system

Abstract

Provide a system. 【Solution means】 Input means for inputting information related to the role and industry that the user aims for, Means for identifying the skills and knowledge required based on the information, Assessment means for evaluating the user's current skill level, Means for analyzing the user's skill deficiencies based on the evaluation results and generating an individualized learning plan, Means for tracking the user's learning progress and providing opinions, Means for sending a notification of business participation when a certain skill level is achieved, Means for providing access to interactive learning content using a smart device, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the evolution of AI technology, the demand for talents with advanced AI skills in various industrial fields is increasing. However, many learners cannot identify the skills they lack and have difficulty in efficient learning. In addition, due to limited opportunities related to AI projects, there is a shortage of platforms for learners to practice the knowledge and skills they have acquired. In such a situation, there is a need for means to enable learners to acquire the necessary skills in an optimal way and be guided to a platform for practice.

Means for Solving the Problems

[0005] This invention provides an input means for users to input information related to their desired role and industry, and identifies the necessary skills and knowledge based on that information. Furthermore, it includes an assessment means for evaluating the user's current skill level, analyzes the user's skill gaps based on the evaluation results, and generates a personalized learning plan. It streamlines learning by tracking the user's learning progress and providing real-time feedback. It also includes a means for sending project participation notifications when a certain skill level is achieved, ensuring learners have opportunities for practical application.

[0006] A "user" is an individual or group that aims to improve their AI skills and knowledge by using the system.

[0007] A "role" refers to the specific duties or position that the user wishes to fulfill within the AI ​​project.

[0008] An "industry" refers to a specific industrial sector where the application of AI technology is being considered, and it represents the area in which users want to utilize their skills.

[0009] "Input means" refers to functions that include interfaces and tools for providing the system with information related to the role and industry that the user aims to play.

[0010] An "assessment tool" is a function that provides tests or questions to evaluate a user's current skill and knowledge level, and analyzes their responses.

[0011] A "skill gap" refers to the difference between a user's current skill level and the skills required for their chosen role or industry.

[0012] A "learning plan" is an outline of a learning method that includes customized learning materials and exercises designed to help users efficiently acquire the skills required for a specific role or industry.

[0013] "Progress" refers to the state or process indicating the extent to which a user has completed learning based on the provided learning plan.

[0014] "Feedback" refers to providing real-time information about the user's learning progress and achievements, as well as areas for improvement.

[0015] A "project participation notification" is a message or alert that informs a user of an opportunity to participate in a relevant AI project once they have reached a certain skill level. [Brief explanation of the drawing]

[0016] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a labeled 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.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention embodies a system that provides users learning AI technology with an efficient learning process, enabling them to play a practical role in the industry. The system begins with the user selecting a role and industry related to their desired AI project, and then, based on that information, identifying the necessary skills and knowledge.

[0038] The server first consults a database based on the information entered by the user to obtain the skill set required for a specific role. Based on this skill set, the server provides an assessment tool to evaluate the user's current skill level. The user answers this assessment via a terminal, and the results are sent to the server.

[0039] The server analyzes the assessment results and identifies the gap between the user's current skills and the required skills. Based on this, the server generates an optimized learning plan for the user. This plan consists of online courses, exercises, and practical scenarios, and is delivered to the user's device. The user then uses their device to access this learning content and acquire the necessary skills.

[0040] Progress based on the learning plan is tracked in real time by the server. The server analyzes the user's progress and sends feedback and advice to the device as needed. This allows the user to learn efficiently.

[0041] When a user is determined to have reached a certain skill level, the server sends a notification informing them of an opportunity to participate in an AI project. This allows the user to gain practical experience and further deepen their skills.

[0042] For example, if a user wishes to work on a project in the "manufacturing" sector as an "AI consultant," the server will present a skill set including data analysis capabilities and industry-specific knowledge as the skills required for that role. The server will assess the user's skill level and provide a learning plan to fill in any gaps. The user will follow this plan, learning through online courses on data analysis and case studies in the manufacturing sector. If they achieve a certain level of performance, they will receive a notification to participate in a related project.

[0043] Thus, the present invention provides a system that supports the transition from acquiring AI skills to putting them into practice, and offers users a means to realize their desired career.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] After the user logs in, the server provides the terminal with an interface for entering role and industry information.

[0047] Step 2:

[0048] Users input their desired role and industry into a terminal and send that information to the server.

[0049] Step 3:

[0050] Based on the information received, the server retrieves a list of the skills and knowledge required for that role from the database.

[0051] Step 4:

[0052] The server generates an assessment test to evaluate the user's current skill level and delivers it to the terminal.

[0053] Step 5:

[0054] Users answer assessment tests using their devices and send the results to the server.

[0055] Step 6:

[0056] The server analyzes user responses and identifies skill gaps.

[0057] Step 7:

[0058] The server generates a personalized learning plan based on the skill gap and provides it to the device.

[0059] Step 8:

[0060] Users follow the provided learning plan and study online courses and exercises through their devices.

[0061] Step 9:

[0062] The server monitors the user's learning progress in real time and analyzes their progress.

[0063] Step 10:

[0064] The server generates and sends feedback and improvement suggestions to the terminal based on the progress.

[0065] Step 11:

[0066] The server sends a notification to the user's device informing them of an opportunity to participate in a relevant AI project once the user reaches their target skill level.

[0067] Step 12:

[0068] Users will check the notification and, if they wish, respond via their device to indicate their intention to participate in the project.

[0069] (Example 1)

[0070] 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."

[0071] In the field of artificial intelligence technology, it is difficult for users to efficiently acquire skills appropriate for diverse roles and industries. Furthermore, finding the optimal learning plan for users, monitoring their progress in real time, and receiving appropriate feedback are not easy. Additionally, there is a lack of adequate support for users to gain relevant practical opportunities once they have acquired sufficient skills. To address these challenges, an integrated system is needed that enables users to efficiently and effectively acquire the necessary skills and participate in their work.

[0072] 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.

[0073] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to, means for identifying the required skills and knowledge based on the information, and evaluation means for assessing the user's current skill level. This allows the user to discover specific skill sets tailored to their interests and goals, and to efficiently acquire those skills through an optimized training plan.

[0074] An "input method" refers to a device or interface used by a user to transmit information related to their desired role or industry to the system.

[0075] "Abilities and knowledge" refers to the specialized skills and information required for a particular job or industry.

[0076] "Evaluation methods" refer to processes and tools for objectively determining a user's current skill level.

[0077] "Skill gap" refers to the gap in skills and knowledge between a user's current skill level and their target skill level.

[0078] An "individualized learning plan" is a learning program customized to improve specific skills based on the user's differences in abilities.

[0079] "Educational progress" is an indicator that shows how far a user has progressed in acquiring skills through the educational plan.

[0080] "Means of providing advice" refers to a system for providing users with feedback and support to improve their learning efficiency.

[0081] A "notification of participation in work" is information that informs a user of opportunities to participate in relevant projects or activities once they have reached a certain skill level.

[0082] A "generative AI model" is an algorithm that automatically creates learning plans, notification messages, and other similar content based on user information.

[0083] A "prompt statement" is an instruction given to an AI model to obtain a specific output.

[0084] This invention provides a system aimed at enabling users learning artificial intelligence technology to efficiently acquire skills and improve their practical abilities in related industries. Specifically, it is implemented in the following way.

[0085] First, the user uses a terminal to input information about their desired role and areas of interest into the system. This information is sent to the server, which then retrieves the corresponding skill sets from its database. A common database management system such as MySQL® is used for this database management.

[0086] The server generates questions using a programming language like Python to assess the user's current skill level. This assessment is provided to the user using an online form tool such as Google Forms. Once the user completes the form, the results are sent to the server and analyzed using libraries such as Pandas.

[0087] Based on the analysis results, the server identifies differences in user abilities and generates personalized learning plans. These plans are optimized for each user by a generating AI model and are structured as online learning platforms, exercises, and practical examples. The generated learning plans are sent to the user's device, and the user proceeds with their learning accordingly.

[0088] Furthermore, the server tracks user progress in real time and provides advice as needed. A learning management system (LMS) and dashboard tools are used for this purpose. Users who achieve a certain level of proficiency receive notifications about relevant work assignments. These notifications are sent via email and push notification services.

[0089] For example, if a user wants to work in the manufacturing sector as an "AI consultant," the server will identify the data analysis skills and industry-specific knowledge required for that role and provide an educational plan that includes online training and case studies. By following this plan, the user will have a higher chance of getting opportunities to participate in projects.

[0090] Examples of prompts include, "Please tell me the skill set required for an AI consultant working on a manufacturing project." This system helps users efficiently improve their skills and actually succeed in their desired job.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Users use a terminal to input information about the roles they aspire to and the industries they are interested in. This input includes desired job types and relevant industry information. This input is sent to the server. The server queries a database based on this information to find the corresponding skill sets. The skill sets obtained from the database are returned to the server.

[0094] Step 2:

[0095] The server identifies the required skill set based on the user's input and then generates assessment questions to evaluate the user's current skill level. A generative AI model is used to automatically generate questions optimized for each role. This generated assessment is sent to the user's terminal as an online form. The user answers the assessment from their terminal and sends the results back to the server.

[0096] Step 3:

[0097] The server analyzes the assessment results received from the user. This analysis is performed using the Python Pandas library. The server identifies the competency gap between the user's current skills and their ideal skill set. As a result of this analysis, the foundational data for an individualized education plan is generated.

[0098] Step 4:

[0099] Based on the analysis results, the server generates an optimal learning plan for the user. This plan incorporates online lessons, exercises, and case studies. The generated learning plan is sent to the user's device as JSON data. The user accesses this plan through their device and begins learning.

[0100] Step 5:

[0101] To track learning progress, the server monitors the user's progress in real time. Based on the progress data sent from the device, the server generates feedback and provides advice to improve learning efficiency. This feedback is sent to the device and displayed to the user.

[0102] Step 6:

[0103] When a user reaches a set skill level, the server generates a notification of relevant work opportunities for that user. The notification text, created by the generation AI model, is sent to the user's device as an email or push notification. Through this notification, the user can gain practical experience.

[0104] (Application Example 1)

[0105] 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."

[0106] In learning AI technology, users require efficient learning processes tailored to their desired roles and industries, but currently, there is a lack of personalized learning methods to achieve this. Furthermore, the insufficient real-time progress monitoring and feedback can prevent users from maximizing the effectiveness of their learning. Additionally, limited access to learning content hinders the efficient acquisition of skills relevant to what users want to learn.

[0107] 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.

[0108] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to; means for identifying the skills and knowledge required based on the information; assessment means for evaluating the user's current skill level; means for generating an individualized learning plan; means for tracking the user's learning progress and providing immediate feedback; and means for providing access to interactive learning content using a smart device. This enables the user to learn efficiently and individually, and to practically acquire the skills necessary for their desired role.

[0109] A "user" is an individual or legal entity that wishes to learn AI technology using the system.

[0110] A "role" refers to the type of job or work related to the AI ​​technology that the user aims to achieve.

[0111] An "industry" refers to a specific industrial sector or area of ​​economic activity that a user is interested in.

[0112] "Input means" refers to a method or device for introducing information about the role and industry that the user aims to play into the system.

[0113] "Skills" refer to the technical or practical abilities required to perform a particular role.

[0114] "Knowledge" refers to the information or understanding that a user should possess in a particular industrial field.

[0115] An "assessment tool" is a method or device for evaluating a user's current skill level.

[0116] An "individualized learning plan" refers to educational content and a program tailored to the user's skill level and learning goals.

[0117] "Learning progress" refers to the extent to which a user has improved their skills and knowledge according to their learning plan.

[0118] "Feedback" refers to advice or comments provided regarding a user's learning progress.

[0119] A "smart device" is an advanced electronic device used to provide learning content interactively.

[0120] "Interactive learning content" refers to educational content that allows users to actively participate in and learn.

[0121] In the system implementing this invention, users can experience an efficient learning process of AI technology using their own smart devices. Users input information about their desired role and industry into the system via input means. This data is analyzed by a server to identify the required skills and knowledge. The server uses assessment means to evaluate the user's current skill level, and based on this evaluation, a personalized learning plan is generated. This learning plan includes online courses, practice problems, and application scenarios.

[0122] Users can learn while receiving feedback in real time using smart devices. The server tracks learning progress and provides immediate feedback based on that data. This allows users to constantly check their progress and areas for improvement, enabling them to learn efficiently. Furthermore, once a certain skill level is reached, users are notified of opportunities to participate in actual projects, allowing them to gain experience in real-world projects.

[0123] As a concrete example, if a user aims to take on a role in the manufacturing industry as an "AI consultant," the server identifies the need for data analysis skills and industry knowledge. Based on this, the server provides the user with specialized online courses and case studies, which the user then uses to proceed with their learning. By generating example prompts such as, "Evaluate the skills required to be an AI consultant and propose an optimal learning plan," the user can experience skill assessment and plan proposal using a generated AI model.

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The user uses a terminal to input information about their desired role and industry. This input is data sent to the server. The server receives this data and begins analysis.

[0127] Step 2:

[0128] The server identifies relevant skills and knowledge from the database based on the received information. The identified information is filtered based on the user's input to generate the required skill set.

[0129] Step 3:

[0130] The server uses assessment tools to generate test data to evaluate the user's current skill level. The user takes this test on a terminal and sends their answers to the server. Based on the user's answers, the server generates a current skill profile.

[0131] Step 4:

[0132] The server compares the user's skill profile with their required skill set and identifies any skill gaps. Based on this, a personalized learning plan is generated for the user. The generated learning plan is then materialized in the form of online lectures, practice problems, and other materials, and sent to the device.

[0133] Step 5:

[0134] Users progress through their learning based on the generated learning plan via their device. Learning progress data is sent to the server in real time, and the server analyzes this data to evaluate learning progress.

[0135] Step 6:

[0136] The server generates opinions and feedback in real time based on the evaluation results of learning progress and sends them to the terminal. This allows users to instantly understand their learning status and adjust their learning plan as needed.

[0137] Step 7:

[0138] When the server determines that a user has reached a certain skill level, it notifies the user of an opportunity to participate in work. This notification allows the user to participate in actual projects and further deepen their skills.

[0139] 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.

[0140] This invention provides a learning experience optimized for each individual user by combining emotion recognition functionality with a system that supports the improvement of users' AI skills. The system aims to improve learning efficiency and reduce stress by considering the user's emotional state during learning, along with their technical progress.

[0141] This system first identifies the necessary skill set based on the role and industry information entered by the user, and then generates a learning plan to fill that gap. The user accesses the learning content through their device and receives real-time progress and evaluation feedback.

[0142] A key feature of this system is its emotion engine. The emotion engine obtains multiple emotional indicators, such as facial expressions, voice tone, and input speed, from input from the user's device to infer their emotional state. The inferred emotional state is sent to the server and used to adjust the learning plan and feedback.

[0143] The server adjusts feedback and learning content based on the user's emotional state. For example, if the server determines that the user is feeling down, it will provide encouraging messages to reduce the user's stress and change the content to a more gradual progression. Conversely, if the server determines that the user is highly motivated, it will adjust the content to provide more challenging tasks.

[0144] For example, when a user aims to become an AI planner, they undergo a planned skills assessment. If the server detects that the user's emotions are unstable, it temporarily lowers the difficulty level and displays encouraging comments. Conversely, if a stable and positive emotional state is detected, it presents a more challenging curriculum to support the user's growth. Furthermore, the timing of learning plan revisions and feedback is optimized based on real-time emotional data.

[0145] Thus, this system, equipped with an emotion engine, considers both the user's technical skills and emotions to provide efficient and adaptive learning support. This approach enables users to achieve their learning goals and smoothly transition to actual projects.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] Users log into the system and enter information related to their desired role and industry via a terminal.

[0149] Step 2:

[0150] The server receives information entered by the user, consults the database, and identifies a list of skills and knowledge required for that role.

[0151] Step 3:

[0152] The server generates an assessment test to evaluate the user's current skill level and sends it to the terminal.

[0153] Step 4:

[0154] Users answer assessment tests via their devices and send the results to the server.

[0155] Step 5:

[0156] The server analyzes the user's test results and generates a personalized learning plan based on the identified skill gaps.

[0157] Step 6:

[0158] The server sends the generated learning plan to the terminal, and the user begins learning based on its contents.

[0159] Step 7:

[0160] The device uses an emotion engine to monitor the user's emotional state and acquire data such as facial expressions and voice.

[0161] Step 8:

[0162] The server receives emotion data sent from the terminal and evaluates the user's emotional state in real time.

[0163] Step 9:

[0164] The server adjusts the content and difficulty level of learning materials based on the user's emotional state and generates encouraging messages and advice as needed.

[0165] Step 10:

[0166] The server sends the adjusted learning content and feedback to the device, and the user proceeds with their learning based on that information.

[0167] Step 11:

[0168] When the server determines that the user has reached their target skill level, it sends a notification to the device inviting them to participate in the relevant AI project.

[0169] Step 12:

[0170] Users can proceed to the next step by checking the notification on their device and replying to indicate their intention to participate in the project.

[0171] (Example 2)

[0172] 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".

[0173] In today's learning environment, users are required to efficiently acquire diverse skills and rapidly improve the abilities necessary for their roles. However, traditional systems have the problem of being limited to feedback based solely on technical progress, and failing to adequately optimize learning according to the emotional state of individual users. This can lead to decreased learning efficiency and an increase in the number of users who experience stress.

[0174] 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.

[0175] In this invention, the server includes data acquisition means for inputting information related to the role and industry the user aims for, means for identifying the required abilities and knowledge based on the information, and means for acquiring the user's emotional state during learning and adjusting the learning plan and evaluation information based on the emotional state. This makes it possible to provide feedback and adjustments to learning content that respond not only to the user's technical progress but also to changes in their emotions, thereby providing an efficient and stress-free learning experience.

[0176] "Data acquisition means" refers to the interface and technology for users to input information related to the role and industry they aspire to, thereby incorporating the user's background information into the system.

[0177] "Competency" is a concept that includes the technical and non-technical skills and knowledge required to perform a particular role.

[0178] "Knowledge" refers to the information and theoretical background required in a particular field, and represents the level of understanding necessary for a user to perform their job duties.

[0179] A "personally optimized learning plan" refers to a learning method and content structure that is customized according to the user's individual skill level and learning progress.

[0180] "Evaluation information" refers to information that provides feedback on the user's progress and skill acquisition level based on their learning activities, and serves as an indicator for users to objectively understand their own learning activities.

[0181] "Emotional state" refers to the psychological and physiological responses that a user exhibits during learning, and the progress and content of the learning are adjusted based on these responses.

[0182] "Adjustment measures" include processes and technologies that modify learning plans and content provided in accordance with the user's progress and emotional state.

[0183] The system of this invention provides a personalized learning experience tailored to the user's skills and emotional state. The server receives role and industry-related information entered by the user through a terminal and uses this information to identify the necessary skills and knowledge. As an evaluation method, the system analyzes the user's skill gap by comparing the user's skill level with industry-standard competency requirements obtained from a database. The system incorporates a generative AI model using an AI algorithm to generate a personalized learning plan.

[0184] Specifically, the device uses a camera and microphone to monitor the user's facial expressions and tone of voice, and estimates their emotional state in real time. The obtained emotional data is sent to a server. The server adjusts the learning plan and evaluation information based on this emotional data. For example, if the user's emotional state is low, the server provides encouraging messages or tasks with reduced difficulty. On the other hand, if the user is highly motivated, it presents challenging tasks to promote growth.

[0185] As a concrete example, consider a scenario where a user aims to become an AI planner. In this scenario, a planned skills assessment is conducted. If the emotion engine detects unstable emotions, the server temporarily lowers the difficulty level and displays encouraging comments on the user's device. Conversely, if stable, positive emotions are detected, a more advanced curriculum is provided.

[0186] Examples of prompt statements include: "If a user aiming to become an AI planner is judged to be in a depressed emotional state, what message or learning adjustment should be given?" and "Please provide examples of challenging tasks that can be offered when a user is highly motivated." This allows the system to provide optimal support tailored to the user.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The user enters information about the role they are pursuing and related industries through a terminal. This input information is sent to the server in text format. The server parses the input information and extracts basic skills and knowledge related to the industry and role from its database. The output of this step is a list of identified skills and knowledge.

[0190] Step 2:

[0191] The server conducts an assessment to evaluate the user's current skill level. The user responds to the skill assessment test using a terminal. The server analyzes the user's response data and calculates the skill gap. This process reveals the difference between the required skill set and the user's current skill level. The output of this step is a detailed report on the user's skill gap.

[0192] Step 3:

[0193] The server uses a generative AI model to create an individually optimized learning plan. This model receives a skills gap report and pre-acquired industry-specific skill requirements as input and generates a learning curriculum best suited to the user. This learning plan, including step-by-step tasks and resources, is sent to the terminal. The output provides a user-specific learning plan.

[0194] Step 4:

[0195] The device monitors the user's emotional state during learning. Specifically, it uses a camera and microphone to sense the user's facial expressions and tone of voice, which are then analyzed by emotion recognition software. The emotional data is sent to a server, which infers the user's emotions in real time. The output of this step is the inferred result of the user's current emotional state.

[0196] Step 5:

[0197] The server adjusts the learning plan and feedback based on the emotional state received from the device. For example, if it determines that the user is feeling down, it lowers the difficulty of the learning tasks and generates and sends an encouraging message to the device. Conversely, if it determines that the user is highly motivated, it suggests a more challenging task. The output of this step is the adjusted learning content and feedback message.

[0198] Step 6:

[0199] The user receives the adjusted learning content and feedback sent from the server on their device. Specifically, they review the assignments and messages displayed on their learning screen and proceed to the next learning activity. The output of this step is an improvement in the learning experience the user receives.

[0200] (Application Example 2)

[0201] 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 device 14 will be referred to as the "terminal."

[0202] While modern learning support systems excel at tracking users' technical progress, they lack flexible feedback and learning plan adjustment capabilities that take into account users' emotional states. This results in a challenge in providing an optimal learning experience, as they fail to adequately address the stress and motivational shifts users experience during their learning journey.

[0203] 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.

[0204] In this invention, the server includes a device for inputting information related to the user's desired role and industry, a device for identifying skills and knowledge, a device for analyzing the user's skill differences and generating an individualized learning plan, an engine for acquiring information for inferring emotions and analyzing the user's emotional state, and a device for adjusting feedback and learning content based on the emotional state. This makes it possible to provide an optimal learning plan and feedback that takes into account the user's emotional state in addition to their technical progress.

[0205] A "device" is a collection of hardware or software designed to perform a specific function.

[0206] "Skills" refer to the knowledge and practical abilities necessary to perform a specific task efficiently and effectively.

[0207] A "personalized learning plan" is an optimal learning route tailored to the user's skill level and emotional state.

[0208] An "emotion inference engine" is a system that analyzes a user's emotional state in real time based on data such as their facial expressions, tone of voice, and input speed.

[0209] "Feedback" refers to evaluations and advice provided regarding a user's learning progress.

[0210] "Learning content" refers to educational materials, exercises, and practical scenarios related to the specific skills and knowledge that the user aims to acquire.

[0211] "Information" refers to data about users and specific insights generated from that data.

[0212] In the system implementing this invention, the user first inputs information related to the role and industry they aspire to work in into a terminal. This terminal can include a general-purpose computer or smartphone. Based on this information, a server identifies the necessary skills and knowledge and generates a personalized learning plan accordingly. This server operates on a cloud-based platform and utilizes Google Cloud's sentiment recognition API and AWS® cloud servers, among others.

[0213] As the user progresses through the learning process, the device captures the user's facial expressions and voice through its camera and microphone, collecting information to infer their emotions. An emotion inference engine processes this data in real time to analyze the user's emotional state. The results of this analysis are sent to a server and used for feedback and adjustments to the learning content.

[0214] The server generates optimal feedback by considering the user's emotional state as well as their technical progress. For example, if a user is feeling stressed, the system will present them with easy tasks and display encouraging messages. On the other hand, if the system determines that the user is relaxed and highly motivated, it will provide them with challenging tasks and new promotional information.

[0215] For example, if a user is studying at home during a holiday, the system will detect their relaxed facial expression and calm tone of voice, and then present content of an appropriate difficulty level. This allows the user to study in a stress-free environment.

[0216] Example prompt: "How can we improve the purchasing experience by taking emotions into account?"

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] The user enters information about their desired role and industry into the device.

[0220] The terminal sends the entered information to the server. Based on this information, the server identifies the necessary skills and knowledge. This provides the basic data for analyzing skill gaps.

[0221] Step 2:

[0222] The server analyzes the user's skill differences based on the acquired data and generates an individualized learning plan.

[0223] The server extracts relevant topics and courses from the database and uses them to build a learning plan. This plan includes optimal course content to improve the user's technical skills. The output is a personalized learning plan.

[0224] Step 3:

[0225] The device captures the user's facial expressions and voice while learning is in progress and sends them to an engine that infers emotions.

[0226] This engine analyzes data input from the camera and microphone to infer the user's emotional state. Based on this inference, the user's current emotional state is output.

[0227] Step 4:

[0228] Emotional state data is sent to the server, which then adjusts the feedback and learning content based on that data.

[0229] The server generates modified learning plans and feedback messages based on the analyzed emotional state and technical progress. Depending on the user's state, actions such as adjusting the difficulty of tasks and selecting appropriate encouraging messages are performed. The output is the adjusted learning plan and feedback.

[0230] Step 5:

[0231] Users continue learning based on the provided learning plan and receive immediate progress feedback from their device.

[0232] This feedback includes advice on the level achieved and areas for improvement. Receiving it immediately allows users to quickly understand the next steps needed and improve learning efficiency. The output is progress feedback.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention embodies a system that provides users learning AI technology with an efficient learning process, enabling them to play a practical role in the industry. The system begins with the user selecting a role and industry related to their desired AI project, and then, based on that information, identifying the necessary skills and knowledge.

[0250] The server first consults a database based on the information entered by the user to obtain the skill set required for a specific role. Based on this skill set, the server provides an assessment tool to evaluate the user's current skill level. The user answers this assessment via a terminal, and the results are sent to the server.

[0251] The server analyzes the assessment results and identifies the gap between the user's current skills and the required skills. Based on this, the server generates an optimized learning plan for the user. This plan consists of online courses, exercises, and practical scenarios, and is delivered to the user's device. The user then uses their device to access this learning content and acquire the necessary skills.

[0252] Progress based on the learning plan is tracked in real time by the server. The server analyzes the user's progress and sends feedback and advice to the device as needed. This allows the user to learn efficiently.

[0253] When a user is determined to have reached a certain skill level, the server sends a notification informing them of an opportunity to participate in an AI project. This allows the user to gain practical experience and further deepen their skills.

[0254] For example, if a user wishes to work on a project in the "manufacturing" sector as an "AI consultant," the server will present a skill set including data analysis capabilities and industry-specific knowledge as the skills required for that role. The server will assess the user's skill level and provide a learning plan to fill in any gaps. The user will follow this plan, learning through online courses on data analysis and case studies in the manufacturing sector. If they achieve a certain level of performance, they will receive a notification to participate in a related project.

[0255] Thus, the present invention provides a system that supports the transition from acquiring AI skills to putting them into practice, and offers users a means to realize their desired career.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] After the user logs in, the server provides the terminal with an interface for entering role and industry information.

[0259] Step 2:

[0260] Users input their desired role and industry into a terminal and send that information to the server.

[0261] Step 3:

[0262] Based on the information received, the server retrieves a list of the skills and knowledge required for that role from the database.

[0263] Step 4:

[0264] The server generates an assessment test to evaluate the user's current skill level and delivers it to the terminal.

[0265] Step 5:

[0266] Users answer assessment tests using their devices and send the results to the server.

[0267] Step 6:

[0268] The server analyzes user responses and identifies skill gaps.

[0269] Step 7:

[0270] The server generates a personalized learning plan based on the skill gap and provides it to the device.

[0271] Step 8:

[0272] Users follow the provided learning plan and study online courses and exercises through their devices.

[0273] Step 9:

[0274] The server monitors the user's learning progress in real time and analyzes their progress.

[0275] Step 10:

[0276] The server generates and sends feedback and improvement suggestions to the terminal based on the progress.

[0277] Step 11:

[0278] The server sends a notification to the user's device informing them of an opportunity to participate in a relevant AI project once the user reaches their target skill level.

[0279] Step 12:

[0280] Users will check the notification and, if they wish, respond via their device to indicate their intention to participate in the project.

[0281] (Example 1)

[0282] 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".

[0283] In the field of artificial intelligence technology, it is difficult for users to efficiently acquire skills suitable for various roles and industries. Also, it is not easy for users to find the optimal learning plan, grasp its progress in real time, and receive appropriate feedback. Furthermore, when users have acquired sufficient skills, there is also a lack of appropriate support for obtaining related practical opportunities. To address these issues, an integrated system is required for users to efficiently and effectively acquire the necessary skills and participate in the business.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0285] In this invention, the server includes an input means for inputting information related to the role and industry that the user aims for, a means for specifying the required capabilities and knowledge based on the information, and an evaluation means for evaluating the current ability level of the user. As a result, the user can know a specific skill set according to their own interests and goals, and can efficiently acquire those skills through an optimized educational plan.

[0286] The "input means" is a device or interface for the user to convey information related to the role and industry they aim for to the system.

[0287] "Capabilities and knowledge" refers to the specialized skills and information required in a specific job or industry.

[0288] The "evaluation means" is a process or tool for objectively judging the current skill level of the user.

[0289] "Skill difference" refers to the lack of skills and knowledge existing between the user's current skill level and the skill level they should aim for.

[0290] An "individualized learning plan" is a learning program customized to improve specific skills based on the user's differences in abilities.

[0291] "Educational progress" is an indicator that shows how far a user has progressed in acquiring skills through the educational plan.

[0292] "Means of providing advice" refers to a system for providing users with feedback and support to improve their learning efficiency.

[0293] A "notification of participation in work" is information that informs a user of opportunities to participate in relevant projects or activities once they have reached a certain skill level.

[0294] A "generative AI model" is an algorithm that automatically creates learning plans, notification messages, and other similar content based on user information.

[0295] A "prompt statement" is an instruction given to an AI model to obtain a specific output.

[0296] This invention provides a system aimed at enabling users learning artificial intelligence technology to efficiently acquire skills and improve their practical abilities in related industries. Specifically, it is implemented in the following way.

[0297] First, the user uses a terminal to input information about their desired role and areas of interest into the system. This information is sent to the server, which then retrieves the corresponding skill sets from its database. A common database management system such as MySQL is used for this database management.

[0298] The server generates questions using a programming language like Python to assess the user's current skill level. This assessment is provided to the user using an online form tool such as Google Forms. Once the user completes the form, the results are sent to the server and analyzed using libraries such as Pandas.

[0299] Based on the analysis results, the server identifies differences in user abilities and generates personalized learning plans. These plans are optimized for each user by a generating AI model and are structured as online learning platforms, exercises, and practical examples. The generated learning plans are sent to the user's device, and the user proceeds with their learning accordingly.

[0300] Furthermore, the server tracks user progress in real time and provides advice as needed. A learning management system (LMS) and dashboard tools are used for this purpose. Users who achieve a certain level of proficiency receive notifications about relevant work assignments. These notifications are sent via email and push notification services.

[0301] For example, if a user wants to work in the manufacturing sector as an "AI consultant," the server will identify the data analysis skills and industry-specific knowledge required for that role and provide an educational plan that includes online training and case studies. By following this plan, the user will have a higher chance of getting opportunities to participate in projects.

[0302] Examples of prompts include, "Please tell me the skill set required for an AI consultant working on a manufacturing project." This system helps users efficiently improve their skills and actually succeed in their desired job.

[0303] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0304] Step 1:

[0305] The user uses the terminal to input information about the role they aim for and the industries they are interested in. The input information includes the desired job type and related industry information. This input is sent to the server. The server queries the database based on this information and searches for the corresponding skill set. The skill set obtained from the database is returned to the server.

[0306] Step 2:

[0307] After identifying the necessary skill set based on the user's input information, the server generates assessment questions to evaluate the user's current skill level. An AI model for generation is used to automatically generate questions optimized for each role. This generated assessment is sent to the terminal as an online form and provided to the user. The user answers this assessment from the terminal and resends the answer results to the server.

[0308] Step 3:

[0309] The server analyzes the assessment results received from the user. This analysis is performed using the Pandas library in Python. The server identifies the ability differences that exist between the user's current skills and the ideal skill set. As a result of this analysis, basic data for an individualized education plan is generated.

[0310] Step 4:

[0311] Based on the results of the analysis, the server generates an optimal education plan for the user. This plan incorporates online education, exercise problems, and practical examples. The generated education plan is sent to the terminal as JSON-formatted data. The user accesses this plan through the terminal and starts learning.

[0312] Step 5:

[0313] To track learning progress, the server monitors the user's progress in real time. Based on the progress data sent from the device, the server generates feedback and provides advice to improve learning efficiency. This feedback is sent to the device and displayed to the user.

[0314] Step 6:

[0315] When a user reaches a set skill level, the server generates a notification of relevant work opportunities for that user. The notification text, created by the generation AI model, is sent to the user's device as an email or push notification. Through this notification, the user can gain practical experience.

[0316] (Application Example 1)

[0317] 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."

[0318] In learning AI technology, users require efficient learning processes tailored to their desired roles and industries, but currently, there is a lack of personalized learning methods to achieve this. Furthermore, the insufficient real-time progress monitoring and feedback can prevent users from maximizing the effectiveness of their learning. Additionally, limited access to learning content hinders the efficient acquisition of skills relevant to what users want to learn.

[0319] 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.

[0320] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to; means for identifying the skills and knowledge required based on the information; assessment means for evaluating the user's current skill level; means for generating an individualized learning plan; means for tracking the user's learning progress and providing immediate feedback; and means for providing access to interactive learning content using a smart device. This enables the user to learn efficiently and individually, and to practically acquire the skills necessary for their desired role.

[0321] A "user" is an individual or legal entity that wishes to learn AI technology using the system.

[0322] A "role" refers to the type of job or work related to the AI ​​technology that the user aims to achieve.

[0323] An "industry" refers to a specific industrial sector or area of ​​economic activity that a user is interested in.

[0324] "Input means" refers to a method or device for introducing information about the role and industry that the user aims to play into the system.

[0325] "Skills" refer to the technical or practical abilities required to perform a particular role.

[0326] "Knowledge" refers to the information or understanding that a user should possess in a particular industrial field.

[0327] An "assessment tool" is a method or device for evaluating a user's current skill level.

[0328] An "individualized learning plan" refers to educational content and a program tailored to the user's skill level and learning goals.

[0329] "Learning progress" refers to the extent to which a user has improved their skills and knowledge according to their learning plan.

[0330] "Feedback" refers to advice or comments provided regarding a user's learning progress.

[0331] A "smart device" is an advanced electronic device used to provide learning content interactively.

[0332] "Interactive learning content" refers to educational content that allows users to actively participate in and learn.

[0333] In the system implementing this invention, users can experience an efficient learning process of AI technology using their own smart devices. Users input information about their desired role and industry into the system via input means. This data is analyzed by a server to identify the required skills and knowledge. The server uses assessment means to evaluate the user's current skill level, and based on this evaluation, a personalized learning plan is generated. This learning plan includes online courses, practice problems, and application scenarios.

[0334] Users can learn while receiving feedback in real time using smart devices. The server tracks learning progress and provides immediate feedback based on that data. This allows users to constantly check their progress and areas for improvement, enabling them to learn efficiently. Furthermore, once a certain skill level is reached, users are notified of opportunities to participate in actual projects, allowing them to gain experience in real-world projects.

[0335] As a concrete example, if a user aims to take on a role in the manufacturing industry as an "AI consultant," the server identifies the need for data analysis skills and industry knowledge. Based on this, the server provides the user with specialized online courses and case studies, which the user then uses to proceed with their learning. By generating example prompts such as, "Evaluate the skills required to be an AI consultant and propose an optimal learning plan," the user can experience skill assessment and plan proposal using a generated AI model.

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The user uses a terminal to input information about their desired role and industry. This input is data sent to the server. The server receives this data and begins analysis.

[0339] Step 2:

[0340] The server identifies relevant skills and knowledge from the database based on the received information. The identified information is filtered based on the user's input to generate the required skill set.

[0341] Step 3:

[0342] The server uses assessment tools to generate test data to evaluate the user's current skill level. The user takes this test on a terminal and sends their answers to the server. Based on the user's answers, the server generates a current skill profile.

[0343] Step 4:

[0344] The server compares the user's skill profile with their required skill set and identifies any skill gaps. Based on this, a personalized learning plan is generated for the user. The generated learning plan is then materialized in the form of online lectures, practice problems, and other materials, and sent to the device.

[0345] Step 5:

[0346] Users progress through their learning based on the generated learning plan via their device. Learning progress data is sent to the server in real time, and the server analyzes this data to evaluate learning progress.

[0347] Step 6:

[0348] The server generates opinions and feedback in real time based on the evaluation results of learning progress and sends them to the terminal. This allows users to instantly understand their learning status and adjust their learning plan as needed.

[0349] Step 7:

[0350] When the server determines that a user has reached a certain skill level, it notifies the user of an opportunity to participate in work. This notification allows the user to participate in actual projects and further deepen their skills.

[0351] 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.

[0352] This invention provides a learning experience optimized for each individual user by combining emotion recognition functionality with a system that supports the improvement of users' AI skills. The system aims to improve learning efficiency and reduce stress by considering the user's emotional state during learning, along with their technical progress.

[0353] This system first identifies the necessary skill set based on the role and industry information entered by the user, and then generates a learning plan to fill that gap. The user accesses the learning content through their device and receives real-time progress and evaluation feedback.

[0354] A key feature of this system is its emotion engine. The emotion engine obtains multiple emotional indicators, such as facial expressions, voice tone, and input speed, from input from the user's device to infer their emotional state. The inferred emotional state is sent to the server and used to adjust the learning plan and feedback.

[0355] The server adjusts feedback and learning content based on the user's emotional state. For example, if the server determines that the user is feeling down, it will provide encouraging messages to reduce the user's stress and change the content to a more gradual progression. Conversely, if the server determines that the user is highly motivated, it will adjust the content to provide more challenging tasks.

[0356] For example, when a user aims to become an AI planner, they undergo a planned skills assessment. If the server detects that the user's emotions are unstable, it temporarily lowers the difficulty level and displays encouraging comments. Conversely, if a stable and positive emotional state is detected, it presents a more challenging curriculum to support the user's growth. Furthermore, the timing of learning plan revisions and feedback is optimized based on real-time emotional data.

[0357] Thus, this system, equipped with an emotion engine, considers both the user's technical skills and emotions to provide efficient and adaptive learning support. This approach enables users to achieve their learning goals and smoothly transition to actual projects.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] Users log into the system and enter information related to their desired role and industry via a terminal.

[0361] Step 2:

[0362] The server receives information entered by the user, consults the database, and identifies a list of skills and knowledge required for that role.

[0363] Step 3:

[0364] The server generates an assessment test to evaluate the user's current skill level and sends it to the terminal.

[0365] Step 4:

[0366] Users answer assessment tests via their devices and send the results to the server.

[0367] Step 5:

[0368] The server analyzes the user's test results and generates a personalized learning plan based on the identified skill gaps.

[0369] Step 6:

[0370] The server sends the generated learning plan to the terminal, and the user begins learning based on its contents.

[0371] Step 7:

[0372] The device uses an emotion engine to monitor the user's emotional state and acquire data such as facial expressions and voice.

[0373] Step 8:

[0374] The server receives emotion data sent from the terminal and evaluates the user's emotional state in real time.

[0375] Step 9:

[0376] The server adjusts the content and difficulty level of learning materials based on the user's emotional state and generates encouraging messages and advice as needed.

[0377] Step 10:

[0378] The server sends the adjusted learning content and feedback to the device, and the user proceeds with their learning based on that information.

[0379] Step 11:

[0380] When the server determines that the user has reached their target skill level, it sends a notification to the device inviting them to participate in the relevant AI project.

[0381] Step 12:

[0382] Users can proceed to the next step by checking the notification on their device and replying to indicate their intention to participate in the project.

[0383] (Example 2)

[0384] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0385] In today's learning environment, users are required to efficiently acquire diverse skills and rapidly improve the abilities necessary for their roles. However, traditional systems have the problem of being limited to feedback based solely on technical progress, and failing to adequately optimize learning according to the emotional state of individual users. This can lead to decreased learning efficiency and an increase in the number of users who experience stress.

[0386] 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.

[0387] In this invention, the server includes data acquisition means for inputting information related to the role and industry the user aims for, means for identifying the required abilities and knowledge based on the information, and means for acquiring the user's emotional state during learning and adjusting the learning plan and evaluation information based on the emotional state. This makes it possible to provide feedback and adjustments to learning content that respond not only to the user's technical progress but also to changes in their emotions, thereby providing an efficient and stress-free learning experience.

[0388] "Data acquisition means" refers to the interface and technology for users to input information related to the role and industry they aspire to, thereby incorporating the user's background information into the system.

[0389] "Competency" is a concept that includes the technical and non-technical skills and knowledge required to perform a particular role.

[0390] "Knowledge" refers to the information and theoretical background required in a particular field, and represents the level of understanding necessary for a user to perform their job duties.

[0391] A "personally optimized learning plan" refers to a learning method and content structure that is customized according to the user's individual skill level and learning progress.

[0392] "Evaluation information" refers to information that provides feedback on the user's progress and skill acquisition level based on their learning activities, and serves as an indicator for users to objectively understand their own learning activities.

[0393] "Emotional state" refers to the psychological and physiological responses that a user exhibits during learning, and the progress and content of the learning are adjusted based on these responses.

[0394] "Adjustment measures" include processes and technologies that modify learning plans and content provided in accordance with the user's progress and emotional state.

[0395] The system of this invention provides a personalized learning experience tailored to the user's skills and emotional state. The server receives role and industry-related information entered by the user through a terminal and uses this information to identify the necessary skills and knowledge. As an evaluation method, the system analyzes the user's skill gap by comparing the user's skill level with industry-standard competency requirements obtained from a database. The system incorporates a generative AI model using an AI algorithm to generate a personalized learning plan.

[0396] Specifically, the device uses a camera and microphone to monitor the user's facial expressions and tone of voice, and estimates their emotional state in real time. The obtained emotional data is sent to a server. The server adjusts the learning plan and evaluation information based on this emotional data. For example, if the user's emotional state is low, the server provides encouraging messages or tasks with reduced difficulty. On the other hand, if the user is highly motivated, it presents challenging tasks to promote growth.

[0397] As a concrete example, consider a scenario where a user aims to become an AI planner. In this scenario, a planned skills assessment is conducted. If the emotion engine detects unstable emotions, the server temporarily lowers the difficulty level and displays encouraging comments on the user's device. Conversely, if stable, positive emotions are detected, a more advanced curriculum is provided.

[0398] Examples of prompt statements include: "If a user aiming to become an AI planner is judged to be in a depressed emotional state, what message or learning adjustment should be given?" and "Please provide examples of challenging tasks that can be offered when a user is highly motivated." This allows the system to provide optimal support tailored to the user.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] The user enters information about the role they are pursuing and related industries through a terminal. This input information is sent to the server in text format. The server parses the input information and extracts basic skills and knowledge related to the industry and role from its database. The output of this step is a list of identified skills and knowledge.

[0402] Step 2:

[0403] The server conducts an assessment to evaluate the user's current skill level. The user responds to the skill assessment test using a terminal. The server analyzes the user's response data and calculates the skill gap. This process reveals the difference between the required skill set and the user's current skill level. The output of this step is a detailed report on the user's skill gap.

[0404] Step 3:

[0405] The server uses a generative AI model to create an individually optimized learning plan. This model receives a skills gap report and pre-acquired industry-specific skill requirements as input and generates a learning curriculum best suited to the user. This learning plan, including step-by-step tasks and resources, is sent to the terminal. The output provides a user-specific learning plan.

[0406] Step 4:

[0407] The device monitors the user's emotional state during learning. Specifically, it uses a camera and microphone to sense the user's facial expressions and tone of voice, which are then analyzed by emotion recognition software. The emotional data is sent to a server, which infers the user's emotions in real time. The output of this step is the inferred result of the user's current emotional state.

[0408] Step 5:

[0409] The server adjusts the learning plan and feedback based on the emotional state received from the device. For example, if it determines that the user is feeling down, it lowers the difficulty of the learning tasks and generates and sends an encouraging message to the device. Conversely, if it determines that the user is highly motivated, it suggests a more challenging task. The output of this step is the adjusted learning content and feedback message.

[0410] Step 6:

[0411] The user receives the adjusted learning content and feedback sent from the server on their device. Specifically, they review the assignments and messages displayed on their learning screen and proceed to the next learning activity. The output of this step is an improvement in the learning experience the user receives.

[0412] (Application Example 2)

[0413] 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."

[0414] While modern learning support systems excel at tracking users' technical progress, they lack flexible feedback and learning plan adjustment capabilities that take into account users' emotional states. This results in a challenge in providing an optimal learning experience, as they fail to adequately address the stress and motivational shifts users experience during their learning journey.

[0415] 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.

[0416] In this invention, the server includes a device for inputting information related to the user's desired role and industry, a device for identifying skills and knowledge, a device for analyzing the user's skill differences and generating an individualized learning plan, an engine for acquiring information for inferring emotions and analyzing the user's emotional state, and a device for adjusting feedback and learning content based on the emotional state. This makes it possible to provide an optimal learning plan and feedback that takes into account the user's emotional state in addition to their technical progress.

[0417] A "device" is a collection of hardware or software designed to perform a specific function.

[0418] "Skills" refer to the knowledge and practical abilities necessary to perform a specific task efficiently and effectively.

[0419] A "personalized learning plan" is an optimal learning route tailored to the user's skill level and emotional state.

[0420] An "emotion inference engine" is a system that analyzes a user's emotional state in real time based on data such as their facial expressions, tone of voice, and input speed.

[0421] "Feedback" refers to evaluations and advice provided regarding a user's learning progress.

[0422] "Learning content" refers to educational materials, exercises, and practical scenarios related to the specific skills and knowledge that the user aims to acquire.

[0423] "Information" refers to data about users and specific insights generated from that data.

[0424] In the system implementing this invention, the user first inputs information related to the role and industry they aspire to work in into a terminal. This terminal can include a general-purpose computer or smartphone. Based on this information, a server identifies the necessary skills and knowledge and generates a personalized learning plan accordingly. This server operates on a cloud-based platform and utilizes Google Cloud's sentiment recognition API and AWS cloud servers, among others.

[0425] As the user progresses through the learning process, the device captures the user's facial expressions and voice through its camera and microphone, collecting information to infer their emotions. An emotion inference engine processes this data in real time to analyze the user's emotional state. The results of this analysis are sent to a server and used for feedback and adjustments to the learning content.

[0426] The server generates optimal feedback by considering the user's emotional state as well as their technical progress. For example, if a user is feeling stressed, the system will present them with easy tasks and display encouraging messages. On the other hand, if the system determines that the user is relaxed and highly motivated, it will provide them with challenging tasks and new promotional information.

[0427] For example, if a user is studying at home during a holiday, the system will detect their relaxed facial expression and calm tone of voice, and then present content of an appropriate difficulty level. This allows the user to study in a stress-free environment.

[0428] Example prompt: "How can we improve the purchasing experience by taking emotions into account?"

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The user enters information about their desired role and industry into the device.

[0432] The terminal sends the entered information to the server. Based on this information, the server identifies the necessary skills and knowledge. This provides the basic data for analyzing skill gaps.

[0433] Step 2:

[0434] The server analyzes the user's skill differences based on the acquired data and generates an individualized learning plan.

[0435] The server extracts relevant topics and courses from the database and uses them to build a learning plan. This plan includes optimal course content to improve the user's technical skills. The output is a personalized learning plan.

[0436] Step 3:

[0437] The device captures the user's facial expressions and voice while learning is in progress and sends them to an engine that infers emotions.

[0438] This engine analyzes data input from the camera and microphone to infer the user's emotional state. Based on this inference, the user's current emotional state is output.

[0439] Step 4:

[0440] Emotional state data is sent to the server, which then adjusts the feedback and learning content based on that data.

[0441] The server generates modified learning plans and feedback messages based on the analyzed emotional state and technical progress. Depending on the user's state, actions such as adjusting the difficulty of tasks and selecting appropriate encouraging messages are performed. The output is the adjusted learning plan and feedback.

[0442] Step 5:

[0443] Users continue learning based on the provided learning plan and receive immediate progress feedback from their device.

[0444] This feedback includes advice on the level achieved and areas for improvement. Receiving it immediately allows users to quickly understand the next steps needed and improve learning efficiency. The output is progress feedback.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention embodies a system that provides users learning AI technology with an efficient learning process, enabling them to play a practical role in the industry. The system begins with the user selecting a role and industry related to their desired AI project, and then, based on that information, identifying the necessary skills and knowledge.

[0462] The server first consults a database based on the information entered by the user to obtain the skill set required for a specific role. Based on this skill set, the server provides an assessment tool to evaluate the user's current skill level. The user answers this assessment via a terminal, and the results are sent to the server.

[0463] The server analyzes the assessment results and identifies the gap between the user's current skills and the required skills. Based on this, the server generates an optimized learning plan for the user. This plan consists of online courses, exercises, and practical scenarios, and is delivered to the user's device. The user then uses their device to access this learning content and acquire the necessary skills.

[0464] Progress based on the learning plan is tracked in real time by the server. The server analyzes the user's progress and sends feedback and advice to the device as needed. This allows the user to learn efficiently.

[0465] When a user is determined to have reached a certain skill level, the server sends a notification informing them of an opportunity to participate in an AI project. This allows the user to gain practical experience and further deepen their skills.

[0466] For example, if a user wishes to work on a project in the "manufacturing" sector as an "AI consultant," the server will present a skill set including data analysis capabilities and industry-specific knowledge as the skills required for that role. The server will assess the user's skill level and provide a learning plan to fill in any gaps. The user will follow this plan, learning through online courses on data analysis and case studies in the manufacturing sector. If they achieve a certain level of performance, they will receive a notification to participate in a related project.

[0467] Thus, the present invention provides a system that supports the transition from acquiring AI skills to putting them into practice, and offers users a means to realize their desired career.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] After the user logs in, the server provides the terminal with an interface for entering role and industry information.

[0471] Step 2:

[0472] Users input their desired role and industry into a terminal and send that information to the server.

[0473] Step 3:

[0474] Based on the information received, the server retrieves a list of the skills and knowledge required for that role from the database.

[0475] Step 4:

[0476] The server generates an assessment test to evaluate the user's current skill level and delivers it to the terminal.

[0477] Step 5:

[0478] Users answer assessment tests using their devices and send the results to the server.

[0479] Step 6:

[0480] The server analyzes user responses and identifies skill gaps.

[0481] Step 7:

[0482] The server generates a personalized learning plan based on the skill gap and provides it to the device.

[0483] Step 8:

[0484] Users follow the provided learning plan and study online courses and exercises through their devices.

[0485] Step 9:

[0486] The server monitors the user's learning progress in real time and analyzes their progress.

[0487] Step 10:

[0488] The server generates and sends feedback and improvement suggestions to the terminal based on the progress.

[0489] Step 11:

[0490] The server sends a notification to the user's device informing them of an opportunity to participate in a relevant AI project once the user reaches their target skill level.

[0491] Step 12:

[0492] Users will check the notification and, if they wish, respond via their device to indicate their intention to participate in the project.

[0493] (Example 1)

[0494] 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."

[0495] In the field of artificial intelligence technology, it is difficult for users to efficiently acquire skills appropriate for diverse roles and industries. Furthermore, finding the optimal learning plan for users, monitoring their progress in real time, and receiving appropriate feedback are not easy. Additionally, there is a lack of adequate support for users to gain relevant practical opportunities once they have acquired sufficient skills. To address these challenges, an integrated system is needed that enables users to efficiently and effectively acquire the necessary skills and participate in their work.

[0496] 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.

[0497] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to, means for identifying the required skills and knowledge based on the information, and evaluation means for assessing the user's current skill level. This allows the user to discover specific skill sets tailored to their interests and goals, and to efficiently acquire those skills through an optimized training plan.

[0498] An "input method" refers to a device or interface used by a user to transmit information related to their desired role or industry to the system.

[0499] "Abilities and knowledge" refers to the specialized skills and information required for a particular job or industry.

[0500] "Evaluation methods" refer to processes and tools for objectively determining a user's current skill level.

[0501] "Skill gap" refers to the gap in skills and knowledge between a user's current skill level and their target skill level.

[0502] An "individualized learning plan" is a learning program customized to improve specific skills based on the user's differences in abilities.

[0503] "Educational progress" is an indicator that shows how far a user has progressed in acquiring skills through the educational plan.

[0504] "Means of providing advice" refers to a system for providing users with feedback and support to improve their learning efficiency.

[0505] A "notification of participation in work" is information that informs a user of opportunities to participate in relevant projects or activities once they have reached a certain skill level.

[0506] A "generative AI model" is an algorithm that automatically creates learning plans, notification messages, and other similar content based on user information.

[0507] A "prompt statement" is an instruction given to an AI model to obtain a specific output.

[0508] This invention provides a system aimed at enabling users learning artificial intelligence technology to efficiently acquire skills and improve their practical abilities in related industries. Specifically, it is implemented in the following way.

[0509] First, the user uses a terminal to input information about their desired role and areas of interest into the system. This information is sent to the server, which then retrieves the corresponding skill sets from its database. A common database management system such as MySQL is used for this database management.

[0510] The server generates questions using a programming language like Python to assess the user's current skill level. This assessment is provided to the user using an online form tool such as Google Forms. Once the user completes the form, the results are sent to the server and analyzed using libraries such as Pandas.

[0511] Based on the analysis results, the server identifies differences in user abilities and generates personalized learning plans. These plans are optimized for each user by a generating AI model and are structured as online learning platforms, exercises, and practical examples. The generated learning plans are sent to the user's device, and the user proceeds with their learning accordingly.

[0512] Furthermore, the server tracks user progress in real time and provides advice as needed. A learning management system (LMS) and dashboard tools are used for this purpose. Users who achieve a certain level of proficiency receive notifications about relevant work assignments. These notifications are sent via email and push notification services.

[0513] For example, if a user wants to work in the manufacturing sector as an "AI consultant," the server will identify the data analysis skills and industry-specific knowledge required for that role and provide an educational plan that includes online training and case studies. By following this plan, the user will have a higher chance of getting opportunities to participate in projects.

[0514] Examples of prompts include, "Please tell me the skill set required for an AI consultant working on a manufacturing project." This system helps users efficiently improve their skills and actually succeed in their desired job.

[0515] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0516] Step 1:

[0517] Users use a terminal to input information about the roles they aspire to and the industries they are interested in. This input includes desired job types and relevant industry information. This input is sent to the server. The server queries a database based on this information to find the corresponding skill sets. The skill sets obtained from the database are returned to the server.

[0518] Step 2:

[0519] The server identifies the required skill set based on the user's input and then generates assessment questions to evaluate the user's current skill level. A generative AI model is used to automatically generate questions optimized for each role. This generated assessment is sent to the user's terminal as an online form. The user answers the assessment from their terminal and sends the results back to the server.

[0520] Step 3:

[0521] The server analyzes the assessment results received from the user. This analysis is performed using the Python Pandas library. The server identifies the competency gap between the user's current skills and their ideal skill set. As a result of this analysis, the foundational data for an individualized education plan is generated.

[0522] Step 4:

[0523] Based on the analysis results, the server generates an optimal learning plan for the user. This plan incorporates online lessons, exercises, and case studies. The generated learning plan is sent to the user's device as JSON data. The user accesses this plan through their device and begins learning.

[0524] Step 5:

[0525] To track learning progress, the server monitors the user's progress in real time. Based on the progress data sent from the device, the server generates feedback and provides advice to improve learning efficiency. This feedback is sent to the device and displayed to the user.

[0526] Step 6:

[0527] When a user reaches a set skill level, the server generates a notification of relevant work opportunities for that user. The notification text, created by the generation AI model, is sent to the user's device as an email or push notification. Through this notification, the user can gain practical experience.

[0528] (Application Example 1)

[0529] 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."

[0530] In learning AI technology, users require efficient learning processes tailored to their desired roles and industries, but currently, there is a lack of personalized learning methods to achieve this. Furthermore, the insufficient real-time progress monitoring and feedback can prevent users from maximizing the effectiveness of their learning. Additionally, limited access to learning content hinders the efficient acquisition of skills relevant to what users want to learn.

[0531] 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.

[0532] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to; means for identifying the skills and knowledge required based on the information; assessment means for evaluating the user's current skill level; means for generating an individualized learning plan; means for tracking the user's learning progress and providing immediate feedback; and means for providing access to interactive learning content using a smart device. This enables the user to learn efficiently and individually, and to practically acquire the skills necessary for their desired role.

[0533] A "user" is an individual or legal entity that wishes to learn AI technology using the system.

[0534] A "role" refers to the type of job or work related to the AI ​​technology that the user aims to achieve.

[0535] An "industry" refers to a specific industrial sector or area of ​​economic activity that a user is interested in.

[0536] "Input means" refers to a method or device for introducing information about the role and industry that the user aims to play into the system.

[0537] "Skills" refer to the technical or practical abilities required to perform a particular role.

[0538] "Knowledge" refers to the information or understanding that a user should possess in a particular industrial field.

[0539] An "assessment tool" is a method or device for evaluating a user's current skill level.

[0540] An "individualized learning plan" refers to educational content and a program tailored to the user's skill level and learning goals.

[0541] "Learning progress" refers to the extent to which a user has improved their skills and knowledge according to their learning plan.

[0542] "Feedback" refers to advice or comments provided regarding a user's learning progress.

[0543] A "smart device" is an advanced electronic device used to provide learning content interactively.

[0544] "Interactive learning content" refers to educational content that allows users to actively participate in and learn.

[0545] In the system implementing this invention, users can experience an efficient learning process of AI technology using their own smart devices. Users input information about their desired role and industry into the system via input means. This data is analyzed by a server to identify the required skills and knowledge. The server uses assessment means to evaluate the user's current skill level, and based on this evaluation, a personalized learning plan is generated. This learning plan includes online courses, practice problems, and application scenarios.

[0546] Users can learn while receiving feedback in real time using smart devices. The server tracks learning progress and provides immediate feedback based on that data. This allows users to constantly check their progress and areas for improvement, enabling them to learn efficiently. Furthermore, once a certain skill level is reached, users are notified of opportunities to participate in actual projects, allowing them to gain experience in real-world projects.

[0547] As a concrete example, if a user aims to take on a role in the manufacturing industry as an "AI consultant," the server identifies the need for data analysis skills and industry knowledge. Based on this, the server provides the user with specialized online courses and case studies, which the user then uses to proceed with their learning. By generating example prompts such as, "Evaluate the skills required to be an AI consultant and propose an optimal learning plan," the user can experience skill assessment and plan proposal using a generated AI model.

[0548] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0549] Step 1:

[0550] The user uses a terminal to input information about their desired role and industry. This input is data sent to the server. The server receives this data and begins analysis.

[0551] Step 2:

[0552] The server identifies relevant skills and knowledge from the database based on the received information. The identified information is filtered based on the user's input to generate the required skill set.

[0553] Step 3:

[0554] The server uses assessment tools to generate test data to evaluate the user's current skill level. The user takes this test on a terminal and sends their answers to the server. Based on the user's answers, the server generates a current skill profile.

[0555] Step 4:

[0556] The server compares the user's skill profile with their required skill set and identifies any skill gaps. Based on this, a personalized learning plan is generated for the user. The generated learning plan is then materialized in the form of online lectures, practice problems, and other materials, and sent to the device.

[0557] Step 5:

[0558] Users progress through their learning based on the generated learning plan via their device. Learning progress data is sent to the server in real time, and the server analyzes this data to evaluate learning progress.

[0559] Step 6:

[0560] The server generates opinions and feedback in real time based on the evaluation results of learning progress and sends them to the terminal. This allows users to instantly understand their learning status and adjust their learning plan as needed.

[0561] Step 7:

[0562] When the server determines that a user has reached a certain skill level, it notifies the user of an opportunity to participate in work. This notification allows the user to participate in actual projects and further deepen their skills.

[0563] 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.

[0564] This invention provides a learning experience optimized for each individual user by combining emotion recognition functionality with a system that supports the improvement of users' AI skills. The system aims to improve learning efficiency and reduce stress by considering the user's emotional state during learning, along with their technical progress.

[0565] This system first identifies the necessary skill set based on the role and industry information entered by the user, and then generates a learning plan to fill that gap. The user accesses the learning content through their device and receives real-time progress and evaluation feedback.

[0566] A key feature of this system is its emotion engine. The emotion engine obtains multiple emotional indicators, such as facial expressions, voice tone, and input speed, from input from the user's device to infer their emotional state. The inferred emotional state is sent to the server and used to adjust the learning plan and feedback.

[0567] The server adjusts feedback and learning content based on the user's emotional state. For example, if the server determines that the user is feeling down, it will provide encouraging messages to reduce the user's stress and change the content to a more gradual progression. Conversely, if the server determines that the user is highly motivated, it will adjust the content to provide more challenging tasks.

[0568] For example, when a user aims to become an AI planner, they undergo a planned skills assessment. If the server detects that the user's emotions are unstable, it temporarily lowers the difficulty level and displays encouraging comments. Conversely, if a stable and positive emotional state is detected, it presents a more challenging curriculum to support the user's growth. Furthermore, the timing of learning plan revisions and feedback is optimized based on real-time emotional data.

[0569] Thus, this system, equipped with an emotion engine, considers both the user's technical skills and emotions to provide efficient and adaptive learning support. This approach enables users to achieve their learning goals and smoothly transition to actual projects.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] Users log into the system and enter information related to their desired role and industry via a terminal.

[0573] Step 2:

[0574] The server receives information entered by the user, consults the database, and identifies a list of skills and knowledge required for that role.

[0575] Step 3:

[0576] The server generates an assessment test to evaluate the user's current skill level and sends it to the terminal.

[0577] Step 4:

[0578] Users answer assessment tests via their devices and send the results to the server.

[0579] Step 5:

[0580] The server analyzes the user's test results and generates a personalized learning plan based on the identified skill gaps.

[0581] Step 6:

[0582] The server sends the generated learning plan to the terminal, and the user begins learning based on its contents.

[0583] Step 7:

[0584] The device uses an emotion engine to monitor the user's emotional state and acquire data such as facial expressions and voice.

[0585] Step 8:

[0586] The server receives emotion data sent from the terminal and evaluates the user's emotional state in real time.

[0587] Step 9:

[0588] The server adjusts the content and difficulty level of learning materials based on the user's emotional state and generates encouraging messages and advice as needed.

[0589] Step 10:

[0590] The server sends the adjusted learning content and feedback to the device, and the user proceeds with their learning based on that information.

[0591] Step 11:

[0592] When the server determines that the user has reached their target skill level, it sends a notification to the device inviting them to participate in the relevant AI project.

[0593] Step 12:

[0594] Users can proceed to the next step by checking the notification on their device and replying to indicate their intention to participate in the project.

[0595] (Example 2)

[0596] 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."

[0597] In today's learning environment, users are required to efficiently acquire diverse skills and rapidly improve the abilities necessary for their roles. However, traditional systems have the problem of being limited to feedback based solely on technical progress, and failing to adequately optimize learning according to the emotional state of individual users. This can lead to decreased learning efficiency and an increase in the number of users who experience stress.

[0598] 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.

[0599] In this invention, the server includes data acquisition means for inputting information related to the role and industry the user aims for, means for identifying the required abilities and knowledge based on the information, and means for acquiring the user's emotional state during learning and adjusting the learning plan and evaluation information based on the emotional state. This makes it possible to provide feedback and adjustments to learning content that respond not only to the user's technical progress but also to changes in their emotions, thereby providing an efficient and stress-free learning experience.

[0600] "Data acquisition means" refers to the interface and technology for users to input information related to the role and industry they aspire to, thereby incorporating the user's background information into the system.

[0601] "Competency" is a concept that includes the technical and non-technical skills and knowledge required to perform a particular role.

[0602] "Knowledge" refers to the information and theoretical background required in a particular field, and represents the level of understanding necessary for a user to perform their job duties.

[0603] A "personally optimized learning plan" refers to a learning method and content structure that is customized according to the user's individual skill level and learning progress.

[0604] "Evaluation information" refers to information that provides feedback on the user's progress and skill acquisition level based on their learning activities, and serves as an indicator for users to objectively understand their own learning activities.

[0605] "Emotional state" refers to the psychological and physiological responses that a user exhibits during learning, and the progress and content of the learning are adjusted based on these responses.

[0606] "Adjustment measures" include processes and technologies that modify learning plans and content provided in accordance with the user's progress and emotional state.

[0607] The system of this invention provides a personalized learning experience tailored to the user's skills and emotional state. The server receives role and industry-related information entered by the user through a terminal and uses this information to identify the necessary skills and knowledge. As an evaluation method, the system analyzes the user's skill gap by comparing the user's skill level with industry-standard competency requirements obtained from a database. The system incorporates a generative AI model using an AI algorithm to generate a personalized learning plan.

[0608] Specifically, the device uses a camera and microphone to monitor the user's facial expressions and tone of voice, and estimates their emotional state in real time. The obtained emotional data is sent to a server. The server adjusts the learning plan and evaluation information based on this emotional data. For example, if the user's emotional state is low, the server provides encouraging messages or tasks with reduced difficulty. On the other hand, if the user is highly motivated, it presents challenging tasks to promote growth.

[0609] As a concrete example, consider a scenario where a user aims to become an AI planner. In this scenario, a planned skills assessment is conducted. If the emotion engine detects unstable emotions, the server temporarily lowers the difficulty level and displays encouraging comments on the user's device. Conversely, if stable, positive emotions are detected, a more advanced curriculum is provided.

[0610] Examples of prompt statements include: "If a user aiming to become an AI planner is judged to be in a depressed emotional state, what message or learning adjustment should be given?" and "Please provide examples of challenging tasks that can be offered when a user is highly motivated." This allows the system to provide optimal support tailored to the user.

[0611] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0612] Step 1:

[0613] The user enters information about the role they are pursuing and related industries through a terminal. This input information is sent to the server in text format. The server parses the input information and extracts basic skills and knowledge related to the industry and role from its database. The output of this step is a list of identified skills and knowledge.

[0614] Step 2:

[0615] The server conducts an assessment to evaluate the user's current skill level. The user responds to the skill assessment test using a terminal. The server analyzes the user's response data and calculates the skill gap. This process reveals the difference between the required skill set and the user's current skill level. The output of this step is a detailed report on the user's skill gap.

[0616] Step 3:

[0617] The server uses a generative AI model to create an individually optimized learning plan. This model receives a skills gap report and pre-acquired industry-specific skill requirements as input and generates a learning curriculum best suited to the user. This learning plan, including step-by-step tasks and resources, is sent to the terminal. The output provides a user-specific learning plan.

[0618] Step 4:

[0619] The device monitors the user's emotional state during learning. Specifically, it uses a camera and microphone to sense the user's facial expressions and tone of voice, which are then analyzed by emotion recognition software. The emotional data is sent to a server, which infers the user's emotions in real time. The output of this step is the inferred result of the user's current emotional state.

[0620] Step 5:

[0621] The server adjusts the learning plan and feedback based on the emotional state received from the device. For example, if it determines that the user is feeling down, it lowers the difficulty of the learning tasks and generates and sends an encouraging message to the device. Conversely, if it determines that the user is highly motivated, it suggests a more challenging task. The output of this step is the adjusted learning content and feedback message.

[0622] Step 6:

[0623] The user receives the adjusted learning content and feedback sent from the server on their device. Specifically, they review the assignments and messages displayed on their learning screen and proceed to the next learning activity. The output of this step is an improvement in the learning experience the user receives.

[0624] (Application Example 2)

[0625] 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."

[0626] While modern learning support systems excel at tracking users' technical progress, they lack flexible feedback and learning plan adjustment capabilities that take into account users' emotional states. This results in a challenge in providing an optimal learning experience, as they fail to adequately address the stress and motivational shifts users experience during their learning journey.

[0627] 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.

[0628] In this invention, the server includes a device for inputting information related to the user's desired role and industry, a device for identifying skills and knowledge, a device for analyzing the user's skill differences and generating an individualized learning plan, an engine for acquiring information for inferring emotions and analyzing the user's emotional state, and a device for adjusting feedback and learning content based on the emotional state. This makes it possible to provide an optimal learning plan and feedback that takes into account the user's emotional state in addition to their technical progress.

[0629] A "device" is a collection of hardware or software designed to perform a specific function.

[0630] "Skills" refer to the knowledge and practical abilities necessary to perform a specific task efficiently and effectively.

[0631] A "personalized learning plan" is an optimal learning route tailored to the user's skill level and emotional state.

[0632] An "emotion inference engine" is a system that analyzes a user's emotional state in real time based on data such as their facial expressions, tone of voice, and input speed.

[0633] "Feedback" refers to evaluations and advice provided regarding a user's learning progress.

[0634] "Learning content" refers to educational materials, exercises, and practical scenarios related to the specific skills and knowledge that the user aims to acquire.

[0635] "Information" refers to data about users and specific insights generated from that data.

[0636] In the system implementing this invention, the user first inputs information related to the role and industry they aspire to work in into a terminal. This terminal can include a general-purpose computer or smartphone. Based on this information, a server identifies the necessary skills and knowledge and generates a personalized learning plan accordingly. This server operates on a cloud-based platform and utilizes Google Cloud's sentiment recognition API and AWS cloud servers, among others.

[0637] As the user progresses through the learning process, the device captures the user's facial expressions and voice through its camera and microphone, collecting information to infer their emotions. An emotion inference engine processes this data in real time to analyze the user's emotional state. The results of this analysis are sent to a server and used for feedback and adjustments to the learning content.

[0638] The server generates optimal feedback by considering the user's emotional state as well as their technical progress. For example, if a user is feeling stressed, the system will present them with easy tasks and display encouraging messages. On the other hand, if the system determines that the user is relaxed and highly motivated, it will provide them with challenging tasks and new promotional information.

[0639] For example, if a user is studying at home during a holiday, the system will detect their relaxed facial expression and calm tone of voice, and then present content of an appropriate difficulty level. This allows the user to study in a stress-free environment.

[0640] Example prompt: "How can we improve the purchasing experience by taking emotions into account?"

[0641] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0642] Step 1:

[0643] The user enters information about their desired role and industry into the device.

[0644] The terminal sends the entered information to the server. Based on this information, the server identifies the necessary skills and knowledge. This provides the basic data for analyzing skill gaps.

[0645] Step 2:

[0646] The server analyzes the user's skill differences based on the acquired data and generates an individualized learning plan.

[0647] The server extracts relevant topics and courses from the database and uses them to build a learning plan. This plan includes optimal course content to improve the user's technical skills. The output is a personalized learning plan.

[0648] Step 3:

[0649] The device captures the user's facial expressions and voice while learning is in progress and sends them to an engine that infers emotions.

[0650] This engine analyzes data input from the camera and microphone to infer the user's emotional state. Based on this inference, the user's current emotional state is output.

[0651] Step 4:

[0652] Emotional state data is sent to the server, which then adjusts the feedback and learning content based on that data.

[0653] The server generates modified learning plans and feedback messages based on the analyzed emotional state and technical progress. Depending on the user's state, actions such as adjusting the difficulty of tasks and selecting appropriate encouraging messages are performed. The output is the adjusted learning plan and feedback.

[0654] Step 5:

[0655] Users continue learning based on the provided learning plan and receive immediate progress feedback from their device.

[0656] This feedback includes advice on the level achieved and areas for improvement. Receiving it immediately allows users to quickly understand the next steps needed and improve learning efficiency. The output is progress feedback.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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).

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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".

[0674] This invention embodies a system that provides users learning AI technology with an efficient learning process, enabling them to play a practical role in the industry. The system begins with the user selecting a role and industry related to their desired AI project, and then, based on that information, identifying the necessary skills and knowledge.

[0675] The server first consults a database based on the information entered by the user to obtain the skill set required for a specific role. Based on this skill set, the server provides an assessment tool to evaluate the user's current skill level. The user answers this assessment via a terminal, and the results are sent to the server.

[0676] The server analyzes the assessment results and identifies the gap between the user's current skills and the required skills. Based on this, the server generates an optimized learning plan for the user. This plan consists of online courses, exercises, and practical scenarios, and is delivered to the user's device. The user then uses their device to access this learning content and acquire the necessary skills.

[0677] Progress based on the learning plan is tracked in real time by the server. The server analyzes the user's progress and sends feedback and advice to the device as needed. This allows the user to learn efficiently.

[0678] When a user is determined to have reached a certain skill level, the server sends a notification informing them of an opportunity to participate in an AI project. This allows the user to gain practical experience and further deepen their skills.

[0679] For example, if a user wishes to work on a project in the "manufacturing" sector as an "AI consultant," the server will present a skill set including data analysis capabilities and industry-specific knowledge as the skills required for that role. The server will assess the user's skill level and provide a learning plan to fill in any gaps. The user will follow this plan, learning through online courses on data analysis and case studies in the manufacturing sector. If they achieve a certain level of performance, they will receive a notification to participate in a related project.

[0680] Thus, the present invention provides a system that supports the transition from acquiring AI skills to putting them into practice, and offers users a means to realize their desired career.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] After the user logs in, the server provides the terminal with an interface for entering role and industry information.

[0684] Step 2:

[0685] Users input their desired role and industry into a terminal and send that information to the server.

[0686] Step 3:

[0687] Based on the information received, the server retrieves a list of the skills and knowledge required for that role from the database.

[0688] Step 4:

[0689] The server generates an assessment test to evaluate the user's current skill level and delivers it to the terminal.

[0690] Step 5:

[0691] Users answer assessment tests using their devices and send the results to the server.

[0692] Step 6:

[0693] The server analyzes user responses and identifies skill gaps.

[0694] Step 7:

[0695] The server generates a personalized learning plan based on the skill gap and provides it to the device.

[0696] Step 8:

[0697] Users follow the provided learning plan and study online courses and exercises through their devices.

[0698] Step 9:

[0699] The server monitors the user's learning progress in real time and analyzes their progress.

[0700] Step 10:

[0701] The server generates and sends feedback and improvement suggestions to the terminal based on the progress.

[0702] Step 11:

[0703] The server sends a notification to the user's device informing them of an opportunity to participate in a relevant AI project once the user reaches their target skill level.

[0704] Step 12:

[0705] Users will check the notification and, if they wish, respond via their device to indicate their intention to participate in the project.

[0706] (Example 1)

[0707] 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".

[0708] In the field of artificial intelligence technology, it is difficult for users to efficiently acquire skills appropriate for diverse roles and industries. Furthermore, finding the optimal learning plan for users, monitoring their progress in real time, and receiving appropriate feedback are not easy. Additionally, there is a lack of adequate support for users to gain relevant practical opportunities once they have acquired sufficient skills. To address these challenges, an integrated system is needed that enables users to efficiently and effectively acquire the necessary skills and participate in their work.

[0709] 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.

[0710] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to, means for identifying the required skills and knowledge based on the information, and evaluation means for assessing the user's current skill level. This allows the user to discover specific skill sets tailored to their interests and goals, and to efficiently acquire those skills through an optimized training plan.

[0711] An "input method" refers to a device or interface used by a user to transmit information related to their desired role or industry to the system.

[0712] "Abilities and knowledge" refers to the specialized skills and information required for a particular job or industry.

[0713] "Evaluation methods" refer to processes and tools for objectively determining a user's current skill level.

[0714] "Skill gap" refers to the gap in skills and knowledge between a user's current skill level and their target skill level.

[0715] An "individualized learning plan" is a learning program customized to improve specific skills based on the user's differences in abilities.

[0716] "Educational progress" is an indicator that shows how far a user has progressed in acquiring skills through the educational plan.

[0717] "Means of providing advice" refers to a system for providing users with feedback and support to improve their learning efficiency.

[0718] A "notification of participation in work" is information that informs a user of opportunities to participate in relevant projects or activities once they have reached a certain skill level.

[0719] A "generative AI model" is an algorithm that automatically creates learning plans, notification messages, and other similar content based on user information.

[0720] A "prompt statement" is an instruction given to an AI model to obtain a specific output.

[0721] This invention provides a system aimed at enabling users learning artificial intelligence technology to efficiently acquire skills and improve their practical abilities in related industries. Specifically, it is implemented in the following way.

[0722] First, the user uses a terminal to input information about their desired role and areas of interest into the system. This information is sent to the server, which then retrieves the corresponding skill sets from its database. A common database management system such as MySQL is used for this database management.

[0723] The server generates questions using a programming language like Python to assess the user's current skill level. This assessment is provided to the user using an online form tool such as Google Forms. Once the user completes the form, the results are sent to the server and analyzed using libraries such as Pandas.

[0724] Based on the analysis results, the server identifies differences in user abilities and generates personalized learning plans. These plans are optimized for each user by a generating AI model and are structured as online learning platforms, exercises, and practical examples. The generated learning plans are sent to the user's device, and the user proceeds with their learning accordingly.

[0725] Furthermore, the server tracks user progress in real time and provides advice as needed. A learning management system (LMS) and dashboard tools are used for this purpose. Users who achieve a certain level of proficiency receive notifications about relevant work assignments. These notifications are sent via email and push notification services.

[0726] For example, if a user wants to work in the manufacturing sector as an "AI consultant," the server will identify the data analysis skills and industry-specific knowledge required for that role and provide an educational plan that includes online training and case studies. By following this plan, the user will have a higher chance of getting opportunities to participate in projects.

[0727] Examples of prompts include, "Please tell me the skill set required for an AI consultant working on a manufacturing project." This system helps users efficiently improve their skills and actually succeed in their desired job.

[0728] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0729] Step 1:

[0730] Users use a terminal to input information about the roles they aspire to and the industries they are interested in. This input includes desired job types and relevant industry information. This input is sent to the server. The server queries a database based on this information to find the corresponding skill sets. The skill sets obtained from the database are returned to the server.

[0731] Step 2:

[0732] The server identifies the required skill set based on the user's input and then generates assessment questions to evaluate the user's current skill level. A generative AI model is used to automatically generate questions optimized for each role. This generated assessment is sent to the user's terminal as an online form. The user answers the assessment from their terminal and sends the results back to the server.

[0733] Step 3:

[0734] The server analyzes the assessment results received from the user. This analysis is performed using the Python Pandas library. The server identifies the competency gap between the user's current skills and their ideal skill set. As a result of this analysis, the foundational data for an individualized education plan is generated.

[0735] Step 4:

[0736] Based on the analysis results, the server generates an optimal learning plan for the user. This plan incorporates online lessons, exercises, and case studies. The generated learning plan is sent to the user's device as JSON data. The user accesses this plan through their device and begins learning.

[0737] Step 5:

[0738] To track learning progress, the server monitors the user's progress in real time. Based on the progress data sent from the device, the server generates feedback and provides advice to improve learning efficiency. This feedback is sent to the device and displayed to the user.

[0739] Step 6:

[0740] When a user reaches a set skill level, the server generates a notification of relevant work opportunities for that user. The notification text, created by the generation AI model, is sent to the user's device as an email or push notification. Through this notification, the user can gain practical experience.

[0741] (Application Example 1)

[0742] 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".

[0743] In learning AI technology, users require efficient learning processes tailored to their desired roles and industries, but currently, there is a lack of personalized learning methods to achieve this. Furthermore, the insufficient real-time progress monitoring and feedback can prevent users from maximizing the effectiveness of their learning. Additionally, limited access to learning content hinders the efficient acquisition of skills relevant to what users want to learn.

[0744] 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.

[0745] In this invention, the server includes input means for inputting information related to the role and industry the user aspires to; means for identifying the skills and knowledge required based on the information; assessment means for evaluating the user's current skill level; means for generating an individualized learning plan; means for tracking the user's learning progress and providing immediate feedback; and means for providing access to interactive learning content using a smart device. This enables the user to learn efficiently and individually, and to practically acquire the skills necessary for their desired role.

[0746] A "user" is an individual or legal entity that wishes to learn AI technology using the system.

[0747] A "role" refers to the type of job or work related to the AI ​​technology that the user aims to achieve.

[0748] An "industry" refers to a specific industrial sector or area of ​​economic activity that a user is interested in.

[0749] "Input means" refers to a method or device for introducing information about the role and industry that the user aims to play into the system.

[0750] "Skills" refer to the technical or practical abilities required to perform a particular role.

[0751] "Knowledge" refers to the information or understanding that a user should possess in a particular industrial field.

[0752] An "assessment tool" is a method or device for evaluating a user's current skill level.

[0753] An "individualized learning plan" refers to educational content and a program tailored to the user's skill level and learning goals.

[0754] "Learning progress" refers to the extent to which a user has improved their skills and knowledge according to their learning plan.

[0755] "Feedback" refers to advice or comments provided regarding a user's learning progress.

[0756] A "smart device" is an advanced electronic device used to provide learning content interactively.

[0757] "Interactive learning content" refers to educational content that allows users to actively participate in and learn.

[0758] In the system implementing this invention, users can experience an efficient learning process of AI technology using their own smart devices. Users input information about their desired role and industry into the system via input means. This data is analyzed by a server to identify the required skills and knowledge. The server uses assessment means to evaluate the user's current skill level, and based on this evaluation, a personalized learning plan is generated. This learning plan includes online courses, practice problems, and application scenarios.

[0759] Users can learn while receiving feedback in real time using smart devices. The server tracks learning progress and provides immediate feedback based on that data. This allows users to constantly check their progress and areas for improvement, enabling them to learn efficiently. Furthermore, once a certain skill level is reached, users are notified of opportunities to participate in actual projects, allowing them to gain experience in real-world projects.

[0760] As a concrete example, if a user aims to take on a role in the manufacturing industry as an "AI consultant," the server identifies the need for data analysis skills and industry knowledge. Based on this, the server provides the user with specialized online courses and case studies, which the user then uses to proceed with their learning. By generating example prompts such as, "Evaluate the skills required to be an AI consultant and propose an optimal learning plan," the user can experience skill assessment and plan proposal using a generated AI model.

[0761] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0762] Step 1:

[0763] The user uses a terminal to input information about their desired role and industry. This input is data sent to the server. The server receives this data and begins analysis.

[0764] Step 2:

[0765] The server identifies relevant skills and knowledge from the database based on the received information. The identified information is filtered based on the user's input to generate the required skill set.

[0766] Step 3:

[0767] The server uses assessment tools to generate test data to evaluate the user's current skill level. The user takes this test on a terminal and sends their answers to the server. Based on the user's answers, the server generates a current skill profile.

[0768] Step 4:

[0769] The server compares the user's skill profile with their required skill set and identifies any skill gaps. Based on this, a personalized learning plan is generated for the user. The generated learning plan is then materialized in the form of online lectures, practice problems, and other materials, and sent to the device.

[0770] Step 5:

[0771] Users progress through their learning based on the generated learning plan via their device. Learning progress data is sent to the server in real time, and the server analyzes this data to evaluate learning progress.

[0772] Step 6:

[0773] The server generates opinions and feedback in real time based on the evaluation results of learning progress and sends them to the terminal. This allows users to instantly understand their learning status and adjust their learning plan as needed.

[0774] Step 7:

[0775] When the server determines that a user has reached a certain skill level, it notifies the user of an opportunity to participate in work. This notification allows the user to participate in actual projects and further deepen their skills.

[0776] 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.

[0777] This invention provides a learning experience optimized for each individual user by combining emotion recognition functionality with a system that supports the improvement of users' AI skills. The system aims to improve learning efficiency and reduce stress by considering the user's emotional state during learning, along with their technical progress.

[0778] This system first identifies the necessary skill set based on the role and industry information entered by the user, and then generates a learning plan to fill that gap. The user accesses the learning content through their device and receives real-time progress and evaluation feedback.

[0779] A key feature of this system is its emotion engine. The emotion engine obtains multiple emotional indicators, such as facial expressions, voice tone, and input speed, from input from the user's device to infer their emotional state. The inferred emotional state is sent to the server and used to adjust the learning plan and feedback.

[0780] The server adjusts feedback and learning content based on the user's emotional state. For example, if the server determines that the user is feeling down, it will provide encouraging messages to reduce the user's stress and change the content to a more gradual progression. Conversely, if the server determines that the user is highly motivated, it will adjust the content to provide more challenging tasks.

[0781] For example, when a user aims to become an AI planner, they undergo a planned skills assessment. If the server detects that the user's emotions are unstable, it temporarily lowers the difficulty level and displays encouraging comments. Conversely, if a stable and positive emotional state is detected, it presents a more challenging curriculum to support the user's growth. Furthermore, the timing of learning plan revisions and feedback is optimized based on real-time emotional data.

[0782] Thus, this system, equipped with an emotion engine, considers both the user's technical skills and emotions to provide efficient and adaptive learning support. This approach enables users to achieve their learning goals and smoothly transition to actual projects.

[0783] The following describes the processing flow.

[0784] Step 1:

[0785] Users log into the system and enter information related to their desired role and industry via a terminal.

[0786] Step 2:

[0787] The server receives information entered by the user, consults the database, and identifies a list of skills and knowledge required for that role.

[0788] Step 3:

[0789] The server generates an assessment test to evaluate the user's current skill level and sends it to the terminal.

[0790] Step 4:

[0791] Users answer assessment tests via their devices and send the results to the server.

[0792] Step 5:

[0793] The server analyzes the user's test results and generates a personalized learning plan based on the identified skill gaps.

[0794] Step 6:

[0795] The server sends the generated learning plan to the terminal, and the user begins learning based on its contents.

[0796] Step 7:

[0797] The device uses an emotion engine to monitor the user's emotional state and acquire data such as facial expressions and voice.

[0798] Step 8:

[0799] The server receives emotion data sent from the terminal and evaluates the user's emotional state in real time.

[0800] Step 9:

[0801] The server adjusts the content and difficulty level of learning materials based on the user's emotional state and generates encouraging messages and advice as needed.

[0802] Step 10:

[0803] The server sends the adjusted learning content and feedback to the device, and the user proceeds with their learning based on that information.

[0804] Step 11:

[0805] When the server determines that the user has reached their target skill level, it sends a notification to the device inviting them to participate in the relevant AI project.

[0806] Step 12:

[0807] Users can proceed to the next step by checking the notification on their device and replying to indicate their intention to participate in the project.

[0808] (Example 2)

[0809] 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".

[0810] In today's learning environment, users are required to efficiently acquire diverse skills and rapidly improve the abilities necessary for their roles. However, traditional systems have the problem of being limited to feedback based solely on technical progress, and failing to adequately optimize learning according to the emotional state of individual users. This can lead to decreased learning efficiency and an increase in the number of users who experience stress.

[0811] 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.

[0812] In this invention, the server includes data acquisition means for inputting information related to the role and industry the user aims for, means for identifying the required abilities and knowledge based on the information, and means for acquiring the user's emotional state during learning and adjusting the learning plan and evaluation information based on the emotional state. This makes it possible to provide feedback and adjustments to learning content that respond not only to the user's technical progress but also to changes in their emotions, thereby providing an efficient and stress-free learning experience.

[0813] "Data acquisition means" refers to the interface and technology for users to input information related to the role and industry they aspire to, thereby incorporating the user's background information into the system.

[0814] "Competency" is a concept that includes the technical and non-technical skills and knowledge required to perform a particular role.

[0815] "Knowledge" refers to the information and theoretical background required in a particular field, and represents the level of understanding necessary for a user to perform their job duties.

[0816] A "personally optimized learning plan" refers to a learning method and content structure that is customized according to the user's individual skill level and learning progress.

[0817] "Evaluation information" refers to information that provides feedback on the user's progress and skill acquisition level based on their learning activities, and serves as an indicator for users to objectively understand their own learning activities.

[0818] "Emotional state" refers to the psychological and physiological responses that a user exhibits during learning, and the progress and content of the learning are adjusted based on these responses.

[0819] "Adjustment measures" include processes and technologies that modify learning plans and content provided in accordance with the user's progress and emotional state.

[0820] The system of this invention provides a personalized learning experience tailored to the user's skills and emotional state. The server receives role and industry-related information entered by the user through a terminal and uses this information to identify the necessary skills and knowledge. As an evaluation method, the system analyzes the user's skill gap by comparing the user's skill level with industry-standard competency requirements obtained from a database. The system incorporates a generative AI model using an AI algorithm to generate a personalized learning plan.

[0821] Specifically, the device uses a camera and microphone to monitor the user's facial expressions and tone of voice, and estimates their emotional state in real time. The obtained emotional data is sent to a server. The server adjusts the learning plan and evaluation information based on this emotional data. For example, if the user's emotional state is low, the server provides encouraging messages or tasks with reduced difficulty. On the other hand, if the user is highly motivated, it presents challenging tasks to promote growth.

[0822] As a concrete example, consider a scenario where a user aims to become an AI planner. In this scenario, a planned skills assessment is conducted. If the emotion engine detects unstable emotions, the server temporarily lowers the difficulty level and displays encouraging comments on the user's device. Conversely, if stable, positive emotions are detected, a more advanced curriculum is provided.

[0823] Examples of prompt statements include: "If a user aiming to become an AI planner is judged to be in a depressed emotional state, what message or learning adjustment should be given?" and "Please provide examples of challenging tasks that can be offered when a user is highly motivated." This allows the system to provide optimal support tailored to the user.

[0824] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0825] Step 1:

[0826] The user enters information about the role they are pursuing and related industries through a terminal. This input information is sent to the server in text format. The server parses the input information and extracts basic skills and knowledge related to the industry and role from its database. The output of this step is a list of identified skills and knowledge.

[0827] Step 2:

[0828] The server conducts an assessment to evaluate the user's current skill level. The user responds to the skill assessment test using a terminal. The server analyzes the user's response data and calculates the skill gap. This process reveals the difference between the required skill set and the user's current skill level. The output of this step is a detailed report on the user's skill gap.

[0829] Step 3:

[0830] The server uses a generative AI model to create an individually optimized learning plan. This model receives a skills gap report and pre-acquired industry-specific skill requirements as input and generates a learning curriculum best suited to the user. This learning plan, including step-by-step tasks and resources, is sent to the terminal. The output provides a user-specific learning plan.

[0831] Step 4:

[0832] The device monitors the user's emotional state during learning. Specifically, it uses a camera and microphone to sense the user's facial expressions and tone of voice, which are then analyzed by emotion recognition software. The emotional data is sent to a server, which infers the user's emotions in real time. The output of this step is the inferred result of the user's current emotional state.

[0833] Step 5:

[0834] The server adjusts the learning plan and feedback based on the emotional state received from the device. For example, if it determines that the user is feeling down, it lowers the difficulty of the learning tasks and generates and sends an encouraging message to the device. Conversely, if it determines that the user is highly motivated, it suggests a more challenging task. The output of this step is the adjusted learning content and feedback message.

[0835] Step 6:

[0836] The user receives the adjusted learning content and feedback sent from the server on their device. Specifically, they review the assignments and messages displayed on their learning screen and proceed to the next learning activity. The output of this step is an improvement in the learning experience the user receives.

[0837] (Application Example 2)

[0838] 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".

[0839] While modern learning support systems excel at tracking users' technical progress, they lack flexible feedback and learning plan adjustment capabilities that take into account users' emotional states. This results in a challenge in providing an optimal learning experience, as they fail to adequately address the stress and motivational shifts users experience during their learning journey.

[0840] 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.

[0841] In this invention, the server includes a device for inputting information related to the user's desired role and industry, a device for identifying skills and knowledge, a device for analyzing the user's skill differences and generating an individualized learning plan, an engine for acquiring information for inferring emotions and analyzing the user's emotional state, and a device for adjusting feedback and learning content based on the emotional state. This makes it possible to provide an optimal learning plan and feedback that takes into account the user's emotional state in addition to their technical progress.

[0842] A "device" is a collection of hardware or software designed to perform a specific function.

[0843] "Skills" refer to the knowledge and practical abilities necessary to perform a specific task efficiently and effectively.

[0844] A "personalized learning plan" is an optimal learning route tailored to the user's skill level and emotional state.

[0845] An "emotion inference engine" is a system that analyzes a user's emotional state in real time based on data such as their facial expressions, tone of voice, and input speed.

[0846] "Feedback" refers to evaluations and advice provided regarding a user's learning progress.

[0847] "Learning content" refers to educational materials, exercises, and practical scenarios related to the specific skills and knowledge that the user aims to acquire.

[0848] "Information" refers to data about users and specific insights generated from that data.

[0849] In the system implementing this invention, the user first inputs information related to the role and industry they aspire to work in into a terminal. This terminal can include a general-purpose computer or smartphone. Based on this information, a server identifies the necessary skills and knowledge and generates a personalized learning plan accordingly. This server operates on a cloud-based platform and utilizes Google Cloud's sentiment recognition API and AWS cloud servers, among others.

[0850] As the user progresses through the learning process, the device captures the user's facial expressions and voice through its camera and microphone, collecting information to infer their emotions. An emotion inference engine processes this data in real time to analyze the user's emotional state. The results of this analysis are sent to a server and used for feedback and adjustments to the learning content.

[0851] The server generates optimal feedback by considering the user's emotional state as well as their technical progress. For example, if a user is feeling stressed, the system will present them with easy tasks and display encouraging messages. On the other hand, if the system determines that the user is relaxed and highly motivated, it will provide them with challenging tasks and new promotional information.

[0852] For example, if a user is studying at home during a holiday, the system will detect their relaxed facial expression and calm tone of voice, and then present content of an appropriate difficulty level. This allows the user to study in a stress-free environment.

[0853] Example prompt: "How can we improve the purchasing experience by taking emotions into account?"

[0854] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0855] Step 1:

[0856] The user enters information about their desired role and industry into the device.

[0857] The terminal sends the entered information to the server. Based on this information, the server identifies the necessary skills and knowledge. This provides the basic data for analyzing skill gaps.

[0858] Step 2:

[0859] The server analyzes the user's skill differences based on the acquired data and generates an individualized learning plan.

[0860] The server extracts relevant topics and courses from the database and uses them to build a learning plan. This plan includes optimal course content to improve the user's technical skills. The output is a personalized learning plan.

[0861] Step 3:

[0862] The device captures the user's facial expressions and voice while learning is in progress and sends them to an engine that infers emotions.

[0863] This engine analyzes data input from the camera and microphone to infer the user's emotional state. Based on this inference, the user's current emotional state is output.

[0864] Step 4:

[0865] Emotional state data is sent to the server, which then adjusts the feedback and learning content based on that data.

[0866] The server generates modified learning plans and feedback messages based on the analyzed emotional state and technical progress. Depending on the user's state, actions such as adjusting the difficulty of tasks and selecting appropriate encouraging messages are performed. The output is the adjusted learning plan and feedback.

[0867] Step 5:

[0868] Users continue learning based on the provided learning plan and receive immediate progress feedback from their device.

[0869] This feedback includes advice on the level achieved and areas for improvement. Receiving it immediately allows users to quickly understand the next steps needed and improve learning efficiency. The output is progress feedback.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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."

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] An input method for users to enter information related to the role they aspire to play and the industry they work in,

[0894] A means for identifying the required skills and knowledge based on the aforementioned information,

[0895] Assessment tools for evaluating the user's current skill level,

[0896] A means for analyzing the user's skill gap based on the aforementioned evaluation results and generating a personalized learning plan,

[0897] A means of tracking the user's learning progress and providing feedback,

[0898] A means of sending project participation notifications when a certain skill level is achieved,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the personalized learning plan includes an online course, exercises, and practice scenarios.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein feedback on the progress is provided in real time.

[0904] "Example 1"

[0905] (Claim 1)

[0906] An input method for users to enter information related to the role they aspire to play and the industry they work in,

[0907] Means for identifying the required abilities and knowledge based on the aforementioned information,

[0908] An evaluation method for assessing the user's current skill level,

[0909] A means for analyzing differences in user abilities based on the aforementioned evaluation results and generating an individualized educational plan,

[0910] A means of monitoring and advising on the user's educational progress,

[0911] A means of sending a notification of participation in work upon achieving a certain level of competence,

[0912] A means for transmitting the generated educational plan to the user's device,

[0913] A means of analyzing progress and sending advice as needed,

[0914] A means for sending the generated notification message to the user's communication device,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the individualized educational plan includes online education, exercises, and practical examples.

[0918] (Claim 3)

[0919] The system according to claim 1, wherein advice on the progress is provided immediately.

[0920] "Application Example 1"

[0921] (Claim 1)

[0922] An input method for users to enter information related to the role they aspire to play and the industry they work in,

[0923] A means for identifying the skills and knowledge required based on the aforementioned information,

[0924] Assessment tools for evaluating the user's current skill level,

[0925] A means for analyzing the user's skill deficiencies based on the aforementioned evaluation results and generating an individualized learning plan,

[0926] A means to track users' learning progress and provide feedback,

[0927] A means of sending a notification of participation in work upon achieving a certain skill level,

[0928] A means of providing access to interactive learning content using smart devices,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, wherein the individualized learning plan includes online courses, practice problems, and application scenarios.

[0932] (Claim 3)

[0933] The system according to claim 1, wherein the opinion on the progress is provided immediately.

[0934] "Example 2 of combining an emotion engine"

[0935] (Claim 1)

[0936] A means of acquiring data for users to input information related to the role and industry they aim for,

[0937] Means for identifying the required abilities and knowledge based on the aforementioned information,

[0938] An evaluation method for assessing the user's current skill level,

[0939] A means for analyzing the user's skill gap based on the aforementioned evaluation results and generating an individually optimized learning plan,

[0940] A means of tracking the user's learning progress and providing evaluation information,

[0941] A means for acquiring the user's emotional state during learning and adjusting the learning plan and evaluation information based on the said emotional state,

[0942] A means of sending notifications when a specific skill level is achieved,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, wherein the individually optimized learning plan includes online learning, exercises, and practice scenarios.

[0946] (Claim 3)

[0947] The system according to claim 1, wherein the aforementioned evaluation information is provided in real time.

[0948] "Application example 2 when combining with an emotional engine"

[0949] (Claim 1)

[0950] A device for inputting information related to the role the user aims for and the industry,

[0951] A device for identifying the skills and knowledge required based on the aforementioned information,

[0952] A means of evaluating the user's current skill level,

[0953] A device that analyzes the skill differences of users based on the aforementioned evaluation results and generates an individualized learning plan,

[0954] A device that monitors the user's learning progress and provides feedback,

[0955] A device that sends a notification of participation in the plan when a certain skill level is achieved,

[0956] An engine that acquires information to infer emotions and analyzes the user's emotional state based on this information,

[0957] A device that adjusts feedback and learning content based on the aforementioned emotional state,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, wherein the individualized learning plan includes online lectures, exercises, and practical scenarios.

[0961] (Claim 3)

[0962] The system according to claim 1, wherein feedback on the progress is provided immediately. [Explanation of Symbols]

[0963] 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. An input method for users to enter information related to the role they aspire to play and the industry they work in, A means for identifying the skills and knowledge required based on the aforementioned information, Assessment tools for evaluating the user's current skill level, A means for analyzing the user's skill deficiencies based on the aforementioned evaluation results and generating an individualized learning plan, A means to track users' learning progress and provide feedback, A means of sending a notification of participation in work upon achieving a certain skill level, A means of providing access to interactive learning content using smart devices, A system that includes this.

2. The system according to claim 1, wherein the individualized learning plan includes online courses, practice problems, and application scenarios.

3. The system according to claim 1, wherein the opinion on the progress is provided immediately.

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