system

Abstract

We provide the system. [Solution] A means of acquiring input information and generating learning data based on job content, A means of distributing the generated training data to users and recording their learning progress, A means of analyzing recorded learning progress and making it visible to stakeholders, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, due to the increasing mobility of human resources and the diversification of business contents, it is difficult to systematize education in an organization. For this reason, there is a demand for an educational method for effectively upgrading skills for new employees and existing employees, but the current situation is that on-site education is still being explored, and there are variations in the effects of education. The object of the present invention is to solve such problems and realize unified and efficient employee education within an organization.

Means for Solving the Problems

[0005] This invention provides a system that includes means for acquiring input information from a user and generating learning data related to job content based on this information; means for distributing the generated learning data to the user and recording learning progress; and means for analyzing the recorded learning progress and visualizing it for relevant parties. Furthermore, the generated learning data includes text data or video data and is generated using generative artificial intelligence. This makes it possible to efficiently and effectively provide personalized educational content to each user.

[0006] "Input information" refers to data that users provide to the system, such as job duties, desired skills, and learning objectives.

[0007] "Learning data" refers to educational content generated based on input information and tailored to the user's job duties and learning objectives.

[0008] "Generative artificial intelligence" refers to artificial intelligence that has the ability to analyze provided input information and combine the necessary information according to the purpose to generate new data.

[0009] "Progress" refers to the state or stage that indicates the user's achievement level based on the learning data.

[0010] "Stakeholders" refers to people inside and outside the organization who are interested in the user's learning situation and progress, such as supervisors and HR personnel.

[0011] "Visualization" is a technique or practice that makes it easier to understand related information by visually representing recorded progress information. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential 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.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] To implement this invention, a system consisting mainly of users, servers, and terminals is utilized to generate educational materials using a generation AI and manage their progress.

[0034] First, the user accesses the system and creates an account. The user provides information such as their job description, the skills they want to acquire, and their preferred learning format (text, video, etc.). This information is sent to and stored on the server.

[0035] Next, the server analyzes the user's input information and uses a generative AI to generate appropriate training data. The generative AI references relevant technical knowledge and market trends from its internal database to construct educational materials tailored to the user's needs. These materials may also include interactive content as needed.

[0036] The generated learning data is delivered from the server to the terminal. The terminal visually presents this data to the user, who then uses the presented learning materials to proceed with self-study. As the user's learning progresses, the terminal records their progress to the server. This information is made visible for access by stakeholders and used to provide feedback based on progress and evaluation.

[0037] As a concrete example, consider a scenario where a user in a company's sales department wishes to learn "new customer acquisition skills." When the user inputs relevant information into the system, the server generates learning data including the latest market trends and promotional techniques. The terminal provides this data, allowing the user to hone their skills according to a designated learning plan. Ultimately, the user's progress and results are aggregated on the server, enabling stakeholders to evaluate the results and plan the next steps.

[0038] In this way, the present invention enables increased efficiency and improved effectiveness of human resource development within organizations.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users access the system and create their own accounts. Through the interface, users input information such as their job description, current skills, and what they want to learn, and send it to the server.

[0042] Step 2:

[0043] The server analyzes the user's input information to understand their needs. The server then sends a request to the generating AI, instructing it to generate optimal training data based on the relevant job description.

[0044] Step 3:

[0045] The generating AI references a database within the server and analyzes relevant proposals, technical knowledge, manuals, and market trend information. Based on this, the AI ​​generates learning materials in text and video formats that are suitable for the user.

[0046] Step 4:

[0047] The server sends the training data created by the generating AI to the terminal. The terminal receives the data and displays it visually to the user, making it ready for learning.

[0048] Step 5:

[0049] Users engage in self-study using learning materials displayed on their devices. To check their learning progress and results, users also participate in interactive quizzes and simulations.

[0050] Step 6:

[0051] The device records the user's learning progress in real time and sends this information to the server. The server stores and analyzes this progress data.

[0052] Step 7:

[0053] The server displays recorded progress data on a dashboard for stakeholders. Administrators and supervisors can monitor users' learning progress through this dashboard and provide feedback or additional learning content as needed.

[0054] (Example 1)

[0055] 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."

[0056] Currently, many workplaces face the challenge of efficiently generating educational content that meets individual learning needs and providing it to users in an appropriate format. Furthermore, there is a need to accurately track learning progress and enable stakeholders to easily evaluate it.

[0057] 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.

[0058] In this invention, the server includes a processing unit for acquiring input information and generating job-related educational content, a processing unit for distributing the generated educational content to recipients and recording the progress of the education, and a processing unit for interpreting the recorded progress of the education and visualizing it for relevant parties. This enables the efficient generation and distribution of educational content tailored to individual learning needs, and makes it easy to manage and evaluate learning progress.

[0059] "Inputted information" refers to data provided by users to the system regarding their job responsibilities and learning preferences.

[0060] "Educational content" refers to text-based and video-based learning materials that include information related to the user's job.

[0061] A "processing device" is a computer device used for analyzing information, generating educational content, and recording and visualizing progress.

[0062] "Generative artificial intelligence" is an artificial intelligence technology that automatically creates content in response to user requests based on input prompts.

[0063] "Progress" refers to records of the learning process, such as the extent to which the user has completed their learning.

[0064] "Stakeholders" refers to individuals or organizations responsible for understanding, evaluating, and providing feedback on users' learning progress.

[0065] To implement this invention, three main components are required: a user, a server, and a terminal. The user first accesses the system using a dedicated web portal or application. Initially, the user creates an account and enters information such as their job description, desired skills, and preferred learning format. This information is sent to the server and stored in a secure database.

[0066] The server then utilizes a generative AI model to analyze the received information. This process employs advanced language processing techniques, such as OpenAI's GPT model, for information analysis and material generation. This generative AI model generates prompts based on user input and uses them to optimize educational content. A possible prompt might be, "Generate materials including the latest market trends regarding new customer acquisition skills." Based on these prompts, the generative AI model creates materials and provides the server with educational content, including interactive elements.

[0067] The generated learning materials are delivered to the terminal by the server. The terminal visually presents these materials to the user, who then proceeds with self-study. The terminal also sends information to the server about how much the user has worked on the materials and their progress, recording the learning progress. This information is further processed on the server and visualized in a dashboard format that is easily accessible to stakeholders.

[0068] For example, suppose a sales representative at a company wants to learn how to acquire new customers. The user inputs this information into the system, and the server generates content incorporating the latest market trends and promotional techniques. This content is delivered to the user via their device, and the user can progress through the learning process according to a specified plan. As the learning progresses, the progress information is aggregated on the server, providing a foundation for efficiently managing the company's talent development program.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] Users access the system through a web portal or application and create an account. Users enter information such as their job description, desired skills, and preferred learning format. This information is sent to the server as user information and securely stored in the database. At this stage, the entered information becomes the foundational data for subsequent processing of the system.

[0072] Step 2:

[0073] The server analyzes user information and generates prompts to send to the AI ​​model. Based on the user's preferences and job responsibilities, it creates specific prompts such as, "Generate training materials that include the latest market trends regarding new customer acquisition skills." The generated prompts are then used as input to the AI ​​model.

[0074] Step 3:

[0075] The generative AI model receives prompt text as input and generates relevant educational content based on it. The AI ​​references internal and external data sources, extracting, processing, and integrating the necessary information. This process may result in content output in formats such as text, images, and videos. The generated educational content is returned to the server.

[0076] Step 4:

[0077] The server receives AI-generated educational content and prepares it for delivery to the user's device. The content format is customized according to the learning style specified by the user. For example, if the content is text-based, interactive elements are added as needed. Once ready for delivery, the server sends the content to the user's device.

[0078] Step 5:

[0079] The terminal presents the user with educational content received from the server. Through a visual interface, the user can efficiently progress through the learning process. The terminal records the user's actions and monitors their learning progress. Progress data is generated each time the user engages with the learning materials.

[0080] Step 6:

[0081] The terminal sends recorded progress data to the server. The server aggregates and analyzes this data, making it available to stakeholders in a visualized format. This visualized progress data serves as foundational material for evaluation and planning the next steps. This enables timely feedback and adjustments to maximize the effectiveness of learning.

[0082] (Application Example 1)

[0083] 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."

[0084] Traditional education systems struggle to provide individually optimized skills necessary for specific jobs, making efficient human resource development a particular challenge in practical environments such as factories. Furthermore, there is a need for a system that can record and manage users' learning progress in real time, allowing administrators to immediately evaluate and implement improvement measures.

[0085] 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.

[0086] In this invention, the server includes means for acquiring input information and generating educational data based on work content; means for presenting the generated educational data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing and evaluating it for relevant parties; and means for displaying interactive educational content using a visual display device. This enables efficient learning tailored to individual work needs, while also allowing learning progress to be managed in real time and immediate evaluation and improvement measures to be taken.

[0087] "Means for acquiring input information" refers to methods and devices for collecting information from users regarding their work content and the skills they wish to acquire.

[0088] "Means for generating educational data based on job content" refers to methods or devices for creating educational materials and content to cultivate the skills necessary for specific tasks, based on collected information.

[0089] "Means for presenting generated educational data to users" refers to methods or devices for displaying or providing generated educational content to users.

[0090] "Means for recording learning progress" refers to methods or devices for recording and managing a user's learning status and progress.

[0091] "Means for analyzing recorded learning progress and visualizing and evaluating it for stakeholders" refers to methods and devices for analyzing collected progress data, displaying it in a way that is easy for stakeholders to understand, and conducting evaluations.

[0092] "Means for displaying interactive educational content using a visual display device" refers to devices or methods for providing educational content visually in a way that allows users to actively participate.

[0093] This system was built to support the skill development of factory workers. The system primarily consists of three elements: a server, terminals (smart glasses), and users.

[0094] The server first processes input information obtained from the user via smart glasses. This input information includes the user's work content and the skills they wish to acquire. Based on this information, the server generates educational content using a generative AI model. The generated content mainly consists of videos and interactive guides. By referring to an internal database and combining the most suitable materials, the server creates learning materials tailored to the user.

[0095] The generated educational content is delivered to the user via smart glasses (e.g., Vuzix, Google Glass®). The smart glasses visually present this content, helping the user efficiently acquire the necessary skills. As the user progresses through the content, their learning progress is sent from the smart glasses to a server and recorded on the server.

[0096] The server analyzes recorded learning progress and visualizes the progress data for administrators and other stakeholders. This allows administrators to understand the user's skill improvement in real time and provide further support as needed.

[0097] For example, if a factory worker needs to learn how to operate a new machine, the system will generate videos of the relevant operating procedures and present them to the worker in real time to support smooth learning. An example of a prompt to implement this process would be, "Generate up-to-date training materials on the operating procedures of the new CNC machine and create content to present a visual guide."

[0098] This system will enable efficient and effective skill acquisition on the factory floor, and is expected to improve overall work efficiency.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The user logs into the system via smart glasses and enters their work details and desired skills. This input information is retrieved through the smart glasses' interface and sent to the server. The server receives this information and stores it for the next processing step.

[0102] Step 2:

[0103] The server analyzes the received input information and uses a generative AI model to generate specific educational content. Here, the generative AI model accesses an internal database, combining relevant technical documents and market information to create the most suitable learning materials for the user. Data processing based on the input information results in the output of customized learning content.

[0104] Step 3:

[0105] The generated educational content is delivered from the server to the device (smart glasses). The smart glasses display the content in the user's field of view and present it in an interactive format. For example, they can play a video that explains the operation procedure of a new machine in detail and present options when the user is ready to proceed to the next step.

[0106] Step 4:

[0107] The user learns content presented through smart glasses, and the smart glasses record their progress. During this process, the user's learning activity (e.g., completion status of each piece of content and frequency of interactions) is recorded in real time.

[0108] Step 5:

[0109] The device sends recorded learning progress to the server. The server receives this data, analyzes the progress, and visualizes it on a dashboard. Administrators use this information to see in real time which workers have acquired which skills and when, and provide additional support or evaluation as needed.

[0110] Step 6:

[0111] The server generates administrator and user feedback based on progress data. This feedback is presented as an improved learning plan or suggestions for the next learning steps, maintaining system consistency.

[0112] 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.

[0113] This invention is a system that combines an emotion engine to make user learning more efficient and effective. This system mainly consists of a user, a server, and a terminal, and has the function of generating learning materials using generative AI and detecting the user's emotional state in real time using an emotion engine.

[0114] First, the user accesses the system, creates an account, and provides input information including job description and learning objectives. The user's input information is sent to the server, and based on this information, the AI ​​generates appropriate training data. The training data includes text data and video data, and is tailored to the learning objectives.

[0115] The generated training data is delivered from the server to the terminal and presented to the user visually. While the user is learning on the terminal, the emotion engine analyzes the user's facial expressions and voice to perform emotion recognition. Based on this emotion recognition result, the server dynamically adjusts the content of the training data to provide a learning experience best suited to the user's emotional state. Furthermore, feedback on the user's emotional state is provided to relevant parties via the server and used to improve the user's learning experience.

[0116] As a concrete example, consider a situation where a user is feeling frustrated while learning a new programming language. When the emotion engine detects this frustration, the server adjusts its approach to provide additional learning materials and hints through generative AI to deepen understanding. In this way, the goal is to provide the user with the optimal learning environment.

[0117] Thus, the present invention can significantly improve the quality and efficiency of education within an organization by enabling real-time monitoring of the user's emotional state using an emotion engine and optimizing learning content based on that monitoring.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The user accesses the system and creates an account. The user enters information about their job description, learning goals, and areas of interest into a terminal and sends it to the server.

[0121] Step 2:

[0122] The server analyzes the submitted input information and sends a request to the generating AI. The AI ​​retrieves relevant information from the database and generates training data (text and video data) tailored to the user's learning needs.

[0123] Step 3:

[0124] The server sends the generated training data to a device equipped with an emotion engine. This allows the user to visually receive the training data via the device.

[0125] Step 4:

[0126] The user views the training data presented on the device and progresses through the learning process. During this time, the emotion engine built into the device analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0127] Step 5:

[0128] The device sends emotional data obtained by the emotion engine to the server. The server uses the emotional data to analyze the user's level of understanding and concentration.

[0129] Step 6:

[0130] Based on the analysis results, the server uses the generating AI again to create additional learning data and hints tailored to the user. This provides optimal content to address any unclear points in the learning process and pique interest.

[0131] Step 7:

[0132] The server sends the generated additional data to the terminal, improving the user's learning experience. Furthermore, it supports the user's sustainable growth by visualizing learning progress and sentiment data for stakeholders and providing feedback.

[0133] (Example 2)

[0134] 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".

[0135] Traditional learning systems fail to optimize content by taking into account the learner's emotional state, resulting in decreased learning efficiency. Furthermore, they struggle to dynamically provide appropriate learning materials for each user, making it difficult to offer a personalized learning experience.

[0136] 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.

[0137] In this invention, the server includes means for generating learning content suitable for job duties and learning objectives based on user input information using generative artificial intelligence, means for analyzing the user's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning content based on the analysis results. This enables the provision of a personalized learning experience and improved learning efficiency.

[0138] "User" refers to an individual or group that uses the system to receive learning content.

[0139] "Input information" refers to information about job duties and learning objectives that users provide to the system.

[0140] "Learning content" refers to educational materials that include text data, video data, and interactive elements generated using generative artificial intelligence.

[0141] "Generative artificial intelligence" refers to artificial intelligence technology that generates information and data based on given instructions or prompts.

[0142] A "terminal" refers to an electronic device used by users to receive learning content and engage in learning.

[0143] An "emotion analysis engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[0144] "Dynamic adjustment" refers to changing the content and format of learning materials in real time based on user sentiment data.

[0145] "Feedback" refers to the means of providing users and stakeholders with information regarding their learning progress and emotional state.

[0146] In a mode for carrying out the invention, this system aims to make user learning efficient and effective. This system mainly consists of a server, a terminal, and an emotion analysis engine including generative artificial intelligence.

[0147] When a user accesses the system, they first create an account and enter information related to their job responsibilities and learning objectives into the terminal. This information forms the basis for generating content tailored to the user's learning needs.

[0148] Upon receiving this information, the server uses generative artificial intelligence to generate appropriate learning content. In this process, prompts are provided to the generative AI, which then constructs specific learning materials tailored to the learning objectives. For example, using the prompt "Generate learning materials on the basic syntax of Python" will generate the corresponding materials.

[0149] The generated learning content is delivered from the server to the user's device. The device then visually presents this content to the user, including text data, video data, or interactive elements.

[0150] As a user learns, the device's emotion analysis engine analyzes the user's facial expressions and voice in real time to recognize their emotional state. This analysis is sent to a server, which dynamically adjusts the learning content based on the user's current emotional state, thereby providing a personalized learning experience.

[0151] For example, if a user feels frustrated while learning a new programming language, the sentiment analysis engine will capture this emotion. Based on this, the server will provide additional content and hints through generative artificial intelligence to aid understanding, allowing the learning process to proceed more smoothly.

[0152] This invention is expected to improve the quality and efficiency of learning, and will bring various benefits to educational settings.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] Users access the system and create an account. Next, they provide information such as their job responsibilities and learning objectives as input to the terminal. This input information serves as foundational data to identify the user's individual learning needs.

[0156] Step 2:

[0157] The terminal sends the acquired input information to the server. The server receives this information and generates a prompt message for the generative AI model. This prompt message may include instructions such as, "Generate training materials related to the job description." This prompts the generative AI model to begin generating data suitable for the purpose.

[0158] Step 3:

[0159] The server uses a generative AI model to generate learning content based on input information and prompts. The generated content is output as text data or video data. In this process, the generative AI model uses data analysis and natural language processing techniques to construct specific learning materials that meet the required content.

[0160] Step 4:

[0161] The server delivers the generated learning content to the device. The device visually presents the received content to the user. The user uses this content to proceed with their learning. Interactive elements function effectively through actions performed on the device.

[0162] Step 5:

[0163] During training, the device analyzes the user's facial expressions and voice via an emotion analysis engine. It receives real-time emotion data as input and identifies the user's emotional state as output. For example, if the user is confused, that data is sent to the server.

[0164] Step 6:

[0165] The server analyzes the received emotional data and dynamically adjusts the learning content. This adjustment is achieved by reusing the generative AI model. For example, if the user's emotional state indicates frustration, the content is reorganized to be easily understood. This process provides an optimal learning experience.

[0166] Step 7:

[0167] The server visualizes learning progress and emotional state for stakeholders and provides feedback. This feedback includes the user's learning outcomes and emotional changes, which stakeholders can use to adjust the educational plan. The data at this stage will be used to improve future learning strategies.

[0168] (Application Example 2)

[0169] 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".

[0170] In today's educational environment, it is not easy to adjust learning content according to the emotional state and comprehension level of individual learners. Especially in virtual learning environments, learners are more likely to experience anxiety and stress, which can affect learning efficiency. This invention aims to enable dynamic adjustment of learning content in response to learners' emotions, thereby providing a more effective and stress-free learning experience.

[0171] 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.

[0172] In this invention, the server includes means for acquiring input information and generating learning data based on job content; means for distributing the generated learning data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing it for relevant parties; and means for sensing the user's emotional state in real time and dynamically adjusting the learning data based on this emotional state. This enables flexible adjustment of learning content in accordance with the learner's emotions.

[0173] "Input information" refers to data obtained from users in order to generate learning data according to job duties and learning objectives.

[0174] "Training data" refers to text and video data generated using generative artificial intelligence to facilitate user education.

[0175] "Means of recording progress" refers to technologies that record how far a user has progressed in their learning and accumulate that information.

[0176] "Means of visualization" refers to technologies that display the recorded progress of learning in a way that is easy for stakeholders to understand.

[0177] "Means of sensing emotional states in real time" refers to technology that analyzes and evaluates a user's emotions in real time based on their facial expressions and voice.

[0178] "Means of dynamic adjustment" refers to technologies for instantly changing and optimizing the content of training data based on the user's emotional state.

[0179] To realize this invention, a user-accessible program is required. The server acquires input information from the user and generates training data based on it. Generative artificial intelligence technology is used to automatically generate text and video data tailored to the user's job duties and learning objectives. Open-source AI models and platforms can be used in this generation process.

[0180] The server distributes the generated training data to the terminal. The terminal has an application installed to visually display the training data, and the user performs the training through this application.

[0181] The device incorporates an emotion engine that detects emotional states in real time. Using technologies such as Microsoft's Face API, it analyzes the user's facial expressions and voice to evaluate their emotional state. This evaluation result is sent to a server, which dynamically adjusts the training data. For example, if the user is feeling stressed, additional training material can be presented.

[0182] For example, when a user is using a VR application to learn how to operate new machinery in a factory, hints and support messages will automatically appear when the user feels anxious. Possible prompts in this case might include: "Write code for a robotic assistant to detect user anxiety in VR training and provide necessary support in real time."

[0183] This system enables flexible learning support tailored to the learner's emotions, which is expected to improve learning efficiency.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The user logs into the system and enters their job description and learning objectives. This information is sent to the server. The input in this step is text data entered by the user, while the output is user information stored on the server.

[0187] Step 2:

[0188] The server generates training data using a generative AI model based on the user input information it receives. This data is represented in text or video format. Specifically, it generates prompt sentences based on the input keywords, and the AI ​​then creates content suitable for training based on these prompts. The input for this step is user information, and the output is the generated training data.

[0189] Step 3:

[0190] The generated training data is delivered from the server to the terminal. An application installed on the terminal receives this data and presents it visually to the user. The user then proceeds with learning based on the presented content. In this step, the input is the training data, and the output is the learning content displayed on the terminal.

[0191] Step 4:

[0192] The device detects the user's emotional state in real time using an emotion engine. The analysis uses a camera and microphone to evaluate the user's emotions based on facial expressions and voice. The input for this step is real-time audio and video data, and the output is the detected emotion data.

[0193] Step 5:

[0194] The server receives emotional data sent from the emotion engine and dynamically adjusts the training data based on it. For example, if anxiety is high, additional learning materials are provided to facilitate understanding. The input for this step is emotional data, and the output is the adjusted training data.

[0195] Step 6:

[0196] The adjusted training data is redistributed to the device and presented to the user. The user continues learning through the new content. In this step, the input is the adjusted training data, and the output is the new content the user sees.

[0197] 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.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] To implement this invention, a system consisting mainly of users, servers, and terminals is utilized to generate educational materials using a generation AI and manage their progress.

[0214] First, the user accesses the system and creates an account. The user provides information such as their job description, the skills they want to acquire, and their preferred learning format (text, video, etc.). This information is sent to and stored on the server.

[0215] Next, the server analyzes the user's input information and uses a generative AI to generate appropriate training data. The generative AI references relevant technical knowledge and market trends from its internal database to construct educational materials tailored to the user's needs. These materials may also include interactive content as needed.

[0216] The generated learning data is delivered from the server to the terminal. The terminal visually presents this data to the user, who then uses the presented learning materials to proceed with self-study. As the user's learning progresses, the terminal records their progress to the server. This information is made visible for access by stakeholders and used to provide feedback based on progress and evaluation.

[0217] As a concrete example, consider a scenario where a user in a company's sales department wishes to learn "new customer acquisition skills." When the user inputs relevant information into the system, the server generates learning data including the latest market trends and promotional techniques. The terminal provides this data, allowing the user to hone their skills according to a designated learning plan. Ultimately, the user's progress and results are aggregated on the server, enabling stakeholders to evaluate the results and plan the next steps.

[0218] In this way, the present invention enables increased efficiency and improved effectiveness of human resource development within organizations.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] Users access the system and create their own accounts. Through the interface, users input information such as their job description, current skills, and what they want to learn, and send it to the server.

[0222] Step 2:

[0223] The server analyzes the user's input information to understand their needs. The server then sends a request to the generating AI, instructing it to generate optimal training data based on the relevant job description.

[0224] Step 3:

[0225] The generating AI references a database within the server and analyzes relevant proposals, technical knowledge, manuals, and market trend information. Based on this, the AI ​​generates learning materials in text and video formats that are suitable for the user.

[0226] Step 4:

[0227] The server sends the training data created by the generating AI to the terminal. The terminal receives the data and displays it visually to the user, making it ready for learning.

[0228] Step 5:

[0229] Users engage in self-study using learning materials displayed on their devices. To check their learning progress and results, users also participate in interactive quizzes and simulations.

[0230] Step 6:

[0231] The device records the user's learning progress in real time and sends this information to the server. The server stores and analyzes this progress data.

[0232] Step 7:

[0233] The server displays recorded progress data on a dashboard for stakeholders. Administrators and supervisors can monitor users' learning progress through this dashboard and provide feedback or additional learning content as needed.

[0234] (Example 1)

[0235] 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."

[0236] Currently, many workplaces face the challenge of efficiently generating educational content that meets individual learning needs and providing it to users in an appropriate format. Furthermore, there is a need to accurately track learning progress and enable stakeholders to easily evaluate it.

[0237] 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.

[0238] In this invention, the server includes a processing unit for acquiring input information and generating job-related educational content, a processing unit for distributing the generated educational content to recipients and recording the progress of the education, and a processing unit for interpreting the recorded progress of the education and visualizing it for relevant parties. This enables the efficient generation and distribution of educational content tailored to individual learning needs, and makes it easy to manage and evaluate learning progress.

[0239] "Inputted information" refers to data provided by users to the system regarding their job responsibilities and learning preferences.

[0240] "Educational content" refers to text-based and video-based learning materials that include information related to the user's job.

[0241] A "processing device" is a computer device used for analyzing information, generating educational content, and recording and visualizing progress.

[0242] "Generative artificial intelligence" is an artificial intelligence technology that automatically creates content in response to user requests based on input prompts.

[0243] "Progress" refers to records of the learning process, such as the extent to which the user has completed their learning.

[0244] "Stakeholders" refers to individuals or organizations responsible for understanding, evaluating, and providing feedback on users' learning progress.

[0245] To implement this invention, three main components are required: a user, a server, and a terminal. The user first accesses the system using a dedicated web portal or application. Initially, the user creates an account and enters information such as their job description, desired skills, and preferred learning format. This information is sent to the server and stored in a secure database.

[0246] The server then utilizes a generative AI model to analyze the received information. This process employs advanced language processing techniques, such as OpenAI's GPT model, for information analysis and material generation. This generative AI model generates prompts based on user input and uses them to optimize educational content. A possible prompt might be, "Generate materials including the latest market trends regarding new customer acquisition skills." Based on these prompts, the generative AI model creates materials and provides the server with educational content, including interactive elements.

[0247] The generated learning materials are delivered to the terminal by the server. The terminal visually presents these materials to the user, who then proceeds with self-study. The terminal also sends information to the server about how much the user has worked on the materials and their progress, recording the learning progress. This information is further processed on the server and visualized in a dashboard format that is easily accessible to stakeholders.

[0248] For example, suppose a sales representative at a company wants to learn how to acquire new customers. The user inputs this information into the system, and the server generates content incorporating the latest market trends and promotional techniques. This content is delivered to the user via their device, and the user can progress through the learning process according to a specified plan. As the learning progresses, the progress information is aggregated on the server, providing a foundation for efficiently managing the company's talent development program.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] Users access the system through a web portal or application and create an account. Users enter information such as their job description, desired skills, and preferred learning format. This information is sent to the server as user information and securely stored in the database. At this stage, the entered information becomes the foundational data for subsequent processing of the system.

[0252] Step 2:

[0253] The server analyzes user information and generates prompts to send to the AI ​​model. Based on the user's preferences and job responsibilities, it creates specific prompts such as, "Generate training materials that include the latest market trends regarding new customer acquisition skills." The generated prompts are then used as input to the AI ​​model.

[0254] Step 3:

[0255] The generative AI model receives prompt text as input and generates relevant educational content based on it. The AI ​​references internal and external data sources, extracting, processing, and integrating the necessary information. This process may result in content output in formats such as text, images, and videos. The generated educational content is returned to the server.

[0256] Step 4:

[0257] The server receives AI-generated educational content and prepares it for delivery to the user's device. The content format is customized according to the learning style specified by the user. For example, if the content is text-based, interactive elements are added as needed. Once ready for delivery, the server sends the content to the user's device.

[0258] Step 5:

[0259] The terminal presents the user with educational content received from the server. Through a visual interface, the user can efficiently progress through the learning process. The terminal records the user's actions and monitors their learning progress. Progress data is generated each time the user engages with the learning materials.

[0260] Step 6:

[0261] The terminal sends recorded progress data to the server. The server aggregates and analyzes this data, making it available to stakeholders in a visualized format. This visualized progress data serves as foundational material for evaluation and planning the next steps. This enables timely feedback and adjustments to maximize the effectiveness of learning.

[0262] (Application Example 1)

[0263] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Traditional education systems struggle to provide individually optimized skills necessary for specific jobs, making efficient human resource development a particular challenge in practical environments such as factories. Furthermore, there is a need for a system that can record and manage users' learning progress in real time, allowing administrators to immediately evaluate and implement improvement measures.

[0265] 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.

[0266] In this invention, the server includes means for acquiring input information and generating educational data based on work content; means for presenting the generated educational data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing and evaluating it for relevant parties; and means for displaying interactive educational content using a visual display device. This enables efficient learning tailored to individual work needs, while also allowing learning progress to be managed in real time and immediate evaluation and improvement measures to be taken.

[0267] "Means for acquiring input information" refers to methods and devices for collecting information from users regarding their work content and the skills they wish to acquire.

[0268] "Means for generating educational data based on job content" refers to methods or devices for creating educational materials and content to cultivate the skills necessary for specific tasks, based on collected information.

[0269] "Means for presenting generated educational data to users" refers to methods or devices for displaying or providing generated educational content to users.

[0270] "Means for recording learning progress" refers to methods or devices for recording and managing a user's learning status and progress.

[0271] "Means for analyzing recorded learning progress and visualizing and evaluating it for stakeholders" refers to methods and devices for analyzing collected progress data, displaying it in a way that is easy for stakeholders to understand, and conducting evaluations.

[0272] "Means for displaying interactive educational content using a visual display device" refers to devices or methods for providing educational content visually in a way that allows users to actively participate.

[0273] This system was built to support the skill development of factory workers. The system primarily consists of three elements: a server, terminals (smart glasses), and users.

[0274] The server first processes input information obtained from the user via smart glasses. This input information includes the user's work content and the skills they wish to acquire. Based on this information, the server generates educational content using a generative AI model. The generated content mainly consists of videos and interactive guides. By referring to an internal database and combining the most suitable materials, the server creates learning materials tailored to the user.

[0275] The generated educational content is delivered to the user via smart glasses (e.g., Vuzix, Google Glass). The smart glasses visually present this content, helping the user efficiently acquire the necessary skills. As the user progresses through the content, their learning progress is sent from the smart glasses to a server and recorded on the server.

[0276] The server analyzes recorded learning progress and visualizes the progress data for administrators and other stakeholders. This allows administrators to understand the user's skill improvement in real time and provide further support as needed.

[0277] For example, if a factory worker needs to learn how to operate a new machine, the system will generate videos of the relevant operating procedures and present them to the worker in real time to support smooth learning. An example of a prompt to implement this process would be, "Generate up-to-date training materials on the operating procedures of the new CNC machine and create content to present a visual guide."

[0278] This system will enable efficient and effective skill acquisition on the factory floor, and is expected to improve overall work efficiency.

[0279] The process of the specific processing in Application Example 1 will be described using FIG. 12.

[0280] Step 1:

[0281] The user logs in to the system through smart glasses and enters the business content and skills to be acquired. This input information is obtained through the interface of the smart glasses and sent to the server. The server receives this information and saves it for the next processing step.

[0282] Step 2:

[0283] The server analyzes the received input information and uses the generated AI model to generate specific educational content. Here, the generated AI model accesses the internal database, combines relevant technical materials and market information, and generates the most suitable teaching materials for the user. Through data processing based on the input information, customized learning content is output.

[0284] Step 3:

[0285] The generated educational content is distributed from the server to the terminal (smart glasses). The smart glasses display the content in the user's field of vision and present it in an interactive format. For example, play a video that details the operation procedure of a new machine and present options when the user is ready to proceed to the next stage.

[0286] Step 4:

[0287] The user learns the content presented via the smart glasses, and the smart glasses record the progress. At this time, the user's learning activities (such as the completion status of each content and the frequency of interactions, etc.) are recorded in real time.

[0288] Step 5:

[0289] The device sends recorded learning progress to the server. The server receives this data, analyzes the progress, and visualizes it on a dashboard. Administrators use this information to see in real time which workers have acquired which skills and when, and provide additional support or evaluation as needed.

[0290] Step 6:

[0291] The server generates administrator and user feedback based on progress data. This feedback is presented as an improved learning plan or suggestions for the next learning steps, maintaining system consistency.

[0292] 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.

[0293] This invention is a system that combines an emotion engine to make user learning more efficient and effective. This system mainly consists of a user, a server, and a terminal, and has the function of generating learning materials using generative AI and detecting the user's emotional state in real time using an emotion engine.

[0294] First, the user accesses the system, creates an account, and provides input information including job description and learning objectives. The user's input information is sent to the server, and based on this information, the AI ​​generates appropriate training data. The training data includes text data and video data, and is tailored to the learning objectives.

[0295] The generated training data is delivered from the server to the terminal and presented to the user visually. While the user is learning on the terminal, the emotion engine analyzes the user's facial expressions and voice to perform emotion recognition. Based on this emotion recognition result, the server dynamically adjusts the content of the training data to provide a learning experience best suited to the user's emotional state. Furthermore, feedback on the user's emotional state is provided to relevant parties via the server and used to improve the user's learning experience.

[0296] As a concrete example, consider a situation where a user is feeling frustrated while learning a new programming language. When the emotion engine detects this frustration, the server adjusts its approach to provide additional learning materials and hints through generative AI to deepen understanding. In this way, the goal is to provide the user with the optimal learning environment.

[0297] Thus, the present invention can significantly improve the quality and efficiency of education within an organization by enabling real-time monitoring of the user's emotional state using an emotion engine and optimizing learning content based on that monitoring.

[0298] The following describes the processing flow.

[0299] Step 1:

[0300] The user accesses the system and creates an account. The user enters information about their job description, learning goals, and areas of interest into a terminal and sends it to the server.

[0301] Step 2:

[0302] The server analyzes the submitted input information and sends a request to the generating AI. The AI ​​retrieves relevant information from the database and generates training data (text and video data) tailored to the user's learning needs.

[0303] Step 3:

[0304] The server transmits the data, together with the generated learning data, to the terminal equipped with the emotion engine. As a result, the user can visually receive the learning data via the terminal.

[0305] Step 4:

[0306] The user browses the learning data presented on the terminal and proceeds with the learning. During this period, the emotion engine installed on the terminal analyzes the user's expression and voice in real time and recognizes the emotional state.

[0307] Step 5:

[0308] The terminal transmits the emotion data obtained by the emotion engine to the server. The server uses the emotion data to analyze the user's comprehension level and concentration state.

[0309] Step 6:

[0310] Based on the analysis results, the server uses the generative AI again to generate additional learning data and hints tailored to the user. This provides the optimal content for bringing out the unclear points and interests in learning.

[0311] Step 7:

[0312] The server sends the generated additional data to the terminal to improve the user's learning experience. Furthermore, by visualizing the learning progress and emotion data to the relevant parties and providing feedback, it supports the continuous growth of the user.

[0313] (Example 2)

[0314] Next, Example 2 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".

[0315] Traditional learning systems fail to optimize content by taking into account the learner's emotional state, resulting in decreased learning efficiency. Furthermore, they struggle to dynamically provide appropriate learning materials for each user, making it difficult to offer a personalized learning experience.

[0316] 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.

[0317] In this invention, the server includes means for generating learning content suitable for job duties and learning objectives based on user input information using generative artificial intelligence, means for analyzing the user's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning content based on the analysis results. This enables the provision of a personalized learning experience and improved learning efficiency.

[0318] "User" refers to an individual or group that uses the system to receive learning content.

[0319] "Input information" refers to information about job duties and learning objectives that users provide to the system.

[0320] "Learning content" refers to educational materials that include text data, video data, and interactive elements generated using generative artificial intelligence.

[0321] "Generative artificial intelligence" refers to artificial intelligence technology that generates information and data based on given instructions or prompts.

[0322] A "terminal" refers to an electronic device used by users to receive learning content and engage in learning.

[0323] An "emotion analysis engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[0324] "Dynamic adjustment" refers to changing the content and format of learning materials in real time based on user sentiment data.

[0325] "Feedback" refers to the means of providing users and stakeholders with information regarding their learning progress and emotional state.

[0326] In a mode for carrying out the invention, this system aims to make user learning efficient and effective. This system mainly consists of a server, a terminal, and an emotion analysis engine including generative artificial intelligence.

[0327] When a user accesses the system, they first create an account and enter information related to their job responsibilities and learning objectives into the terminal. This information forms the basis for generating content tailored to the user's learning needs.

[0328] Upon receiving this information, the server uses generative artificial intelligence to generate appropriate learning content. In this process, prompts are provided to the generative AI, which then constructs specific learning materials tailored to the learning objectives. For example, using the prompt "Generate learning materials on the basic syntax of Python" will generate the corresponding materials.

[0329] The generated learning content is delivered from the server to the user's device. The device then visually presents this content to the user, including text data, video data, or interactive elements.

[0330] As a user learns, the device's emotion analysis engine analyzes the user's facial expressions and voice in real time to recognize their emotional state. This analysis is sent to a server, which dynamically adjusts the learning content based on the user's current emotional state, thereby providing a personalized learning experience.

[0331] For example, if a user feels frustrated while learning a new programming language, the sentiment analysis engine will capture this emotion. Based on this, the server will provide additional content and hints through generative artificial intelligence to aid understanding, allowing the learning process to proceed more smoothly.

[0332] This invention is expected to improve the quality and efficiency of learning, and will bring various benefits to educational settings.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] Users access the system and create an account. Next, they provide information such as their job responsibilities and learning objectives as input to the terminal. This input information serves as foundational data to identify the user's individual learning needs.

[0336] Step 2:

[0337] The terminal sends the acquired input information to the server. The server receives this information and generates a prompt message for the generative AI model. This prompt message may include instructions such as, "Generate training materials related to the job description." This prompts the generative AI model to begin generating data suitable for the purpose.

[0338] Step 3:

[0339] The server uses a generative AI model to generate learning content based on input information and prompts. The generated content is output as text data or video data. In this process, the generative AI model uses data analysis and natural language processing techniques to construct specific learning materials that meet the required content.

[0340] Step 4:

[0341] The server delivers the generated learning content to the device. The device visually presents the received content to the user. The user uses this content to proceed with their learning. Interactive elements function effectively through actions performed on the device.

[0342] Step 5:

[0343] During training, the device analyzes the user's facial expressions and voice via an emotion analysis engine. It receives real-time emotion data as input and identifies the user's emotional state as output. For example, if the user is confused, that data is sent to the server.

[0344] Step 6:

[0345] The server analyzes the received emotional data and dynamically adjusts the learning content. This adjustment is achieved by reusing the generative AI model. For example, if the user's emotional state indicates frustration, the content is reorganized to be easily understood. This process provides an optimal learning experience.

[0346] Step 7:

[0347] The server visualizes learning progress and emotional state for stakeholders and provides feedback. This feedback includes the user's learning outcomes and emotional changes, which stakeholders can use to adjust the educational plan. The data at this stage will be used to improve future learning strategies.

[0348] (Application Example 2)

[0349] 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."

[0350] In today's educational environment, it is not easy to adjust learning content according to the emotional state and comprehension level of individual learners. Especially in virtual learning environments, learners are more likely to experience anxiety and stress, which can affect learning efficiency. This invention aims to enable dynamic adjustment of learning content in response to learners' emotions, thereby providing a more effective and stress-free learning experience.

[0351] 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.

[0352] In this invention, the server includes means for acquiring input information and generating learning data based on job content; means for distributing the generated learning data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing it for relevant parties; and means for sensing the user's emotional state in real time and dynamically adjusting the learning data based on this emotional state. This enables flexible adjustment of learning content in accordance with the learner's emotions.

[0353] "Input information" refers to data obtained from users in order to generate learning data according to job duties and learning objectives.

[0354] "Training data" refers to text and video data generated using generative artificial intelligence to facilitate user education.

[0355] "Means of recording progress" refers to technologies that record how far a user has progressed in their learning and accumulate that information.

[0356] "Means of visualization" refers to technologies that display the recorded progress of learning in a way that is easy for stakeholders to understand.

[0357] "Means of sensing emotional states in real time" refers to technology that analyzes and evaluates a user's emotions in real time based on their facial expressions and voice.

[0358] "Means of dynamic adjustment" refers to technologies for instantly changing and optimizing the content of training data based on the user's emotional state.

[0359] To realize this invention, a user-accessible program is required. The server acquires input information from the user and generates training data based on it. Generative artificial intelligence technology is used to automatically generate text and video data tailored to the user's job duties and learning objectives. Open-source AI models and platforms can be used in this generation process.

[0360] The server distributes the generated training data to the terminal. The terminal has an application installed to visually display the training data, and the user performs the training through this application.

[0361] The device incorporates an emotion engine that detects emotional states in real time. Using technologies such as Microsoft's Face API, it analyzes the user's facial expressions and voice to evaluate their emotional state. This evaluation result is sent to a server, which dynamically adjusts the training data. For example, if the user is feeling stressed, additional training material can be presented.

[0362] For example, when a user is using a VR application to learn how to operate new machinery in a factory, hints and support messages will automatically appear when the user feels anxious. Possible prompts in this case might include: "Write code for a robotic assistant to detect user anxiety in VR training and provide necessary support in real time."

[0363] This system enables flexible learning support tailored to the learner's emotions, which is expected to improve learning efficiency.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The user logs into the system and enters their job description and learning objectives. This information is sent to the server. The input in this step is text data entered by the user, while the output is user information stored on the server.

[0367] Step 2:

[0368] The server generates training data using a generative AI model based on the user input information it receives. This data is represented in text or video format. Specifically, it generates prompt sentences based on the input keywords, and the AI ​​then creates content suitable for training based on these prompts. The input for this step is user information, and the output is the generated training data.

[0369] Step 3:

[0370] The generated training data is delivered from the server to the terminal. An application installed on the terminal receives this data and presents it visually to the user. The user then proceeds with learning based on the presented content. In this step, the input is the training data, and the output is the learning content displayed on the terminal.

[0371] Step 4:

[0372] The device detects the user's emotional state in real time using an emotion engine. The analysis uses a camera and microphone to evaluate the user's emotions based on facial expressions and voice. The input for this step is real-time audio and video data, and the output is the detected emotion data.

[0373] Step 5:

[0374] The server receives emotional data sent from the emotion engine and dynamically adjusts the training data based on it. For example, if anxiety is high, additional learning materials are provided to facilitate understanding. The input for this step is emotional data, and the output is the adjusted training data.

[0375] Step 6:

[0376] The adjusted training data is redistributed to the device and presented to the user. The user continues learning through the new content. In this step, the input is the adjusted training data, and the output is the new content the user sees.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] To implement this invention, a system consisting mainly of users, servers, and terminals is utilized to generate educational materials using a generation AI and manage their progress.

[0394] First, the user accesses the system and creates an account. The user provides information such as their job description, the skills they want to acquire, and their preferred learning format (text, video, etc.). This information is sent to and stored on the server.

[0395] Next, the server analyzes the user's input information and uses a generative AI to generate appropriate training data. The generative AI references relevant technical knowledge and market trends from its internal database to construct educational materials tailored to the user's needs. These materials may also include interactive content as needed.

[0396] The generated learning data is delivered from the server to the terminal. The terminal visually presents this data to the user, who then uses the presented learning materials to proceed with self-study. As the user's learning progresses, the terminal records their progress to the server. This information is made visible for access by stakeholders and used to provide feedback based on progress and evaluation.

[0397] As a concrete example, consider a scenario where a user in a company's sales department wishes to learn "new customer acquisition skills." When the user inputs relevant information into the system, the server generates learning data including the latest market trends and promotional techniques. The terminal provides this data, allowing the user to hone their skills according to a designated learning plan. Ultimately, the user's progress and results are aggregated on the server, enabling stakeholders to evaluate the results and plan the next steps.

[0398] In this way, the present invention enables increased efficiency and improved effectiveness of human resource development within organizations.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] Users access the system and create their own accounts. Through the interface, users input information such as their job description, current skills, and what they want to learn, and send it to the server.

[0402] Step 2:

[0403] The server analyzes the user's input information to understand their needs. The server then sends a request to the generating AI, instructing it to generate optimal training data based on the relevant job description.

[0404] Step 3:

[0405] The generating AI references a database within the server and analyzes relevant proposals, technical knowledge, manuals, and market trend information. Based on this, the AI ​​generates learning materials in text and video formats that are suitable for the user.

[0406] Step 4:

[0407] The server sends the training data created by the generating AI to the terminal. The terminal receives the data and displays it visually to the user, making it ready for learning.

[0408] Step 5:

[0409] Users engage in self-study using learning materials displayed on their devices. To check their learning progress and results, users also participate in interactive quizzes and simulations.

[0410] Step 6:

[0411] The device records the user's learning progress in real time and sends this information to the server. The server stores and analyzes this progress data.

[0412] Step 7:

[0413] The server displays recorded progress data on a dashboard for stakeholders. Administrators and supervisors can monitor users' learning progress through this dashboard and provide feedback or additional learning content as needed.

[0414] (Example 1)

[0415] 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."

[0416] Currently, many workplaces face the challenge of efficiently generating educational content that meets individual learning needs and providing it to users in an appropriate format. Furthermore, there is a need to accurately track learning progress and enable stakeholders to easily evaluate it.

[0417] 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.

[0418] In this invention, the server includes a processing unit for acquiring input information and generating job-related educational content, a processing unit for distributing the generated educational content to recipients and recording the progress of the education, and a processing unit for interpreting the recorded progress of the education and visualizing it for relevant parties. This enables the efficient generation and distribution of educational content tailored to individual learning needs, and makes it easy to manage and evaluate learning progress.

[0419] "Inputted information" refers to data provided by users to the system regarding their job responsibilities and learning preferences.

[0420] "Educational content" refers to text-based and video-based learning materials that include information related to the user's job.

[0421] A "processing device" is a computer device used for analyzing information, generating educational content, and recording and visualizing progress.

[0422] "Generative artificial intelligence" is an artificial intelligence technology that automatically creates content in response to user requests based on input prompts.

[0423] "Progress" refers to records of the learning process, such as the extent to which the user has completed their learning.

[0424] "Stakeholders" refers to individuals or organizations responsible for understanding, evaluating, and providing feedback on users' learning progress.

[0425] To implement this invention, three main components are required: a user, a server, and a terminal. The user first accesses the system using a dedicated web portal or application. Initially, the user creates an account and enters information such as their job description, desired skills, and preferred learning format. This information is sent to the server and stored in a secure database.

[0426] The server then utilizes a generative AI model to analyze the received information. This process employs advanced language processing techniques, such as OpenAI's GPT model, for information analysis and material generation. This generative AI model generates prompts based on user input and uses them to optimize educational content. A possible prompt might be, "Generate materials including the latest market trends regarding new customer acquisition skills." Based on these prompts, the generative AI model creates materials and provides the server with educational content, including interactive elements.

[0427] The generated learning materials are delivered to the terminal by the server. The terminal visually presents these materials to the user, who then proceeds with self-study. The terminal also sends information to the server about how much the user has worked on the materials and their progress, recording the learning progress. This information is further processed on the server and visualized in a dashboard format that is easily accessible to stakeholders.

[0428] For example, suppose a sales representative at a company wants to learn how to acquire new customers. The user inputs this information into the system, and the server generates content incorporating the latest market trends and promotional techniques. This content is delivered to the user via their device, and the user can progress through the learning process according to a specified plan. As the learning progresses, the progress information is aggregated on the server, providing a foundation for efficiently managing the company's talent development program.

[0429] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0430] Step 1:

[0431] Users access the system through a web portal or application and create an account. Users enter information such as their job description, desired skills, and preferred learning format. This information is sent to the server as user information and securely stored in the database. At this stage, the entered information becomes the foundational data for subsequent processing of the system.

[0432] Step 2:

[0433] The server analyzes user information and generates prompts to send to the AI ​​model. Based on the user's preferences and job responsibilities, it creates specific prompts such as, "Generate training materials that include the latest market trends regarding new customer acquisition skills." The generated prompts are then used as input to the AI ​​model.

[0434] Step 3:

[0435] The generative AI model receives prompt text as input and generates relevant educational content based on it. The AI ​​references internal and external data sources, extracting, processing, and integrating the necessary information. This process may result in content output in formats such as text, images, and videos. The generated educational content is returned to the server.

[0436] Step 4:

[0437] The server receives AI-generated educational content and prepares it for delivery to the user's device. The content format is customized according to the learning style specified by the user. For example, if the content is text-based, interactive elements are added as needed. Once ready for delivery, the server sends the content to the user's device.

[0438] Step 5:

[0439] The terminal presents the user with educational content received from the server. Through a visual interface, the user can efficiently progress through the learning process. The terminal records the user's actions and monitors their learning progress. Progress data is generated each time the user engages with the learning materials.

[0440] Step 6:

[0441] The terminal sends recorded progress data to the server. The server aggregates and analyzes this data, making it available to stakeholders in a visualized format. This visualized progress data serves as foundational material for evaluation and planning the next steps. This enables timely feedback and adjustments to maximize the effectiveness of learning.

[0442] (Application Example 1)

[0443] 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."

[0444] Traditional education systems struggle to provide individually optimized skills necessary for specific jobs, making efficient human resource development a particular challenge in practical environments such as factories. Furthermore, there is a need for a system that can record and manage users' learning progress in real time, allowing administrators to immediately evaluate and implement improvement measures.

[0445] 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.

[0446] In this invention, the server includes means for acquiring input information and generating educational data based on work content; means for presenting the generated educational data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing and evaluating it for relevant parties; and means for displaying interactive educational content using a visual display device. This enables efficient learning tailored to individual work needs, while also allowing learning progress to be managed in real time and immediate evaluation and improvement measures to be taken.

[0447] "Means for acquiring input information" refers to methods and devices for collecting information from users regarding their work content and the skills they wish to acquire.

[0448] "Means for generating educational data based on job content" refers to methods or devices for creating educational materials and content to cultivate the skills necessary for specific tasks, based on collected information.

[0449] "Means for presenting generated educational data to users" refers to methods or devices for displaying or providing generated educational content to users.

[0450] "Means for recording learning progress" refers to methods or devices for recording and managing a user's learning status and progress.

[0451] "Means for analyzing recorded learning progress and visualizing and evaluating it for stakeholders" refers to methods and devices for analyzing collected progress data, displaying it in a way that is easy for stakeholders to understand, and conducting evaluations.

[0452] "Means for displaying interactive educational content using a visual display device" refers to devices or methods for providing educational content visually in a way that allows users to actively participate.

[0453] This system was built to support the skill development of factory workers. The system primarily consists of three elements: a server, terminals (smart glasses), and users.

[0454] The server first processes input information obtained from the user via smart glasses. This input information includes the user's work content and the skills they wish to acquire. Based on this information, the server generates educational content using a generative AI model. The generated content mainly consists of videos and interactive guides. By referring to an internal database and combining the most suitable materials, the server creates learning materials tailored to the user.

[0455] The generated educational content is delivered to the user via smart glasses (e.g., Vuzix, Google Glass). The smart glasses visually present this content, helping the user efficiently acquire the necessary skills. As the user progresses through the content, their learning progress is sent from the smart glasses to a server and recorded on the server.

[0456] The server analyzes recorded learning progress and visualizes the progress data for administrators and other stakeholders. This allows administrators to understand the user's skill improvement in real time and provide further support as needed.

[0457] For example, if a factory worker needs to learn how to operate a new machine, the system will generate videos of the relevant operating procedures and present them to the worker in real time to support smooth learning. An example of a prompt to implement this process would be, "Generate up-to-date training materials on the operating procedures of the new CNC machine and create content to present a visual guide."

[0458] This system will enable efficient and effective skill acquisition on the factory floor, and is expected to improve overall work efficiency.

[0459] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0460] Step 1:

[0461] The user logs into the system via smart glasses and enters their work details and desired skills. This input information is retrieved through the smart glasses' interface and sent to the server. The server receives this information and stores it for the next processing step.

[0462] Step 2:

[0463] The server analyzes the received input information and uses a generative AI model to generate specific educational content. Here, the generative AI model accesses an internal database, combining relevant technical documents and market information to create the most suitable learning materials for the user. Data processing based on the input information results in the output of customized learning content.

[0464] Step 3:

[0465] The generated educational content is delivered from the server to the device (smart glasses). The smart glasses display the content in the user's field of view and present it in an interactive format. For example, they can play a video that explains the operation procedure of a new machine in detail and present options when the user is ready to proceed to the next step.

[0466] Step 4:

[0467] The user learns content presented through smart glasses, and the smart glasses record their progress. During this process, the user's learning activity (e.g., completion status of each piece of content and frequency of interactions) is recorded in real time.

[0468] Step 5:

[0469] The device sends recorded learning progress to the server. The server receives this data, analyzes the progress, and visualizes it on a dashboard. Administrators use this information to see in real time which workers have acquired which skills and when, and provide additional support or evaluation as needed.

[0470] Step 6:

[0471] The server generates administrator and user feedback based on progress data. This feedback is presented as an improved learning plan or suggestions for the next learning steps, maintaining system consistency.

[0472] 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.

[0473] This invention is a system that combines an emotion engine to make user learning more efficient and effective. This system mainly consists of a user, a server, and a terminal, and has the function of generating learning materials using generative AI and detecting the user's emotional state in real time using an emotion engine.

[0474] First, the user accesses the system, creates an account, and provides input information including job description and learning objectives. The user's input information is sent to the server, and based on this information, the AI ​​generates appropriate training data. The training data includes text data and video data, and is tailored to the learning objectives.

[0475] The generated training data is delivered from the server to the terminal and presented to the user visually. While the user is learning on the terminal, the emotion engine analyzes the user's facial expressions and voice to perform emotion recognition. Based on this emotion recognition result, the server dynamically adjusts the content of the training data to provide a learning experience best suited to the user's emotional state. Furthermore, feedback on the user's emotional state is provided to relevant parties via the server and used to improve the user's learning experience.

[0476] As a concrete example, consider a situation where a user is feeling frustrated while learning a new programming language. When the emotion engine detects this frustration, the server adjusts its approach to provide additional learning materials and hints through generative AI to deepen understanding. In this way, the goal is to provide the user with the optimal learning environment.

[0477] Thus, the present invention can significantly improve the quality and efficiency of education within an organization by enabling real-time monitoring of the user's emotional state using an emotion engine and optimizing learning content based on that monitoring.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The user accesses the system and creates an account. The user enters information about their job description, learning goals, and areas of interest into a terminal and sends it to the server.

[0481] Step 2:

[0482] The server analyzes the submitted input information and sends a request to the generating AI. The AI ​​retrieves relevant information from the database and generates training data (text and video data) tailored to the user's learning needs.

[0483] Step 3:

[0484] The server sends the generated training data to a device equipped with an emotion engine. This allows the user to visually receive the training data via the device.

[0485] Step 4:

[0486] The user views the training data presented on the device and progresses through the learning process. During this time, the emotion engine built into the device analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0487] Step 5:

[0488] The device sends emotional data obtained by the emotion engine to the server. The server uses the emotional data to analyze the user's level of understanding and concentration.

[0489] Step 6:

[0490] Based on the analysis results, the server uses the generating AI again to create additional learning data and hints tailored to the user. This provides optimal content to address any unclear points in the learning process and pique interest.

[0491] Step 7:

[0492] The server sends the generated additional data to the terminal, improving the user's learning experience. Furthermore, it supports the user's sustainable growth by visualizing learning progress and sentiment data for stakeholders and providing feedback.

[0493] (Example 2)

[0494] 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."

[0495] Traditional learning systems fail to optimize content by taking into account the learner's emotional state, resulting in decreased learning efficiency. Furthermore, they struggle to dynamically provide appropriate learning materials for each user, making it difficult to offer a personalized learning experience.

[0496] 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.

[0497] In this invention, the server includes means for generating learning content suitable for job duties and learning objectives based on user input information using generative artificial intelligence, means for analyzing the user's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning content based on the analysis results. This enables the provision of a personalized learning experience and improved learning efficiency.

[0498] "User" refers to an individual or group that uses the system to receive learning content.

[0499] "Input information" refers to information about job duties and learning objectives that users provide to the system.

[0500] "Learning content" refers to educational materials that include text data, video data, and interactive elements generated using generative artificial intelligence.

[0501] "Generative artificial intelligence" refers to artificial intelligence technology that generates information and data based on given instructions or prompts.

[0502] A "terminal" refers to an electronic device used by users to receive learning content and engage in learning.

[0503] An "emotion analysis engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[0504] "Dynamic adjustment" refers to changing the content and format of learning materials in real time based on user sentiment data.

[0505] "Feedback" refers to the means of providing users and stakeholders with information regarding their learning progress and emotional state.

[0506] In a mode for carrying out the invention, this system aims to make user learning efficient and effective. This system mainly consists of a server, a terminal, and an emotion analysis engine including generative artificial intelligence.

[0507] When a user accesses the system, they first create an account and enter information related to their job responsibilities and learning objectives into the terminal. This information forms the basis for generating content tailored to the user's learning needs.

[0508] Upon receiving this information, the server uses generative artificial intelligence to generate appropriate learning content. In this process, prompts are provided to the generative AI, which then constructs specific learning materials tailored to the learning objectives. For example, using the prompt "Generate learning materials on the basic syntax of Python" will generate the corresponding materials.

[0509] The generated learning content is delivered from the server to the user's device. The device then visually presents this content to the user, including text data, video data, or interactive elements.

[0510] As a user learns, the device's emotion analysis engine analyzes the user's facial expressions and voice in real time to recognize their emotional state. This analysis is sent to a server, which dynamically adjusts the learning content based on the user's current emotional state, thereby providing a personalized learning experience.

[0511] For example, if a user feels frustrated while learning a new programming language, the sentiment analysis engine will capture this emotion. Based on this, the server will provide additional content and hints through generative artificial intelligence to aid understanding, allowing the learning process to proceed more smoothly.

[0512] This invention is expected to improve the quality and efficiency of learning, and will bring various benefits to educational settings.

[0513] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0514] Step 1:

[0515] Users access the system and create an account. Next, they provide information such as their job responsibilities and learning objectives as input to the terminal. This input information serves as foundational data to identify the user's individual learning needs.

[0516] Step 2:

[0517] The terminal sends the acquired input information to the server. The server receives this information and generates a prompt message for the generative AI model. This prompt message may include instructions such as, "Generate training materials related to the job description." This prompts the generative AI model to begin generating data suitable for the purpose.

[0518] Step 3:

[0519] The server uses a generative AI model to generate learning content based on input information and prompts. The generated content is output as text data or video data. In this process, the generative AI model uses data analysis and natural language processing techniques to construct specific learning materials that meet the required content.

[0520] Step 4:

[0521] The server delivers the generated learning content to the device. The device visually presents the received content to the user. The user uses this content to proceed with their learning. Interactive elements function effectively through actions performed on the device.

[0522] Step 5:

[0523] During training, the device analyzes the user's facial expressions and voice via an emotion analysis engine. It receives real-time emotion data as input and identifies the user's emotional state as output. For example, if the user is confused, that data is sent to the server.

[0524] Step 6:

[0525] The server analyzes the received emotional data and dynamically adjusts the learning content. This adjustment is achieved by reusing the generative AI model. For example, if the user's emotional state indicates frustration, the content is reorganized to be easily understood. This process provides an optimal learning experience.

[0526] Step 7:

[0527] The server visualizes learning progress and emotional state for stakeholders and provides feedback. This feedback includes the user's learning outcomes and emotional changes, which stakeholders can use to adjust the educational plan. The data at this stage will be used to improve future learning strategies.

[0528] (Application Example 2)

[0529] 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."

[0530] In today's educational environment, it is not easy to adjust learning content according to the emotional state and comprehension level of individual learners. Especially in virtual learning environments, learners are more likely to experience anxiety and stress, which can affect learning efficiency. This invention aims to enable dynamic adjustment of learning content in response to learners' emotions, thereby providing a more effective and stress-free learning experience.

[0531] 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.

[0532] In this invention, the server includes means for acquiring input information and generating learning data based on job content; means for distributing the generated learning data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing it for relevant parties; and means for sensing the user's emotional state in real time and dynamically adjusting the learning data based on this emotional state. This enables flexible adjustment of learning content in accordance with the learner's emotions.

[0533] "Input information" refers to data obtained from users in order to generate learning data according to job duties and learning objectives.

[0534] "Training data" refers to text and video data generated using generative artificial intelligence to facilitate user education.

[0535] "Means of recording progress" refers to technologies that record how far a user has progressed in their learning and accumulate that information.

[0536] "Means of visualization" refers to technologies that display the recorded progress of learning in a way that is easy for stakeholders to understand.

[0537] "Means of sensing emotional states in real time" refers to technology that analyzes and evaluates a user's emotions in real time based on their facial expressions and voice.

[0538] "Means of dynamic adjustment" refers to technologies for instantly changing and optimizing the content of training data based on the user's emotional state.

[0539] To realize this invention, a user-accessible program is required. The server acquires input information from the user and generates training data based on it. Generative artificial intelligence technology is used to automatically generate text and video data tailored to the user's job duties and learning objectives. Open-source AI models and platforms can be used in this generation process.

[0540] The server distributes the generated training data to the terminal. The terminal has an application installed to visually display the training data, and the user performs the training through this application.

[0541] The device incorporates an emotion engine that detects emotional states in real time. Using technologies such as Microsoft's Face API, it analyzes the user's facial expressions and voice to evaluate their emotional state. This evaluation result is sent to a server, which dynamically adjusts the training data. For example, if the user is feeling stressed, additional training material can be presented.

[0542] For example, when a user is using a VR application to learn how to operate new machinery in a factory, hints and support messages will automatically appear when the user feels anxious. Possible prompts in this case might include: "Write code for a robotic assistant to detect user anxiety in VR training and provide necessary support in real time."

[0543] This system enables flexible learning support tailored to the learner's emotions, which is expected to improve learning efficiency.

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The user logs into the system and enters their job description and learning objectives. This information is sent to the server. The input in this step is text data entered by the user, while the output is user information stored on the server.

[0547] Step 2:

[0548] The server generates training data using a generative AI model based on the user input information it receives. This data is represented in text or video format. Specifically, it generates prompt sentences based on the input keywords, and the AI ​​then creates content suitable for training based on these prompts. The input for this step is user information, and the output is the generated training data.

[0549] Step 3:

[0550] The generated training data is delivered from the server to the terminal. An application installed on the terminal receives this data and presents it visually to the user. The user then proceeds with learning based on the presented content. In this step, the input is the training data, and the output is the learning content displayed on the terminal.

[0551] Step 4:

[0552] The device detects the user's emotional state in real time using an emotion engine. The analysis uses a camera and microphone to evaluate the user's emotions based on facial expressions and voice. The input for this step is real-time audio and video data, and the output is the detected emotion data.

[0553] Step 5:

[0554] The server receives emotional data sent from the emotion engine and dynamically adjusts the training data based on it. For example, if anxiety is high, additional learning materials are provided to facilitate understanding. The input for this step is emotional data, and the output is the adjusted training data.

[0555] Step 6:

[0556] The adjusted training data is redistributed to the device and presented to the user. The user continues learning through the new content. In this step, the input is the adjusted training data, and the output is the new content the user sees.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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".

[0574] To implement this invention, a system consisting mainly of users, servers, and terminals is utilized to generate educational materials using a generation AI and manage their progress.

[0575] First, the user accesses the system and creates an account. The user provides information such as their job description, the skills they want to acquire, and their preferred learning format (text, video, etc.). This information is sent to and stored on the server.

[0576] Next, the server analyzes the user's input information and uses a generative AI to generate appropriate training data. The generative AI references relevant technical knowledge and market trends from its internal database to construct educational materials tailored to the user's needs. These materials may also include interactive content as needed.

[0577] The generated learning data is delivered from the server to the terminal. The terminal visually presents this data to the user, who then uses the presented learning materials to proceed with self-study. As the user's learning progresses, the terminal records their progress to the server. This information is made visible for access by stakeholders and used to provide feedback based on progress and evaluation.

[0578] As a concrete example, consider a scenario where a user in a company's sales department wishes to learn "new customer acquisition skills." When the user inputs relevant information into the system, the server generates learning data including the latest market trends and promotional techniques. The terminal provides this data, allowing the user to hone their skills according to a designated learning plan. Ultimately, the user's progress and results are aggregated on the server, enabling stakeholders to evaluate the results and plan the next steps.

[0579] In this way, the present invention enables increased efficiency and improved effectiveness of human resource development within organizations.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] Users access the system and create their own accounts. Through the interface, users input information such as their job description, current skills, and what they want to learn, and send it to the server.

[0583] Step 2:

[0584] The server analyzes the user's input information to understand their needs. The server then sends a request to the generating AI, instructing it to generate optimal training data based on the relevant job description.

[0585] Step 3:

[0586] The generating AI references a database within the server and analyzes relevant proposals, technical knowledge, manuals, and market trend information. Based on this, the AI ​​generates learning materials in text and video formats that are suitable for the user.

[0587] Step 4:

[0588] The server sends the training data created by the generating AI to the terminal. The terminal receives the data and displays it visually to the user, making it ready for learning.

[0589] Step 5:

[0590] Users engage in self-study using learning materials displayed on their devices. To check their learning progress and results, users also participate in interactive quizzes and simulations.

[0591] Step 6:

[0592] The device records the user's learning progress in real time and sends this information to the server. The server stores and analyzes this progress data.

[0593] Step 7:

[0594] The server displays recorded progress data on a dashboard for stakeholders. Administrators and supervisors can monitor users' learning progress through this dashboard and provide feedback or additional learning content as needed.

[0595] (Example 1)

[0596] 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".

[0597] Currently, many workplaces face the challenge of efficiently generating educational content that meets individual learning needs and providing it to users in an appropriate format. Furthermore, there is a need to accurately track learning progress and enable stakeholders to easily evaluate it.

[0598] 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.

[0599] In this invention, the server includes a processing unit for acquiring input information and generating job-related educational content, a processing unit for distributing the generated educational content to recipients and recording the progress of the education, and a processing unit for interpreting the recorded progress of the education and visualizing it for relevant parties. This enables the efficient generation and distribution of educational content tailored to individual learning needs, and makes it easy to manage and evaluate learning progress.

[0600] "Inputted information" refers to data provided by users to the system regarding their job responsibilities and learning preferences.

[0601] "Educational content" refers to text-based and video-based learning materials that include information related to the user's job.

[0602] A "processing device" is a computer device used for analyzing information, generating educational content, and recording and visualizing progress.

[0603] "Generative artificial intelligence" is an artificial intelligence technology that automatically creates content in response to user requests based on input prompts.

[0604] "Progress" refers to records of the learning process, such as the extent to which the user has completed their learning.

[0605] "Stakeholders" refers to individuals or organizations responsible for understanding, evaluating, and providing feedback on users' learning progress.

[0606] To implement this invention, three main components are required: a user, a server, and a terminal. The user first accesses the system using a dedicated web portal or application. Initially, the user creates an account and enters information such as their job description, desired skills, and preferred learning format. This information is sent to the server and stored in a secure database.

[0607] The server then utilizes a generative AI model to analyze the received information. This process employs advanced language processing techniques, such as OpenAI's GPT model, for information analysis and material generation. This generative AI model generates prompts based on user input and uses them to optimize educational content. A possible prompt might be, "Generate materials including the latest market trends regarding new customer acquisition skills." Based on these prompts, the generative AI model creates materials and provides the server with educational content, including interactive elements.

[0608] The generated learning materials are delivered to the terminal by the server. The terminal visually presents these materials to the user, who then proceeds with self-study. The terminal also sends information to the server about how much the user has worked on the materials and their progress, recording the learning progress. This information is further processed on the server and visualized in a dashboard format that is easily accessible to stakeholders.

[0609] For example, suppose a sales representative at a company wants to learn how to acquire new customers. The user inputs this information into the system, and the server generates content incorporating the latest market trends and promotional techniques. This content is delivered to the user via their device, and the user can progress through the learning process according to a specified plan. As the learning progresses, the progress information is aggregated on the server, providing a foundation for efficiently managing the company's talent development program.

[0610] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0611] Step 1:

[0612] Users access the system through a web portal or application and create an account. Users enter information such as their job description, desired skills, and preferred learning format. This information is sent to the server as user information and securely stored in the database. At this stage, the entered information becomes the foundational data for subsequent processing of the system.

[0613] Step 2:

[0614] The server analyzes user information and generates prompts to send to the AI ​​model. Based on the user's preferences and job responsibilities, it creates specific prompts such as, "Generate training materials that include the latest market trends regarding new customer acquisition skills." The generated prompts are then used as input to the AI ​​model.

[0615] Step 3:

[0616] The generative AI model receives prompt text as input and generates relevant educational content based on it. The AI ​​references internal and external data sources, extracting, processing, and integrating the necessary information. This process may result in content output in formats such as text, images, and videos. The generated educational content is returned to the server.

[0617] Step 4:

[0618] The server receives AI-generated educational content and prepares it for delivery to the user's device. The content format is customized according to the learning style specified by the user. For example, if the content is text-based, interactive elements are added as needed. Once ready for delivery, the server sends the content to the user's device.

[0619] Step 5:

[0620] The terminal presents the user with educational content received from the server. Through a visual interface, the user can efficiently progress through the learning process. The terminal records the user's actions and monitors their learning progress. Progress data is generated each time the user engages with the learning materials.

[0621] Step 6:

[0622] The terminal sends recorded progress data to the server. The server aggregates and analyzes this data, making it available to stakeholders in a visualized format. This visualized progress data serves as foundational material for evaluation and planning the next steps. This enables timely feedback and adjustments to maximize the effectiveness of learning.

[0623] (Application Example 1)

[0624] 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".

[0625] Traditional education systems struggle to provide individually optimized skills necessary for specific jobs, making efficient human resource development a particular challenge in practical environments such as factories. Furthermore, there is a need for a system that can record and manage users' learning progress in real time, allowing administrators to immediately evaluate and implement improvement measures.

[0626] 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.

[0627] In this invention, the server includes means for acquiring input information and generating educational data based on work content; means for presenting the generated educational data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing and evaluating it for relevant parties; and means for displaying interactive educational content using a visual display device. This enables efficient learning tailored to individual work needs, while also allowing learning progress to be managed in real time and immediate evaluation and improvement measures to be taken.

[0628] "Means for acquiring input information" refers to methods and devices for collecting information from users regarding their work content and the skills they wish to acquire.

[0629] "Means for generating educational data based on job content" refers to methods or devices for creating educational materials and content to cultivate the skills necessary for specific tasks, based on collected information.

[0630] "Means for presenting generated educational data to users" refers to methods or devices for displaying or providing generated educational content to users.

[0631] "Means for recording learning progress" refers to methods or devices for recording and managing a user's learning status and progress.

[0632] "Means for analyzing recorded learning progress and visualizing and evaluating it for stakeholders" refers to methods and devices for analyzing collected progress data, displaying it in a way that is easy for stakeholders to understand, and conducting evaluations.

[0633] "Means for displaying interactive educational content using a visual display device" refers to devices or methods for providing educational content visually in a way that allows users to actively participate.

[0634] This system was built to support the skill development of factory workers. The system primarily consists of three elements: a server, terminals (smart glasses), and users.

[0635] The server first processes input information obtained from the user via smart glasses. This input information includes the user's work content and the skills they wish to acquire. Based on this information, the server generates educational content using a generative AI model. The generated content mainly consists of videos and interactive guides. By referring to an internal database and combining the most suitable materials, the server creates learning materials tailored to the user.

[0636] The generated educational content is delivered to the user via smart glasses (e.g., Vuzix, Google Glass). The smart glasses visually present this content, helping the user efficiently acquire the necessary skills. As the user progresses through the content, their learning progress is sent from the smart glasses to a server and recorded on the server.

[0637] The server analyzes recorded learning progress and visualizes the progress data for administrators and other stakeholders. This allows administrators to understand the user's skill improvement in real time and provide further support as needed.

[0638] For example, if a factory worker needs to learn how to operate a new machine, the system will generate videos of the relevant operating procedures and present them to the worker in real time to support smooth learning. An example of a prompt to implement this process would be, "Generate up-to-date training materials on the operating procedures of the new CNC machine and create content to present a visual guide."

[0639] This system will enable efficient and effective skill acquisition on the factory floor, and is expected to improve overall work efficiency.

[0640] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0641] Step 1:

[0642] The user logs into the system via smart glasses and enters their work details and desired skills. This input information is retrieved through the smart glasses' interface and sent to the server. The server receives this information and stores it for the next processing step.

[0643] Step 2:

[0644] The server analyzes the received input information and uses a generative AI model to generate specific educational content. Here, the generative AI model accesses an internal database, combining relevant technical documents and market information to create the most suitable learning materials for the user. Data processing based on the input information results in the output of customized learning content.

[0645] Step 3:

[0646] The generated educational content is delivered from the server to the device (smart glasses). The smart glasses display the content in the user's field of view and present it in an interactive format. For example, they can play a video that explains the operation procedure of a new machine in detail and present options when the user is ready to proceed to the next step.

[0647] Step 4:

[0648] The user learns content presented through smart glasses, and the smart glasses record their progress. During this process, the user's learning activity (e.g., completion status of each piece of content and frequency of interactions) is recorded in real time.

[0649] Step 5:

[0650] The device sends recorded learning progress to the server. The server receives this data, analyzes the progress, and visualizes it on a dashboard. Administrators use this information to see in real time which workers have acquired which skills and when, and provide additional support or evaluation as needed.

[0651] Step 6:

[0652] The server generates administrator and user feedback based on progress data. This feedback is presented as an improved learning plan or suggestions for the next learning steps, maintaining system consistency.

[0653] 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.

[0654] This invention is a system that combines an emotion engine to make user learning more efficient and effective. This system mainly consists of a user, a server, and a terminal, and has the function of generating learning materials using generative AI and detecting the user's emotional state in real time using an emotion engine.

[0655] First, the user accesses the system, creates an account, and provides input information including job description and learning objectives. The user's input information is sent to the server, and based on this information, the AI ​​generates appropriate training data. The training data includes text data and video data, and is tailored to the learning objectives.

[0656] The generated training data is delivered from the server to the terminal and presented to the user visually. While the user is learning on the terminal, the emotion engine analyzes the user's facial expressions and voice to perform emotion recognition. Based on this emotion recognition result, the server dynamically adjusts the content of the training data to provide a learning experience best suited to the user's emotional state. Furthermore, feedback on the user's emotional state is provided to relevant parties via the server and used to improve the user's learning experience.

[0657] As a concrete example, consider a situation where a user is feeling frustrated while learning a new programming language. When the emotion engine detects this frustration, the server adjusts its approach to provide additional learning materials and hints through generative AI to deepen understanding. In this way, the goal is to provide the user with the optimal learning environment.

[0658] Thus, the present invention can significantly improve the quality and efficiency of education within an organization by enabling real-time monitoring of the user's emotional state using an emotion engine and optimizing learning content based on that monitoring.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The user accesses the system and creates an account. The user enters information about their job description, learning goals, and areas of interest into a terminal and sends it to the server.

[0662] Step 2:

[0663] The server analyzes the submitted input information and sends a request to the generating AI. The AI ​​retrieves relevant information from the database and generates training data (text and video data) tailored to the user's learning needs.

[0664] Step 3:

[0665] The server sends the generated training data to a device equipped with an emotion engine. This allows the user to visually receive the training data via the device.

[0666] Step 4:

[0667] The user views the training data presented on the device and progresses through the learning process. During this time, the emotion engine built into the device analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0668] Step 5:

[0669] The device sends emotional data obtained by the emotion engine to the server. The server uses the emotional data to analyze the user's level of understanding and concentration.

[0670] Step 6:

[0671] Based on the analysis results, the server uses the generating AI again to create additional learning data and hints tailored to the user. This provides optimal content to address any unclear points in the learning process and pique interest.

[0672] Step 7:

[0673] The server sends the generated additional data to the terminal, improving the user's learning experience. Furthermore, it supports the user's sustainable growth by visualizing learning progress and sentiment data for stakeholders and providing feedback.

[0674] (Example 2)

[0675] 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".

[0676] Traditional learning systems fail to optimize content by taking into account the learner's emotional state, resulting in decreased learning efficiency. Furthermore, they struggle to dynamically provide appropriate learning materials for each user, making it difficult to offer a personalized learning experience.

[0677] 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.

[0678] In this invention, the server includes means for generating learning content suitable for job duties and learning objectives based on user input information using generative artificial intelligence, means for analyzing the user's emotional state in real time using an emotion analysis engine, and means for dynamically adjusting the learning content based on the analysis results. This enables the provision of a personalized learning experience and improved learning efficiency.

[0679] "User" refers to an individual or group that uses the system to receive learning content.

[0680] "Input information" refers to information about job duties and learning objectives that users provide to the system.

[0681] "Learning content" refers to educational materials that include text data, video data, and interactive elements generated using generative artificial intelligence.

[0682] "Generative artificial intelligence" refers to artificial intelligence technology that generates information and data based on given instructions or prompts.

[0683] A "terminal" refers to an electronic device used by users to receive learning content and engage in learning.

[0684] An "emotion analysis engine" refers to a system that analyzes a user's facial expressions and voice to recognize their emotional state in real time.

[0685] "Dynamic adjustment" refers to changing the content and format of learning materials in real time based on user sentiment data.

[0686] "Feedback" refers to the means of providing users and stakeholders with information regarding their learning progress and emotional state.

[0687] In a mode for carrying out the invention, this system aims to make user learning efficient and effective. This system mainly consists of a server, a terminal, and an emotion analysis engine including generative artificial intelligence.

[0688] When a user accesses the system, they first create an account and enter information related to their job responsibilities and learning objectives into the terminal. This information forms the basis for generating content tailored to the user's learning needs.

[0689] Upon receiving this information, the server uses generative artificial intelligence to generate appropriate learning content. In this process, prompts are provided to the generative AI, which then constructs specific learning materials tailored to the learning objectives. For example, using the prompt "Generate learning materials on the basic syntax of Python" will generate the corresponding materials.

[0690] The generated learning content is delivered from the server to the user's device. The device then visually presents this content to the user, including text data, video data, or interactive elements.

[0691] As a user learns, the device's emotion analysis engine analyzes the user's facial expressions and voice in real time to recognize their emotional state. This analysis is sent to a server, which dynamically adjusts the learning content based on the user's current emotional state, thereby providing a personalized learning experience.

[0692] For example, if a user feels frustrated while learning a new programming language, the sentiment analysis engine will capture this emotion. Based on this, the server will provide additional content and hints through generative artificial intelligence to aid understanding, allowing the learning process to proceed more smoothly.

[0693] This invention is expected to improve the quality and efficiency of learning, and will bring various benefits to educational settings.

[0694] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0695] Step 1:

[0696] Users access the system and create an account. Next, they provide information such as their job responsibilities and learning objectives as input to the terminal. This input information serves as foundational data to identify the user's individual learning needs.

[0697] Step 2:

[0698] The terminal sends the acquired input information to the server. The server receives this information and generates a prompt message for the generative AI model. This prompt message may include instructions such as, "Generate training materials related to the job description." This prompts the generative AI model to begin generating data suitable for the purpose.

[0699] Step 3:

[0700] The server uses a generative AI model to generate learning content based on input information and prompts. The generated content is output as text data or video data. In this process, the generative AI model uses data analysis and natural language processing techniques to construct specific learning materials that meet the required content.

[0701] Step 4:

[0702] The server delivers the generated learning content to the device. The device visually presents the received content to the user. The user uses this content to proceed with their learning. Interactive elements function effectively through actions performed on the device.

[0703] Step 5:

[0704] During training, the device analyzes the user's facial expressions and voice via an emotion analysis engine. It receives real-time emotion data as input and identifies the user's emotional state as output. For example, if the user is confused, that data is sent to the server.

[0705] Step 6:

[0706] The server analyzes the received emotional data and dynamically adjusts the learning content. This adjustment is achieved by reusing the generative AI model. For example, if the user's emotional state indicates frustration, the content is reorganized to be easily understood. This process provides an optimal learning experience.

[0707] Step 7:

[0708] The server visualizes learning progress and emotional state for stakeholders and provides feedback. This feedback includes the user's learning outcomes and emotional changes, which stakeholders can use to adjust the educational plan. The data at this stage will be used to improve future learning strategies.

[0709] (Application Example 2)

[0710] 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".

[0711] In today's educational environment, it is not easy to adjust learning content according to the emotional state and comprehension level of individual learners. Especially in virtual learning environments, learners are more likely to experience anxiety and stress, which can affect learning efficiency. This invention aims to enable dynamic adjustment of learning content in response to learners' emotions, thereby providing a more effective and stress-free learning experience.

[0712] 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.

[0713] In this invention, the server includes means for acquiring input information and generating learning data based on job content; means for distributing the generated learning data to the user and recording learning progress; means for analyzing the recorded learning progress and visualizing it for relevant parties; and means for sensing the user's emotional state in real time and dynamically adjusting the learning data based on this emotional state. This enables flexible adjustment of learning content in accordance with the learner's emotions.

[0714] "Input information" refers to data obtained from users in order to generate learning data according to job duties and learning objectives.

[0715] "Training data" refers to text and video data generated using generative artificial intelligence to facilitate user education.

[0716] "Means of recording progress" refers to technologies that record how far a user has progressed in their learning and accumulate that information.

[0717] "Means of visualization" refers to technologies that display the recorded progress of learning in a way that is easy for stakeholders to understand.

[0718] "Means of sensing emotional states in real time" refers to technology that analyzes and evaluates a user's emotions in real time based on their facial expressions and voice.

[0719] "Means of dynamic adjustment" refers to technologies for instantly changing and optimizing the content of training data based on the user's emotional state.

[0720] To realize this invention, a user-accessible program is required. The server acquires input information from the user and generates training data based on it. Generative artificial intelligence technology is used to automatically generate text and video data tailored to the user's job duties and learning objectives. Open-source AI models and platforms can be used in this generation process.

[0721] The server distributes the generated training data to the terminal. The terminal has an application installed to visually display the training data, and the user performs the training through this application.

[0722] The device incorporates an emotion engine that detects emotional states in real time. Using technologies such as Microsoft's Face API, it analyzes the user's facial expressions and voice to evaluate their emotional state. This evaluation result is sent to a server, which dynamically adjusts the training data. For example, if the user is feeling stressed, additional training material can be presented.

[0723] For example, when a user is using a VR application to learn how to operate new machinery in a factory, hints and support messages will automatically appear when the user feels anxious. Possible prompts in this case might include: "Write code for a robotic assistant to detect user anxiety in VR training and provide necessary support in real time."

[0724] This system enables flexible learning support tailored to the learner's emotions, which is expected to improve learning efficiency.

[0725] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0726] Step 1:

[0727] The user logs into the system and enters their job description and learning objectives. This information is sent to the server. The input in this step is text data entered by the user, while the output is user information stored on the server.

[0728] Step 2:

[0729] The server generates training data using a generative AI model based on the user input information it receives. This data is represented in text or video format. Specifically, it generates prompt sentences based on the input keywords, and the AI ​​then creates content suitable for training based on these prompts. The input for this step is user information, and the output is the generated training data.

[0730] Step 3:

[0731] The generated training data is delivered from the server to the terminal. An application installed on the terminal receives this data and presents it visually to the user. The user then proceeds with learning based on the presented content. In this step, the input is the training data, and the output is the learning content displayed on the terminal.

[0732] Step 4:

[0733] The device detects the user's emotional state in real time using an emotion engine. The analysis uses a camera and microphone to evaluate the user's emotions based on facial expressions and voice. The input for this step is real-time audio and video data, and the output is the detected emotion data.

[0734] Step 5:

[0735] The server receives emotional data sent from the emotion engine and dynamically adjusts the training data based on it. For example, if anxiety is high, additional learning materials are provided to facilitate understanding. The input for this step is emotional data, and the output is the adjusted training data.

[0736] Step 6:

[0737] The adjusted training data is redistributed to the device and presented to the user. The user continues learning through the new content. In this step, the input is the adjusted training data, and the output is the new content the user sees.

[0738] 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.

[0739] 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.

[0740] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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."

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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 as being incorporated by reference.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] A means of acquiring input information and generating learning data based on job content,

[0762] A means of distributing the generated training data to users and recording their learning progress,

[0763] A means of analyzing recorded learning progress and making it visible to stakeholders,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, wherein the generated training data includes text data or video data.

[0767] (Claim 3)

[0768] The system according to claim 1, wherein the generated learning data is generated using generative artificial intelligence.

[0769] "Example 1"

[0770] (Claim 1)

[0771] A processing device for acquiring input information and generating job-related educational content,

[0772] A processing device that delivers generated educational content to recipients and records the progress of the education,

[0773] A processing device that interprets recorded educational progress and visualizes it for stakeholders,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, wherein the generated educational content includes text information or video information.

[0777] (Claim 3)

[0778] The system according to claim 1, wherein educational content is generated using generative artificial intelligence.

[0779] "Application Example 1"

[0780] (Claim 1)

[0781] A means of acquiring input information and generating educational data based on the work content,

[0782] A means of presenting the generated educational data to the user and recording learning progress,

[0783] A means of analyzing recorded learning progress and making it visible and evaluateable to stakeholders,

[0784] A means of displaying interactive educational content using a visual display device,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the generated educational data includes text data or video data.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the generated educational data is generated using generative artificial intelligence.

[0790] "Example 2 of combining an emotion engine"

[0791] (Claim 1)

[0792] A means of generating learning content suitable for job duties and learning objectives based on user input information, using generative artificial intelligence,

[0793] A means of delivering the generated learning content to the user's device and presenting it visually,

[0794] A means of analyzing the user's emotional state obtained through the terminal in real time using an emotion analysis engine,

[0795] A means to dynamically adjust learning content based on analysis results and optimize the user's learning experience,

[0796] A means of visualizing recorded learning progress and emotional states for stakeholders and providing feedback,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the generated learning content includes text data, video data, and interactive elements.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the generated learning content is generated using generative artificial intelligence via prompt statements.

[0802] "Application example 2 when combining with an emotional engine"

[0803] (Claim 1)

[0804] A means of acquiring input information and generating learning data based on job content,

[0805] A means of distributing the generated training data to users and recording their learning progress,

[0806] A means of analyzing recorded learning progress and making it visible to stakeholders,

[0807] A means for sensing the user's emotional state in real time and dynamically adjusting the training data based on this emotional state,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the generated learning data includes text data or video data, and is further provided as supplementary material when a user engages in a learning experience in a virtual environment.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the generated learning data is generated using generative artificial intelligence, and means for analyzing the user's emotions are implemented. [Explanation of Symbols]

[0813] 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. A means of acquiring input information and generating learning data based on job content, A means of distributing the generated training data to users and recording their learning progress, A means of analyzing recorded learning progress and making it visible to stakeholders, A system that includes this.

2. The system according to claim 1, wherein the generated training data includes text data or video data.

3. The system according to claim 1, wherein the generated learning data is generated using generative artificial intelligence.

Images