system

Abstract

We provide the system. [Solution] A means for collecting data for human resource development, structuring the data, and storing it in a storage device, A method for training a model using a machine learning algorithm based on stored data, A means for creating a skill profile for each individual and generating an educational plan corresponding to that profile, A means of presenting learning content based on individualized educational plans and tracking learning progress, A means of providing support to resolve business problems by responding in real time based on individual input, A means of collecting feedback from users and using that feedback to improve the model, A system that includes this.

Description

【Technical Field】 , 【0005】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a modern corporate environment, it is difficult to efficiently implement personnel training suitable for individual needs. In particular, not only new employees but also second-year graduates and employees with rich work experience are required to provide appropriate education according to their respective skill levels and career stages. Furthermore, in order to achieve efficient training, real-time support for employees and continuous updating of educational content are also necessary, and there is a problem that the cost and time burden of personnel training increase with conventional methods. 【Means for Solving the Problems】 【0005】 This invention provides means for collecting and structuring data and storing it in a storage device, and means for training a model using a machine learning algorithm based on the stored data, in order to solve these challenges in human resource development. By using this model, a system is constructed that generates training plans tailored to each employee's skill profile and provides learning content based on individual training plans. It also has the function of providing support for resolving work-related problems through real-time responses and improving the model based on feedback. Furthermore, it is possible to promote skill improvement at all career stages by automatically updating the model when there are changes in work rules and always providing the latest and most optimal learning content. 【0006】 "Data collection" is the process of systematically gathering information to obtain the data necessary for analysis and processing. 【0007】 A "storage device" is a device for temporarily or permanently storing digital information, and it enables the reading and writing of data. 【0008】 A "machine learning algorithm" is a collection of mathematical methods that allow computers to learn patterns from large amounts of data and make predictions and decisions. 【0009】 A "skill profile" is a dataset that compiles information on an individual's abilities and job-related skills, representing their areas of expertise and experience. 【0010】 An "educational plan" is a set of educational activities or programs organized to achieve specific learning objectives. 【0011】 "Learning content" refers to educational materials and information designed to provide learners with knowledge and skills, and is provided in various formats such as text, video, and audio. 【0012】 "Real-time response" refers to a system's ability to respond immediately to user input, enabling rapid information provision and support. 【0013】 Feedback is a process of providing evaluations and information about specific actions or results, and using that information to improve or take the next step. [Brief explanation of the drawing] 【0014】 [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 the data processing device and 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] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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. 【Embodiments for Carrying Out the Invention】 【0015】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0018】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a numbered storage is one or more nonvolatile storage devices that store various programs and various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 This invention provides an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0036】 server 【0037】 The server is responsible for centrally collecting and storing company operational manuals and internal regulations in a database. This data is then used to train an AI model using machine learning algorithms. The trained model analyzes employee skill profiles and generates personalized training plans optimized for each profile. Furthermore, the server responds to user questions and inquiries in real time, supporting problem-solving in the workplace. 【0038】 terminal 【0039】 The terminal serves as the user interface, providing users with a means to view their learning plan and check their progress. Through the terminal, users can access learning content and submit questions to the server as needed. The terminal provides a smooth operation and user experience through a simple and intuitive interface. 【0040】 User 【0041】 Users are the end users of this system and are primarily the ones who will be learning. Through their devices, users input their individual skill profiles and utilize the learning content provided based on those profiles. Users also provide feedback on their learning experience and AI support, which helps in the continuous improvement of the system. 【0042】 Specific example 【0043】 For example, when a new employee is onboarded, the server analyzes manuals related to various business processes and generates individual learning modules. The new employee then uses a terminal to progress through these modules sequentially. When specific work scenarios are presented and questions arise, the user can instantly send questions from their terminal to the server and receive answers. This kind of feedback loop allows users to effectively improve their practical skills. 【0044】 The AI ​​agent mentor system of the present invention enables customized education and support tailored to diverse career stages and skill levels, efficiently promoting talent development throughout the organization. 【0045】 The following describes the processing flow. 【0046】 Step 1: 【0047】 The server collects internal business manuals and company regulations and stores them in a database. This data is structured using text analysis tools, and important information is extracted. 【0048】 Step 2: 【0049】 The server uses structured data to execute machine learning algorithms and train a generative AI model. This model is designed to provide optimal education and support based on the specific tasks performed. 【0050】 Step 3: 【0051】 Through the terminal, users input information such as their skills, work history, and desired career goals. This information is sent to the server and stored as an individual skills profile. 【0052】 Step 4: 【0053】 The server analyzes the user's skill profile and generates an individualized learning plan based on it. This plan includes learning modules, progress indicators, and evaluation criteria. 【0054】 Step 5: 【0055】 The device allows users to access generated learning plans and provides an intuitive interface. Users can follow the plan and monitor their progress in real time. 【0056】 Step 6: 【0057】 If a user encounters questions or problems during learning, they can send them to the server via their device. The server uses a trained AI model to answer the questions in real time and provide appropriate support. 【0058】 Step 7: 【0059】 After the training is complete, the user provides feedback. This feedback is sent from the device to the server and used to evaluate and improve the AI ​​model. 【0060】 Step 8: 【0061】 The server updates the AI ​​model based on feedback and newly collected data, providing an even more optimized learning plan and support in subsequent sessions. 【0062】 (Example 1) 【0063】 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." 【0064】 Traditional talent development systems have struggled to efficiently create personalized training plans and provide customized support tailored to each individual's skill level. Furthermore, flexibly adapting to new work regulations and information updates, and continuously keeping learning content up-to-date, has also been a challenge. 【0065】 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. 【0066】 In this invention, the server includes means for collecting information, structuring the information, and storing it in a memory device; means for training a model using analytical techniques based on the stored information; and means for creating individual competency profiles and generating training plans corresponding to those profiles. This enables the rapid creation and distribution of individual training plans, the provision of up-to-date learning content tailored to diverse work environments, and skill improvement across the entire organization. 【0067】 "Information" refers to data and knowledge collected from both inside and outside the organization, and primarily includes operational manuals, internal regulations, and guidelines related to operations. 【0068】 A "storage device" is a hardware device or system for electronically storing information, and generally refers to a database system. 【0069】 "Analytical techniques" refer to the techniques used to analyze data and information and to learn or train models, and include machine learning algorithms and natural language processing. 【0070】 A "competency profile" is a systematic record of each individual's skills, knowledge, and experience, forming the basis for an individualized educational plan. 【0071】 An "educational plan" refers to a learning program designed based on an individual's ability profile, and includes the purpose, content, and sequence of learning. 【0072】 "Educational content" refers to specific learning materials and information provided to individuals in accordance with a particular educational plan, and includes textbooks, videos, simulations, etc. 【0073】 "Tracking" refers to the process of monitoring and recording learners' progress and achievements, and is used to evaluate and adjust learning effectiveness. 【0074】 "Support" refers to assistance provided to help individuals solve work-related problems, and includes real-time advice and assistance with educational content. 【0075】 "Opinions" refer to feedback and suggestions collected from users, which are used to improve the system and model. 【0076】 "Computing device" refers to an electronic device used by users to access educational content, and includes computers and tablet devices. 【0077】 An "interface" is a platform for interaction between a user and a computing device, and includes means for accessing the educational content provided. 【0078】 This invention is an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0079】 The server has the function of collecting information, structuring it, and storing it in a database. This collection includes internal business policies and guidelines of the company, and a "database management system" is used as the storage device. The server also uses a "machine learning framework" as an "analysis technique" to train a generative AI model using the collected data. With this trained model, the server analyzes each individual's ability profile and generates an optimal training plan. In addition, the server receives prompts and responds instantly using a natural language processing model to support the resolution of business problems. 【0080】 The device allows users to access the educational plan provided to them. Through the device, users can view the educational content and check their progress. This functionality is implemented using user interface technology to facilitate intuitive operation. The specific learning content is diverse, including text, videos, and interactive simulations, allowing users to efficiently progress through their studies. 【0081】 The user is the learner in this system. Through their terminal, users can input their individual skill profiles and access learning content provided based on those profiles. They can also send prompts to the server, such as "Please tell me the key steps in the next project," to instantly obtain relevant information. In this way, users continuously improve their skills with the support they receive. 【0082】 A concrete example is the onboarding process for new employees. The server automatically generates individual learning modules based on detailed information about business processes. New employees access these modules using terminals and acquire the necessary knowledge sequentially. When questions arise, they can ask them immediately through prompts and receive answers in real time. This cyclical process greatly helps improve the user's practical skills. 【0083】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0084】 Step 1: 【0085】 The server collects necessary information from data sources both inside and outside the company. It receives unstructured data related to business policies and guidelines as input. The "data collection and analysis module" uses this data to organize, structure, and store it in a database. The output is structured data stored in the database. 【0086】 Step 2: 【0087】 The server analyzes the stored data and trains a model using machine learning libraries. It uses the accumulated data in the database as input. Through analytical techniques, it builds a generative AI model and trains it. In this process, it efficiently processes the data and extracts patterns. The output is the trained AI model, which is used in the next step. 【0088】 Step 3: 【0089】 The server uses a trained AI model to analyze the user's ability profile. It receives user skill information and historical data as input. Based on this information, the AI ​​model generates an individually optimized training plan. The output is a customized training plan for each user. 【0090】 Step 4: 【0091】 The terminal displays the educational plan received from the server to the user through a user interface. It receives educational plan data as input and processes it for visual presentation within the interface. The output is educational content that the user can intuitively interact with. 【0092】 Step 5: 【0093】 Users access the indicated educational content using their devices and progress through the learning process. The device receives user actions and learning progress data as input. This data is sent to a server to measure learning effectiveness and modify the educational plan as needed. Outputs include the user's learning progress and feedback. 【0094】 Step 6: 【0095】 During training, the user sends questions to the server using prompts. The input is the user's question, sent from the terminal to the server as a prompt. The server uses natural language processing to generate an appropriate response and sends the reply back to the terminal. The output is a real-time response to the user. 【0096】 Step 7: 【0097】 The server collects user feedback and uses it as data to improve the model. It receives user opinions and evaluation data as input. Based on this, it improves the AI ​​model and educational content. The output is the improved system components. 【0098】 (Application Example 1) 【0099】 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." 【0100】 In order to improve individual skills and develop human resources, there is a need to provide training plans optimized for each individual. Furthermore, support for quickly acquiring work skills in on-site settings such as factories is essential. Current systems lack sufficient real-time feedback and customized support, and there is a need to efficiently address this challenge. 【0101】 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. 【0102】 In this invention, the server includes means for creating an individual's skill profile and generating an educational plan corresponding to that profile; means for presenting learning information based on the individual educational plan and monitoring the learning progress; and means for visually displaying operating procedures on-site and supporting the use of the device. This provides each user with a customized educational plan, enabling efficient skill improvement. Furthermore, it also supports rapid on-site work acquisition. 【0103】 "Human resource development" is the process of improving the knowledge and skills of individuals and groups in order to achieve better results in their work and duties. 【0104】 "Data" refers to records of facts and information, which are the subject of analysis and processing. 【0105】 "Structuring" is the process of organizing data and making it easier to use. 【0106】 A "storage device" is a piece of hardware or system that can store information and retrieve it as needed. 【0107】 A "machine learning algorithm" is a mathematical method that uses data to enable computers to automatically learn and make predictions and decisions. 【0108】 A "model" is a structure that represents real-world phenomena or processes from a mathematical or computer perspective. 【0109】 A "competency profile" is a set of information used to summarize and evaluate an individual's skills and knowledge. 【0110】 An "educational plan" is a guideline for a step-by-step learning process designed to help learners achieve their goals. 【0111】 "Learning information" refers to the learning materials and data necessary for instruction that learners need. 【0112】 "Monitoring" is the act of observing the trends in processes and data and analyzing them as needed. 【0113】 "Real-time" is a term that describes the ability to process information and respond almost instantaneously. 【0114】 "Support" is the process of providing assistance or aid to make an activity or project successful. 【0115】 "Feedback" is information provided based on the results of an action or process, and is intended to encourage improvement and adjustment. 【0116】 The system for implementing the present invention consists of three components: a server, a terminal, and a user. 【0117】 The server aggregates information within the company and stores it in a database. This enables data analysis using machine learning algorithms. The trained generative AI model automatically generates customized training plans based on each user's skill profile. Furthermore, the server responds to user questions in real time and provides guidance and support related to work. 【0118】 The terminal is a device that allows users to interactively review their educational plans and manage their progress. Specific hardware examples include smart glasses and tablet devices. These devices feature an intuitive interface that allows users to access learning information with simple operations. When users use smart glasses in the field, the operating procedures are visually displayed, assisting them in using the device. 【0119】 Users progress through their learning plan via their devices and provide feedback on their skills. This feedback is received by the server and used to further improve and refine the model. 【0120】 As a concrete example, operators on a new factory line can learn how to operate the system through smart glasses, enabling them to perform tasks quickly without prior training. An example of a prompt in this scenario would be, "Write an AI agent program that provides personalized training advice to individual employees based on factory work procedures." 【0121】 The software used includes machine learning frameworks for data analysis (e.g., TENSORFLOW®) and natural language processing tools (e.g., the Transformers library). These are used for data processing, model training, and then for prediction and educational guidance using the generated models. 【0122】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0123】 Step 1: The server collects and stores the data. 【0124】 The server collects internal company manuals and regulations and stores them in a structured format in a database. This input data is used to generate model training and educational plans, as described later. 【0125】 Step 2: The server trains the machine learning model. 【0126】 The server uses stored data to train a generated AI model using machine learning algorithms. Past work data and skill profiles are used as input, and the optimized model is output through training. 【0127】 Step 3: The server generates individual education plans. 【0128】 Using a trained model, the server automatically generates an educational plan based on each user's skill profile. The input is the user's skill data, and the output is a customized learning plan. This plan shows exactly what skills the user will learn and in what order. 【0129】 Step 4: The device displays learning information and monitors progress. 【0130】 The terminal visually presents learning information based on the educational plan generated for the user. The input is the learning plan generated by the server, and the output is the monitoring of the user's learning progress, with the progress reported to the server as needed. 【0131】 Step 5: The user provides feedback and the server processes it. 【0132】 Users provide feedback through their devices regarding their learning progress and any questions they have during the learning process. This feedback, including user experiences and opinions, is received by the server and used to further improve the generated AI model. The processing of this feedback is done to improve the accuracy of the model and the quality of the educational plan. 【0133】 Step 6: The server provides on-site instructions and support. 【0134】 The server visually displays the necessary operating procedures to the user on-site via smart glasses. Inputs include real-time operation status and work processes, and the output provides visually displayed procedures, thereby supporting the user's on-site work. 【0135】 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. 【0136】 This invention relates to an AI agent mentor system that combines an emotion engine that recognizes the emotional state of users. This system significantly improves conventional talent development processes and provides more personalized educational support to individual employees. 【0137】 server 【0138】 The server is responsible for collecting and organizing internal business manuals and company regulations into a database. It also uses this data to train an AI model using machine learning algorithms. The trained model automatically generates training plans tailored to each user's individual skill profile. Furthermore, by integrating an emotion engine, the server analyzes user input and behavioral patterns, and adjusts training content according to their emotional state. 【0139】 terminal 【0140】 The device provides a user interface, enabling users to view their learning plans and check their progress. Through the device, users can access learning content and send questions to the server as needed. The device incorporates sensors that analyze the user's voice and facial expressions, allowing the emotion engine to detect the user's emotional state in real time. 【0141】 User 【0142】 Users, as learners, continue their daily learning through their devices. The user's emotional state is periodically analyzed, and the server adjusts the learning content based on the results. For example, if the server determines that the user is tired, it can present content with a lower difficulty level or recommend a break. Furthermore, feedback based on emotional state is used to further improve the system. 【0143】 Specific example 【0144】 For example, suppose an employee has joined a new project and is learning the job responsibilities. The server uses an AI model to generate a training plan based on the job responsibilities and presents the learning content through the user's device. As the user progresses through the learning process, the device's camera reads their facial expressions, and the emotion engine detects that the user's stress levels are increasing. In response, the server temporarily adjusts the training plan and provides the user with relaxing content and encouraging messages. In this way, by utilizing the emotion engine, a more appropriate and comfortable learning environment can be provided to the user. 【0145】 This invention enables customized support and learning experiences for individual users, resulting in efficient and flexible human resource development. 【0146】 The following describes the processing flow. 【0147】 Step 1: 【0148】 The server collects internal business manuals and company regulations, structures this information through text analysis, and stores it in a database. At this stage, key points related to business operations are extracted. 【0149】 Step 2: 【0150】 The server trains an AI model using machine learning algorithms based on structured data. This model is designed to learn the most effective training methods relevant to the task at hand. 【0151】 Step 3: 【0152】 The user enters their skills and work history into the terminal. The terminal sends this information to the server to create the user's profile. 【0153】 Step 4: 【0154】 The server generates individualized training plans based on user profiles. These plans aim to enhance the skills necessary for each user's job and include corresponding learning content. 【0155】 Step 5: 【0156】 The device presents learning content to the user and tracks their progress. It also analyzes voice and facial expressions using an emotion engine to monitor the user's emotional state in real time. 【0157】 Step 6: 【0158】 If the emotion engine detects the user's stress level or fatigue, the server will adjust the difficulty level of the learning content or suggest content that recommends taking a break. 【0159】 Step 7: 【0160】 As users progress through the learning content, if they encounter any questions, they can send them to the server via their device. The server then uses a trained AI model to respond and help resolve the issues. 【0161】 Step 8: 【0162】 After training, users provide feedback. This feedback is sent from the device to the server and used to improve the AI ​​model and adjust the training plan. 【0163】 Step 9: 【0164】 When new operational rules are introduced, the server automatically adds them to the database and updates the AI ​​model to ensure that the latest and most appropriate educational content is provided. 【0165】 Step 10: 【0166】 The server continuously optimizes the entire system based on feedback and new data to improve the user's educational experience and operational efficiency. 【0167】 (Example 2) 【0168】 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". 【0169】 In modern talent development processes, a lack of flexible and individualized educational support tailored to each individual's professional skills and emotional state is a challenge. Furthermore, delays in providing educational resources that keep pace with rapidly changing work regulations hinder efficient and effective talent development. 【0170】 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. 【0171】 In this invention, the server includes means for collecting information, organizing the information, and storing it in a storage medium; means for training a model using a learning algorithm based on the stored information; means for creating individual skill profiles and generating educational plans corresponding to the profiles; and means for detecting emotional states and adjusting the educational plans based on the detection results. This enables accurate and flexible educational support tailored to the professional abilities and emotional states of individual users, allowing for a rapid response to solving business challenges. 【0172】 "Information" refers to documents such as company operational manuals and internal regulations stored in a database. 【0173】 "Organization" refers to the process of classifying collected information into categories and storing it in a storage medium in an easily accessible format. 【0174】 A "storage medium" is a device or system used to store information in digital format. 【0175】 A "learning algorithm" is a method or technique for analyzing data from collected information and improving the performance of a model based on the results of that analysis. 【0176】 A "model" is a computational system trained using machine learning algorithms, used for making predictions and classifications. 【0177】 A "skill profile" is a dataset that represents a user's professional skills and knowledge level. 【0178】 An "educational plan" is an individualized learning plan designed based on the user's skill profile. 【0179】 "Emotional state" refers to the user's emotional and psychological condition, and is detected through voice, facial expressions, and other means. 【0180】 "Educational resources" refer to a collection of teaching materials and information designed to support the user's learning process. 【0181】 This invention provides a system for realizing personalized talent development support. In this system, the server, terminal, and user interact as follows: 【0182】 The server first aggregates information such as company operational manuals and internal regulations and records it in a database. This information is digitized using OCR technology and organized into categories using text analysis tools. For training, TensorFlow and PyTorch are used as machine learning libraries to build models based on generative AI models such as BERT and GPT. This generates training plans tailored to the user. 【0183】 The terminal provides an interface for receiving user input. A camera and microphone are used to capture the user's emotional state in real time. The terminal hardware integrates OpenFace and Emotion API as facial expression analysis tools, which automatically detect the user's emotional state and send the analysis results to the server. 【0184】 Users progress through their learning based on an educational plan provided via their device. Their learning progress is sent from the device to the server as feedback, and the educational plan is adjusted as needed. Users can also input questions and doubts into the system as prompts. For example, by inputting prompts such as "What sports do you recommend for relaxation?" or "What should I do if I feel I'm progressing too slowly?", the server uses a generative AI model to provide appropriate answers and suggestions. This enables flexible learning support tailored to each individual user. 【0185】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0186】 Step 1: 【0187】 The server collects internal business manuals and company regulations as information and stores it in a database. Paper documents are used as input, which is converted into digital data using OCR technology, and then organized and processed using text analysis tools. The output is a collection of digital information categorized by type. Specifically, the server uses a scanner to read paper documents and converts the images into text data. 【0188】 Step 2: 【0189】 The server trains a generative AI model using machine learning libraries based on organized data. This process uses pre-collected digital information as input to train a generative AI model (e.g., BERT or GPT). The output is the trained AI model, which can automatically generate educational plans. Specifically, it supplies training data to the model and tunes hyperparameters to improve the model's accuracy. 【0190】 Step 3: 【0191】 The terminal provides a user interface and creates an environment for users to view educational plans and progress through their studies. The input is the educational plan retrieved from the server, and the output is the learning content provided to the user. Specifically, the user solves tasks through the terminal, the terminal temporarily saves the results, and records progress. 【0192】 Step 4: 【0193】 The device detects the user's emotional state. This process uses the user's facial expressions and voice as input, which are analyzed using OpenFace and the Emotion API. The output is user emotional state data, which is sent to a server. Specifically, the device uses its built-in camera and microphone to capture data in real time and send it to the analysis tool. 【0194】 Step 5: 【0195】 The server adjusts the educational plan based on emotional state data. The input is emotional state data sent from the terminal, and the server uses data analysis functions to analyze the user's psychological state. The output is the adjusted educational plan. Specifically, the server modifies difficulty levels and optimizes the learning order to provide the user with the best possible learning experience. 【0196】 Step 6: 【0197】 The user enters prompts through the terminal, sending questions and requests to the system. In this process, the prompts received from the user become input and are processed by the server via an AI model. The output is specific answers and advice for the user. For example, if the user enters the prompt "Tell me some sports I can play when I want to relax," the server generates relevant information and returns it to the terminal. 【0198】 (Application Example 2) 【0199】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0200】 In human resource development, there is a need to provide optimal support tailored to the emotional state of each individual user. However, existing systems can handle support based on users' skills and progress, but they struggle to grasp and respond to emotional states in real time. As a result, it is not possible to quickly optimize educational content in response to fluctuations in users' stress levels and motivation, hindering effective human resource development. 【0201】 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. 【0202】 In this invention, the server includes means for analyzing facial expressions and voice tone to analyze the emotional state and estimate the user's emotional state; means for presenting the user with the most suitable communication method based on the estimated emotional state information; and means for presenting personalized learning content according to the emotional state. This enables individualized responses according to the user's emotional state, making it possible to create a more effective and comfortable human resource development environment. 【0203】 "Human resource development" refers to the process of education and training conducted to improve the skills and abilities of individual users. 【0204】 "Data collection" is the act of systematically gathering information for a specific purpose. 【0205】 "Structured data" refers to data that is organized consistently and stored in a memory device. 【0206】 A "machine learning algorithm" is a methodology for computers to learn from experience and perform pattern recognition using data. 【0207】 A "skill profile" is a collection of information about the abilities and characteristics of an individual user. 【0208】 An "educational plan" is a blueprint for the learning process, created based on set goals. 【0209】 "Learning content" refers to a collection of educational materials and information provided to users for learning purposes. 【0210】 "Tracking" is the act of tracking and recording progress and activities. 【0211】 "Responding in real time" means reacting instantly to user input and circumstances. 【0212】 "Work-related problems" refer to challenges or obstacles that arise in the workplace or within work activities. 【0213】 "Feedback" refers to the reactions and opinions provided by users. 【0214】 "Facial expression analysis" is a technique that infers emotions from the state and movement of a person's face. 【0215】 "Voice tone analysis" is a technique that infers emotions from the tone and quality of a person's voice. 【0216】 "Emotional state" refers to the psychological and emotional condition of a user at a given point in time. 【0217】 "To suggest communication methods" means to propose the most suitable means of interaction for the target audience. 【0218】 The system implementing this invention consists of three components: a server, a terminal, and a user. The roles of each component and specific examples are shown below. 【0219】 server 【0220】 The server is responsible for collecting, structuring, and storing data on talent development accumulated within the company. This data is used to train AI models using machine learning algorithms, particularly TensorFlow based on Python. The trained models automatically generate training plans tailored to each user's skill profile. Furthermore, by incorporating an emotion engine, the system estimates the user's emotional state and optimizes the training content based on that, thereby more effectively supporting talent development. 【0221】 terminal 【0222】 The device provides a user interface that allows users to check their educational plans and progress. Sensors embedded in smart glasses or robots analyze the user's facial expressions and voice tone. This allows for real-time assessment of their emotional state and transmission to a server. This data is processed by an emotion engine and presented to the user as appropriate feedback. 【0223】 User 【0224】 Users utilize their devices as the primary tool for their daily learning. Their emotional state is regularly analyzed, and optimized learning content is provided based on the results. For example, if a user is experiencing stress, the difficulty level of the learning material can be adjusted, or content designed to boost motivation can be added. 【0225】 One specific example of this implementation is that the emotion engine detects the emotional state of residents in a care facility and, if necessary, sends a suggestion to the caregiver to "play music to help them relax." 【0226】 An example of a prompt message is: "Analyze the image and audio data of the care recipient and infer their emotional state. If the recipient appears anxious, suggest appropriate responses." 【0227】 In this way, servers, terminals, and users work together as a unified team, making it possible to create a more personalized talent development environment. 【0228】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0229】 Step 1: 【0230】 The server collects employee training data from a central database within the company and structures that data. This process involves categorizing and tagging the data and storing it in memory. This input data forms the basis for subsequent model training using machine learning algorithms. 【0231】 Step 2: 【0232】 The server trains a generative AI model using structured data. It uses machine learning algorithms, specifically Python and TensorFlow, to generate individualized training plans based on skill profiles. The input for this step is structured data, and the output is a highly accurate AI model. 【0233】 Step 3: 【0234】 The device presents the generated educational plan and learning content to the user. It utilizes smart glasses or robotic display functions to track learning progress. The input is the educational plan from the AI ​​model, and the output is the specific learning content displayed to the user. 【0235】 Step 4: 【0236】 The device uses sensors to analyze the user's facial expressions and voice tone in real time. This analysis estimates the user's emotional state. Input is data from the camera and voice sensors, and output is data representing the user's emotional state. 【0237】 Step 5: 【0238】 The server analyzes emotional states obtained using an emotion engine and adjusts educational content based on that information. It also selects and presents the most appropriate communication method for the user. The input is emotional state data, and the output is adjusted learning content and communication guidelines. 【0239】 Step 6: 【0240】 Users continue their daily learning based on feedback from their devices. This feedback is used to improve the entire system and helps optimize future educational content. The input is the user's learning experience, and the output is the improved learning program. 【0241】 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. 【0242】 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. 【0243】 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. 【0244】 [Second Embodiment] 【0245】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0246】 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. 【0247】 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). 【0248】 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. 【0249】 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. 【0250】 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). 【0251】 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. 【0252】 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. 【0253】 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. 【0254】 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. 【0255】 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. 【0256】 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". 【0257】 This invention provides an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0258】 server 【0259】 The server is responsible for centrally collecting and storing company operational manuals and internal regulations in a database. This data is then used to train an AI model using machine learning algorithms. The trained model analyzes employee skill profiles and generates personalized training plans optimized for each profile. Furthermore, the server responds to user questions and inquiries in real time, supporting problem-solving in the workplace. 【0260】 terminal 【0261】 The terminal serves as the user interface, providing users with a means to view their learning plan and check their progress. Through the terminal, users can access learning content and submit questions to the server as needed. The terminal provides a smooth operation and user experience through a simple and intuitive interface. 【0262】 User 【0263】 Users are the end users of this system and are primarily the ones who will be learning. Through their devices, users input their individual skill profiles and utilize the learning content provided based on those profiles. Users also provide feedback on their learning experience and AI support, which helps in the continuous improvement of the system. 【0264】 Specific example 【0265】 For example, when a new employee is onboarded, the server analyzes manuals related to various business processes and generates individual learning modules. The new employee then uses a terminal to progress through these modules sequentially. When specific work scenarios are presented and questions arise, the user can instantly send questions from their terminal to the server and receive answers. This kind of feedback loop allows users to effectively improve their practical skills. 【0266】 The AI ​​agent mentor system of the present invention enables customized education and support tailored to diverse career stages and skill levels, efficiently promoting talent development throughout the organization. 【0267】 The following describes the processing flow. 【0268】 Step 1: 【0269】 The server collects internal business manuals and company regulations and stores them in a database. This data is structured using text analysis tools, and important information is extracted. 【0270】 Step 2: 【0271】 The server uses structured data to execute machine learning algorithms and train a generative AI model. This model is designed to provide optimal education and support based on the specific tasks performed. 【0272】 Step 3: 【0273】 Through the terminal, users input information such as their skills, work history, and desired career goals. This information is sent to the server and stored as an individual skills profile. 【0274】 Step 4: 【0275】 The server analyzes the user's skill profile and generates an individualized learning plan based on it. This plan includes learning modules, progress indicators, and evaluation criteria. 【0276】 Step 5: 【0277】 The device allows users to access generated learning plans and provides an intuitive interface. Users can follow the plan and monitor their progress in real time. 【0278】 Step 6: 【0279】 If the user has questions or problems during learning, they send a question to the server through the terminal. The server uses the trained AI model to answer the question in real time and provides appropriate support. 【0280】 Step 7: 【0281】 After learning, the user provides feedback. This feedback is sent from the terminal to the server and used for evaluating and improving the AI model. 【0282】 Step 8: 【0283】 The server updates the AI model based on the feedback and newly collected data, and provides an optimized learning plan and support in subsequent sessions. 【0284】 (Example 1) 【0285】 Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0286】 In the conventional personnel training system, it was difficult to efficiently create an education plan optimized for each individual or provide customized support according to each individual's skill level. Also, it was a challenge to flexibly respond to updates of new work rules and information and keep the learning content up-to-date continuously. 【0287】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0288】 In this invention, the server includes means for collecting information, structuring the information, and storing it in a memory device; means for training a model using analytical techniques based on the stored information; and means for creating individual competency profiles and generating training plans corresponding to those profiles. This enables the rapid creation and distribution of individual training plans, the provision of up-to-date learning content tailored to diverse work environments, and skill improvement across the entire organization. 【0289】 "Information" refers to data and knowledge collected from both inside and outside the organization, and primarily includes operational manuals, internal regulations, and guidelines related to operations. 【0290】 A "storage device" is a hardware device or system for electronically storing information, and generally refers to a database system. 【0291】 "Analytical techniques" refer to the techniques used to analyze data and information and to learn or train models, and include machine learning algorithms and natural language processing. 【0292】 A "competency profile" is a systematic record of each individual's skills, knowledge, and experience, forming the basis for an individualized educational plan. 【0293】 An "educational plan" refers to a learning program designed based on an individual's ability profile, and includes the purpose, content, and sequence of learning. 【0294】 "Educational content" refers to specific learning materials and information provided to individuals in accordance with a particular educational plan, and includes textbooks, videos, simulations, etc. 【0295】 "Tracking" refers to the process of monitoring and recording learners' progress and achievements, and is used to evaluate and adjust learning effectiveness. 【0296】 "Support" refers to assistance provided to help individuals solve work-related problems, and includes real-time advice and assistance with educational content. 【0297】 "Opinions" refer to feedback and suggestions collected from users, which are used to improve the system and model. 【0298】 "Computing device" refers to an electronic device used by users to access educational content, and includes computers and tablet devices. 【0299】 An "interface" is a platform for interaction between a user and a computing device, and includes means for accessing the educational content provided. 【0300】 This invention is an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0301】 The server has the function of collecting information, structuring it, and storing it in a database. This collection includes internal business policies and guidelines of the company, and a "database management system" is used as the storage device. The server also uses a "machine learning framework" as an "analysis technique" to train a generative AI model using the collected data. With this trained model, the server analyzes each individual's ability profile and generates an optimal training plan. In addition, the server receives prompts and responds instantly using a natural language processing model to support the resolution of business problems. 【0302】 The device allows users to access the educational plan provided to them. Through the device, users can view the educational content and check their progress. This functionality is implemented using user interface technology to facilitate intuitive operation. The specific learning content is diverse, including text, videos, and interactive simulations, allowing users to efficiently progress through their studies. 【0303】 The user is the entity that learns through this system. The user can input their respective skill profiles via a terminal and utilize the learning content provided based on them. Also, as an example of a prompt sentence, by sending a question such as "Please teach me the important procedures in the next project" to the server, relevant information can be obtained immediately. In this way, the user continuously improves their skills while receiving the given support. 【0304】 As a specific example, there is an onboarding process for new employees. The server automatically generates individual learning modules based on detailed information regarding business processes. New employees access these modules using a terminal and sequentially acquire the necessary knowledge. When doubts arise, questions can be immediately asked through the prompt sentence, and answers can be obtained in real time. This cyclic process greatly helps improve the user's practical capabilities. 【0305】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0306】 Step 1: 【0307】 The server collects the necessary information from data sources inside and outside the company. As input, it receives unstructured data regarding business policies and guidelines. This is used by the "Data Collection and Analysis Module" to organize, structure the information, and store it in a database. The output is the structured data stored in the database. 【0308】 Step 2: 【0309】 The server analyzes the stored data and trains the model using a machine learning library. As input, the accumulated data in the database is used. Through analysis techniques, a generative AI model is constructed and learned through training. In this process, the data is efficiently processed and patterns are extracted. The output is the trained AI model, which is used in the next step. 【0310】 Step 3: 【0311】 The server uses a trained AI model to analyze the user's ability profile. It receives user skill information and historical data as input. Based on this information, the AI ​​model generates an individually optimized training plan. The output is a customized training plan for each user. 【0312】 Step 4: 【0313】 The terminal displays the educational plan received from the server to the user through a user interface. It receives educational plan data as input and processes it for visual presentation within the interface. The output is educational content that the user can intuitively interact with. 【0314】 Step 5: 【0315】 Users access the indicated educational content using their devices and progress through the learning process. The device receives user actions and learning progress data as input. This data is sent to a server to measure learning effectiveness and modify the educational plan as needed. Outputs include the user's learning progress and feedback. 【0316】 Step 6: 【0317】 During training, the user sends questions to the server using prompts. The input is the user's question, sent from the terminal to the server as a prompt. The server uses natural language processing to generate an appropriate response and sends the reply back to the terminal. The output is a real-time response to the user. 【0318】 Step 7: 【0319】 The server collects user feedback and uses it as data to improve the model. It receives user opinions and evaluation data as input. Based on this, it improves the AI ​​model and educational content. The output is the improved system components. 【0320】 (Application Example 1) 【0321】 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." 【0322】 In order to improve individual skills and develop human resources, there is a need to provide training plans optimized for each individual. Furthermore, support for quickly acquiring work skills in on-site settings such as factories is essential. Current systems lack sufficient real-time feedback and customized support, and there is a need to efficiently address this challenge. 【0323】 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. 【0324】 In this invention, the server includes means for creating an individual's skill profile and generating an educational plan corresponding to that profile; means for presenting learning information based on the individual educational plan and monitoring the learning progress; and means for visually displaying operating procedures on-site and supporting the use of the device. This provides each user with a customized educational plan, enabling efficient skill improvement. Furthermore, it also supports rapid on-site work acquisition. 【0325】 "Human resource development" is the process of improving the knowledge and skills of individuals and groups in order to achieve better results in their work and duties. 【0326】 "Data" refers to records of facts and information, which are the subject of analysis and processing. 【0327】 "Structuring" is the process of organizing data and making it easier to use. 【0328】 A "storage device" is a piece of hardware or system that can store information and retrieve it as needed. 【0329】 A "machine learning algorithm" is a mathematical method that uses data to enable computers to automatically learn and make predictions and decisions. 【0330】 A "model" is a structure that represents real-world phenomena or processes from a mathematical or computer perspective. 【0331】 A "competency profile" is a set of information used to summarize and evaluate an individual's skills and knowledge. 【0332】 An "educational plan" is a guideline for a step-by-step learning process designed to help learners achieve their goals. 【0333】 "Learning information" refers to the learning materials and data necessary for instruction that learners need. 【0334】 "Monitoring" is the act of observing the trends in processes and data and analyzing them as needed. 【0335】 "Real-time" is a term that describes the ability to process information and respond almost instantaneously. 【0336】 "Support" is the process of providing assistance or aid to make an activity or project successful. 【0337】 "Feedback" is information provided based on the results of an action or process, and is intended to encourage improvement and adjustment. 【0338】 The system for implementing the present invention consists of three components: a server, a terminal, and a user. 【0339】 The server aggregates information within the company and stores it in a database. This enables data analysis using machine learning algorithms. The trained generative AI model automatically generates customized training plans based on each user's skill profile. Furthermore, the server responds to user questions in real time and provides guidance and support related to work. 【0340】 The terminal is a device that allows users to interactively review their educational plans and manage their progress. Specific hardware examples include smart glasses and tablet devices. These devices feature an intuitive interface that allows users to access learning information with simple operations. When users use smart glasses in the field, the operating procedures are visually displayed, assisting them in using the device. 【0341】 Users progress through their learning plan via their devices and provide feedback on their skills. This feedback is received by the server and used to further improve and refine the model. 【0342】 As a concrete example, operators on a new factory line can learn how to operate the system through smart glasses, enabling them to perform tasks quickly without prior training. An example of a prompt in this scenario would be, "Write an AI agent program that provides personalized training advice to individual employees based on factory work procedures." 【0343】 The software used includes machine learning frameworks for data analysis (e.g., TensorFlow) and natural language processing tools (e.g., the Transformers library). These are used to process data, train models, and then perform predictions and educational guidance using the generated models. 【0344】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0345】 Step 1: The server collects and stores the data. 【0346】 The server collects internal company manuals and regulations and stores them in a structured format in a database. This input data is used to generate model training and educational plans, as described later. 【0347】 Step 2: The server trains the machine learning model. 【0348】 The server uses stored data to train a generated AI model using machine learning algorithms. Past work data and skill profiles are used as input, and the optimized model is output through training. 【0349】 Step 3: The server generates individual education plans. 【0350】 Using a trained model, the server automatically generates an educational plan based on each user's skill profile. The input is the user's skill data, and the output is a customized learning plan. This plan shows exactly what skills the user will learn and in what order. 【0351】 Step 4: The device displays learning information and monitors progress. 【0352】 The terminal visually presents learning information based on the educational plan generated for the user. The input is the learning plan generated by the server, and the output is the monitoring of the user's learning progress, with the progress reported to the server as needed. 【0353】 Step 5: The user provides feedback and the server processes it. 【0354】 Users provide feedback through their devices regarding their learning progress and any questions they have during the learning process. This feedback, including user experiences and opinions, is received by the server and used to further improve the generated AI model. The processing of this feedback is done to improve the accuracy of the model and the quality of the educational plan. 【0355】 Step 6: The server provides on-site instructions and support. 【0356】 The server visually displays the necessary operating procedures to the user on-site via smart glasses. Inputs include real-time operation status and work processes, and the output provides visually displayed procedures, thereby supporting the user's on-site work. 【0357】 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. 【0358】 This invention relates to an AI agent mentor system that combines an emotion engine that recognizes the emotional state of users. This system significantly improves conventional talent development processes and provides more personalized educational support to individual employees. 【0359】 server 【0360】 The server is responsible for collecting and organizing internal business manuals and company regulations into a database. It also uses this data to train an AI model using machine learning algorithms. The trained model automatically generates training plans tailored to each user's individual skill profile. Furthermore, by integrating an emotion engine, the server analyzes user input and behavioral patterns, and adjusts training content according to their emotional state. 【0361】 terminal 【0362】 The device provides a user interface, enabling users to view their learning plans and check their progress. Through the device, users can access learning content and send questions to the server as needed. The device incorporates sensors that analyze the user's voice and facial expressions, allowing the emotion engine to detect the user's emotional state in real time. 【0363】 User 【0364】 Users, as learners, continue their daily learning through their devices. The user's emotional state is periodically analyzed, and the server adjusts the learning content based on the results. For example, if the server determines that the user is tired, it can present content with a lower difficulty level or recommend a break. Furthermore, feedback based on emotional state is used to further improve the system. 【0365】 Specific example 【0366】 For example, suppose an employee has joined a new project and is learning the job responsibilities. The server uses an AI model to generate a training plan based on the job responsibilities and presents the learning content through the user's device. As the user progresses through the learning process, the device's camera reads their facial expressions, and the emotion engine detects that the user's stress levels are increasing. In response, the server temporarily adjusts the training plan and provides the user with relaxing content and encouraging messages. In this way, by utilizing the emotion engine, a more appropriate and comfortable learning environment can be provided to the user. 【0367】 This invention enables customized support and learning experiences for individual users, resulting in efficient and flexible human resource development. 【0368】 The following describes the processing flow. 【0369】 Step 1: 【0370】 The server collects internal business manuals and company regulations, structures this information through text analysis, and stores it in a database. At this stage, key points related to business operations are extracted. 【0371】 Step 2: 【0372】 The server trains an AI model using machine learning algorithms based on structured data. This model is designed to learn the most effective training methods relevant to the task at hand. 【0373】 Step 3: 【0374】 The user enters their skills and work history into the terminal. The terminal sends this information to the server to create the user's profile. 【0375】 Step 4: 【0376】 The server generates individualized training plans based on user profiles. These plans aim to enhance the skills necessary for each user's job and include corresponding learning content. 【0377】 Step 5: 【0378】 The device presents learning content to the user and tracks their progress. It also analyzes voice and facial expressions using an emotion engine to monitor the user's emotional state in real time. 【0379】 Step 6: 【0380】 If the emotion engine detects the user's stress level or fatigue, the server will adjust the difficulty level of the learning content or suggest content that recommends taking a break. 【0381】 Step 7: 【0382】 As users progress through the learning content, if they encounter any questions, they can send them to the server via their device. The server then uses a trained AI model to respond and help resolve the issues. 【0383】 Step 8: 【0384】 After training, users provide feedback. This feedback is sent from the device to the server and used to improve the AI ​​model and adjust the training plan. 【0385】 Step 9: 【0386】 When new operational rules are introduced, the server automatically adds them to the database and updates the AI ​​model to ensure that the latest and most appropriate educational content is provided. 【0387】 Step 10: 【0388】 The server continuously optimizes the entire system based on feedback and new data to improve the user's educational experience and operational efficiency. 【0389】 (Example 2) 【0390】 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". 【0391】 In modern talent development processes, a lack of flexible and individualized educational support tailored to each individual's professional skills and emotional state is a challenge. Furthermore, delays in providing educational resources that keep pace with rapidly changing work regulations hinder efficient and effective talent development. 【0392】 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. 【0393】 In this invention, the server includes means for collecting information, organizing the information, and storing it in a storage medium; means for training a model using a learning algorithm based on the stored information; means for creating individual skill profiles and generating educational plans corresponding to the profiles; and means for detecting emotional states and adjusting the educational plans based on the detection results. This enables accurate and flexible educational support tailored to the professional abilities and emotional states of individual users, allowing for a rapid response to solving business challenges. 【0394】 "Information" refers to documents such as company operational manuals and internal regulations stored in a database. 【0395】 "Organization" refers to the process of classifying collected information into categories and storing it in a storage medium in an easily accessible format. 【0396】 A "storage medium" is a device or system used to store information in digital format. 【0397】 A "learning algorithm" is a method or technique for analyzing data from collected information and improving the performance of a model based on the results of that analysis. 【0398】 A "model" is a computational system trained using machine learning algorithms, used for making predictions and classifications. 【0399】 A "skill profile" is a dataset that represents a user's professional skills and knowledge level. 【0400】 An "educational plan" is an individualized learning plan designed based on the user's skill profile. 【0401】 "Emotional state" refers to the user's emotional and psychological condition, and is detected through voice, facial expressions, and other means. 【0402】 "Educational resources" refer to a collection of teaching materials and information designed to support the user's learning process. 【0403】 This invention provides a system for realizing personalized talent development support. In this system, the server, terminal, and user interact as follows: 【0404】 The server first aggregates information such as company operational manuals and internal regulations and records it in a database. This information is digitized using OCR technology and organized into categories using text analysis tools. For training, TensorFlow and PyTorch are used as machine learning libraries to build models based on generative AI models such as BERT and GPT. This generates training plans tailored to the user. 【0405】 The terminal provides an interface for receiving user input. A camera and microphone are used to capture the user's emotional state in real time. The terminal hardware integrates OpenFace and Emotion API as facial expression analysis tools, which automatically detect the user's emotional state and send the analysis results to the server. 【0406】 Users progress through their learning based on an educational plan provided via their device. Their learning progress is sent from the device to the server as feedback, and the educational plan is adjusted as needed. Users can also input questions and doubts into the system as prompts. For example, by inputting prompts such as "What sports do you recommend for relaxation?" or "What should I do if I feel I'm progressing too slowly?", the server uses a generative AI model to provide appropriate answers and suggestions. This enables flexible learning support tailored to each individual user. 【0407】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0408】 Step 1: 【0409】 The server collects internal business manuals and company regulations as information and stores it in a database. Paper documents are used as input, which is converted into digital data using OCR technology, and then organized and processed using text analysis tools. The output is a collection of digital information categorized by type. Specifically, the server uses a scanner to read paper documents and converts the images into text data. 【0410】 Step 2: 【0411】 The server trains a generative AI model using machine learning libraries based on organized data. This process uses pre-collected digital information as input to train a generative AI model (e.g., BERT or GPT). The output is the trained AI model, which can automatically generate educational plans. Specifically, it supplies training data to the model and tunes hyperparameters to improve the model's accuracy. 【0412】 Step 3: 【0413】 The terminal provides a user interface and creates an environment for users to view educational plans and progress through their studies. The input is the educational plan retrieved from the server, and the output is the learning content provided to the user. Specifically, the user solves tasks through the terminal, the terminal temporarily saves the results, and records progress. 【0414】 Step 4: 【0415】 The device detects the user's emotional state. This process uses the user's facial expressions and voice as input, which are analyzed using OpenFace and the Emotion API. The output is user emotional state data, which is sent to a server. Specifically, the device uses its built-in camera and microphone to capture data in real time and send it to the analysis tool. 【0416】 Step 5: 【0417】 The server adjusts the educational plan based on emotional state data. The input is emotional state data sent from the terminal, and the server uses data analysis functions to analyze the user's psychological state. The output is the adjusted educational plan. Specifically, the server modifies difficulty levels and optimizes the learning order to provide the user with the best possible learning experience. 【0418】 Step 6: 【0419】 The user enters prompts through the terminal, sending questions and requests to the system. In this process, the prompts received from the user become input and are processed by the server via an AI model. The output is specific answers and advice for the user. For example, if the user enters the prompt "Tell me some sports I can play when I want to relax," the server generates relevant information and returns it to the terminal. 【0420】 (Application Example 2) 【0421】 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 as the "terminal". 【0422】 In human resource development, there is a need to provide optimal support tailored to the emotional state of each individual user. However, existing systems can handle support based on users' skills and progress, but they struggle to grasp and respond to emotional states in real time. As a result, it is not possible to quickly optimize educational content in response to fluctuations in users' stress levels and motivation, hindering effective human resource development. 【0423】 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. 【0424】 In this invention, the server includes means for analyzing facial expressions and voice tone to analyze the emotional state and estimate the user's emotional state; means for presenting the user with the most suitable communication method based on the estimated emotional state information; and means for presenting personalized learning content according to the emotional state. This enables individualized responses according to the user's emotional state, making it possible to create a more effective and comfortable human resource development environment. 【0425】 "Human resource development" refers to the process of education and training conducted to improve the skills and abilities of individual users. 【0426】 "Data collection" is the act of systematically gathering information for a specific purpose. 【0427】 "Structured data" refers to data that is organized consistently and stored in a memory device. 【0428】 A "machine learning algorithm" is a methodology for computers to learn from experience and perform pattern recognition using data. 【0429】 A "skill profile" is a collection of information about the abilities and characteristics of an individual user. 【0430】 An "educational plan" is a blueprint for the learning process, created based on set goals. 【0431】 "Learning content" refers to a collection of educational materials and information provided to users for learning purposes. 【0432】 "Tracking" is the act of tracking and recording progress and activities. 【0433】 "Responding in real time" means reacting instantly to user input and circumstances. 【0434】 "Work-related problems" refer to challenges or obstacles that arise in the workplace or within work activities. 【0435】 "Feedback" refers to the reactions and opinions provided by users. 【0436】 "Facial expression analysis" is a technique that infers emotions from the state and movement of a person's face. 【0437】 "Voice tone analysis" is a technique that infers emotions from the tone and quality of a person's voice. 【0438】 "Emotional state" refers to the psychological and emotional condition of a user at a given point in time. 【0439】 "To suggest communication methods" means to propose the most suitable means of interaction for the target audience. 【0440】 The system implementing this invention consists of three components: a server, a terminal, and a user. The roles of each component and specific examples are shown below. 【0441】 server 【0442】 The server is responsible for collecting, structuring, and storing data on talent development accumulated within the company. This data is used to train AI models using machine learning algorithms, particularly TensorFlow based on Python. The trained models automatically generate training plans tailored to each user's skill profile. Furthermore, by incorporating an emotion engine, the system estimates the user's emotional state and optimizes the training content based on that, thereby more effectively supporting talent development. 【0443】 terminal 【0444】 The device provides a user interface that allows users to check their educational plans and progress. Sensors embedded in smart glasses or robots analyze the user's facial expressions and voice tone. This allows for real-time assessment of their emotional state and transmission to a server. This data is processed by an emotion engine and presented to the user as appropriate feedback. 【0445】 User 【0446】 Users utilize their devices as the primary tool for their daily learning. Their emotional state is regularly analyzed, and optimized learning content is provided based on the results. For example, if a user is experiencing stress, the difficulty level of the learning material can be adjusted, or content designed to boost motivation can be added. 【0447】 One specific example of this implementation is that the emotion engine detects the emotional state of residents in a care facility and, if necessary, sends a suggestion to the caregiver to "play music to help them relax." 【0448】 An example of a prompt message is: "Analyze the image and audio data of the care recipient and infer their emotional state. If the recipient appears anxious, suggest appropriate responses." 【0449】 In this way, servers, terminals, and users work together as a unified team, making it possible to create a more personalized talent development environment. 【0450】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0451】 Step 1: 【0452】 The server collects employee training data from a central database within the company and structures that data. This process involves categorizing and tagging the data and storing it in memory. This input data forms the basis for subsequent model training using machine learning algorithms. 【0453】 Step 2: 【0454】 The server trains a generative AI model using structured data. It uses machine learning algorithms, specifically Python and TensorFlow, to generate individualized training plans based on skill profiles. The input for this step is structured data, and the output is a highly accurate AI model. 【0455】 Step 3: 【0456】 The device presents the generated educational plan and learning content to the user. It utilizes smart glasses or robotic display functions to track learning progress. The input is the educational plan from the AI ​​model, and the output is the specific learning content displayed to the user. 【0457】 Step 4: 【0458】 The device uses sensors to analyze the user's facial expressions and voice tone in real time. This analysis estimates the user's emotional state. Input is data from the camera and voice sensors, and output is data representing the user's emotional state. 【0459】 Step 5: 【0460】 The server analyzes emotional states obtained using an emotion engine and adjusts educational content based on that information. It also selects and presents the most appropriate communication method for the user. The input is emotional state data, and the output is adjusted learning content and communication guidelines. 【0461】 Step 6: 【0462】 Users continue their daily learning based on feedback from their devices. This feedback is used to improve the entire system and helps optimize future educational content. The input is the user's learning experience, and the output is the improved learning program. 【0463】 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. 【0464】 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. 【0465】 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. 【0466】 [Third Embodiment] 【0467】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0468】 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. 【0469】 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). 【0470】 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. 【0471】 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. 【0472】 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). 【0473】 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. 【0474】 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. 【0475】 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. 【0476】 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. 【0477】 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. 【0478】 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". 【0479】 This invention provides an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0480】 server 【0481】 The server is responsible for centrally collecting and storing company operational manuals and internal regulations in a database. This data is then used to train an AI model using machine learning algorithms. The trained model analyzes employee skill profiles and generates personalized training plans optimized for each profile. Furthermore, the server responds to user questions and inquiries in real time, supporting problem-solving in the workplace. 【0482】 terminal 【0483】 The terminal serves as the user interface, providing users with a means to view their learning plan and check their progress. Through the terminal, users can access learning content and submit questions to the server as needed. The terminal provides a smooth operation and user experience through a simple and intuitive interface. 【0484】 User 【0485】 Users are the end users of this system and are primarily the ones who will be learning. Through their devices, users input their individual skill profiles and utilize the learning content provided based on those profiles. Users also provide feedback on their learning experience and AI support, which helps in the continuous improvement of the system. 【0486】 Specific example 【0487】 For example, when a new employee is onboarded, the server analyzes manuals related to various business processes and generates individual learning modules. The new employee then uses a terminal to progress through these modules sequentially. When specific work scenarios are presented and questions arise, the user can instantly send questions from their terminal to the server and receive answers. This kind of feedback loop allows users to effectively improve their practical skills. 【0488】 The AI ​​agent mentor system of the present invention enables customized education and support tailored to diverse career stages and skill levels, efficiently promoting talent development throughout the organization. 【0489】 The following describes the processing flow. 【0490】 Step 1: 【0491】 The server collects internal business manuals and company regulations and stores them in a database. This data is structured using text analysis tools, and important information is extracted. 【0492】 Step 2: 【0493】 The server uses structured data to execute machine learning algorithms and train a generative AI model. This model is designed to provide optimal education and support based on the specific tasks performed. 【0494】 Step 3: 【0495】 Through the terminal, users input information such as their skills, work history, and desired career goals. This information is sent to the server and stored as an individual skills profile. 【0496】 Step 4: 【0497】 The server analyzes the user's skill profile and generates an individualized learning plan based on it. This plan includes learning modules, progress indicators, and evaluation criteria. 【0498】 Step 5: 【0499】 The device allows users to access generated learning plans and provides an intuitive interface. Users can follow the plan and monitor their progress in real time. 【0500】 Step 6: 【0501】 If a user encounters questions or problems during learning, they can send them to the server via their device. The server uses a trained AI model to answer the questions in real time and provide appropriate support. 【0502】 Step 7: 【0503】 After the training is complete, the user provides feedback. This feedback is sent from the device to the server and used to evaluate and improve the AI ​​model. 【0504】 Step 8: 【0505】 The server updates the AI ​​model based on feedback and newly collected data, providing an even more optimized learning plan and support in subsequent sessions. 【0506】 (Example 1) 【0507】 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." 【0508】 Traditional talent development systems have struggled to efficiently create personalized training plans and provide customized support tailored to each individual's skill level. Furthermore, flexibly adapting to new work regulations and information updates, and continuously keeping learning content up-to-date, has also been a challenge. 【0509】 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. 【0510】 In this invention, the server includes means for collecting information, structuring the information, and storing it in a memory device; means for training a model using analytical techniques based on the stored information; and means for creating individual competency profiles and generating training plans corresponding to those profiles. This enables the rapid creation and distribution of individual training plans, the provision of up-to-date learning content tailored to diverse work environments, and skill improvement across the entire organization. 【0511】 "Information" refers to data and knowledge collected from both inside and outside the organization, and primarily includes operational manuals, internal regulations, and guidelines related to operations. 【0512】 A "storage device" is a hardware device or system for electronically storing information, and generally refers to a database system. 【0513】 "Analytical techniques" refer to the techniques used to analyze data and information and to learn or train models, and include machine learning algorithms and natural language processing. 【0514】 A "competency profile" is a systematic record of each individual's skills, knowledge, and experience, forming the basis for an individualized educational plan. 【0515】 An "educational plan" refers to a learning program designed based on an individual's ability profile, and includes the purpose, content, and sequence of learning. 【0516】 "Educational content" refers to specific learning materials and information provided to individuals in accordance with a particular educational plan, and includes textbooks, videos, simulations, etc. 【0517】 "Tracking" refers to the process of monitoring and recording learners' progress and achievements, and is used to evaluate and adjust learning effectiveness. 【0518】 "Support" refers to assistance provided to help individuals solve work-related problems, and includes real-time advice and assistance with educational content. 【0519】 "Opinions" refer to feedback and suggestions collected from users, which are used to improve the system and model. 【0520】 "Computing device" refers to an electronic device used by users to access educational content, and includes computers and tablet devices. 【0521】 An "interface" is a platform for interaction between a user and a computing device, and includes means for accessing the educational content provided. 【0522】 This invention is an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0523】 The server has the function of collecting information, structuring it, and storing it in a database. This collection includes internal business policies and guidelines of the company, and a "database management system" is used as the storage device. The server also uses a "machine learning framework" as an "analysis technique" to train a generative AI model using the collected data. With this trained model, the server analyzes each individual's ability profile and generates an optimal training plan. In addition, the server receives prompts and responds instantly using a natural language processing model to support the resolution of business problems. 【0524】 The device allows users to access the educational plan provided to them. Through the device, users can view the educational content and check their progress. This functionality is implemented using user interface technology to facilitate intuitive operation. The specific learning content is diverse, including text, videos, and interactive simulations, allowing users to efficiently progress through their studies. 【0525】 The user is the learner in this system. Through their terminal, users can input their individual skill profiles and access learning content provided based on those profiles. They can also send prompts to the server, such as "Please tell me the key steps in the next project," to instantly obtain relevant information. In this way, users continuously improve their skills with the support they receive. 【0526】 A concrete example is the onboarding process for new employees. The server automatically generates individual learning modules based on detailed information about business processes. New employees access these modules using terminals and acquire the necessary knowledge sequentially. When questions arise, they can ask them immediately through prompts and receive answers in real time. This cyclical process greatly helps improve the user's practical skills. 【0527】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0528】 Step 1: 【0529】 The server collects necessary information from data sources both inside and outside the company. It receives unstructured data related to business policies and guidelines as input. The "data collection and analysis module" uses this data to organize, structure, and store it in a database. The output is structured data stored in the database. 【0530】 Step 2: 【0531】 The server analyzes the stored data and trains a model using machine learning libraries. It uses the accumulated data in the database as input. Through analytical techniques, it builds a generative AI model and trains it. In this process, it efficiently processes the data and extracts patterns. The output is the trained AI model, which is used in the next step. 【0532】 Step 3: 【0533】 The server uses a trained AI model to analyze the user's ability profile. It receives user skill information and historical data as input. Based on this information, the AI ​​model generates an individually optimized training plan. The output is a customized training plan for each user. 【0534】 Step 4: 【0535】 The terminal displays the educational plan received from the server to the user through a user interface. It receives educational plan data as input and processes it for visual presentation within the interface. The output is educational content that the user can intuitively interact with. 【0536】 Step 5: 【0537】 Users access the indicated educational content using their devices and progress through the learning process. The device receives user actions and learning progress data as input. This data is sent to a server to measure learning effectiveness and modify the educational plan as needed. Outputs include the user's learning progress and feedback. 【0538】 Step 6: 【0539】 During training, the user sends questions to the server using prompts. The input is the user's question, sent from the terminal to the server as a prompt. The server uses natural language processing to generate an appropriate response and sends the reply back to the terminal. The output is a real-time response to the user. 【0540】 Step 7: 【0541】 The server collects user feedback and uses it as data to improve the model. It receives user opinions and evaluation data as input. Based on this, it improves the AI ​​model and educational content. The output is the improved system components. 【0542】 (Application Example 1) 【0543】 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." 【0544】 In order to improve individual skills and develop human resources, there is a need to provide training plans optimized for each individual. Furthermore, support for quickly acquiring work skills in on-site settings such as factories is essential. Current systems lack sufficient real-time feedback and customized support, and there is a need to efficiently address this challenge. 【0545】 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. 【0546】 In this invention, the server includes means for creating an individual's skill profile and generating an educational plan corresponding to that profile; means for presenting learning information based on the individual educational plan and monitoring the learning progress; and means for visually displaying operating procedures on-site and supporting the use of the device. This provides each user with a customized educational plan, enabling efficient skill improvement. Furthermore, it also supports rapid on-site work acquisition. 【0547】 "Human resource development" is the process of improving the knowledge and skills of individuals and groups in order to achieve better results in their work and duties. 【0548】 "Data" refers to records of facts and information, which are the subject of analysis and processing. 【0549】 "Structuring" is the process of organizing data and making it easier to use. 【0550】 A "storage device" is a piece of hardware or system that can store information and retrieve it as needed. 【0551】 A "machine learning algorithm" is a mathematical method that uses data to enable computers to automatically learn and make predictions and decisions. 【0552】 A "model" is a structure that represents real-world phenomena or processes from a mathematical or computer perspective. 【0553】 A "competency profile" is a set of information used to summarize and evaluate an individual's skills and knowledge. 【0554】 An "educational plan" is a guideline for a step-by-step learning process designed to help learners achieve their goals. 【0555】 "Learning information" refers to the learning materials and data necessary for instruction that learners need. 【0556】 "Monitoring" is the act of observing the trends in processes and data and analyzing them as needed. 【0557】 "Real-time" is a term that describes the ability to process information and respond almost instantaneously. 【0558】 "Support" is the process of providing assistance or aid to make an activity or project successful. 【0559】 "Feedback" is information provided based on the results of an action or process, and is intended to encourage improvement and adjustment. 【0560】 The system for implementing the present invention consists of three components: a server, a terminal, and a user. 【0561】 The server aggregates information within the company and stores it in a database. This enables data analysis using machine learning algorithms. The trained generative AI model automatically generates customized training plans based on each user's skill profile. Furthermore, the server responds to user questions in real time and provides guidance and support related to work. 【0562】 The terminal is a device that allows users to interactively review their educational plans and manage their progress. Specific hardware examples include smart glasses and tablet devices. These devices feature an intuitive interface that allows users to access learning information with simple operations. When users use smart glasses in the field, the operating procedures are visually displayed, assisting them in using the device. 【0563】 Users progress through their learning plan via their devices and provide feedback on their skills. This feedback is received by the server and used to further improve and refine the model. 【0564】 As a concrete example, operators on a new factory line can learn how to operate the system through smart glasses, enabling them to perform tasks quickly without prior training. An example of a prompt in this scenario would be, "Write an AI agent program that provides personalized training advice to individual employees based on factory work procedures." 【0565】 The software used includes machine learning frameworks for data analysis (e.g., TensorFlow) and natural language processing tools (e.g., the Transformers library). These are used to process data, train models, and then perform predictions and educational guidance using the generated models. 【0566】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0567】 Step 1: The server collects and stores the data. 【0568】 The server collects internal company manuals and regulations and stores them in a structured format in a database. This input data is used to generate model training and educational plans, as described later. 【0569】 Step 2: The server trains the machine learning model. 【0570】 The server uses stored data to train a generated AI model using machine learning algorithms. Past work data and skill profiles are used as input, and the optimized model is output through training. 【0571】 Step 3: The server generates individual education plans. 【0572】 Using a trained model, the server automatically generates an educational plan based on each user's skill profile. The input is the user's skill data, and the output is a customized learning plan. This plan shows exactly what skills the user will learn and in what order. 【0573】 Step 4: The device displays learning information and monitors progress. 【0574】 The terminal visually presents learning information based on the educational plan generated for the user. The input is the learning plan generated by the server, and the output is the monitoring of the user's learning progress, with the progress reported to the server as needed. 【0575】 Step 5: The user provides feedback and the server processes it. 【0576】 Users provide feedback through their devices regarding their learning progress and any questions they have during the learning process. This feedback, including user experiences and opinions, is received by the server and used to further improve the generated AI model. The processing of this feedback is done to improve the accuracy of the model and the quality of the educational plan. 【0577】 Step 6: The server provides on-site instructions and support. 【0578】 The server visually displays the necessary operating procedures to the user on-site via smart glasses. Inputs include real-time operation status and work processes, and the output provides visually displayed procedures, thereby supporting the user's on-site work. 【0579】 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. 【0580】 This invention relates to an AI agent mentor system that combines an emotion engine that recognizes the emotional state of users. This system significantly improves conventional talent development processes and provides more personalized educational support to individual employees. 【0581】 server 【0582】 The server is responsible for collecting and organizing internal business manuals and company regulations into a database. It also uses this data to train an AI model using machine learning algorithms. The trained model automatically generates training plans tailored to each user's individual skill profile. Furthermore, by integrating an emotion engine, the server analyzes user input and behavioral patterns, and adjusts training content according to their emotional state. 【0583】 terminal 【0584】 The device provides a user interface, enabling users to view their learning plans and check their progress. Through the device, users can access learning content and send questions to the server as needed. The device incorporates sensors that analyze the user's voice and facial expressions, allowing the emotion engine to detect the user's emotional state in real time. 【0585】 User 【0586】 Users, as learners, continue their daily learning through their devices. The user's emotional state is periodically analyzed, and the server adjusts the learning content based on the results. For example, if the server determines that the user is tired, it can present content with a lower difficulty level or recommend a break. Furthermore, feedback based on emotional state is used to further improve the system. 【0587】 Specific example 【0588】 For example, suppose an employee has joined a new project and is learning the job responsibilities. The server uses an AI model to generate a training plan based on the job responsibilities and presents the learning content through the user's device. As the user progresses through the learning process, the device's camera reads their facial expressions, and the emotion engine detects that the user's stress levels are increasing. In response, the server temporarily adjusts the training plan and provides the user with relaxing content and encouraging messages. In this way, by utilizing the emotion engine, a more appropriate and comfortable learning environment can be provided to the user. 【0589】 This invention enables customized support and learning experiences for individual users, resulting in efficient and flexible human resource development. 【0590】 The following describes the processing flow. 【0591】 Step 1: 【0592】 The server collects internal business manuals and company regulations, structures this information through text analysis, and stores it in a database. At this stage, key points related to business operations are extracted. 【0593】 Step 2: 【0594】 The server trains an AI model using machine learning algorithms based on structured data. This model is designed to learn the most effective training methods relevant to the task at hand. 【0595】 Step 3: 【0596】 The user enters their skills and work history into the terminal. The terminal sends this information to the server to create the user's profile. 【0597】 Step 4: 【0598】 The server generates individualized training plans based on user profiles. These plans aim to enhance the skills necessary for each user's job and include corresponding learning content. 【0599】 Step 5: 【0600】 The device presents learning content to the user and tracks their progress. It also analyzes voice and facial expressions using an emotion engine to monitor the user's emotional state in real time. 【0601】 Step 6: 【0602】 If the emotion engine detects the user's stress level or fatigue, the server will adjust the difficulty level of the learning content or suggest content that recommends taking a break. 【0603】 Step 7: 【0604】 As users progress through the learning content, if they encounter any questions, they can send them to the server via their device. The server then uses a trained AI model to respond and help resolve the issues. 【0605】 Step 8: 【0606】 After training, users provide feedback. This feedback is sent from the device to the server and used to improve the AI ​​model and adjust the training plan. 【0607】 Step 9: 【0608】 When new operational rules are introduced, the server automatically adds them to the database and updates the AI ​​model to ensure that the latest and most appropriate educational content is provided. 【0609】 Step 10: 【0610】 The server continuously optimizes the entire system based on feedback and new data to improve the user's educational experience and operational efficiency. 【0611】 (Example 2) 【0612】 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." 【0613】 In modern talent development processes, a lack of flexible and individualized educational support tailored to each individual's professional skills and emotional state is a challenge. Furthermore, delays in providing educational resources that keep pace with rapidly changing work regulations hinder efficient and effective talent development. 【0614】 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. 【0615】 In this invention, the server includes means for collecting information, organizing the information, and storing it in a storage medium; means for training a model using a learning algorithm based on the stored information; means for creating individual skill profiles and generating educational plans corresponding to the profiles; and means for detecting emotional states and adjusting the educational plans based on the detection results. This enables accurate and flexible educational support tailored to the professional abilities and emotional states of individual users, allowing for a rapid response to solving business challenges. 【0616】 "Information" refers to documents such as company operational manuals and internal regulations stored in a database. 【0617】 "Organization" refers to the process of classifying collected information into categories and storing it in a storage medium in an easily accessible format. 【0618】 A "storage medium" is a device or system used to store information in digital format. 【0619】 A "learning algorithm" is a method or technique for analyzing data from collected information and improving the performance of a model based on the results of that analysis. 【0620】 A "model" is a computational system trained using machine learning algorithms, used for making predictions and classifications. 【0621】 A "skill profile" is a dataset that represents a user's professional skills and knowledge level. 【0622】 An "educational plan" is an individualized learning plan designed based on the user's skill profile. 【0623】 "Emotional state" refers to the user's emotional and psychological condition, and is detected through voice, facial expressions, and other means. 【0624】 "Educational resources" refer to a collection of teaching materials and information designed to support the user's learning process. 【0625】 This invention provides a system for realizing personalized talent development support. In this system, the server, terminal, and user interact as follows: 【0626】 The server first aggregates information such as company operational manuals and internal regulations and records it in a database. This information is digitized using OCR technology and organized into categories using text analysis tools. For training, TensorFlow and PyTorch are used as machine learning libraries to build models based on generative AI models such as BERT and GPT. This generates training plans tailored to the user. 【0627】 The terminal provides an interface for receiving user input. A camera and microphone are used to capture the user's emotional state in real time. The terminal hardware integrates OpenFace and Emotion API as facial expression analysis tools, which automatically detect the user's emotional state and send the analysis results to the server. 【0628】 Users progress through their learning based on an educational plan provided via their device. Their learning progress is sent from the device to the server as feedback, and the educational plan is adjusted as needed. Users can also input questions and doubts into the system as prompts. For example, by inputting prompts such as "What sports do you recommend for relaxation?" or "What should I do if I feel I'm progressing too slowly?", the server uses a generative AI model to provide appropriate answers and suggestions. This enables flexible learning support tailored to each individual user. 【0629】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0630】 Step 1: 【0631】 The server collects internal business manuals and company regulations as information and stores it in a database. Paper documents are used as input, which is converted into digital data using OCR technology, and then organized and processed using text analysis tools. The output is a collection of digital information categorized by type. Specifically, the server uses a scanner to read paper documents and converts the images into text data. 【0632】 Step 2: 【0633】 The server trains a generative AI model using machine learning libraries based on organized data. This process uses pre-collected digital information as input to train a generative AI model (e.g., BERT or GPT). The output is the trained AI model, which can automatically generate educational plans. Specifically, it supplies training data to the model and tunes hyperparameters to improve the model's accuracy. 【0634】 Step 3: 【0635】 The terminal provides a user interface and creates an environment for users to view educational plans and progress through their studies. The input is the educational plan retrieved from the server, and the output is the learning content provided to the user. Specifically, the user solves tasks through the terminal, the terminal temporarily saves the results, and records progress. 【0636】 Step 4: 【0637】 The device detects the user's emotional state. This process uses the user's facial expressions and voice as input, which are analyzed using OpenFace and the Emotion API. The output is user emotional state data, which is sent to a server. Specifically, the device uses its built-in camera and microphone to capture data in real time and send it to the analysis tool. 【0638】 Step 5: 【0639】 The server adjusts the educational plan based on emotional state data. The input is emotional state data sent from the terminal, and the server uses data analysis functions to analyze the user's psychological state. The output is the adjusted educational plan. Specifically, the server modifies difficulty levels and optimizes the learning order to provide the user with the best possible learning experience. 【0640】 Step 6: 【0641】 The user enters prompts through the terminal, sending questions and requests to the system. In this process, the prompts received from the user become input and are processed by the server via an AI model. The output is specific answers and advice for the user. For example, if the user enters the prompt "Tell me some sports I can play when I want to relax," the server generates relevant information and returns it to the terminal. 【0642】 (Application Example 2) 【0643】 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." 【0644】 In human resource development, there is a need to provide optimal support tailored to the emotional state of each individual user. However, existing systems can handle support based on users' skills and progress, but they struggle to grasp and respond to emotional states in real time. As a result, it is not possible to quickly optimize educational content in response to fluctuations in users' stress levels and motivation, hindering effective human resource development. 【0645】 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. 【0646】 In this invention, the server includes means for analyzing facial expressions and voice tone to analyze the emotional state and estimate the user's emotional state; means for presenting the user with the most suitable communication method based on the estimated emotional state information; and means for presenting personalized learning content according to the emotional state. This enables individualized responses according to the user's emotional state, making it possible to create a more effective and comfortable human resource development environment. 【0647】 "Human resource development" refers to the process of education and training conducted to improve the skills and abilities of individual users. 【0648】 "Data collection" is the act of systematically gathering information for a specific purpose. 【0649】 "Structured data" refers to data that is organized consistently and stored in a memory device. 【0650】 A "machine learning algorithm" is a methodology for computers to learn from experience and perform pattern recognition using data. 【0651】 A "skill profile" is a collection of information about the abilities and characteristics of an individual user. 【0652】 An "educational plan" is a blueprint for the learning process, created based on set goals. 【0653】 "Learning content" refers to a collection of educational materials and information provided to users for learning purposes. 【0654】 "Tracking" is the act of tracking and recording progress and activities. 【0655】 "Responding in real time" means reacting instantly to user input and circumstances. 【0656】 "Work-related problems" refer to challenges or obstacles that arise in the workplace or within work activities. 【0657】 "Feedback" refers to the reactions and opinions provided by users. 【0658】 "Facial expression analysis" is a technique that infers emotions from the state and movement of a person's face. 【0659】 "Voice tone analysis" is a technique that infers emotions from the tone and quality of a person's voice. 【0660】 "Emotional state" refers to the psychological and emotional condition of a user at a given point in time. 【0661】 "To suggest communication methods" means to propose the most suitable means of interaction for the target audience. 【0662】 The system implementing this invention consists of three components: a server, a terminal, and a user. The roles of each component and specific examples are shown below. 【0663】 server 【0664】 The server is responsible for collecting, structuring, and storing data on talent development accumulated within the company. This data is used to train AI models using machine learning algorithms, particularly TensorFlow based on Python. The trained models automatically generate training plans tailored to each user's skill profile. Furthermore, by incorporating an emotion engine, the system estimates the user's emotional state and optimizes the training content based on that, thereby more effectively supporting talent development. 【0665】 terminal 【0666】 The device provides a user interface that allows users to check their educational plans and progress. Sensors embedded in smart glasses or robots analyze the user's facial expressions and voice tone. This allows for real-time assessment of their emotional state and transmission to a server. This data is processed by an emotion engine and presented to the user as appropriate feedback. 【0667】 User 【0668】 Users utilize their devices as the primary tool for their daily learning. Their emotional state is regularly analyzed, and optimized learning content is provided based on the results. For example, if a user is experiencing stress, the difficulty level of the learning material can be adjusted, or content designed to boost motivation can be added. 【0669】 One specific example of this implementation is that the emotion engine detects the emotional state of residents in a care facility and, if necessary, sends a suggestion to the caregiver to "play music to help them relax." 【0670】 An example of a prompt message is: "Analyze the image and audio data of the care recipient and infer their emotional state. If the recipient appears anxious, suggest appropriate responses." 【0671】 In this way, servers, terminals, and users work together as a unified team, making it possible to create a more personalized talent development environment. 【0672】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0673】 Step 1: 【0674】 The server collects employee training data from a central database within the company and structures that data. This process involves categorizing and tagging the data and storing it in memory. This input data forms the basis for subsequent model training using machine learning algorithms. 【0675】 Step 2: 【0676】 The server trains a generative AI model using structured data. It uses machine learning algorithms, specifically Python and TensorFlow, to generate individualized training plans based on skill profiles. The input for this step is structured data, and the output is a highly accurate AI model. 【0677】 Step 3: 【0678】 The device presents the generated educational plan and learning content to the user. It utilizes smart glasses or robotic display functions to track learning progress. The input is the educational plan from the AI ​​model, and the output is the specific learning content displayed to the user. 【0679】 Step 4: 【0680】 The device uses sensors to analyze the user's facial expressions and voice tone in real time. This analysis estimates the user's emotional state. Input is data from the camera and voice sensors, and output is data representing the user's emotional state. 【0681】 Step 5: 【0682】 The server analyzes emotional states obtained using an emotion engine and adjusts educational content based on that information. It also selects and presents the most appropriate communication method for the user. The input is emotional state data, and the output is adjusted learning content and communication guidelines. 【0683】 Step 6: 【0684】 Users continue their daily learning based on feedback from their devices. This feedback is used to improve the entire system and helps optimize future educational content. The input is the user's learning experience, and the output is the improved learning program. 【0685】 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. 【0686】 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. 【0687】 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. 【0688】 [Fourth Embodiment] 【0689】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0690】 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. 【0691】 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). 【0692】 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. 【0693】 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. 【0694】 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). 【0695】 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. 【0696】 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. 【0697】 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. 【0698】 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. 【0699】 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. 【0700】 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. 【0701】 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". 【0702】 This invention provides an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0703】 server 【0704】 The server is responsible for centrally collecting and storing company operational manuals and internal regulations in a database. This data is then used to train an AI model using machine learning algorithms. The trained model analyzes employee skill profiles and generates personalized training plans optimized for each profile. Furthermore, the server responds to user questions and inquiries in real time, supporting problem-solving in the workplace. 【0705】 terminal 【0706】 The terminal serves as the user interface, providing users with a means to view their learning plan and check their progress. Through the terminal, users can access learning content and submit questions to the server as needed. The terminal provides a smooth operation and user experience through a simple and intuitive interface. 【0707】 User 【0708】 Users are the end users of this system and are primarily the ones who will be learning. Through their devices, users input their individual skill profiles and utilize the learning content provided based on those profiles. Users also provide feedback on their learning experience and AI support, which helps in the continuous improvement of the system. 【0709】 Specific example 【0710】 For example, when a new employee is onboarded, the server analyzes manuals related to various business processes and generates individual learning modules. The new employee then uses a terminal to progress through these modules sequentially. When specific work scenarios are presented and questions arise, the user can instantly send questions from their terminal to the server and receive answers. This kind of feedback loop allows users to effectively improve their practical skills. 【0711】 The AI ​​agent mentor system of the present invention enables customized education and support tailored to diverse career stages and skill levels, efficiently promoting talent development throughout the organization. 【0712】 The following describes the processing flow. 【0713】 Step 1: 【0714】 The server collects internal business manuals and company regulations and stores them in a database. This data is structured using text analysis tools, and important information is extracted. 【0715】 Step 2: 【0716】 The server uses structured data to execute machine learning algorithms and train a generative AI model. This model is designed to provide optimal education and support based on the specific tasks performed. 【0717】 Step 3: 【0718】 Through the terminal, users input information such as their skills, work history, and desired career goals. This information is sent to the server and stored as an individual skills profile. 【0719】 Step 4: 【0720】 The server analyzes the user's skill profile and generates an individualized learning plan based on it. This plan includes learning modules, progress indicators, and evaluation criteria. 【0721】 Step 5: 【0722】 The device allows users to access generated learning plans and provides an intuitive interface. Users can follow the plan and monitor their progress in real time. 【0723】 Step 6: 【0724】 If a user encounters questions or problems during learning, they can send them to the server via their device. The server uses a trained AI model to answer the questions in real time and provide appropriate support. 【0725】 Step 7: 【0726】 After the training is complete, the user provides feedback. This feedback is sent from the device to the server and used to evaluate and improve the AI ​​model. 【0727】 Step 8: 【0728】 The server updates the AI ​​model based on feedback and newly collected data, providing an even more optimized learning plan and support in subsequent sessions. 【0729】 (Example 1) 【0730】 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". 【0731】 Traditional talent development systems have struggled to efficiently create personalized training plans and provide customized support tailored to each individual's skill level. Furthermore, flexibly adapting to new work regulations and information updates, and continuously keeping learning content up-to-date, has also been a challenge. 【0732】 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. 【0733】 In this invention, the server includes means for collecting information, structuring the information, and storing it in a memory device; means for training a model using analytical techniques based on the stored information; and means for creating individual competency profiles and generating training plans corresponding to those profiles. This enables the rapid creation and distribution of individual training plans, the provision of up-to-date learning content tailored to diverse work environments, and skill improvement across the entire organization. 【0734】 "Information" refers to data and knowledge collected from both inside and outside the organization, and primarily includes operational manuals, internal regulations, and guidelines related to operations. 【0735】 A "storage device" is a hardware device or system for electronically storing information, and generally refers to a database system. 【0736】 "Analytical techniques" refer to the techniques used to analyze data and information and to learn or train models, and include machine learning algorithms and natural language processing. 【0737】 A "competency profile" is a systematic record of each individual's skills, knowledge, and experience, forming the basis for an individualized educational plan. 【0738】 An "educational plan" refers to a learning program designed based on an individual's ability profile, and includes the purpose, content, and sequence of learning. 【0739】 "Educational content" refers to specific learning materials and information provided to individuals in accordance with a particular educational plan, and includes textbooks, videos, simulations, etc. 【0740】 "Tracking" refers to the process of monitoring and recording learners' progress and achievements, and is used to evaluate and adjust learning effectiveness. 【0741】 "Support" refers to assistance provided to help individuals solve work-related problems, and includes real-time advice and assistance with educational content. 【0742】 "Opinions" refer to feedback and suggestions collected from users, which are used to improve the system and model. 【0743】 "Computing device" refers to an electronic device used by users to access educational content, and includes computers and tablet devices. 【0744】 An "interface" is a platform for interaction between a user and a computing device, and includes means for accessing the educational content provided. 【0745】 This invention is an AI agent mentor system aimed at improving individual skills and efficient talent development. This system consists of three main components: a server, a terminal, and a user. 【0746】 The server has the function of collecting information, structuring it, and storing it in a database. This collection includes internal business policies and guidelines of the company, and a "database management system" is used as the storage device. The server also uses a "machine learning framework" as an "analysis technique" to train a generative AI model using the collected data. With this trained model, the server analyzes each individual's ability profile and generates an optimal training plan. In addition, the server receives prompts and responds instantly using a natural language processing model to support the resolution of business problems. 【0747】 The device allows users to access the educational plan provided to them. Through the device, users can view the educational content and check their progress. This functionality is implemented using user interface technology to facilitate intuitive operation. The specific learning content is diverse, including text, videos, and interactive simulations, allowing users to efficiently progress through their studies. 【0748】 The user is the learner in this system. Through their terminal, users can input their individual skill profiles and access learning content provided based on those profiles. They can also send prompts to the server, such as "Please tell me the key steps in the next project," to instantly obtain relevant information. In this way, users continuously improve their skills with the support they receive. 【0749】 A concrete example is the onboarding process for new employees. The server automatically generates individual learning modules based on detailed information about business processes. New employees access these modules using terminals and acquire the necessary knowledge sequentially. When questions arise, they can ask them immediately through prompts and receive answers in real time. This cyclical process greatly helps improve the user's practical skills. 【0750】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0751】 Step 1: 【0752】 The server collects necessary information from data sources both inside and outside the company. It receives unstructured data related to business policies and guidelines as input. The "data collection and analysis module" uses this data to organize, structure, and store it in a database. The output is structured data stored in the database. 【0753】 Step 2: 【0754】 The server analyzes the stored data and trains a model using machine learning libraries. It uses the accumulated data in the database as input. Through analytical techniques, it builds a generative AI model and trains it. In this process, it efficiently processes the data and extracts patterns. The output is the trained AI model, which is used in the next step. 【0755】 Step 3: 【0756】 The server uses a trained AI model to analyze the user's ability profile. It receives user skill information and historical data as input. Based on this information, the AI ​​model generates an individually optimized training plan. The output is a customized training plan for each user. 【0757】 Step 4: 【0758】 The terminal displays the educational plan received from the server to the user through a user interface. It receives educational plan data as input and processes it for visual presentation within the interface. The output is educational content that the user can intuitively interact with. 【0759】 Step 5: 【0760】 Users access the indicated educational content using their devices and progress through the learning process. The device receives user actions and learning progress data as input. This data is sent to a server to measure learning effectiveness and modify the educational plan as needed. Outputs include the user's learning progress and feedback. 【0761】 Step 6: 【0762】 During training, the user sends questions to the server using prompts. The input is the user's question, sent from the terminal to the server as a prompt. The server uses natural language processing to generate an appropriate response and sends the reply back to the terminal. The output is a real-time response to the user. 【0763】 Step 7: 【0764】 The server collects user feedback and uses it as data to improve the model. It receives user opinions and evaluation data as input. Based on this, it improves the AI ​​model and educational content. The output is the improved system components. 【0765】 (Application Example 1) 【0766】 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". 【0767】 In order to improve individual skills and develop human resources, there is a need to provide training plans optimized for each individual. Furthermore, support for quickly acquiring work skills in on-site settings such as factories is essential. Current systems lack sufficient real-time feedback and customized support, and there is a need to efficiently address this challenge. 【0768】 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. 【0769】 In this invention, the server includes means for creating an individual's skill profile and generating an educational plan corresponding to that profile; means for presenting learning information based on the individual educational plan and monitoring the learning progress; and means for visually displaying operating procedures on-site and supporting the use of the device. This provides each user with a customized educational plan, enabling efficient skill improvement. Furthermore, it also supports rapid on-site work acquisition. 【0770】 "Human resource development" is the process of improving the knowledge and skills of individuals and groups in order to achieve better results in their work and duties. 【0771】 "Data" refers to records of facts and information, which are the subject of analysis and processing. 【0772】 "Structuring" is the process of organizing data and making it easier to use. 【0773】 A "storage device" is a piece of hardware or system that can store information and retrieve it as needed. 【0774】 A "machine learning algorithm" is a mathematical method that uses data to enable computers to automatically learn and make predictions and decisions. 【0775】 A "model" is a structure that represents real-world phenomena or processes from a mathematical or computer perspective. 【0776】 A "competency profile" is a set of information used to summarize and evaluate an individual's skills and knowledge. 【0777】 An "educational plan" is a guideline for a step-by-step learning process designed to help learners achieve their goals. 【0778】 "Learning information" refers to the learning materials and data necessary for instruction that learners need. 【0779】 "Monitoring" is the act of observing the trends in processes and data and analyzing them as needed. 【0780】 "Real-time" is a term that describes the ability to process information and respond almost instantaneously. 【0781】 "Support" is the process of providing assistance or aid to make an activity or project successful. 【0782】 "Feedback" is information provided based on the results of an action or process, and is intended to encourage improvement and adjustment. 【0783】 The system for implementing the present invention consists of three components: a server, a terminal, and a user. 【0784】 The server aggregates information within the company and stores it in a database. This enables data analysis using machine learning algorithms. The trained generative AI model automatically generates customized training plans based on each user's skill profile. Furthermore, the server responds to user questions in real time and provides guidance and support related to work. 【0785】 The terminal is a device that allows users to interactively review their educational plans and manage their progress. Specific hardware examples include smart glasses and tablet devices. These devices feature an intuitive interface that allows users to access learning information with simple operations. When users use smart glasses in the field, the operating procedures are visually displayed, assisting them in using the device. 【0786】 Users progress through their learning plan via their devices and provide feedback on their skills. This feedback is received by the server and used to further improve and refine the model. 【0787】 As a concrete example, operators on a new factory line can learn how to operate the system through smart glasses, enabling them to perform tasks quickly without prior training. An example of a prompt in this scenario would be, "Write an AI agent program that provides personalized training advice to individual employees based on factory work procedures." 【0788】 The software used includes machine learning frameworks for data analysis (e.g., TensorFlow) and natural language processing tools (e.g., the Transformers library). These are used to process data, train models, and then perform predictions and educational guidance using the generated models. 【0789】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0790】 Step 1: The server collects and stores the data. 【0791】 The server collects internal company manuals and regulations and stores them in a structured format in a database. This input data is used to generate model training and educational plans, as described later. 【0792】 Step 2: The server trains the machine learning model. 【0793】 The server uses stored data to train a generated AI model using machine learning algorithms. Past work data and skill profiles are used as input, and the optimized model is output through training. 【0794】 Step 3: The server generates individual education plans. 【0795】 Using a trained model, the server automatically generates an educational plan based on each user's skill profile. The input is the user's skill data, and the output is a customized learning plan. This plan shows exactly what skills the user will learn and in what order. 【0796】 Step 4: The device displays learning information and monitors progress. 【0797】 The terminal visually presents learning information based on the educational plan generated for the user. The input is the learning plan generated by the server, and the output is the monitoring of the user's learning progress, with the progress reported to the server as needed. 【0798】 Step 5: The user provides feedback and the server processes it. 【0799】 Users provide feedback through their devices regarding their learning progress and any questions they have during the learning process. This feedback, including user experiences and opinions, is received by the server and used to further improve the generated AI model. The processing of this feedback is done to improve the accuracy of the model and the quality of the educational plan. 【0800】 Step 6: The server provides on-site instructions and support. 【0801】 The server visually displays the necessary operating procedures to the user on-site via smart glasses. Inputs include real-time operation status and work processes, and the output provides visually displayed procedures, thereby supporting the user's on-site work. 【0802】 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. 【0803】 This invention relates to an AI agent mentor system that combines an emotion engine that recognizes the emotional state of users. This system significantly improves conventional talent development processes and provides more personalized educational support to individual employees. 【0804】 server 【0805】 The server is responsible for collecting and organizing internal business manuals and company regulations into a database. It also uses this data to train an AI model using machine learning algorithms. The trained model automatically generates training plans tailored to each user's individual skill profile. Furthermore, by integrating an emotion engine, the server analyzes user input and behavioral patterns, and adjusts training content according to their emotional state. 【0806】 terminal 【0807】 The device provides a user interface, enabling users to view their learning plans and check their progress. Through the device, users can access learning content and send questions to the server as needed. The device incorporates sensors that analyze the user's voice and facial expressions, allowing the emotion engine to detect the user's emotional state in real time. 【0808】 User 【0809】 Users, as learners, continue their daily learning through their devices. The user's emotional state is periodically analyzed, and the server adjusts the learning content based on the results. For example, if the server determines that the user is tired, it can present content with a lower difficulty level or recommend a break. Furthermore, feedback based on emotional state is used to further improve the system. 【0810】 Specific example 【0811】 For example, suppose an employee has joined a new project and is learning the job responsibilities. The server uses an AI model to generate a training plan based on the job responsibilities and presents the learning content through the user's device. As the user progresses through the learning process, the device's camera reads their facial expressions, and the emotion engine detects that the user's stress levels are increasing. In response, the server temporarily adjusts the training plan and provides the user with relaxing content and encouraging messages. In this way, by utilizing the emotion engine, a more appropriate and comfortable learning environment can be provided to the user. 【0812】 This invention enables customized support and learning experiences for individual users, resulting in efficient and flexible human resource development. 【0813】 The following describes the processing flow. 【0814】 Step 1: 【0815】 The server collects internal business manuals and company regulations, structures this information through text analysis, and stores it in a database. At this stage, key points related to business operations are extracted. 【0816】 Step 2: 【0817】 The server trains an AI model using machine learning algorithms based on structured data. This model is designed to learn the most effective training methods relevant to the task at hand. 【0818】 Step 3: 【0819】 The user enters their skills and work history into the terminal. The terminal sends this information to the server to create the user's profile. 【0820】 Step 4: 【0821】 The server generates individualized training plans based on user profiles. These plans aim to enhance the skills necessary for each user's job and include corresponding learning content. 【0822】 Step 5: 【0823】 The device presents learning content to the user and tracks their progress. It also analyzes voice and facial expressions using an emotion engine to monitor the user's emotional state in real time. 【0824】 Step 6: 【0825】 If the emotion engine detects the user's stress level or fatigue, the server will adjust the difficulty level of the learning content or suggest content that recommends taking a break. 【0826】 Step 7: 【0827】 As users progress through the learning content, if they encounter any questions, they can send them to the server via their device. The server then uses a trained AI model to respond and help resolve the issues. 【0828】 Step 8: 【0829】 After training, users provide feedback. This feedback is sent from the device to the server and used to improve the AI ​​model and adjust the training plan. 【0830】 Step 9: 【0831】 When new operational rules are introduced, the server automatically adds them to the database and updates the AI ​​model to ensure that the latest and most appropriate educational content is provided. 【0832】 Step 10: 【0833】 The server continuously optimizes the entire system based on feedback and new data to improve the user's educational experience and operational efficiency. 【0834】 (Example 2) 【0835】 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". 【0836】 In modern talent development processes, a lack of flexible and individualized educational support tailored to each individual's professional skills and emotional state is a challenge. Furthermore, delays in providing educational resources that keep pace with rapidly changing work regulations hinder efficient and effective talent development. 【0837】 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. 【0838】 In this invention, the server includes means for collecting information, organizing the information, and storing it in a storage medium; means for training a model using a learning algorithm based on the stored information; means for creating individual skill profiles and generating educational plans corresponding to the profiles; and means for detecting emotional states and adjusting the educational plans based on the detection results. This enables accurate and flexible educational support tailored to the professional abilities and emotional states of individual users, allowing for a rapid response to solving business challenges. 【0839】 "Information" refers to documents such as company operational manuals and internal regulations stored in a database. 【0840】 "Organization" refers to the process of classifying collected information into categories and storing it in a storage medium in an easily accessible format. 【0841】 A "storage medium" is a device or system used to store information in digital format. 【0842】 A "learning algorithm" is a method or technique for analyzing data from collected information and improving the performance of a model based on the results of that analysis. 【0843】 A "model" is a computational system trained using machine learning algorithms, used for making predictions and classifications. 【0844】 A "skill profile" is a dataset that represents a user's professional skills and knowledge level. 【0845】 An "educational plan" is an individualized learning plan designed based on the user's skill profile. 【0846】 "Emotional state" refers to the user's emotional and psychological condition, and is detected through voice, facial expressions, and other means. 【0847】 "Educational resources" refer to a collection of teaching materials and information designed to support the user's learning process. 【0848】 This invention provides a system for realizing personalized talent development support. In this system, the server, terminal, and user interact as follows: 【0849】 The server first aggregates information such as company operational manuals and internal regulations and records it in a database. This information is digitized using OCR technology and organized into categories using text analysis tools. For training, TensorFlow and PyTorch are used as machine learning libraries to build models based on generative AI models such as BERT and GPT. This generates training plans tailored to the user. 【0850】 The terminal provides an interface for receiving user input. A camera and microphone are used to capture the user's emotional state in real time. The terminal hardware integrates OpenFace and Emotion API as facial expression analysis tools, which automatically detect the user's emotional state and send the analysis results to the server. 【0851】 Users progress through their learning based on an educational plan provided via their device. Their learning progress is sent from the device to the server as feedback, and the educational plan is adjusted as needed. Users can also input questions and doubts into the system as prompts. For example, by inputting prompts such as "What sports do you recommend for relaxation?" or "What should I do if I feel I'm progressing too slowly?", the server uses a generative AI model to provide appropriate answers and suggestions. This enables flexible learning support tailored to each individual user. 【0852】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0853】 Step 1: 【0854】 The server collects internal business manuals and company regulations as information and stores it in a database. Paper documents are used as input, which is converted into digital data using OCR technology, and then organized and processed using text analysis tools. The output is a collection of digital information categorized by type. Specifically, the server uses a scanner to read paper documents and converts the images into text data. 【0855】 Step 2: 【0856】 The server trains a generative AI model using machine learning libraries based on organized data. This process uses pre-collected digital information as input to train a generative AI model (e.g., BERT or GPT). The output is the trained AI model, which can automatically generate educational plans. Specifically, it supplies training data to the model and tunes hyperparameters to improve the model's accuracy. 【0857】 Step 3: 【0858】 The terminal provides a user interface and creates an environment for users to view educational plans and progress through their studies. The input is the educational plan retrieved from the server, and the output is the learning content provided to the user. Specifically, the user solves tasks through the terminal, the terminal temporarily saves the results, and records progress. 【0859】 Step 4: 【0860】 The device detects the user's emotional state. This process uses the user's facial expressions and voice as input, which are analyzed using OpenFace and the Emotion API. The output is user emotional state data, which is sent to a server. Specifically, the device uses its built-in camera and microphone to capture data in real time and send it to the analysis tool. 【0861】 Step 5: 【0862】 The server adjusts the educational plan based on emotional state data. The input is emotional state data sent from the terminal, and the server uses data analysis functions to analyze the user's psychological state. The output is the adjusted educational plan. Specifically, the server modifies difficulty levels and optimizes the learning order to provide the user with the best possible learning experience. 【0863】 Step 6: 【0864】 The user enters prompts through the terminal, sending questions and requests to the system. In this process, the prompts received from the user become input and are processed by the server via an AI model. The output is specific answers and advice for the user. For example, if the user enters the prompt "Tell me some sports I can play when I want to relax," the server generates relevant information and returns it to the terminal. 【0865】 (Application Example 2) 【0866】 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". 【0867】 In human resource development, there is a need to provide optimal support tailored to the emotional state of each individual user. However, existing systems can handle support based on users' skills and progress, but they struggle to grasp and respond to emotional states in real time. As a result, it is not possible to quickly optimize educational content in response to fluctuations in users' stress levels and motivation, hindering effective human resource development. 【0868】 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. 【0869】 In this invention, the server includes means for analyzing facial expressions and voice tone to analyze the emotional state and estimate the user's emotional state; means for presenting the user with the most suitable communication method based on the estimated emotional state information; and means for presenting personalized learning content according to the emotional state. This enables individualized responses according to the user's emotional state, making it possible to create a more effective and comfortable human resource development environment. 【0870】 "Human resource development" refers to the process of education and training conducted to improve the skills and abilities of individual users. 【0871】 "Data collection" is the act of systematically gathering information for a specific purpose. 【0872】 "Structured data" refers to data that is organized consistently and stored in a memory device. 【0873】 A "machine learning algorithm" is a methodology for computers to learn from experience and perform pattern recognition using data. 【0874】 A "skill profile" is a collection of information about the abilities and characteristics of an individual user. 【0875】 An "educational plan" is a blueprint for the learning process, created based on set goals. 【0876】 "Learning content" refers to a collection of educational materials and information provided to users for learning purposes. 【0877】 "Tracking" is the act of tracking and recording progress and activities. 【0878】 "Responding in real time" means reacting instantly to user input and circumstances. 【0879】 "Work-related problems" refer to challenges or obstacles that arise in the workplace or within work activities. 【0880】 "Feedback" refers to the reactions and opinions provided by users. 【0881】 "Facial expression analysis" is a technique that infers emotions from the state and movement of a person's face. 【0882】 "Voice tone analysis" is a technique that infers emotions from the tone and quality of a person's voice. 【0883】 "Emotional state" refers to the psychological and emotional condition of a user at a given point in time. 【0884】 "To suggest communication methods" means to propose the most suitable means of interaction for the target audience. 【0885】 The system implementing this invention consists of three components: a server, a terminal, and a user. The roles of each component and specific examples are shown below. 【0886】 server 【0887】 The server is responsible for collecting, structuring, and storing data on talent development accumulated within the company. This data is used to train AI models using machine learning algorithms, particularly TensorFlow based on Python. The trained models automatically generate training plans tailored to each user's skill profile. Furthermore, by incorporating an emotion engine, the system estimates the user's emotional state and optimizes the training content based on that, thereby more effectively supporting talent development. 【0888】 terminal 【0889】 The device provides a user interface that allows users to check their educational plans and progress. Sensors embedded in smart glasses or robots analyze the user's facial expressions and voice tone. This allows for real-time assessment of their emotional state and transmission to a server. This data is processed by an emotion engine and presented to the user as appropriate feedback. 【0890】 User 【0891】 Users utilize their devices as the primary tool for their daily learning. Their emotional state is regularly analyzed, and optimized learning content is provided based on the results. For example, if a user is experiencing stress, the difficulty level of the learning material can be adjusted, or content designed to boost motivation can be added. 【0892】 One specific example of this implementation is that the emotion engine detects the emotional state of residents in a care facility and, if necessary, sends a suggestion to the caregiver to "play music to help them relax." 【0893】 An example of a prompt message is: "Analyze the image and audio data of the care recipient and infer their emotional state. If the recipient appears anxious, suggest appropriate responses." 【0894】 In this way, servers, terminals, and users work together as a unified team, making it possible to create a more personalized talent development environment. 【0895】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0896】 Step 1: 【0897】 The server collects employee training data from a central database within the company and structures that data. This process involves categorizing and tagging the data and storing it in memory. This input data forms the basis for subsequent model training using machine learning algorithms. 【0898】 Step 2: 【0899】 The server trains a generative AI model using structured data. It uses machine learning algorithms, specifically Python and TensorFlow, to generate individualized training plans based on skill profiles. The input for this step is structured data, and the output is a highly accurate AI model. 【0900】 Step 3: 【0901】 The device presents the generated educational plan and learning content to the user. It utilizes smart glasses or robotic display functions to track learning progress. The input is the educational plan from the AI ​​model, and the output is the specific learning content displayed to the user. 【0902】 Step 4: 【0903】 The device uses sensors to analyze the user's facial expressions and voice tone in real time. This analysis estimates the user's emotional state. Input is data from the camera and voice sensors, and output is data representing the user's emotional state. 【0904】 Step 5: 【0905】 The server analyzes emotional states obtained using an emotion engine and adjusts educational content based on that information. It also selects and presents the most appropriate communication method for the user. The input is emotional state data, and the output is adjusted learning content and communication guidelines. 【0906】 Step 6: 【0907】 Users continue their daily learning based on feedback from their devices. This feedback is used to improve the entire system and helps optimize future educational content. The input is the user's learning experience, and the output is the improved learning program. 【0908】 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. 【0909】 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. 【0910】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0911】 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. 【0912】 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. 【0913】 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. 【0914】 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. 【0915】 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. 【0916】 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." 【0917】 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. 【0918】 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. 【0919】 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. 【0920】 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. 【0921】 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. 【0922】 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. 【0923】 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 this memory. 【0924】 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. 【0925】 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. 【0926】 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. 【0927】 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. 【0928】 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. 【0929】 The following is further disclosed regarding the embodiments described above. 【0930】 (Claim 1) 【0931】 A means for collecting data for human resource development, structuring the data, and storing it in a storage device, 【0932】 A method for training a model using a machine learning algorithm based on stored data, 【0933】 A means for creating a skill profile for each individual and generating an educational plan corresponding to that profile, 【0934】 A means of presenting learning content based on individualized educational plans and tracking learning progress, 【0935】 A means of providing support to resolve business problems by responding in real time based on individual input, 【0936】 A means of collecting feedback from users and using that feedback to improve the model, 【0937】 A system that includes this. 【0938】 (Claim 2) 【0939】 When new operational rules are introduced, the model is automatically updated to provide the latest learning content. 【0940】 The system according to claim 1. 【0941】 (Claim 3) 【0942】 We provide customized support that is suitable for both recent graduates and individuals with extensive work experience. 【0943】 Promoting skill development at all career stages 【0944】 The system according to claim 1. 【0945】 "Example 1" 【0946】 (Claim 1) 【0947】 A means for collecting information, structuring the information, and storing it in a memory device, 【0948】 A means of training a model using analytical techniques based on stored information, 【0949】 A means for creating an individual's ability profile and generating an educational plan corresponding to that profile, 【0950】 A means of presenting educational content based on individual educational plans and tracking educational progress, 【0951】 A means of providing support for resolving business problems by responding instantly based on individual input, 【0952】 A means of collecting feedback from users and using that feedback to improve the model, 【0953】 A means of providing an interface that allows access to educational content via a computing device, 【0954】 A system that includes this. 【0955】 (Claim 2) 【0956】 The system according to claim 1, which automatically modifies the model and provides the latest training content in response to updates to new business regulations. 【0957】 (Claim 3) 【0958】 The system according to claim 1, which provides individualized support tailored to diverse occupational stages and skill levels, and promotes skill improvement throughout the organization. 【0959】 "Application Example 1" 【0960】 (Claim 1) 【0961】 A means for collecting data for human resource development, structuring the data, and storing it in a storage device, 【0962】 A method for training a model using a machine learning algorithm based on stored data, 【0963】 A means for creating an individual's ability profile and generating an educational plan corresponding to that profile, 【0964】 A means of presenting learning information based on individualized educational plans and monitoring learning progress, 【0965】 A means of providing support for resolving business problems by responding in real time based on individual input, 【0966】 A means of collecting feedback from users and using that feedback to improve the model, 【0967】 A means of visually demonstrating operating procedures on-site and supporting the use of the device, 【0968】 A system that includes this. 【0969】 (Claim 2) 【0970】 The system according to claim 1, which automatically updates the model and provides the latest learning information when there are changes to new business rules. 【0971】 (Claim 3) 【0972】 The system according to claim 1, which provides customized support that is adaptable to individuals with diverse experiences and promotes skill development at all career stages. 【0973】 "Example 2 of combining an emotion engine" 【0974】 (Claim 1) 【0975】 A means for collecting information, organizing the information, and storing it in a storage medium, 【0976】 A method for training a model using a learning algorithm based on stored information, 【0977】 A means for creating individual skill profiles and generating educational plans corresponding to those profiles, 【0978】 A means of presenting educational resources based on individualized educational plans and tracking educational achievement, 【0979】 A means of providing support to solve business problems by responding immediately based on individual input, 【0980】 A means of detecting emotional states and adjusting educational plans based on the detection results, 【0981】 A means of collecting feedback from users and using that feedback to improve the model, 【0982】 A system that includes this. 【0983】 (Claim 2) 【0984】 The system according to claim 1, which automatically updates the model and provides the latest educational resources when there are changes to the new business regulations. 【0985】 (Claim 3) 【0986】 We provide individualized support that is adaptable to individuals at different stages of their careers. 【0987】 The system according to claim 1, which promotes skill improvement at all levels of occupation. 【0988】 "Application example 2 when combining with an emotional engine" 【0989】 (Claim 1) 【0990】 A means for collecting data for human resource development, structuring the data, and storing it in a storage device, 【0991】 A method for training a model using a machine learning algorithm based on stored data, 【0992】 A means for creating a skill profile for each individual and generating an educational plan corresponding to that profile, 【0993】 A means of presenting learning content based on individualized educational plans and tracking learning progress, 【0994】 A means of providing support to resolve business problems by responding in real time based on individual input, 【0995】 A means of collecting feedback from users and using that feedback to improve the model, 【0996】 A means for analyzing facial expressions and voice tone to estimate the user's emotional state, 【0997】 A means of presenting the most suitable communication method to the user based on information about their emotional state, 【0998】 A system that includes this. 【0999】 (Claim 2) 【1000】 The system according to claim 1, which automatically updates the model and provides the latest learning content when there are changes to the new business rules. 【1001】 (Claim 3) 【1002】 The system according to claim 1, which provides customized support that is adaptable to both recent graduates and individuals with extensive work experience, and promotes skill improvement based on the emotional state of each user. [Explanation of symbols] 【1003】 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

[Claim 1] A means for collecting data for human resource development, structuring the data, and storing it in a storage device, A method for training a model using a machine learning algorithm based on stored data, A means for creating a skill profile for each individual and generating an educational plan corresponding to that profile, A means of presenting learning content based on individualized educational plans and tracking learning progress, A means of providing support to resolve business problems by responding in real time based on individual input, A means of collecting feedback from users and using that feedback to improve the model, A system that includes this. [Claim 2] When new operational rules are introduced, the model is automatically updated to provide the latest learning content. The system according to claim 1. [Claim 3] We provide customized support that is suitable for both recent graduates and individuals with extensive work experience. Promoting skill development at all career stages The system according to claim 1.

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