voice, or behavior.

[0582] A "server" is a computer system that processes data centrally and transmits information to other terminals or devices.

[0583] "Dynamic adjustment" means modifying or optimizing the learning plan as needed, based on employee sentiment data and learning progress.

[0584] "Visualization" means representing data and information in the form of graphs, images, and other visual aids to make them easier to understand.

[0585] "Industry trends" refer to the current development and changes and trends in a particular field or industry as a whole.

[0586] The system for implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server interacts with the company's database to retrieve employee personal information, skill levels, and past work history, and generates individual learning plans based on this information. It also receives emotion data from the emotion engine and dynamically adjusts the learning plans. The software used employs an emotion recognition library (e.g., Microsoft Azure Emotion API) to analyze emotions from facial expressions and voice data.

[0587] The terminal serves as a learning environment for employees, accepting learning plans sent from the server and tracking and recording their learning progress. Emotional data is also collected from the terminal and sent to the server. Hardware used includes emotion-recognizing cameras (e.g., Intel RealSense) and tablet devices (e.g., iPad). This allows for real-time tracking of emotional changes during actual learning sessions and enables the presentation of relaxation content or tasks as needed.

[0588] Users can engage in tasks that involve mental activity and reactions through their devices, and monitor their own progress. For example, if an employee is undergoing training to improve their advanced presentation skills, the emotion engine can detect the employee's tension, and the server can provide relaxing music or simple break content to improve learning efficiency.

[0589] An example of a prompt in a generative AI model would be: "Explain how to use a neural network to perform real-time sentiment analysis of a work operator and generate a customized learning plan." This would make the learning plan more effective and personalized, and promote employee skill development.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The server retrieves employee personal information, skill levels, and work history from the company's database as input. Based on this data, it analyzes the employee's skill gaps and generates appropriate learning plans. Utilizing a generative AI model, it selects the most suitable learning materials and assignments for each employee and creates a learning plan. The output is a customized learning plan.

[0593] Step 2:

[0594] The terminal receives the learning plan sent from the server and provides an interface for the user to begin learning. The user progresses through the terminal, recording their progress in real time as input data. This progress data is periodically sent to the server, and a learning log is generated as output.

[0595] Step 3:

[0596] The emotion engine acquires the user's facial expressions and voice as input data and analyzes them using an emotion recognition library. This emotion data is sent to the server as information representing the user's psychological state. Specifically, it processes the data to read stress and concentration levels from facial expressions and obtains the emotional state as output.

[0597] Step 4:

[0598] The server receives emotional data and learning progress data as input and dynamically adjusts the learning plan based on them. For example, if the server determines that the user is experiencing stress, it will add relaxation music or simple tasks to the learning plan. This adjusted learning plan is then output and sent to the terminal.

[0599] Step 5:

[0600] The user receives a customized learning plan via their device and then proceeds with their studies. This step involves the user utilizing provided relaxation content to learn in a relaxed state, resulting in a more efficient learning experience.

[0601] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0608] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0610] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0611] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0612] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0613] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0614] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0615] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] This invention relates to a system for supporting employee skill development, providing an optimized learning plan for each employee and creating an efficient learning environment. The system consists of three main elements: a server, terminals, and users.

[0619] The server connects to the company's database and retrieves detailed information about each employee, including their work history, skills, interests, and career goals. Based on this data, it analyzes the gap between the employee's current skill set and the skills the company requires. Based on this analysis, the server automatically generates a customized learning plan for each employee. The learning plan incorporates relevant learning resources such as videos, quizzes, and assignments, and is designed to reflect industry trends.

[0620] The device receives custom learning plans sent from the server and notifies the user. When the user begins learning activities on the device, the device tracks progress in real time and sends learning results and quiz results to the server. This allows learning progress to be monitored continuously, and alerts and reminders are sent to the user as needed.

[0621] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress and achievements on a dashboard. The learning plan is reviewed regularly and optimized according to business needs and the user's learning progress.

[0622] For example, if an employee wants to improve their "project management" skills, the server will create a learning plan based on the employee's job responsibilities and interests, including video tutorials and practical quizzes related to project management. By completing these tasks, the user can acquire new skills in a planned and efficient manner.

[0623] This allows for a more flexible and individually optimized learning experience than traditional training methods, supporting employee skill development and enhancing the company's competitiveness.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server connects to the company's employee database and retrieves information such as employees' work history, skills, interests, and career goals.

[0627] Step 2:

[0628] The server analyzes the acquired data to identify the gap between the current skill set and the skills required by the company. The results of the analysis are used in the next step.

[0629] Step 3:

[0630] The server automatically generates customized learning plans based on gap analysis for each employee. These learning plans include relevant videos, quizzes, and assignments.

[0631] Step 4:

[0632] The server sends the generated learning plan to each employee's terminal and notifies them of the plan details.

[0633] Step 5:

[0634] Users can review the learning plan received on their device and begin their learning activities. Learning can proceed at the user's own pace.

[0635] Step 6:

[0636] The device tracks the user's learning activities and sends progress data, such as the completion of each assignment and quiz results, to the server in real time.

[0637] Step 7:

[0638] The server updates the learning dashboard based on the received progress data, providing visual feedback to users and company administrators.

[0639] Step 8:

[0640] The server re-evaluates and adjusts the learning plan as needed, based on the user's learning progress and new industry trends. The revised plan is then notified to the user again.

[0641] By performing the above steps recursively, we can continuously provide an optimized learning environment for each individual employee.

[0642] (Example 1)

[0643] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] To efficiently improve employee skills, it is necessary to provide each employee with a learning plan optimized for their individual needs. However, traditional, uniform training methods have failed to adequately address individual needs, resulting in insufficient learning efficiency and effectiveness. Furthermore, there is a lack of appropriate feedback and adjustments regarding learning progress, making it difficult to optimize learning to suit each employee's pace.

[0645] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0646] In this invention, the server includes means for acquiring employee attributes, means for analyzing the acquired employee attributes and identifying skill gaps, and means for customizing learning content using a generation AI model based on the analysis results. This makes it possible to provide each employee with a customized learning plan and support efficient and effective skill acquisition.

[0647] "Employee attributes" refer to information that describes an employee's individual characteristics, such as their work history, skills, interests, and career goals.

[0648] A "skill gap" refers to the shortcomings or discrepancies between an employee's current skills and the skill set required by a company.

[0649] A "generative AI model" is a computer model that uses artificial intelligence technology to generate learning content and plans from data.

[0650] "Customizing learning content" refers to adjusting and optimizing learning plans and materials based on the individual attributes of each employee.

[0651] "Progress information" refers to data that shows the results and progress achieved by employees through their learning activities.

[0652] "Trend information" refers to data that reflects the latest changes and trends in an industry or market.

[0653] "Visualization" refers to representing information in an easily understandable form using visual means such as graphs and charts.

[0654] This invention is a system designed to support employee skill development, providing an efficient learning environment by offering an optimized learning plan for each individual employee. The system consists of three main elements: a server, terminals, and users.

[0655] The server has the capability to connect to the company's database and retrieve attribute information for each employee. This information includes the employee's work history, skills, interests, and career goals. The server uses data analysis software to analyze the retrieved information and identify gaps between current skills and required skills. Based on the analysis results, it automatically generates individually customized learning plans using a generative AI model. These plans include relevant videos, quizzes, and assignments.

[0656] The device receives custom learning plans sent from the server and notifies the user. Using a dedicated learning application, it displays the learning plan and tracks the user's progress in real time as they begin their learning activities. The device can also feed back the results to the server as the learning progresses.

[0657] Users can improve their skills by following a learning plan using a terminal as an employee. Users can learn at their own pace and visually check their progress on a dashboard on the terminal. In addition, the learning content and plan are dynamically reviewed and optimized by the server according to the user's progress and industry trends. For example, for an employee who wants to improve their project management skills by working backward, the server will create a learning plan that includes appropriate content based on the employee's current situation.

[0658] Examples of prompt messages are as follows:

[0659] "Create a learning plan to improve project management skills. Consider the user's current skill level, interests, and career goals, and include appropriate content."

[0660] This system allows for the provision of a learning experience optimized for each employee, compared to traditional, uniform training methods, and can support the improvement of a company's competitiveness.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The server connects to the company's database and retrieves employee attribute information. Input includes employee IDs, work history, skills, interests, and career goals stored in the database. The server formats this information for analysis and generates an organized attribute dataset as output. This process involves data extraction using SQL queries.

[0664] Step 2:

[0665] The server analyzes the acquired attribute dataset to identify the gap between employees' current skills and the required skills. Inputs include employees' skill sets and the company's defined target skill sets. The server compares this information using data analysis algorithms and generates a skills gap report as output. This step primarily involves statistical analysis and variance analysis.

[0666] Step 3:

[0667] The server generates a customized learning plan using a generative AI model based on the skills gap report. Inputs include skills gap information and employee learning styles and interests. The server provides this information to the generative AI model as prompts, generating a custom learning plan incorporating appropriate learning content as output. This process involves content selection and optimization using AI technology.

[0668] Step 4:

[0669] The device receives a custom learning plan sent from the server and notifies the user. The input is learning plan data from the server. The device uses a dedicated learning application to visually present the learning plan to the user as output, prompting them to begin learning. This includes push notifications and displays in the user interface.

[0670] Step 5:

[0671] Users use their devices to engage in learning activities according to their learning plan. The input is the content of the learning plan displayed on the device. Users refer to this content while watching videos, taking quizzes, and answering assignments, and learning progress data is generated as output. The user's learning activities are recorded, and the learning results are sent from the device to the server.

[0672] Step 6:

[0673] The server analyzes learning progress data sent from the terminal and generates feedback. Input includes user progress data and learning outcomes. The server analyzes this data, adjusts the learning plan as needed, and generates a new, optimized learning plan as output. This step involves generating progress-based feedback and dynamically adjusting the plan.

[0674] (Application Example 1)

[0675] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] While there is a need for efficient skill development and enhanced expertise among workers in modern industrial settings, traditional education systems struggle to flexibly provide training tailored to individual needs. Furthermore, it is difficult to track workers' progress and provide optimal learning resources in real time. In particular, there is a lack of efficient means to integrate practical training programs utilizing virtual reality technology.

[0677] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0678] In this invention, the server includes means for acquiring worker information, means for providing practical training in a virtual reality environment, and means for visualizing the progress of the training. This enables workers to receive practical training tailored to their individual needs and to efficiently improve their skills.

[0679] The term "worker" refers to an individual engaged in a specific job or task, and is the target of improvement in their skills and knowledge.

[0680] "Information" refers to data related to the worker, such as their work history, skills, and current progress.

[0681] A "learning plan" refers to a collection of individually customized training programs and resources designed to improve the skills of workers.

[0682] "Providing" refers to the act of handing over the generated training plan to the workers and encouraging them to carry out the training.

[0683] "Tracking" is the process of recording training progress in real time and monitoring the progress of workers.

[0684] "Adjustment" is a step in updating and optimizing the learning plan according to the worker's progress.

[0685] A "virtual reality environment" refers to a computer-generated three-dimensional space where workers can realistically experience practical training.

[0686] "Visualization" is a technique for displaying data graphically so that workers can easily understand their own progress.

[0687] "Industry trends" refer to changes and advancements in technology and needs within an industry, and indicate the necessary skills and knowledge based on those changes.

[0688] The system that realizes this invention includes three main components: a server, a terminal, and a user. First, the server retrieves worker information from the company's database and analyzes skill gaps using an AI model. It performs the analysis using Python and TensorFlow and generates an individualized learning plan. This plan includes the training resources necessary for improving the workers' skills and includes practical VR content using a virtual reality development environment such as Unity.

[0689] Next, the terminal uses smart glasses or a tablet to provide the worker with a generated learning plan in real time. As the worker views the VR content and progresses through the training, the terminal sends its progress to the server, which then adjusts the learning plan based on this data. The progress data is displayed on a graphical dashboard, allowing the worker to easily understand their own learning progress.

[0690] Through this system, users (workers) can train at their own pace. This enables efficient skill acquisition through immediate feedback and flexible learning programs.

[0691] For example, when conducting training on operating new machinery in a factory, the server distributes the relevant VR content and provides it to the workers via their terminals. The workers experience the actual operating procedures in a virtual space, and the server analyzes the results to determine whether further training is necessary.

[0692] An example of a prompt to input into a generative AI model is, "Please suggest VR training materials to help factory workers efficiently learn a new manufacturing process." This prompt is used to get the AI ​​to generate specific learning resources.

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The server retrieves worker information from the company's database. It receives data such as worker work history, skills, interests, and career goals as input, and supplies this information to an analysis program as output. Data retrieval is performed through database queries.

[0696] Step 2:

[0697] The server uses an AI model to analyze the skill gap based on the acquired worker information. The input for this step is the worker's current skill set, and the output is an analysis showing the difference between that skill set and the required skills. This analysis is performed using Python and TensorFlow.

[0698] Step 3:

[0699] The server generates a customized learning plan for each worker based on the analysis results. The input is the analysis results of the skill gap, and the output is a learning plan that includes relevant training resources and VR content. The generated plan is temporarily stored on the server.

[0700] Step 4:

[0701] The server sends the generated training plan to the terminal. The input is the generated training plan, which is then ready to be displayed on the terminal as output. The transmission takes place via network communication.

[0702] Step 5:

[0703] The terminal notifies the user of the received training plan and displays the training content. The input is the training plan sent from the server, and the output is visualized training information. Notification and visualization are performed via the terminal's display.

[0704] Step 6:

[0705] The user (worker) runs VR content through a terminal and begins training. The input is the displayed training content, and the output is data on the training progress. The actions performed during execution depend on the user's input.

[0706] Step 7:

[0707] The device tracks training progress in real time and sends progress data to the server. The input is the user's training progress information, and the output is the transmitted data. Tracking is performed via built-in sensors.

[0708] Step 8:

[0709] The server further adjusts the learning plan based on the received progress data. The input is real-time progress data, and the output is the updated learning plan. The adjustments are optimized by an algorithm.

[0710] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0711] This invention is a system designed to support employee skill development, enabling the analysis of emotions during the employee's learning process and the customization of learning plans accordingly. The system consists of four main components: a server, a terminal, a user, and an emotion engine.

[0712] The server connects with the company's database to retrieve employee personal information, current skills, work history, and career goals. Based on this data, it analyzes each employee's skill gaps and automatically generates custom learning plans. The learning content includes relevant videos, quizzes, and assignments, and is tailored based on industry trends and company needs.

[0713] The emotion engine uses the user's facial expressions and voice data to recognize their emotional state during learning in real time. The emotion data recognized by this engine is sent to the server and used to adjust the learning plan. For example, if the user is feeling stressed, the server will incorporate more relaxing content and tones into the learning plan.

[0714] The device serves as the learning environment for the user, receiving learning plans and sentiment-based adjustments from the server. On the device, the user completes assignments and records their progress. Progress and sentiment data are also sent to the server to ensure the learning experience is constantly optimized.

[0715] Users can improve their skills by following a learning plan on their device. The learning content provided on the device enables effective and efficient learning. Furthermore, the emotional engine provides a learning environment that takes the user's emotions into account, reducing stress and allowing for more focused learning.

[0716] As a concrete example, suppose an employee aims to improve their "presentation skills." In this case, the learning plan would include relevant videos and practical exercises. If the emotion engine detects the user's tension during learning, the server would recommend pre-prepared relaxation content to help the user relax and refocus on learning.

[0717] Thus, the present invention dynamically adjusts the learning plan according to the user's emotions, providing an optimized learning experience for each individual employee, and consequently promoting employee skill development and improved corporate competitiveness.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The server connects to the company's database and retrieves information such as each employee's work history, skills, interests, and career goals.

[0721] Step 2:

[0722] The server analyzes skill gaps based on the acquired information and automatically generates individualized learning plans. These learning plans include videos, quizzes, and assignments.

[0723] Step 3:

[0724] The generated learning plan is sent from the server to the terminal, and the employee is notified that learning has begun.

[0725] Step 4:

[0726] The user progresses through the learning process according to the learning plan provided using the device. During the learning process, the user's facial expressions and voice data are collected by the device's sensors.

[0727] Step 5:

[0728] The device sends the collected sensor data to an emotion engine to analyze the user's emotional state.

[0729] Step 6:

[0730] Emotional data obtained from the emotion engine is sent to the server. The server adjusts the learning plan accordingly, reflecting the user's emotional state. For example, if a user is experiencing high stress, the server recommends relaxation content.

[0731] Step 7:

[0732] The server reflects learning progress and emotion-based adjustments on a dashboard, providing visual feedback to users and administrators.

[0733] Step 8:

[0734] Users can check their learning progress and receive feedback on their device, and adjust their learning pace and methods as needed.

[0735] Through this series of processes, the user's emotions are incorporated into the learning experience, and a learning environment optimized for each individual is provided.

[0736] (Example 2)

[0737] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0738] In today's work environment, improving employee skills requires providing flexible training plans tailored to individual skills and career goals. However, traditional systems struggle to adapt dynamic training plans to learners' emotional states and learning progress, making it difficult to provide the optimal learning method for each employee. Furthermore, selecting learning materials that appropriately reflect industry trends and employee sentiments remains challenging.

[0739] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0740] In this invention, the server includes means for acquiring employee attribute information, means for automatically generating and dynamically adapting educational plans using a generative AI model, and means for acquiring and optimizing learners' emotional states using an emotion analysis device. This makes it possible to provide a flexible and efficient learning environment tailored to each individual employee.

[0741] "Employee attribute information" refers to information including employees' personal information, skills, work history, and career goals.

[0742] An "educational plan" refers to learning content and assignments designed to improve employees' skills, and includes materials such as videos, quizzes, and assignments.

[0743] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and automatically performs specific tasks or generates data.

[0744] An "emotion analysis device" refers to technology that analyzes data such as a user's facial expressions and voice to recognize their emotional state.

[0745] "Dynamic adaptation" refers to a process of adjusting and changing content in real time based on the learner's situation and feedback.

[0746] "Learner's emotional state" refers to an individual's emotional condition during learning (e.g., relaxed, stressed, excited, etc.).

[0747] "Optimization" refers to adjusting or improving conditions or states to be the most appropriate for a particular purpose.

[0748] The following system configurations are possible for carrying out this invention.

[0749] The server first accesses the company's database to retrieve employee attribute information. This information includes employee personal details, skills, work history, and career goals, and is collected using database management systems such as SQL queries. Next, it utilizes a generative AI model to automatically generate individual employee training plans. These training plans include relevant videos, quizzes, and assignments, which are tailored to the company's needs and industry trends.

[0750] The emotion analysis device is connected to the terminal and recognizes the user's emotional state in real time through facial expressions, voice, etc. This analysis device analyzes video and audio data displayed to the user during learning and identifies emotional states such as stress and excitement. The recognition results are sent to a server and used to optimize the educational plan.

[0751] The terminal is a device for users to engage in learning activities and has the function of presenting educational plans sent from the server. On the terminal, the user's progress as they work on assignments is recorded, and this progress information and emotional information are sent to the server to provide a more effective and personalized learning experience.

[0752] Through this system, users can efficiently progress through their learning based on a personalized educational plan. To help users relax and engage in learning, the server adjusts the content as needed based on emotional data obtained from an emotion analysis device.

[0753] For example, if a user wants to improve their "presentation skills," the server will suggest relevant educational videos and practical training exercises. Also, if the emotion analyzer detects signs of tension during learning, the server will provide pre-prepared relaxation content.

[0754] An example of a prompt for a generative AI model could be a text-based instruction such as, "The user is feeling stressed; please add relaxing content to the learning plan." In this way, the learning experience for learners is constantly optimized, and useful skill development becomes possible for companies as well.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The server accesses the company's database and retrieves employee attribute information. The employee's ID is provided as input, and SQL queries are executed based on this ID to search for the necessary data. The output is a dataset containing personal information, current skills, work history, and career goals.

[0758] Step 2:

[0759] The server automatically generates educational plans using a generative AI model based on collected attribute information. Employee attribute data is supplied to the AI ​​model as input, and the most appropriate educational content is selected based on that data. As output, a customized educational plan is generated for each employee. This plan includes relevant videos, quizzes, and assignments.

[0760] Step 3:

[0761] The device collects the user's facial expressions and voice data in real time through an emotion analysis device. The user's learning audio and video streams are provided as input, and the facial recognition algorithm analyzes this data. The output provides data about the user's current emotional state.

[0762] Step 4:

[0763] The server receives emotional state data sent from the terminal and dynamically adjusts the educational plan. The input consists of emotional data and an existing educational plan, which a generative AI model analyzes and modifies as needed. The output is an adjusted educational plan, sometimes with added relaxing content.

[0764] Step 5:

[0765] Users complete assignments according to the educational plan displayed on their devices. User inputs include data on the progress of learning content and quiz answers. Outputs include learning progress data, which is sent from the device to the server.

[0766] Step 6:

[0767] The server proposes the next steps and generates feedback based on the received learning progress data. Progress data is input to the server, where the AI ​​analyzes it. As output, a feedback report is generated, containing suggestions for the next learning content and areas for improvement, and presented to the user.

[0768] (Application Example 2)

[0769] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] Providing a uniform learning plan to employees without considering their emotional state during the skill development process can reduce learning efficiency. Furthermore, if adjustments are not made based on each employee's emotions and stress levels, decreased motivation and learning interruptions may occur. Therefore, it is necessary to dynamically adjust learning plans based on employees' emotions to provide a more effective and customized learning experience.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0772] In this invention, the server includes means for acquiring and analyzing emotional data, means for generating a learning plan based on employee information, and means for dynamically adjusting the learning plan based on the emotional data. This makes it possible to provide a learning plan adapted to the emotional state of each employee.

[0773] An "employee" is an individual hired to perform a specific task within an organization such as a company or factory.

[0774] "Information" refers to the collection of all data necessary for learning, including personal information, skill levels, work history, or sentiment data related to the employee.

[0775] A "learning plan" is a guideline for the process that includes the structure and progress management of learning materials and assignments developed to improve employees' skills.

[0776] "Emotional data" refers to information that indicates an employee's psychological state at a given moment, obtained from their facial expressions, voice, or behavior.

[0777] A "server" is a computer system that processes data centrally and transmits information to other terminals or devices.

[0778] "Dynamic adjustment" means modifying or optimizing the learning plan as needed, based on employee sentiment data and learning progress.

[0779] "Visualization" means representing data and information in the form of graphs, images, and other visual aids to make them easier to understand.

[0780] "Industry trends" refer to the current development and changes and trends in a particular field or industry as a whole.

[0781] The system for implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server interacts with the company's database to retrieve employee personal information, skill levels, and past work history, and generates individual learning plans based on this information. It also receives emotion data from the emotion engine and dynamically adjusts the learning plans. The software used employs an emotion recognition library (e.g., Microsoft Azure Emotion API) to analyze emotions from facial expressions and voice data.

[0782] The terminal serves as a learning environment for employees, accepting learning plans sent from the server and tracking and recording their learning progress. Emotional data is also collected from the terminal and sent to the server. Hardware used includes emotion-recognizing cameras (e.g., Intel RealSense) and tablet devices (e.g., iPad). This allows for real-time tracking of emotional changes during actual learning sessions and enables the presentation of relaxation content or tasks as needed.

[0783] Users can engage in tasks that involve mental activity and reactions through their devices, and monitor their own progress. For example, if an employee is undergoing training to improve their advanced presentation skills, the emotion engine can detect the employee's tension, and the server can provide relaxing music or simple break content to improve learning efficiency.

[0784] An example of a prompt in a generative AI model would be: "Explain how to use a neural network to perform real-time sentiment analysis of a work operator and generate a customized learning plan." This would make the learning plan more effective and personalized, and promote employee skill development.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The server retrieves employee personal information, skill levels, and work history from the company's database as input. Based on this data, it analyzes the employee's skill gaps and generates appropriate learning plans. Utilizing a generative AI model, it selects the most suitable learning materials and assignments for each employee and creates a learning plan. The output is a customized learning plan.

[0788] Step 2:

[0789] The terminal receives the learning plan sent from the server and provides an interface for the user to begin learning. The user progresses through the terminal, recording their progress in real time as input data. This progress data is periodically sent to the server, and a learning log is generated as output.

[0790] Step 3:

[0791] The emotion engine acquires the user's facial expressions and voice as input data and analyzes them using an emotion recognition library. This emotion data is sent to the server as information representing the user's psychological state. Specifically, it processes the data to read stress and concentration levels from facial expressions and obtains the emotional state as output.

[0792] Step 4:

[0793] The server receives emotional data and learning progress data as input and dynamically adjusts the learning plan based on them. For example, if the server determines that the user is experiencing stress, it will add relaxation music or simple tasks to the learning plan. This adjusted learning plan is then output and sent to the terminal.

[0794] Step 5:

[0795] The user receives a customized learning plan via their device and then proceeds with their studies. This step involves the user utilizing provided relaxation content to learn in a relaxed state, resulting in a more efficient learning experience.

[0796] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0797] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0799] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0800] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0801] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0802] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0803] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0804] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0805] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0806] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0807] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0808] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0809] 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.

[0810] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0811] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0812] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0813] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0814] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0815] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0816] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] Means of obtaining employee information,

[0820] Means for generating a learning plan based on the employee's information,

[0821] A means of providing the generated learning plan to the employee,

[0822] A means of tracking and recording employee learning progress,

[0823] A means of adjusting the learning plan according to learning progress,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for visualizing the learning progress of an employee.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for selecting learning resources based on industry trends.

[0829] "Example 1"

[0830] (Claim 1)

[0831] Means for obtaining employee attributes,

[0832] A means of analyzing acquired employee attributes to identify skill gaps,

[0833] A means of customizing the learning content using an AI model based on the analysis results,

[0834] A means of sending customized learning content to employees,

[0835] A means of tracking and recording the progress of employee learning activities,

[0836] A means of dynamically adjusting learning content based on progress information,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, further comprising means for visualizing employee learning progress information.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising means for selecting learning resources based on trend information.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] Means of obtaining worker information,

[0845] Means for generating a learning plan based on the worker's information,

[0846] A means of providing the generated learning plan to the worker,

[0847] A means of tracking and recording the learning progress of workers,

[0848] A means of adjusting the learning plan according to learning progress,

[0849] A means of providing workers with practical training in a virtual reality environment,

[0850] A means of visualizing the progress of training,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising means for suggesting the next step based on visualized training progress.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for selecting training resources based on industry trends.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] Means for obtaining employee attribute information,

[0859] A means for automatically generating an educational plan based on the attribute information of the employee,

[0860] A means of dynamically adapting educational plans using generative AI models,

[0861] A means of acquiring the emotional state of learners using an emotion analysis device,

[0862] A means of optimizing educational plans based on learners' emotional information,

[0863] A means of displaying the educational plan on an electronic device and recording its progress,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for visualizing the learning progress and emotional state of an employee.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising means for selecting learning materials based on industrial analysis.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] Means of obtaining employee information,

[0872] Means for generating a learning plan based on the employee's information,

[0873] A means of providing the generated learning plan to the employee,

[0874] A means of tracking and recording employee learning progress,

[0875] A means of adjusting the learning plan according to learning progress,

[0876] Methods for acquiring and analyzing emotional data,

[0877] A means of dynamically adjusting the learning plan based on emotional data,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, further comprising means for visualizing the learning progress of an employee.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for selecting learning resources based on industry trends. [Explanation of Symbols]

[0883] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining worker information, Means for generating a learning plan based on the worker's information, A means of providing the generated learning plan to the worker, A means of tracking and recording the learning progress of workers, A means of adjusting the learning plan according to learning progress, A means of providing workers with practical training in a virtual reality environment, A means of visualizing the progress of training, A system that includes this.

2. The system according to claim 1, further comprising means for suggesting the next step based on visualized training progress.

3. The system according to claim 1, further comprising means for selecting training resources based on industry trends.

Images