system

Abstract

We provide the system. [Solution] A means for receiving input information from a user and generating a user profile that includes individual learning goals and career goals, A means for collecting and automatically analyzing program information from educational databases and public databases, A method for selecting and scoring optimized educational programs based on analyzed data and user profiles to generate a list of suggestions, A means of presenting a list of suggestions to the user, providing a learning portal based on the selected program, and managing progress, A means of collecting feedback data after the program is completed and analyzing it for future program improvements, A system that includes this.

Description

【Technical Field】 , , 【0005】 【0001】 The technology of this disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern society, effective personnel training corresponding to diversified industry needs and individual career goals is required. However, existing educational programs remain at a general lineup and cannot sufficiently support individual learning needs and skill development. Therefore, it is important to optimize educational content applicable to each user and improve learning efficiency. Furthermore, in enterprises, resource management for strategically promoting employee skill improvement has become an issue. 【Means for Solving the Problems】 【0005】 This invention provides a system that clarifies learning and career goals based on information input by the user and automatically analyzes program information collected from educational and public databases. Specifically, it analyzes program content using natural language processing technology, selects the most suitable program for the user using machine learning algorithms, and scores it. It also presents the user with a list of suggestions, provides a learning portal based on the selected program, and manages progress. Furthermore, it collects feedback from the user after program completion and analyzes it to improve the quality of future suggestions, thereby realizing human resource development that matches the individual needs of each user. 【0006】 A "user profile" is a collection of data that identifies and organizes individual learning and career goals based on information entered by the user. 【0007】 An "educational database" is a source of information that systematically collects and stores information such as educational programs, courses, and curricula. 【0008】 A "public database" is a collection of data that contains information that is generally accessible on the internet. 【0009】 "Natural language processing technology" is a technology that enables computers to understand and analyze human language. 【0010】 A "machine learning algorithm" is a methodology for analyzing data based on experience to make predictions and decisions about the future. 【0011】 "Scoring" is an evaluation method that quantifies the suitability of an educational program based on the user's profile. 【0012】 A "learning portal" is a dedicated website or application that allows users to take online courses for educational programs of their choice. 【0013】 "Progress management" is the process of regularly recording, evaluating, and managing the user's learning progress. 【0014】 "Feedback" refers to the opinions and evaluations that users provide after completing an educational program. 【0015】 The "suggestion list" is a list that presents the most suitable educational program for the user based on the analysis results. [Brief explanation of the drawing] 【0016】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0017】 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. 【0018】 First, the language used in the following description will be explained. 【0019】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0020】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0021】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0022】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0023】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0024】 [First Embodiment] 【0025】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0026】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0027】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0028】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0029】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0030】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0031】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0032】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0033】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0034】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0035】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0036】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0037】 The system of this invention is implemented through an overall process that includes user input information, data collection from public databases, automated program analysis, proposal of scored educational programs, progress management, and feedback analysis. 【0038】 Users input their individual learning and career goals using their devices. The system then generates a user profile, clarifying their educational needs. Based on the information provided by the user, the system is designed to provide the most appropriate educational support. 【0039】 The server collects information on the latest educational programs and learning courses from educational and public databases. This information is analyzed in detail using natural language processing technology, thereby revealing the content, suitability, and difficulty level of each program. 【0040】 Next, the server uses a machine learning algorithm to match the user's profile with program information. During this process, the program's suitability is quantified and scored. Programs with high scores are then proposed as a list of programs deemed optimal for the user. 【0041】 The terminal presents the user with a list of scored program suggestions, displaying details of each program (e.g., course content, duration, and cost). Based on this information, the user selects the most suitable program and begins studying. 【0042】 When a user takes a selected program, the server monitors and manages their progress through the learning portal. Progress management includes evaluating how much the user has completed and what stage they are currently in. 【0043】 Furthermore, after the user completes the program, they input feedback through their terminal. The server collects this feedback and analyzes it for future suggestions and program improvements. 【0044】 For example, if a user inputs that they want to learn a new programming language, the server searches and analyzes relevant online courses and suggests several courses that are best suited to the user. The user can then take the chosen course online and monitor their progress. 【0045】 In this way, the system of the present invention provides an efficient educational environment that responds to the individual learning needs of each user, supporting continuous skill development and career advancement. 【0046】 The following describes the processing flow. 【0047】 Step 1: 【0048】 Users use a terminal to input their learning and career goals, creating an individual user profile. This information is stored by the system and used as foundational data in subsequent processes. 【0049】 Step 2: 【0050】 The server collects information about educational programs and courses from educational databases and public databases. Web scraping techniques and API-based access are used for data collection. 【0051】 Step 3: 【0052】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of each program's content, target skills, and difficulty level. This analysis extracts the program's characteristics. 【0053】 Step 4: 【0054】 The server matches user profile information with analyzed program information. Using machine learning algorithms, it selects the most suitable program for each user and scores the suitability of each program. 【0055】 Step 5: 【0056】 The server generates a list of optimized program suggestions based on the scoring results. This list prioritizes and compiles programs recommended to the user. 【0057】 Step 6: 【0058】 The terminal displays a list of suggestions to the user, providing detailed information about each program. The user can then select a program of interest from the list. 【0059】 Step 7: 【0060】 Once a user selects a program, a learning portal is provided via their device. This allows the user to begin online program enrollment and receive learning support as needed. 【0061】 Step 8: 【0062】 The server manages the user's learning progress in real time and periodically records the progress. It monitors whether the user's progress is proceeding normally and provides support as needed. 【0063】 Step 9: 【0064】 After the user completes the program, they provide feedback on their device. The user enters their thoughts and suggestions for improvement and sends them to the system as feedback. 【0065】 Step 10: 【0066】 The server analyzes the collected feedback to improve the quality of future program proposals and to develop new programs. This feedback analysis forms an improvement cycle for the entire system. 【0067】 (Example 1) 【0068】 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." 【0069】 In today's educational environment, selecting the optimal learning program tailored to each user's individual learning needs and goals presents a challenge. Furthermore, efficiently managing learning progress and providing appropriate feedback are crucial elements in improving educational outcomes. Additionally, it is necessary to effectively utilize user feedback to enhance the quality of educational programs. 【0070】 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. 【0071】 In this invention, the server includes means for receiving input information from the user and generating a user profile; means for collecting program information from an information set including educational information and a publicly available information source, and automatically analyzing it; and means for selecting an optimized educational program based on the analyzed data and the user profile, scoring it, and generating a set of suggestions. This makes it possible to propose an educational program optimized for each individual user and manage their progress, thereby improving the quality of education. 【0072】 A "user profile" is a collection of information, including the user's learning goals and career goals, generated based on information input by the user. 【0073】 "Educational information" refers to detailed information about learning programs and educational courses, and is obtained from publicly available sources and information collections. 【0074】 An "information collection" refers to a database or information source for collecting educational information and other related information. 【0075】 "Public information sources" refer to platforms and data stores that provide information and data that is publicly available. 【0076】 "Analysis" is the process of processing collected data using natural language processing techniques to clarify the content and suitability of each program. 【0077】 "Scoring" is a method of quantifying the degree of fit between a user profile and an educational program, and evaluating the priority of the program. 【0078】 The "proposal set" is a list of optimized educational programs that are presented to the user. 【0079】 "Learning media" refers to online platforms and resources for learning that are provided based on a selected program. 【0080】 "Progress management" is the process of monitoring and evaluating a user's learning progress and providing reminders and feedback as needed. 【0081】 "Evaluation information" refers to feedback data collected from users after the program is completed, which is then analyzed to improve the program. 【0082】 One embodiment of the present invention is a system that enables users to select the optimal educational program according to their learning goals and to effectively advance their learning. This system aims to provide users with customized educational programs by collecting and analyzing educational information, manage learning progress, and continuously improve the program using feedback. 【0083】 Users use a terminal to input their individual learning and career goals. This information is sent to the server, and a user profile is generated. The profile includes data such as learning goals, skill levels, and the characteristics of desired educational programs. 【0084】 The server accesses information sets and public sources containing educational information via the internet to collect the latest educational program data. Specifically, it uses APIs to retrieve information from online course platforms and analyzes the data using natural language processing techniques. Program content, suitability, difficulty level, and other aspects are analyzed. 【0085】 Based on the collected and analyzed program information, the server uses a computational learning algorithm to score the suitability of the user profile to the programs. Programs with high scores are generated as a set of suggested programs deemed optimal for the user. This information is sent to the terminal, and the user selects a program from the presented options and takes part in the program via the learning media. 【0086】 Furthermore, the server monitors and manages the user's learning progress. This includes monitoring progress, sending reminders, and generating progress reports. After completing the program, users input evaluation information from their terminals, and the server analyzes this information to improve the educational program. 【0087】 For example, if a user enters "I want to learn Python programming," the server will analyze relevant online courses and generate a prompt suggesting the most suitable courses. This prompt will take the following form: "If a user enters 'I want to learn Python programming,' please suggest the most suitable online courses." 【0088】 In this way, the present invention addresses the individual learning needs of users and provides an efficient and appropriate educational environment. 【0089】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0090】 Step 1: 【0091】 The user uses a terminal to input individual learning and career goals. The terminal sends the input information to the server, initiating the generation of a user profile. Specifically, the user inputs data such as desired subjects and fields of study, skill level, and desired completion time. The server receives the input information and generates profile data that clarifies the user's needs. 【0092】 Step 2: 【0093】 The server collects program information from information sets and public sources, including educational information. It retrieves data on educational programs and learning courses from online platforms and analyzes the data using natural language processing techniques. Specifically, it uses APIs to collect information from each platform and performs keyword extraction and content analysis. The input is the collected raw program data, and the output is the analyzed program data, including attribute information. 【0094】 Step 3: 【0095】 The server uses a computational learning algorithm to score the suitability of user profiles to programs based on the analyzed program data. Specifically, it selects programs according to the user's learning goals and skill level, assigns numerical values ​​to each, and determines a ranking. The input is the analyzed program data and user profile, and the output is the scoring result. 【0096】 Step 4: 【0097】 The server generates a set of scored program proposals and sends them to the terminal. The terminal displays the proposal set to the user and provides detailed information about each program. Specifically, it displays a list of proposed programs on the screen, allowing the user to check the details of each program (course content, duration, cost, etc.). The input is the scoring result, and the output is a list of selected proposed programs. 【0098】 Step 5: 【0099】 The user selects a program from the presented suggestions and begins learning through the learning media. The server manages the learning progress based on the selected program. Specifically, it logs into the learning platform and monitors the progress of each session. The input is the program selected by the user, and the output is learning progress data. 【0100】 Step 6: 【0101】 After the user completes the program, they input evaluation information via a terminal. The server collects the feedback and identifies areas for program improvement through analysis. Specifically, it provides the user with a survey form to collect opinions on the program's content and teaching methods. The input is user feedback, and the output is analytical data that helps in suggesting improvements. 【0102】 (Application Example 1) 【0103】 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." 【0104】 Traditional learning systems have struggled to provide optimal learning resources in real time according to the user's learning needs and progress. Furthermore, they lacked sufficient visual learning support, and there was a demand for learning environments adapted to specific situations. This limited flexible learning tailored to individual user needs. 【0105】 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. 【0106】 In this invention, the server includes means for receiving information from the user and generating user characteristic information, means for collecting and automatically analyzing educational course information from a database, means for selecting learning courses and generating recommendation lists based on the analyzed data, and means for analyzing visual information and providing learning resources in real time. This enables efficient educational support that meets the user's needs by providing learning resources tailored to the situation in real time using visual information. 【0107】 "Information" refers to data related to learning goals and career goals provided by individual users. 【0108】 "User characteristic information" refers to data about a specific user, generated based on learning goals and career goals collected from the user. 【0109】 "Educational course information" refers to detailed data about learning programs and courses collected from educational databases and public databases. 【0110】 "Analysis" is the process of analyzing data using natural language processing techniques and machine learning algorithms. 【0111】 The "recommendation list" is a list of programs that are numerically evaluated and suggested as the most suitable learning course for the user based on analyzed data. 【0112】 "Visual information" refers to data obtained from a user's visual experience and environment. 【0113】 "Learning resources" refer to the information, learning materials, and support tools that users need when learning. 【0114】 This invention is a comprehensive system for receiving information from users and providing an optimal learning environment. The server plays a central role in this system, generating user characteristic information based on user input. For example, users can input their learning goals and career goals through voice input devices or keyboards. The server stores this information in a database and uses it to create user profiles. 【0115】 Next, the server collects information from educational and public databases and automatically analyzes it. This analysis uses natural language processing techniques and machine learning algorithms. For example, it utilizes Google® Cloud Natural Language and Tensorflow® as APIs. Using these technologies, it evaluates the content, difficulty level, and suitability of educational course information to select appropriate learning courses. 【0116】 The server scores the selected learning courses for the user, generates a suggestion list, and presents it through the device. The courses and resources selected by the user are displayed on the smart glasses' screen. To utilize visual information and provide resources in real time, the device monitors the user's environment using a combination of a camera and microphone. This helps the user to learn efficiently on the spot. 【0117】 For example, if a user who wants to learn a new language while sightseeing voice-inputs "How do you say this phrase in the local language?", the server will immediately search for relevant learning resources and display them on the smart glasses. Using such prompts, users can learn while leveraging their immediate experience. 【0118】 An example of an input prompt for a generative AI model might be, "Describe the process of analyzing a user's voice-based question and providing relevant learning information in real time." This prompt allows the system to respond quickly, further enriching the user's learning experience. 【0119】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0120】 Step 1: 【0121】 Users input learning and career goals using a voice input device or keyboard. This information is sent to the server as input data. The server generates user characteristics information and stores it in a database. Generating user characteristics information involves data processing that analyzes the input information and integrates it into existing user profiles. 【0122】 Step 2: 【0123】 The server collects educational course information from educational databases and public databases. This collected data is automatically analyzed using natural language processing technology (e.g., Google Cloud Natural Language). As a result of the analysis, metadata such as the content, suitability, and difficulty level of the educational courses is generated. Based on this, the educational course information is stored as data on the server. 【0124】 Step 3: 【0125】 The server uses analyzed educational course information and user characteristics information to select the most suitable learning course. This selection process utilizes machine learning algorithms (e.g., TensorFlow). The algorithm matches user needs with the characteristics of the educational courses and performs a numerical evaluation. Courses deemed optimal are scored and output as a recommendation list. 【0126】 Step 4: 【0127】 The server sends the generated recommendation list to the terminal and presents it to the user. Detailed information about the educational course selected by the user (e.g., course content, duration, cost, etc.) is visually displayed on the terminal's screen. Furthermore, if audio guidance is needed, the terminal provides it. The user can then begin learning based on this information. 【0128】 Step 5: 【0129】 The device's camera captures visual information generated by the user during learning in real time. The acquired video information is sent to a server for visual information analysis. Through visual information analysis, learning resources adapted to the user's current learning environment and situation are supported in real time. 【0130】 Step 6: 【0131】 When a user completes a learning program, they input feedback information into the server via their device. This feedback data is stored in a database to improve the program. The server analyzes the collected feedback and uses it to improve algorithms and suggest new learning programs, thereby enabling continuous system improvement. 【0132】 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. 【0133】 The system of this invention incorporates an emotion engine that recognizes user emotions, aiming to make individual learning experiences more interactive and personalized. This system functions by integrating the processing of user input information, program information collected from public databases, automatic analysis, and the user's emotional state. 【0134】 Users use their devices to input learning and career goals, creating individual profiles. In addition, an emotion engine analyzes voice input and text data from the user to infer their emotional state. This information is stored as part of the user profile and used in subsequent processes. 【0135】 The server automatically analyzes educational program information collected from educational and public databases. This analysis uses natural language processing technology to gain a detailed understanding of the program's content, suitability, and target skills. 【0136】 The analysis results are matched with the user's profile and emotional state to generate optimized suggestions tailored to their emotions. Specifically, for example, if the user is showing signs of stress, simpler and shorter programs will be prioritized. 【0137】 The terminal presents the user with a list of suggestions retrieved from the server and displays detailed information about each program. The user selects a program from the options optimized based on their emotional state and begins studying. 【0138】 During learning, the server continuously monitors the user's emotional state through the emotion engine and adjusts the learning portal accordingly. This adjustment includes pacing the learning content and enhancing engagement. 【0139】 After completing the program, the user provides feedback via their device. The emotion engine analyzes this feedback, detects the user's emotional response, and incorporates it into future learning experiences. 【0140】 For example, if a user wants to learn a new programming language but is likely to become discouraged by the initial difficulty level, the system will sense the user's feelings and recommend more beginner-friendly exercises or supplementary lectures. This adaptive support allows users to continue learning effectively and sustainably. 【0141】 Thus, the system of the present invention enables the provision of individually optimized learning that utilizes the user's emotion recognition, thereby improving the user's skills and increasing their satisfaction. 【0142】 The following describes the processing flow. 【0143】 Step 1: 【0144】 Users create a profile by entering their learning and career goals through their device. The device displays options and input forms, allowing users to intuitively enter the necessary information. 【0145】 Step 2: 【0146】 The server builds a user profile based on the input information and activates the emotion engine. It analyzes the user's voice and text data in real time to recognize their emotional state. This analysis utilizes natural language processing and speech recognition technologies. 【0147】 Step 3: 【0148】 The server collects program information from educational and public databases. This includes information on the latest course content, difficulty level, and target skills. APIs and web scraping techniques are used to access the databases. 【0149】 Step 4: 【0150】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of the content and characteristics of each program. This analysis is then compared with the user's profile information and emotional state. 【0151】 Step 5: 【0152】 The server uses machine learning algorithms to select the most suitable educational program. This selection process scores the program's suitability and personalizes the recommendations based on the user's emotional state. 【0153】 Step 6: 【0154】 The terminal presents the user with a list of suggestions sent from the server. This list prioritizes the educational program best suited to the user's situation and includes detailed information about each program. 【0155】 Step 7: 【0156】 When a user selects a program, they access a learning portal through their device. The portal provides video lectures and practice problems, allowing users to learn at their own pace. 【0157】 Step 8: 【0158】 The server continuously monitors the user's emotional changes during learning using an emotion engine. Based on the user's emotional state, feedback is displayed in real time to adjust the difficulty of the learning content and enhance engagement. 【0159】 Step 9: 【0160】 After the user completes the program, they can provide feedback from their device. This includes evaluations of the program, suggestions for improvement, and their overall impressions, as well as emotional feedback. 【0161】 Step 10: 【0162】 The server analyzes the collected feedback using an emotion engine and incorporates the user's emotion-based responses into new learning suggestions. These analysis results will be used to improve and customize the system in the future. 【0163】 (Example 2) 【0164】 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 will be referred to as the "terminal." 【0165】 Traditional learning systems often fail to adequately optimize the learning experience by considering the user's learning progress and emotional state, leading to user stress and inefficient learning. Furthermore, the lack of personalized learning tasks makes it difficult to improve user satisfaction and sustained motivation. 【0166】 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. 【0167】 In this invention, the server includes means for receiving input from the user and generating user identification information including individual goals; means for collecting data from publicly available information and automatically analyzing it; and means for selecting and evaluating optimized information according to the emotional state based on the analyzed data and user identification information, and generating a list. This enables users to learn efficiently, improve satisfaction, and provide appropriate learning content according to their emotions. 【0168】 "User identification information" refers to information generated based on user input, including individual learning goals and career goals. 【0169】 "Publicly available information" refers to educational programs and related information collected from external sources such as educational databases. 【0170】 "Automatically analyzing" refers to the process of analyzing collected information using machine learning algorithms and natural language processing. 【0171】 "Emotional state" refers to the user's mental and emotional condition as inferred by the emotion engine through the user's voice input and text data. 【0172】 "Optimized information" refers to the content of a learning program that has been adjusted based on analyzed data to best suit the user's emotional state and identification information. 【0173】 "Generating a list" refers to creating a list of selected educational programs to evaluate and recommend to users. 【0174】 In order to implement the system based on this invention, the user, server, and terminal each need to play specific roles. The user accesses the learning system and inputs their learning goals and career goals through the terminal. The terminal has an emotion engine built in that analyzes voice and text input to estimate the user's emotional state and stores it as user identification information. 【0175】 The server collects educational program information from educational databases and publicly available information sources. Using natural language processing technology, it automatically analyzes the collected program content to gain a detailed understanding of the program's overview, target audience, and level. Furthermore, by comparing the analysis results with the user's emotional state through an emotion engine, it selects the most suitable educational program for the user, evaluates the information, and creates a list of recommendations for the user. 【0176】 The device presents this list of suggestions to the user and provides a learning portal based on the program selected by the user. During learning, the server uses an emotion engine to continuously monitor the user's emotional state and optimize the learning experience individually by adjusting the pacing and engagement of the learning content. 【0177】 As a concrete example, consider a scenario where a user is motivated to learn a new programming language but is likely to become discouraged in the early stages of learning. This system effectively supports learning by sensing the user's emotions and suggesting beginner-friendly materials and additional lectures. Through this approach, users can learn continuously and efficiently towards their goals. 【0178】 An example of a prompt for a generative AI model would be: "Please generate program suggestions that take user emotions into consideration and reduce stress. For example, if a user is likely to get discouraged in the early stages of learning, please select beginner-friendly resources." 【0179】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0180】 Step 1: 【0181】 Users access the system using a terminal and input their learning and career goals. This process inputs the user's basic learning motivation and desired skills as digital data. The terminal receives this information and stores it as user identification information. 【0182】 Step 2: 【0183】 The device sends voice input and text data to an emotion engine to analyze the user's emotional state. Specifically, the device analyzes voice tone and text keywords to estimate emotions such as stress and joy. This analysis result is added to the user identification information. 【0184】 Step 3: 【0185】 The server accesses external educational databases and publicly available information sources to collect educational program information. The user's learning objectives are considered as input. The server uses natural language processing techniques to analyze the program content, level, target audience, etc., and outputs this information as structured data. 【0186】 Step 4: 【0187】 The server matches the obtained program information with the user's emotional state and identification information. The server uses an emotion engine to filter the information and select a program optimized for the user. The selected programs are generated as a suggestion list. 【0188】 Step 5: 【0189】 The terminal displays a list of suggestions received from the server to the user. Its function is to present information such as an overview of each program, estimated time required, and difficulty level, making it easy for the user to select. The user chooses a program that matches their emotional state and provides input to begin learning. 【0190】 Step 6: 【0191】 As learning progresses, the server continuously monitors the user's emotional state through the emotion engine. Real-time responses from the user are taken in as input. The server understands the progress of the learning content and adjusts pacing and engagement as needed, thereby dynamically adjusting the learning portal. 【0192】 Step 7: 【0193】 After completing the program, the user provides feedback via the device. The device analyzes the feedback using an emotion engine, obtaining new data on the user's emotional response. This information is used to improve future learning programs. 【0194】 (Application Example 2) 【0195】 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". 【0196】 While traditional educational support systems offer personalization based on users' learning and career goals, they lack consideration for users' emotional states, limiting their ability to optimize individual learning experiences. In situations where users are prone to stress or frustration, support and program adjustments are not provided in response to their emotions, making it difficult to maintain effective learning. This results in challenges such as decreased learning efficiency and reduced user satisfaction. 【0197】 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. 【0198】 This invention includes a server that receives input information and emotional data from the user and generates a user profile including individual learning goals, career goals, and emotional states; a server that collects and automatically analyzes program information from an educational database and a public database; and a server that, based on the analyzed data and the user profile, selects and scores an optimized educational program that takes emotional states into account and generates a list of suggestions. This enables individual adjustments based on emotional analysis, making the user's learning experience more interactive and personalized. 【0199】 A "user profile" is a collection of information related to an individual user, including learning goals, career goals, and emotional state. 【0200】 "Emotional state" refers to data that indicates the emotions a user is feeling at a particular moment, and is detected through emotion analysis. 【0201】 An "educational database" is a database that collects information related to education and is used to gather program information. 【0202】 A "public database" is a database that is accessible to the general public and provides information related to education and learning. 【0203】 "Program information" refers to information that includes details about educational programs tailored to the user's learning objectives. 【0204】 "Sentiment analysis" is a process that detects a user's current emotional state based on their text and voice data. 【0205】 The "suggestion list" is a list of optimal educational programs generated based on analyzed data and user profiles. 【0206】 To realize this invention, the user first provides input information using a terminal, generating a user profile that includes learning goals, career goals, and emotional state. The terminal is equipped with a camera and microphone, which can acquire data for emotion analysis. For emotion analysis, deep learning frameworks such as TensorFlow and PyTorch are used to automatically detect the emotional state from audio and video data. 【0207】 The server collects relevant program information from educational and public databases based on profile data obtained from the user. In this process, it analyzes the program content using natural language processing technologies such as SpaCy and NLTK, and optimizes the educational programs suggested to the user using machine learning algorithms. 【0208】 Furthermore, the server performs scoring that takes the user's emotional state into account, generates an optimized list of suggestions, and presents it to the user via the terminal. The user then selects their learning activities based on this list, and the server provides a learning portal based on the selected program. This portal is adjusted in real time according to the user's emotional state to improve learning efficiency. 【0209】 After learning progresses, users provide feedback again from their devices. The server collects this feedback and analyzes the data using sentiment analysis to improve the program. Based on this feedback, future learning programs will be more personalized. 【0210】 For example, if a user finds learning a new language difficult and experiences stress, the server analyzes the user's emotions and suggests a modified learning plan that includes easier tasks and practice exercises, making it easier for the user to continue learning. 【0211】 To support this process, it is possible to use a generative AI model and prompt it with the following statements: 【0212】 "If a user shows signs of stress while learning English vocabulary, how can you adjust the learning menu? Please provide three specific suggestions." 【0213】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0214】 Step 1: 【0215】 The device receives audio and video data from the user as input. This data is stored in internal memory for sentiment analysis. 【0216】 Step 2: 【0217】 The audio and video data acquired by the device are analyzed using TensorFlow to obtain the user's emotional state as output. In this process, a deep learning model analyzes features such as voice tone and facial expressions to detect emotions. 【0218】 Step 3: 【0219】 The device receives learning and career goals directly from the user and generates a user profile along with their emotional state. The generated profile is then sent to the server. 【0220】 Step 4: 【0221】 The server collects program information from educational and public databases. During this process, it uses SpaCy or NLTK to analyze the data using natural language processing and extract educational programs suitable for the user profile. 【0222】 Step 5: 【0223】 The server considers the user's emotional state and scores the extracted programs. This generates an optimized list of suggestions tailored to the user's emotions and outputs it to the terminal. 【0224】 Step 6: 【0225】 The terminal presents the user with a list of suggestions received from the server. The user selects a learning program from the presented list, and the selection is sent from the terminal to the server. 【0226】 Step 7: 【0227】 The server builds a learning portal based on the selected program and provides it to the user via the terminal. This portal has the functionality to manage progress while monitoring the user's emotional state. 【0228】 Step 8: 【0229】 After completing the training, the user provides feedback through their device. The device sends the feedback to a server, which analyzes it to help optimize future programs. 【0230】 Step 9: 【0231】 The server stores the analysis results based on feedback in an internal database, which are then used to select the next learning program. This enables continuous personalization for individual users. 【0232】 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. 【0233】 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. 【0234】 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. 【0235】 [Second Embodiment] 【0236】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0237】 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. 【0238】 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). 【0239】 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. 【0240】 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. 【0241】 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). 【0242】 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. 【0243】 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. 【0244】 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. 【0245】 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. 【0246】 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. 【0247】 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". 【0248】 The system of this invention is implemented through an overall process that includes user input information, data collection from public databases, automated program analysis, proposal of scored educational programs, progress management, and feedback analysis. 【0249】 Users input their individual learning and career goals using their devices. The system then generates a user profile, clarifying their educational needs. Based on the information provided by the user, the system is designed to provide the most appropriate educational support. 【0250】 The server collects information on the latest educational programs and learning courses from educational and public databases. This information is analyzed in detail using natural language processing technology, thereby revealing the content, suitability, and difficulty level of each program. 【0251】 Next, the server uses a machine learning algorithm to match the user's profile with program information. During this process, the program's suitability is quantified and scored. Programs with high scores are then proposed as a list of programs deemed optimal for the user. 【0252】 The terminal presents the user with a list of scored program suggestions, displaying details of each program (e.g., course content, duration, and cost). Based on this information, the user selects the most suitable program and begins studying. 【0253】 When a user takes a selected program, the server monitors and manages their progress through the learning portal. Progress management includes evaluating how much the user has completed and what stage they are currently in. 【0254】 Furthermore, after the user completes the program, they input feedback through their terminal. The server collects this feedback and analyzes it for future suggestions and program improvements. 【0255】 For example, if a user inputs that they want to learn a new programming language, the server searches and analyzes relevant online courses and suggests several courses that are best suited to the user. The user can then take the chosen course online and monitor their progress. 【0256】 In this way, the system of the present invention provides an efficient educational environment that responds to the individual learning needs of each user, supporting continuous skill development and career advancement. 【0257】 The following describes the processing flow. 【0258】 Step 1: 【0259】 Users use a terminal to input their learning and career goals, creating an individual user profile. This information is stored by the system and used as foundational data in subsequent processes. 【0260】 Step 2: 【0261】 The server collects information about educational programs and courses from educational databases and public databases. Web scraping techniques and API-based access are used for data collection. 【0262】 Step 3: 【0263】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of each program's content, target skills, and difficulty level. This analysis extracts the program's characteristics. 【0264】 Step 4: 【0265】 The server matches user profile information with analyzed program information. Using machine learning algorithms, it selects the most suitable program for each user and scores the suitability of each program. 【0266】 Step 5: 【0267】 The server generates a list of optimized program suggestions based on the scoring results. This list prioritizes and compiles programs recommended to the user. 【0268】 Step 6: 【0269】 The terminal displays a list of suggestions to the user, providing detailed information about each program. The user can then select a program of interest from the list. 【0270】 Step 7: 【0271】 Once a user selects a program, a learning portal is provided via their device. This allows the user to begin online program enrollment and receive learning support as needed. 【0272】 Step 8: 【0273】 The server manages the user's learning progress in real time and periodically records the progress. It monitors whether the user's progress is proceeding normally and provides support as needed. 【0274】 Step 9: 【0275】 After the user completes the program, they provide feedback on their device. The user enters their thoughts and suggestions for improvement and sends them to the system as feedback. 【0276】 Step 10: 【0277】 The server analyzes the collected feedback to improve the quality of future program proposals and to develop new programs. This feedback analysis forms an improvement cycle for the entire system. 【0278】 (Example 1) 【0279】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0280】 In a modern educational environment, there is a problem that it is difficult to select an optimal learning program according to the learning needs and goals of individual users. Also, efficiently managing the progress of learning and providing appropriate feedback are important factors in improving educational outcomes. Furthermore, it is necessary to effectively utilize feedback from users to improve the quality of educational programs. 【0281】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0282】 In this invention, the server includes means for receiving input information from a user and generating a user profile, means for collecting program information from an information set including educational information and a public information source and automatically analyzing it, and means for selecting an optimized educational program based on the analyzed data and the user profile, scoring it, and generating a proposal set. As a result, it becomes possible to propose an educational program optimized for each user and manage the progress, and the quality of education can be improved. 【0283】 The "user profile" is a set of information including the learning goals and career goals of a user, generated based on input information from the user. 【0284】 The "educational information" refers to detailed information about learning programs and educational courses, and is obtained from public information sources and information sets. 【0285】 The "information set" refers to a database or information source for collecting educational information and other related information. 【0286】 The "public information source" represents a platform or data store that provides generally public information and data. 【0287】 "Analysis" is a process of processing the collected data using natural language analysis technology to clarify the content and suitability of each program. 【0288】 "Scoring" is a method of quantifying the degree of fit between a user profile and an educational program, and evaluating the priority of the program. 【0289】 The "proposal set" is a list of optimized educational programs that are presented to the user. 【0290】 "Learning media" refers to online platforms and resources for learning that are provided based on a selected program. 【0291】 "Progress management" is the process of monitoring and evaluating a user's learning progress and providing reminders and feedback as needed. 【0292】 "Evaluation information" refers to feedback data collected from users after the program is completed, which is then analyzed to improve the program. 【0293】 One embodiment of the present invention is a system that enables users to select the optimal educational program according to their learning goals and to effectively advance their learning. This system aims to provide users with customized educational programs by collecting and analyzing educational information, manage learning progress, and continuously improve the program using feedback. 【0294】 Users use a terminal to input their individual learning and career goals. This information is sent to the server, and a user profile is generated. The profile includes data such as learning goals, skill levels, and the characteristics of desired educational programs. 【0295】 The server accesses information sets and public sources containing educational information via the internet to collect the latest educational program data. Specifically, it uses APIs to retrieve information from online course platforms and analyzes the data using natural language processing techniques. Program content, suitability, difficulty level, and other aspects are analyzed. 【0296】 Based on the collected and analyzed program information, the server uses a computational learning algorithm to score the suitability of the user profile to the programs. Programs with high scores are generated as a set of suggested programs deemed optimal for the user. This information is sent to the terminal, and the user selects a program from the presented options and takes part in the program via the learning media. 【0297】 Furthermore, the server monitors and manages the user's learning progress. This includes monitoring progress, sending reminders, and generating progress reports. After completing the program, users input evaluation information from their terminals, and the server analyzes this information to improve the educational program. 【0298】 For example, if a user enters "I want to learn Python programming," the server will analyze relevant online courses and generate a prompt suggesting the most suitable courses. This prompt will take the following form: "If a user enters 'I want to learn Python programming,' please suggest the most suitable online courses." 【0299】 In this way, the present invention addresses the individual learning needs of users and provides an efficient and appropriate educational environment. 【0300】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0301】 Step 1: 【0302】 The user uses a terminal to input individual learning and career goals. The terminal sends the input information to the server, initiating the generation of a user profile. Specifically, the user inputs data such as desired subjects and fields of study, skill level, and desired completion time. The server receives the input information and generates profile data that clarifies the user's needs. 【0303】 Step 2: 【0304】 The server collects program information from an information set containing educational information and a public information source. It obtains data on educational programs and learning courses from an online platform and analyzes the data using natural language analysis technology. As specific operations, it uses APIs to collect information from each platform and performs keyword extraction and content analysis. The input is the raw program data collected, and the output is the program data that has been analyzed and contains attribute information. 【0305】 Step 3: 【0306】 Based on the analyzed program data, the server scores the fitness between the user profile and the program using a computational learning algorithm. As specific operations, it selects programs according to the user's learning goals and skill levels, quantifies each of them, and determines the evaluation ranking. The input is the analyzed program data and the user profile, and the output is the scoring result. 【0307】 Step 4: 【0308】 The server generates a set of proposed programs that have been scored and sends them to the terminal. The terminal displays the set of proposals to the user and provides detailed information about each program. As specific operations, it displays a list of proposed programs on the screen so that the user can check the details (lecture content, duration, fees, etc.) of each program. The input is the scoring result, and the output is the selected list of proposed programs. 【0309】 Step 5: 【0310】 The user selects from the presented proposed programs and starts taking courses through the learning media. The server manages the progress of learning based on the selected program. As specific operations, it logs in to the learning platform and monitors the process of advancing each session. The input is the program selected by the user, and the output is the learning progress data. 【0311】 Step 6: 【0312】 After the user completes the program, they input evaluation information via a terminal. The server collects the feedback and identifies areas for program improvement through analysis. Specifically, it provides the user with a survey form to collect opinions on the program's content and teaching methods. The input is user feedback, and the output is analytical data that helps in suggesting improvements. 【0313】 (Application Example 1) 【0314】 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." 【0315】 Traditional learning systems have struggled to provide optimal learning resources in real time according to the user's learning needs and progress. Furthermore, they lacked sufficient visual learning support, and there was a demand for learning environments adapted to specific situations. This limited flexible learning tailored to individual user needs. 【0316】 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. 【0317】 In this invention, the server includes means for receiving information from the user and generating user characteristic information, means for collecting and automatically analyzing educational course information from a database, means for selecting learning courses and generating recommendation lists based on the analyzed data, and means for analyzing visual information and providing learning resources in real time. This enables efficient educational support that meets the user's needs by providing learning resources tailored to the situation in real time using visual information. 【0318】 "Information" refers to data related to learning goals and career goals provided by individual users. 【0319】 "User characteristic information" refers to data about a specific user, generated based on learning goals and career goals collected from the user. 【0320】 "Educational course information" refers to detailed data about learning programs and courses collected from educational databases and public databases. 【0321】 "Analysis" is the process of analyzing data using natural language processing techniques and machine learning algorithms. 【0322】 The "recommendation list" is a list of programs that are numerically evaluated and suggested as the most suitable learning course for the user based on analyzed data. 【0323】 "Visual information" refers to data obtained from a user's visual experience and environment. 【0324】 "Learning resources" refer to the information, learning materials, and support tools that users need when learning. 【0325】 This invention is a comprehensive system for receiving information from users and providing an optimal learning environment. The server plays a central role in this system, generating user characteristic information based on user input. For example, users can input their learning goals and career goals through voice input devices or keyboards. The server stores this information in a database and uses it to create user profiles. 【0326】 Next, the server collects information from educational and public databases and automatically analyzes it. This analysis uses natural language processing techniques and machine learning algorithms. For example, it utilizes Google Cloud Natural Language and TensorFlow as APIs. Using these technologies, it evaluates the content, difficulty level, and suitability of educational course information to select appropriate learning courses. 【0327】 The server scores the selected learning courses for the user, generates a suggestion list, and presents it through the device. The courses and resources selected by the user are displayed on the smart glasses' screen. To utilize visual information and provide resources in real time, the device monitors the user's environment using a combination of a camera and microphone. This helps the user to learn efficiently on the spot. 【0328】 For example, if a user who wants to learn a new language while sightseeing voice-inputs "How do you say this phrase in the local language?", the server will immediately search for relevant learning resources and display them on the smart glasses. Using such prompts, users can learn while leveraging their immediate experience. 【0329】 An example of an input prompt for a generative AI model might be, "Describe the process of analyzing a user's voice-based question and providing relevant learning information in real time." This prompt allows the system to respond quickly, further enriching the user's learning experience. 【0330】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0331】 Step 1: 【0332】 Users input learning and career goals using a voice input device or keyboard. This information is sent to the server as input data. The server generates user characteristics information and stores it in a database. Generating user characteristics information involves data processing that analyzes the input information and integrates it into existing user profiles. 【0333】 Step 2: 【0334】 The server collects educational course information from educational databases and public databases. This collected data is automatically analyzed using natural language processing technology (e.g., Google Cloud Natural Language). As a result of the analysis, metadata such as the content, suitability, and difficulty level of the educational courses is generated. Based on this, the educational course information is stored as data on the server. 【0335】 Step 3: 【0336】 The server uses analyzed educational course information and user characteristics information to select the most suitable learning course. This selection process utilizes machine learning algorithms (e.g., TensorFlow). The algorithm matches user needs with the characteristics of the educational courses and performs a numerical evaluation. Courses deemed optimal are scored and output as a recommendation list. 【0337】 Step 4: 【0338】 The server sends the generated recommendation list to the terminal and presents it to the user. Detailed information about the educational course selected by the user (e.g., course content, duration, cost, etc.) is visually displayed on the terminal's screen. Furthermore, if audio guidance is needed, the terminal provides it. The user can then begin learning based on this information. 【0339】 Step 5: 【0340】 The device's camera captures visual information generated by the user during learning in real time. The acquired video information is sent to a server for visual information analysis. Through visual information analysis, learning resources adapted to the user's current learning environment and situation are supported in real time. 【0341】 Step 6: 【0342】 When a user completes a learning program, they input feedback information into the server via their device. This feedback data is stored in a database to improve the program. The server analyzes the collected feedback and uses it to improve algorithms and suggest new learning programs, thereby enabling continuous system improvement. 【0343】 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. 【0344】 The system of this invention incorporates an emotion engine that recognizes user emotions, aiming to make individual learning experiences more interactive and personalized. This system functions by integrating the processing of user input information, program information collected from public databases, automatic analysis, and the user's emotional state. 【0345】 Users use their devices to input learning and career goals, creating individual profiles. In addition, an emotion engine analyzes voice input and text data from the user to infer their emotional state. This information is stored as part of the user profile and used in subsequent processes. 【0346】 The server automatically analyzes educational program information collected from educational and public databases. This analysis uses natural language processing technology to gain a detailed understanding of the program's content, suitability, and target skills. 【0347】 The analysis results are matched with the user's profile and emotional state to generate optimized suggestions tailored to their emotions. Specifically, for example, if the user is showing signs of stress, simpler and shorter programs will be prioritized. 【0348】 The terminal presents the user with a list of suggestions retrieved from the server and displays detailed information about each program. The user selects a program from the options optimized based on their emotional state and begins studying. 【0349】 During learning, the server continuously monitors the user's emotional state through the emotion engine and adjusts the learning portal accordingly. This adjustment includes pacing the learning content and enhancing engagement. 【0350】 After completing the program, the user provides feedback via their device. The emotion engine analyzes this feedback, detects the user's emotional response, and incorporates it into future learning experiences. 【0351】 For example, if a user wants to learn a new programming language but is likely to become discouraged by the initial difficulty level, the system will sense the user's feelings and recommend more beginner-friendly exercises or supplementary lectures. This adaptive support allows users to continue learning effectively and sustainably. 【0352】 Thus, the system of the present invention enables the provision of individually optimized learning that utilizes the user's emotion recognition, thereby improving the user's skills and increasing their satisfaction. 【0353】 The following describes the processing flow. 【0354】 Step 1: 【0355】 Users create a profile by entering their learning and career goals through their device. The device displays options and input forms, allowing users to intuitively enter the necessary information. 【0356】 Step 2: 【0357】 The server builds a user profile based on the input information and activates the emotion engine. It analyzes the user's voice and text data in real time to recognize their emotional state. This analysis utilizes natural language processing and speech recognition technologies. 【0358】 Step 3: 【0359】 The server collects program information from educational and public databases. This includes information on the latest course content, difficulty level, and target skills. APIs and web scraping techniques are used to access the databases. 【0360】 Step 4: 【0361】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of the content and characteristics of each program. This analysis is then compared with the user's profile information and emotional state. 【0362】 Step 5: 【0363】 The server uses machine learning algorithms to select the most suitable educational program. This selection process scores the program's suitability and personalizes the recommendations based on the user's emotional state. 【0364】 Step 6: 【0365】 The terminal presents the user with a list of suggestions sent from the server. This list prioritizes the educational program best suited to the user's situation and includes detailed information about each program. 【0366】 Step 7: 【0367】 When a user selects a program, they access a learning portal through their device. The portal provides video lectures and practice problems, allowing users to learn at their own pace. 【0368】 Step 8: 【0369】 The server continuously monitors the user's emotional changes during learning using an emotion engine. Based on the user's emotional state, feedback is displayed in real time to adjust the difficulty of the learning content and enhance engagement. 【0370】 Step 9: 【0371】 After the user completes the program, they can provide feedback from their device. This includes evaluations of the program, suggestions for improvement, and their overall impressions, as well as emotional feedback. 【0372】 Step 10: 【0373】 The server analyzes the collected feedback using an emotion engine and incorporates the user's emotion-based responses into new learning suggestions. These analysis results will be used to improve and customize the system in the future. 【0374】 (Example 2) 【0375】 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". 【0376】 Traditional learning systems often fail to adequately optimize the learning experience by considering the user's learning progress and emotional state, leading to user stress and inefficient learning. Furthermore, the lack of personalized learning tasks makes it difficult to improve user satisfaction and sustained motivation. 【0377】 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. 【0378】 In this invention, the server includes means for receiving input from the user and generating user identification information including individual goals; means for collecting data from publicly available information and automatically analyzing it; and means for selecting and evaluating optimized information according to the emotional state based on the analyzed data and user identification information, and generating a list. This enables users to learn efficiently, improve satisfaction, and provide appropriate learning content according to their emotions. 【0379】 "User identification information" refers to information generated based on user input, including individual learning goals and career goals. 【0380】 "Publicly available information" refers to educational programs and related information collected from external sources such as educational databases. 【0381】 "Automatically analyzing" refers to the process of analyzing collected information using machine learning algorithms and natural language processing. 【0382】 "Emotional state" refers to the user's mental and emotional condition as inferred by the emotion engine through the user's voice input and text data. 【0383】 "Optimized information" refers to the content of a learning program that has been adjusted based on analyzed data to best suit the user's emotional state and identification information. 【0384】 "Generating a list" refers to creating a list of selected educational programs to evaluate and recommend to users. 【0385】 In order to implement the system based on this invention, the user, server, and terminal each need to play specific roles. The user accesses the learning system and inputs their learning goals and career goals through the terminal. The terminal has an emotion engine built in that analyzes voice and text input to estimate the user's emotional state and stores it as user identification information. 【0386】 The server collects educational program information from educational databases and publicly available information sources. Using natural language processing technology, it automatically analyzes the collected program content to gain a detailed understanding of the program's overview, target audience, and level. Furthermore, by comparing the analysis results with the user's emotional state through an emotion engine, it selects the most suitable educational program for the user, evaluates the information, and creates a list of recommendations for the user. 【0387】 The device presents this list of suggestions to the user and provides a learning portal based on the program selected by the user. During learning, the server uses an emotion engine to continuously monitor the user's emotional state and optimize the learning experience individually by adjusting the pacing and engagement of the learning content. 【0388】 As a concrete example, consider a scenario where a user is motivated to learn a new programming language but is likely to become discouraged in the early stages of learning. This system effectively supports learning by sensing the user's emotions and suggesting beginner-friendly materials and additional lectures. Through this approach, users can learn continuously and efficiently towards their goals. 【0389】 An example of a prompt for a generative AI model would be: "Please generate program suggestions that take user emotions into consideration and reduce stress. For example, if a user is likely to get discouraged in the early stages of learning, please select beginner-friendly resources." 【0390】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0391】 Step 1: 【0392】 Users access the system using a terminal and input their learning and career goals. This process inputs the user's basic learning motivation and desired skills as digital data. The terminal receives this information and stores it as user identification information. 【0393】 Step 2: 【0394】 The device sends voice input and text data to an emotion engine to analyze the user's emotional state. Specifically, the device analyzes voice tone and text keywords to estimate emotions such as stress and joy. This analysis result is added to the user identification information. 【0395】 Step 3: 【0396】 The server accesses external educational databases and publicly available information sources to collect educational program information. The user's learning objectives are considered as input. The server uses natural language processing techniques to analyze the program content, level, target audience, etc., and outputs this information as structured data. 【0397】 Step 4: 【0398】 The server matches the obtained program information with the user's emotional state and identification information. The server uses an emotion engine to filter the information and select a program optimized for the user. The selected programs are generated as a suggestion list. 【0399】 Step 5: 【0400】 The terminal displays a list of suggestions received from the server to the user. Its function is to present information such as an overview of each program, estimated time required, and difficulty level, making it easy for the user to select. The user chooses a program that matches their emotional state and provides input to begin learning. 【0401】 Step 6: 【0402】 As learning progresses, the server continuously monitors the user's emotional state through the emotion engine. Real-time responses from the user are taken in as input. The server understands the progress of the learning content and adjusts pacing and engagement as needed, thereby dynamically adjusting the learning portal. 【0403】 Step 7: 【0404】 After completing the program, the user provides feedback via the device. The device analyzes the feedback using an emotion engine, obtaining new data on the user's emotional response. This information is used to improve future learning programs. 【0405】 (Application Example 2) 【0406】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0407】 While traditional educational support systems offer personalization based on users' learning and career goals, they lack consideration for users' emotional states, limiting their ability to optimize individual learning experiences. In situations where users are prone to stress or frustration, support and program adjustments are not provided in response to their emotions, making it difficult to maintain effective learning. This results in challenges such as decreased learning efficiency and reduced user satisfaction. 【0408】 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. 【0409】 This invention includes a server that receives input information and emotional data from the user and generates a user profile including individual learning goals, career goals, and emotional states; a server that collects and automatically analyzes program information from an educational database and a public database; and a server that, based on the analyzed data and the user profile, selects and scores an optimized educational program that takes emotional states into account and generates a list of suggestions. This enables individual adjustments based on emotional analysis, making the user's learning experience more interactive and personalized. 【0410】 A "user profile" is a collection of information related to an individual user, including learning goals, career goals, and emotional state. 【0411】 "Emotional state" refers to data that indicates the emotions a user is feeling at a particular moment, and is detected through emotion analysis. 【0412】 An "educational database" is a database that collects information related to education and is used to gather program information. 【0413】 A "public database" is a database that is accessible to the general public and provides information related to education and learning. 【0414】 "Program information" refers to information that includes details about educational programs tailored to the user's learning objectives. 【0415】 "Sentiment analysis" is a process that detects a user's current emotional state based on their text and voice data. 【0416】 The "suggestion list" is a list of optimal educational programs generated based on analyzed data and user profiles. 【0417】 To realize this invention, the user first provides input information using a terminal, generating a user profile that includes learning goals, career goals, and emotional state. The terminal is equipped with a camera and microphone, which can acquire data for emotion analysis. For emotion analysis, deep learning frameworks such as TensorFlow and PyTorch are used to automatically detect the emotional state from audio and video data. 【0418】 The server collects relevant program information from educational and public databases based on profile data obtained from the user. In this process, it analyzes the program content using natural language processing technologies such as SpaCy and NLTK, and optimizes the educational programs suggested to the user using machine learning algorithms. 【0419】 Furthermore, the server performs scoring that takes the user's emotional state into account, generates an optimized list of suggestions, and presents it to the user via the terminal. The user then selects their learning activities based on this list, and the server provides a learning portal based on the selected program. This portal is adjusted in real time according to the user's emotional state to improve learning efficiency. 【0420】 After learning progresses, users provide feedback again from their devices. The server collects this feedback and analyzes the data using sentiment analysis to improve the program. Based on this feedback, future learning programs will be more personalized. 【0421】 For example, if a user finds learning a new language difficult and experiences stress, the server analyzes the user's emotions and suggests a modified learning plan that includes easier tasks and practice exercises, making it easier for the user to continue learning. 【0422】 To support this process, it is possible to use a generative AI model and prompt it with the following statements: 【0423】 "If a user shows signs of stress while learning English vocabulary, how can you adjust the learning menu? Please provide three specific suggestions." 【0424】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0425】 Step 1: 【0426】 The device receives audio and video data from the user as input. This data is stored in internal memory for sentiment analysis. 【0427】 Step 2: 【0428】 The audio and video data acquired by the device are analyzed using TensorFlow to obtain the user's emotional state as output. In this process, a deep learning model analyzes features such as voice tone and facial expressions to detect emotions. 【0429】 Step 3: 【0430】 The device receives learning and career goals directly from the user and generates a user profile along with their emotional state. The generated profile is then sent to the server. 【0431】 Step 4: 【0432】 The server collects program information from educational and public databases. During this process, it uses SpaCy or NLTK to analyze the data using natural language processing and extract educational programs suitable for the user profile. 【0433】 Step 5: 【0434】 The server considers the user's emotional state and scores the extracted programs. This generates an optimized list of suggestions tailored to the user's emotions and outputs it to the terminal. 【0435】 Step 6: 【0436】 The terminal presents the user with a list of suggestions received from the server. The user selects a learning program from the presented list, and the selection is sent from the terminal to the server. 【0437】 Step 7: 【0438】 The server builds a learning portal based on the selected program and provides it to the user via the terminal. This portal has the functionality to manage progress while monitoring the user's emotional state. 【0439】 Step 8: 【0440】 After completing the training, the user provides feedback through their device. The device sends the feedback to a server, which analyzes it to help optimize future programs. 【0441】 Step 9: 【0442】 The server stores the analysis results based on feedback in an internal database, which are then used to select the next learning program. This enables continuous personalization for individual users. 【0443】 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. 【0444】 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. 【0445】 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. 【0446】 [Third Embodiment] 【0447】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0448】 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. 【0449】 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). 【0450】 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. 【0451】 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. 【0452】 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). 【0453】 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. 【0454】 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. 【0455】 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. 【0456】 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. 【0457】 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. 【0458】 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". 【0459】 The system of this invention is implemented through an overall process that includes user input information, data collection from public databases, automated program analysis, proposal of scored educational programs, progress management, and feedback analysis. 【0460】 Users input their individual learning and career goals using their devices. The system then generates a user profile, clarifying their educational needs. Based on the information provided by the user, the system is designed to provide the most appropriate educational support. 【0461】 The server collects information on the latest educational programs and learning courses from educational and public databases. This information is analyzed in detail using natural language processing technology, thereby revealing the content, suitability, and difficulty level of each program. 【0462】 Next, the server uses a machine learning algorithm to match the user's profile with program information. During this process, the program's suitability is quantified and scored. Programs with high scores are then proposed as a list of programs deemed optimal for the user. 【0463】 The terminal presents the user with a list of scored program suggestions, displaying details of each program (e.g., course content, duration, and cost). Based on this information, the user selects the most suitable program and begins studying. 【0464】 When a user takes a selected program, the server monitors and manages their progress through the learning portal. Progress management includes evaluating how much the user has completed and what stage they are currently in. 【0465】 Furthermore, after the user completes the program, they input feedback through their terminal. The server collects this feedback and analyzes it for future suggestions and program improvements. 【0466】 For example, if a user inputs that they want to learn a new programming language, the server searches and analyzes relevant online courses and suggests several courses that are best suited to the user. The user can then take the chosen course online and monitor their progress. 【0467】 In this way, the system of the present invention provides an efficient educational environment that responds to the individual learning needs of each user, supporting continuous skill development and career advancement. 【0468】 The following describes the processing flow. 【0469】 Step 1: 【0470】 Users use a terminal to input their learning and career goals, creating an individual user profile. This information is stored by the system and used as foundational data in subsequent processes. 【0471】 Step 2: 【0472】 The server collects information about educational programs and courses from educational databases and public databases. Web scraping techniques and API-based access are used for data collection. 【0473】 Step 3: 【0474】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of each program's content, target skills, and difficulty level. This analysis extracts the program's characteristics. 【0475】 Step 4: 【0476】 The server matches user profile information with analyzed program information. Using machine learning algorithms, it selects the most suitable program for each user and scores the suitability of each program. 【0477】 Step 5: 【0478】 The server generates a list of optimized program suggestions based on the scoring results. This list prioritizes and compiles programs recommended to the user. 【0479】 Step 6: 【0480】 The terminal displays a list of suggestions to the user, providing detailed information about each program. The user can then select a program of interest from the list. 【0481】 Step 7: 【0482】 Once a user selects a program, a learning portal is provided via their device. This allows the user to begin online program enrollment and receive learning support as needed. 【0483】 Step 8: 【0484】 The server manages the user's learning progress in real time and periodically records the progress. It monitors whether the user's progress is proceeding normally and provides support as needed. 【0485】 Step 9: 【0486】 After the user completes the program, they provide feedback on their device. The user enters their thoughts and suggestions for improvement and sends them to the system as feedback. 【0487】 Step 10: 【0488】 The server analyzes the collected feedback to improve the quality of future program proposals and to develop new programs. This feedback analysis forms an improvement cycle for the entire system. 【0489】 (Example 1) 【0490】 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." 【0491】 In today's educational environment, selecting the optimal learning program tailored to each user's individual learning needs and goals presents a challenge. Furthermore, efficiently managing learning progress and providing appropriate feedback are crucial elements in improving educational outcomes. Additionally, it is necessary to effectively utilize user feedback to enhance the quality of educational programs. 【0492】 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. 【0493】 In this invention, the server includes means for receiving input information from the user and generating a user profile; means for collecting program information from an information set including educational information and a publicly available information source, and automatically analyzing it; and means for selecting an optimized educational program based on the analyzed data and the user profile, scoring it, and generating a set of suggestions. This makes it possible to propose an educational program optimized for each individual user and manage their progress, thereby improving the quality of education. 【0494】 A "user profile" is a collection of information, including the user's learning goals and career goals, generated based on information input by the user. 【0495】 "Educational information" refers to detailed information about learning programs and educational courses, and is obtained from publicly available sources and information collections. 【0496】 An "information collection" refers to a database or information source for collecting educational information and other related information. 【0497】 "Public information sources" refer to platforms and data stores that provide information and data that is publicly available. 【0498】 "Analysis" is the process of processing collected data using natural language processing techniques to clarify the content and suitability of each program. 【0499】 "Scoring" is a method of quantifying the degree of fit between a user profile and an educational program, and evaluating the priority of the program. 【0500】 The "proposal set" is a list of optimized educational programs that are presented to the user. 【0501】 "Learning media" refers to online platforms and resources for learning that are provided based on a selected program. 【0502】 "Progress management" is the process of monitoring and evaluating a user's learning progress and providing reminders and feedback as needed. 【0503】 "Evaluation information" refers to feedback data collected from users after the program is completed, which is then analyzed to improve the program. 【0504】 One embodiment of the present invention is a system that enables users to select the optimal educational program according to their learning goals and to effectively advance their learning. This system aims to provide users with customized educational programs by collecting and analyzing educational information, manage learning progress, and continuously improve the program using feedback. 【0505】 Users use a terminal to input their individual learning and career goals. This information is sent to the server, and a user profile is generated. The profile includes data such as learning goals, skill levels, and the characteristics of desired educational programs. 【0506】 The server accesses information sets and public sources containing educational information via the internet to collect the latest educational program data. Specifically, it uses APIs to retrieve information from online course platforms and analyzes the data using natural language processing techniques. Program content, suitability, difficulty level, and other aspects are analyzed. 【0507】 Based on the collected and analyzed program information, the server uses a computational learning algorithm to score the suitability of the user profile to the programs. Programs with high scores are generated as a set of suggested programs deemed optimal for the user. This information is sent to the terminal, and the user selects a program from the presented options and takes part in the program via the learning media. 【0508】 Furthermore, the server monitors and manages the user's learning progress. This includes monitoring progress, sending reminders, and generating progress reports. After completing the program, users input evaluation information from their terminals, and the server analyzes this information to improve the educational program. 【0509】 For example, if a user enters "I want to learn Python programming," the server will analyze relevant online courses and generate a prompt suggesting the most suitable courses. This prompt will take the following form: "If a user enters 'I want to learn Python programming,' please suggest the most suitable online courses." 【0510】 In this way, the present invention addresses the individual learning needs of users and provides an efficient and appropriate educational environment. 【0511】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0512】 Step 1: 【0513】 The user uses a terminal to input individual learning and career goals. The terminal sends the input information to the server, initiating the generation of a user profile. Specifically, the user inputs data such as desired subjects and fields of study, skill level, and desired completion time. The server receives the input information and generates profile data that clarifies the user's needs. 【0514】 Step 2: 【0515】 The server collects program information from information sets and public sources, including educational information. It retrieves data on educational programs and learning courses from online platforms and analyzes the data using natural language processing techniques. Specifically, it uses APIs to collect information from each platform and performs keyword extraction and content analysis. The input is the collected raw program data, and the output is the analyzed program data, including attribute information. 【0516】 Step 3: 【0517】 The server uses a computational learning algorithm to score the suitability of user profiles to programs based on the analyzed program data. Specifically, it selects programs according to the user's learning goals and skill level, assigns numerical values ​​to each, and determines a ranking. The input is the analyzed program data and user profile, and the output is the scoring result. 【0518】 Step 4: 【0519】 The server generates a set of scored program proposals and sends them to the terminal. The terminal displays the proposal set to the user and provides detailed information about each program. Specifically, it displays a list of proposed programs on the screen, allowing the user to check the details of each program (course content, duration, cost, etc.). The input is the scoring result, and the output is a list of selected proposed programs. 【0520】 Step 5: 【0521】 The user selects a program from the presented suggestions and begins learning through the learning media. The server manages the learning progress based on the selected program. Specifically, it logs into the learning platform and monitors the progress of each session. The input is the program selected by the user, and the output is learning progress data. 【0522】 Step 6: 【0523】 After the user completes the program, they input evaluation information via a terminal. The server collects the feedback and identifies areas for program improvement through analysis. Specifically, it provides the user with a survey form to collect opinions on the program's content and teaching methods. The input is user feedback, and the output is analytical data that helps in suggesting improvements. 【0524】 (Application Example 1) 【0525】 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." 【0526】 Traditional learning systems have struggled to provide optimal learning resources in real time according to the user's learning needs and progress. Furthermore, they lacked sufficient visual learning support, and there was a demand for learning environments adapted to specific situations. This limited flexible learning tailored to individual user needs. 【0527】 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. 【0528】 In this invention, the server includes means for receiving information from the user and generating user characteristic information, means for collecting and automatically analyzing educational course information from a database, means for selecting learning courses and generating recommendation lists based on the analyzed data, and means for analyzing visual information and providing learning resources in real time. This enables efficient educational support that meets the user's needs by providing learning resources tailored to the situation in real time using visual information. 【0529】 "Information" refers to data related to learning goals and career goals provided by individual users. 【0530】 "User characteristic information" refers to data about a specific user, generated based on learning goals and career goals collected from the user. 【0531】 "Educational course information" refers to detailed data about learning programs and courses collected from educational databases and public databases. 【0532】 "Analysis" is the process of analyzing data using natural language processing techniques and machine learning algorithms. 【0533】 The "recommendation list" is a list of programs that are numerically evaluated and suggested as the most suitable learning course for the user based on analyzed data. 【0534】 "Visual information" refers to data obtained from a user's visual experience and environment. 【0535】 "Learning resources" refer to the information, learning materials, and support tools that users need when learning. 【0536】 This invention is a comprehensive system for receiving information from users and providing an optimal learning environment. The server plays a central role in this system, generating user characteristic information based on user input. For example, users can input their learning goals and career goals through voice input devices or keyboards. The server stores this information in a database and uses it to create user profiles. 【0537】 Next, the server collects information from educational and public databases and automatically analyzes it. This analysis uses natural language processing techniques and machine learning algorithms. For example, it utilizes Google Cloud Natural Language and TensorFlow as APIs. Using these technologies, it evaluates the content, difficulty level, and suitability of educational course information to select appropriate learning courses. 【0538】 The server scores the selected learning courses for the user, generates a suggestion list, and presents it through the device. The courses and resources selected by the user are displayed on the smart glasses' screen. To utilize visual information and provide resources in real time, the device monitors the user's environment using a combination of a camera and microphone. This helps the user to learn efficiently on the spot. 【0539】 For example, if a user who wants to learn a new language while sightseeing voice-inputs "How do you say this phrase in the local language?", the server will immediately search for relevant learning resources and display them on the smart glasses. Using such prompts, users can learn while leveraging their immediate experience. 【0540】 An example of an input prompt for a generative AI model might be, "Describe the process of analyzing a user's voice-based question and providing relevant learning information in real time." This prompt allows the system to respond quickly, further enriching the user's learning experience. 【0541】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0542】 Step 1: 【0543】 Users input learning and career goals using a voice input device or keyboard. This information is sent to the server as input data. The server generates user characteristics information and stores it in a database. Generating user characteristics information involves data processing that analyzes the input information and integrates it into existing user profiles. 【0544】 Step 2: 【0545】 The server collects educational course information from educational databases and public databases. This collected data is automatically analyzed using natural language processing technology (e.g., Google Cloud Natural Language). As a result of the analysis, metadata such as the content, suitability, and difficulty level of the educational courses is generated. Based on this, the educational course information is stored as data on the server. 【0546】 Step 3: 【0547】 The server uses analyzed educational course information and user characteristics information to select the most suitable learning course. This selection process utilizes machine learning algorithms (e.g., TensorFlow). The algorithm matches user needs with the characteristics of the educational courses and performs a numerical evaluation. Courses deemed optimal are scored and output as a recommendation list. 【0548】 Step 4: 【0549】 The server sends the generated recommendation list to the terminal and presents it to the user. Detailed information about the educational course selected by the user (e.g., course content, duration, cost, etc.) is visually displayed on the terminal's screen. Furthermore, if audio guidance is needed, the terminal provides it. The user can then begin learning based on this information. 【0550】 Step 5: 【0551】 The device's camera captures visual information generated by the user during learning in real time. The acquired video information is sent to a server for visual information analysis. Through visual information analysis, learning resources adapted to the user's current learning environment and situation are supported in real time. 【0552】 Step 6: 【0553】 When a user completes a learning program, they input feedback information into the server via their device. This feedback data is stored in a database to improve the program. The server analyzes the collected feedback and uses it to improve algorithms and suggest new learning programs, thereby enabling continuous system improvement. 【0554】 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. 【0555】 The system of this invention incorporates an emotion engine that recognizes user emotions, aiming to make individual learning experiences more interactive and personalized. This system functions by integrating the processing of user input information, program information collected from public databases, automatic analysis, and the user's emotional state. 【0556】 Users use their devices to input learning and career goals, creating individual profiles. In addition, an emotion engine analyzes voice input and text data from the user to infer their emotional state. This information is stored as part of the user profile and used in subsequent processes. 【0557】 The server automatically analyzes educational program information collected from educational and public databases. This analysis uses natural language processing technology to gain a detailed understanding of the program's content, suitability, and target skills. 【0558】 The analysis results are matched with the user's profile and emotional state to generate optimized suggestions tailored to their emotions. Specifically, for example, if the user is showing signs of stress, simpler and shorter programs will be prioritized. 【0559】 The terminal presents the user with a list of suggestions retrieved from the server and displays detailed information about each program. The user selects a program from the options optimized based on their emotional state and begins studying. 【0560】 During learning, the server continuously monitors the user's emotional state through the emotion engine and adjusts the learning portal accordingly. This adjustment includes pacing the learning content and enhancing engagement. 【0561】 After completing the program, the user provides feedback via their device. The emotion engine analyzes this feedback, detects the user's emotional response, and incorporates it into future learning experiences. 【0562】 For example, if a user wants to learn a new programming language but is likely to become discouraged by the initial difficulty level, the system will sense the user's feelings and recommend more beginner-friendly exercises or supplementary lectures. This adaptive support allows users to continue learning effectively and sustainably. 【0563】 Thus, the system of the present invention enables the provision of individually optimized learning that utilizes the user's emotion recognition, thereby improving the user's skills and increasing their satisfaction. 【0564】 The following describes the processing flow. 【0565】 Step 1: 【0566】 Users create a profile by entering their learning and career goals through their device. The device displays options and input forms, allowing users to intuitively enter the necessary information. 【0567】 Step 2: 【0568】 The server builds a user profile based on the input information and activates the emotion engine. It analyzes the user's voice and text data in real time to recognize their emotional state. This analysis utilizes natural language processing and speech recognition technologies. 【0569】 Step 3: 【0570】 The server collects program information from educational and public databases. This includes information on the latest course content, difficulty level, and target skills. APIs and web scraping techniques are used to access the databases. 【0571】 Step 4: 【0572】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of the content and characteristics of each program. This analysis is then compared with the user's profile information and emotional state. 【0573】 Step 5: 【0574】 The server uses machine learning algorithms to select the most suitable educational program. This selection process scores the program's suitability and personalizes the recommendations based on the user's emotional state. 【0575】 Step 6: 【0576】 The terminal presents the user with a list of suggestions sent from the server. This list prioritizes the educational program best suited to the user's situation and includes detailed information about each program. 【0577】 Step 7: 【0578】 When a user selects a program, they access a learning portal through their device. The portal provides video lectures and practice problems, allowing users to learn at their own pace. 【0579】 Step 8: 【0580】 The server continuously monitors the user's emotional changes during learning using an emotion engine. Based on the user's emotional state, feedback is displayed in real time to adjust the difficulty of the learning content and enhance engagement. 【0581】 Step 9: 【0582】 After the user completes the program, they can provide feedback from their device. This includes evaluations of the program, suggestions for improvement, and their overall impressions, as well as emotional feedback. 【0583】 Step 10: 【0584】 The server analyzes the collected feedback using an emotion engine and incorporates the user's emotion-based responses into new learning suggestions. These analysis results will be used to improve and customize the system in the future. 【0585】 (Example 2) 【0586】 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." 【0587】 Traditional learning systems often fail to adequately optimize the learning experience by considering the user's learning progress and emotional state, leading to user stress and inefficient learning. Furthermore, the lack of personalized learning tasks makes it difficult to improve user satisfaction and sustained motivation. 【0588】 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. 【0589】 In this invention, the server includes means for receiving input from the user and generating user identification information including individual goals; means for collecting data from publicly available information and automatically analyzing it; and means for selecting and evaluating optimized information according to the emotional state based on the analyzed data and user identification information, and generating a list. This enables users to learn efficiently, improve satisfaction, and provide appropriate learning content according to their emotions. 【0590】 "User identification information" refers to information generated based on user input, including individual learning goals and career goals. 【0591】 "Publicly available information" refers to educational programs and related information collected from external sources such as educational databases. 【0592】 "Automatically analyzing" refers to the process of analyzing collected information using machine learning algorithms and natural language processing. 【0593】 "Emotional state" refers to the user's mental and emotional condition as inferred by the emotion engine through the user's voice input and text data. 【0594】 "Optimized information" refers to the content of a learning program that has been adjusted based on analyzed data to best suit the user's emotional state and identification information. 【0595】 "Generating a list" refers to creating a list of selected educational programs to evaluate and recommend to users. 【0596】 In order to implement the system based on this invention, the user, server, and terminal each need to play specific roles. The user accesses the learning system and inputs their learning goals and career goals through the terminal. The terminal has an emotion engine built in that analyzes voice and text input to estimate the user's emotional state and stores it as user identification information. 【0597】 The server collects educational program information from educational databases and publicly available information sources. Using natural language processing technology, it automatically analyzes the collected program content to gain a detailed understanding of the program's overview, target audience, and level. Furthermore, by comparing the analysis results with the user's emotional state through an emotion engine, it selects the most suitable educational program for the user, evaluates the information, and creates a list of recommendations for the user. 【0598】 The device presents this list of suggestions to the user and provides a learning portal based on the program selected by the user. During learning, the server uses an emotion engine to continuously monitor the user's emotional state and optimize the learning experience individually by adjusting the pacing and engagement of the learning content. 【0599】 As a concrete example, consider a scenario where a user is motivated to learn a new programming language but is likely to become discouraged in the early stages of learning. This system effectively supports learning by sensing the user's emotions and suggesting beginner-friendly materials and additional lectures. Through this approach, users can learn continuously and efficiently towards their goals. 【0600】 An example of a prompt for a generative AI model would be: "Please generate program suggestions that take user emotions into consideration and reduce stress. For example, if a user is likely to get discouraged in the early stages of learning, please select beginner-friendly resources." 【0601】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0602】 Step 1: 【0603】 Users access the system using a terminal and input their learning and career goals. This process inputs the user's basic learning motivation and desired skills as digital data. The terminal receives this information and stores it as user identification information. 【0604】 Step 2: 【0605】 The device sends voice input and text data to an emotion engine to analyze the user's emotional state. Specifically, the device analyzes voice tone and text keywords to estimate emotions such as stress and joy. This analysis result is added to the user identification information. 【0606】 Step 3: 【0607】 The server accesses external educational databases and publicly available information sources to collect educational program information. The user's learning objectives are considered as input. The server uses natural language processing techniques to analyze the program content, level, target audience, etc., and outputs this information as structured data. 【0608】 Step 4: 【0609】 The server matches the obtained program information with the user's emotional state and identification information. The server uses an emotion engine to filter the information and select a program optimized for the user. The selected programs are generated as a suggestion list. 【0610】 Step 5: 【0611】 The terminal displays a list of suggestions received from the server to the user. Its function is to present information such as an overview of each program, estimated time required, and difficulty level, making it easy for the user to select. The user chooses a program that matches their emotional state and provides input to begin learning. 【0612】 Step 6: 【0613】 As learning progresses, the server continuously monitors the user's emotional state through the emotion engine. Real-time responses from the user are taken in as input. The server understands the progress of the learning content and adjusts pacing and engagement as needed, thereby dynamically adjusting the learning portal. 【0614】 Step 7: 【0615】 After completing the program, the user provides feedback via the device. The device analyzes the feedback using an emotion engine, obtaining new data on the user's emotional response. This information is used to improve future learning programs. 【0616】 (Application Example 2) 【0617】 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." 【0618】 While traditional educational support systems offer personalization based on users' learning and career goals, they lack consideration for users' emotional states, limiting their ability to optimize individual learning experiences. In situations where users are prone to stress or frustration, support and program adjustments are not provided in response to their emotions, making it difficult to maintain effective learning. This results in challenges such as decreased learning efficiency and reduced user satisfaction. 【0619】 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. 【0620】 This invention includes a server that receives input information and emotional data from the user and generates a user profile including individual learning goals, career goals, and emotional states; a server that collects and automatically analyzes program information from an educational database and a public database; and a server that, based on the analyzed data and the user profile, selects and scores an optimized educational program that takes emotional states into account and generates a list of suggestions. This enables individual adjustments based on emotional analysis, making the user's learning experience more interactive and personalized. 【0621】 A "user profile" is a collection of information related to an individual user, including learning goals, career goals, and emotional state. 【0622】 "Emotional state" refers to data that indicates the emotions a user is feeling at a particular moment, and is detected through emotion analysis. 【0623】 An "educational database" is a database that collects information related to education and is used to gather program information. 【0624】 A "public database" is a database that is accessible to the general public and provides information related to education and learning. 【0625】 "Program information" refers to information that includes details about educational programs tailored to the user's learning objectives. 【0626】 "Sentiment analysis" is a process that detects a user's current emotional state based on their text and voice data. 【0627】 The "suggestion list" is a list of optimal educational programs generated based on analyzed data and user profiles. 【0628】 To realize this invention, the user first provides input information using a terminal, generating a user profile that includes learning goals, career goals, and emotional state. The terminal is equipped with a camera and microphone, which can acquire data for emotion analysis. For emotion analysis, deep learning frameworks such as TensorFlow and PyTorch are used to automatically detect the emotional state from audio and video data. 【0629】 The server collects relevant program information from educational and public databases based on profile data obtained from the user. In this process, it analyzes the program content using natural language processing technologies such as SpaCy and NLTK, and optimizes the educational programs suggested to the user using machine learning algorithms. 【0630】 Furthermore, the server performs scoring that takes the user's emotional state into account, generates an optimized list of suggestions, and presents it to the user via the terminal. The user then selects their learning activities based on this list, and the server provides a learning portal based on the selected program. This portal is adjusted in real time according to the user's emotional state to improve learning efficiency. 【0631】 After learning progresses, users provide feedback again from their devices. The server collects this feedback and analyzes the data using sentiment analysis to improve the program. Based on this feedback, future learning programs will be more personalized. 【0632】 For example, if a user finds learning a new language difficult and experiences stress, the server analyzes the user's emotions and suggests a modified learning plan that includes easier tasks and practice exercises, making it easier for the user to continue learning. 【0633】 To support this process, it is possible to use a generative AI model and prompt it with the following statements: 【0634】 "If a user shows signs of stress while learning English vocabulary, how can you adjust the learning menu? Please provide three specific suggestions." 【0635】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0636】 Step 1: 【0637】 The device receives audio and video data from the user as input. This data is stored in internal memory for sentiment analysis. 【0638】 Step 2: 【0639】 The audio and video data acquired by the device are analyzed using TensorFlow to obtain the user's emotional state as output. In this process, a deep learning model analyzes features such as voice tone and facial expressions to detect emotions. 【0640】 Step 3: 【0641】 The device receives learning and career goals directly from the user and generates a user profile along with their emotional state. The generated profile is then sent to the server. 【0642】 Step 4: 【0643】 The server collects program information from educational and public databases. During this process, it uses SpaCy or NLTK to analyze the data using natural language processing and extract educational programs suitable for the user profile. 【0644】 Step 5: 【0645】 The server considers the user's emotional state and scores the extracted programs. This generates an optimized list of suggestions tailored to the user's emotions and outputs it to the terminal. 【0646】 Step 6: 【0647】 The terminal presents the user with a list of suggestions received from the server. The user selects a learning program from the presented list, and the selection is sent from the terminal to the server. 【0648】 Step 7: 【0649】 The server builds a learning portal based on the selected program and provides it to the user via the terminal. This portal has the functionality to manage progress while monitoring the user's emotional state. 【0650】 Step 8: 【0651】 After completing the training, the user provides feedback through their device. The device sends the feedback to a server, which analyzes it to help optimize future programs. 【0652】 Step 9: 【0653】 The server stores the analysis results based on feedback in an internal database, which are then used to select the next learning program. This enables continuous personalization for individual users. 【0654】 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. 【0655】 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. 【0656】 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. 【0657】 [Fourth Embodiment] 【0658】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0659】 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. 【0660】 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). 【0661】 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. 【0662】 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. 【0663】 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). 【0664】 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. 【0665】 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. 【0666】 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. 【0667】 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. 【0668】 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. 【0669】 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. 【0670】 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". 【0671】 The system of this invention is implemented through an overall process that includes user input information, data collection from public databases, automated program analysis, proposal of scored educational programs, progress management, and feedback analysis. 【0672】 Users input their individual learning and career goals using their devices. The system then generates a user profile, clarifying their educational needs. Based on the information provided by the user, the system is designed to provide the most appropriate educational support. 【0673】 The server collects information on the latest educational programs and learning courses from educational and public databases. This information is analyzed in detail using natural language processing technology, thereby revealing the content, suitability, and difficulty level of each program. 【0674】 Next, the server uses a machine learning algorithm to match the user's profile with program information. During this process, the program's suitability is quantified and scored. Programs with high scores are then proposed as a list of programs deemed optimal for the user. 【0675】 The terminal presents the user with a list of scored program suggestions, displaying details of each program (e.g., course content, duration, and cost). Based on this information, the user selects the most suitable program and begins studying. 【0676】 When a user takes a selected program, the server monitors and manages their progress through the learning portal. Progress management includes evaluating how much the user has completed and what stage they are currently in. 【0677】 Furthermore, after the user completes the program, they input feedback through their terminal. The server collects this feedback and analyzes it for future suggestions and program improvements. 【0678】 For example, if a user inputs that they want to learn a new programming language, the server searches and analyzes relevant online courses and suggests several courses that are best suited to the user. The user can then take the chosen course online and monitor their progress. 【0679】 In this way, the system of the present invention provides an efficient educational environment that responds to the individual learning needs of each user, supporting continuous skill development and career advancement. 【0680】 The following describes the processing flow. 【0681】 Step 1: 【0682】 Users use a terminal to input their learning and career goals, creating an individual user profile. This information is stored by the system and used as foundational data in subsequent processes. 【0683】 Step 2: 【0684】 The server collects information about educational programs and courses from educational databases and public databases. Web scraping techniques and API-based access are used for data collection. 【0685】 Step 3: 【0686】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of each program's content, target skills, and difficulty level. This analysis extracts the program's characteristics. 【0687】 Step 4: 【0688】 The server matches user profile information with analyzed program information. Using machine learning algorithms, it selects the most suitable program for each user and scores the suitability of each program. 【0689】 Step 5: 【0690】 The server generates a list of optimized program suggestions based on the scoring results. This list prioritizes and compiles programs recommended to the user. 【0691】 Step 6: 【0692】 The terminal displays a list of suggestions to the user, providing detailed information about each program. The user can then select a program of interest from the list. 【0693】 Step 7: 【0694】 Once a user selects a program, a learning portal is provided via their device. This allows the user to begin online program enrollment and receive learning support as needed. 【0695】 Step 8: 【0696】 The server manages the user's learning progress in real time and periodically records the progress. It monitors whether the user's progress is proceeding normally and provides support as needed. 【0697】 Step 9: 【0698】 After the user completes the program, they provide feedback on their device. The user enters their thoughts and suggestions for improvement and sends them to the system as feedback. 【0699】 Step 10: 【0700】 The server analyzes the collected feedback to improve the quality of future program proposals and to develop new programs. This feedback analysis forms an improvement cycle for the entire system. 【0701】 (Example 1) 【0702】 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". 【0703】 In today's educational environment, selecting the optimal learning program tailored to each user's individual learning needs and goals presents a challenge. Furthermore, efficiently managing learning progress and providing appropriate feedback are crucial elements in improving educational outcomes. Additionally, it is necessary to effectively utilize user feedback to enhance the quality of educational programs. 【0704】 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. 【0705】 In this invention, the server includes means for receiving input information from the user and generating a user profile; means for collecting program information from an information set including educational information and a publicly available information source, and automatically analyzing it; and means for selecting an optimized educational program based on the analyzed data and the user profile, scoring it, and generating a set of suggestions. This makes it possible to propose an educational program optimized for each individual user and manage their progress, thereby improving the quality of education. 【0706】 A "user profile" is a collection of information, including the user's learning goals and career goals, generated based on information input by the user. 【0707】 "Educational information" refers to detailed information about learning programs and educational courses, and is obtained from publicly available sources and information collections. 【0708】 An "information collection" refers to a database or information source for collecting educational information and other related information. 【0709】 "Public information sources" refer to platforms and data stores that provide information and data that is publicly available. 【0710】 "Analysis" is the process of processing collected data using natural language processing techniques to clarify the content and suitability of each program. 【0711】 "Scoring" is a method of quantifying the degree of fit between a user profile and an educational program, and evaluating the priority of the program. 【0712】 The "proposal set" is a list of optimized educational programs that are presented to the user. 【0713】 "Learning media" refers to online platforms and resources for learning that are provided based on a selected program. 【0714】 "Progress management" is the process of monitoring and evaluating a user's learning progress and providing reminders and feedback as needed. 【0715】 "Evaluation information" refers to feedback data collected from users after the program is completed, which is then analyzed to improve the program. 【0716】 One embodiment of the present invention is a system that enables users to select the optimal educational program according to their learning goals and to effectively advance their learning. This system aims to provide users with customized educational programs by collecting and analyzing educational information, manage learning progress, and continuously improve the program using feedback. 【0717】 Users use a terminal to input their individual learning and career goals. This information is sent to the server, and a user profile is generated. The profile includes data such as learning goals, skill levels, and the characteristics of desired educational programs. 【0718】 The server accesses information sets and public sources containing educational information via the internet to collect the latest educational program data. Specifically, it uses APIs to retrieve information from online course platforms and analyzes the data using natural language processing techniques. Program content, suitability, difficulty level, and other aspects are analyzed. 【0719】 Based on the collected and analyzed program information, the server uses a computational learning algorithm to score the suitability of the user profile to the programs. Programs with high scores are generated as a set of suggested programs deemed optimal for the user. This information is sent to the terminal, and the user selects a program from the presented options and takes part in the program via the learning media. 【0720】 Furthermore, the server monitors and manages the user's learning progress. This includes monitoring progress, sending reminders, and generating progress reports. After completing the program, users input evaluation information from their terminals, and the server analyzes this information to improve the educational program. 【0721】 For example, if a user enters "I want to learn Python programming," the server will analyze relevant online courses and generate a prompt suggesting the most suitable courses. This prompt will take the following form: "If a user enters 'I want to learn Python programming,' please suggest the most suitable online courses." 【0722】 In this way, the present invention addresses the individual learning needs of users and provides an efficient and appropriate educational environment. 【0723】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0724】 Step 1: 【0725】 The user uses a terminal to input individual learning and career goals. The terminal sends the input information to the server, initiating the generation of a user profile. Specifically, the user inputs data such as desired subjects and fields of study, skill level, and desired completion time. The server receives the input information and generates profile data that clarifies the user's needs. 【0726】 Step 2: 【0727】 The server collects program information from information sets and public sources, including educational information. It retrieves data on educational programs and learning courses from online platforms and analyzes the data using natural language processing techniques. Specifically, it uses APIs to collect information from each platform and performs keyword extraction and content analysis. The input is the collected raw program data, and the output is the analyzed program data, including attribute information. 【0728】 Step 3: 【0729】 The server uses a computational learning algorithm to score the suitability of user profiles to programs based on the analyzed program data. Specifically, it selects programs according to the user's learning goals and skill level, assigns numerical values ​​to each, and determines a ranking. The input is the analyzed program data and user profile, and the output is the scoring result. 【0730】 Step 4: 【0731】 The server generates a set of scored program proposals and sends them to the terminal. The terminal displays the proposal set to the user and provides detailed information about each program. Specifically, it displays a list of proposed programs on the screen, allowing the user to check the details of each program (course content, duration, cost, etc.). The input is the scoring result, and the output is a list of selected proposed programs. 【0732】 Step 5: 【0733】 The user selects a program from the presented suggestions and begins learning through the learning media. The server manages the learning progress based on the selected program. Specifically, it logs into the learning platform and monitors the progress of each session. The input is the program selected by the user, and the output is learning progress data. 【0734】 Step 6: 【0735】 After the user completes the program, they input evaluation information via a terminal. The server collects the feedback and identifies areas for program improvement through analysis. Specifically, it provides the user with a survey form to collect opinions on the program's content and teaching methods. The input is user feedback, and the output is analytical data that helps in suggesting improvements. 【0736】 (Application Example 1) 【0737】 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". 【0738】 Traditional learning systems have struggled to provide optimal learning resources in real time according to the user's learning needs and progress. Furthermore, they lacked sufficient visual learning support, and there was a demand for learning environments adapted to specific situations. This limited flexible learning tailored to individual user needs. 【0739】 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. 【0740】 In this invention, the server includes means for receiving information from the user and generating user characteristic information, means for collecting and automatically analyzing educational course information from a database, means for selecting learning courses and generating recommendation lists based on the analyzed data, and means for analyzing visual information and providing learning resources in real time. This enables efficient educational support that meets the user's needs by providing learning resources tailored to the situation in real time using visual information. 【0741】 "Information" refers to data related to learning goals and career goals provided by individual users. 【0742】 "User characteristic information" refers to data about a specific user, generated based on learning goals and career goals collected from the user. 【0743】 "Educational course information" refers to detailed data about learning programs and courses collected from educational databases and public databases. 【0744】 "Analysis" is the process of analyzing data using natural language processing techniques and machine learning algorithms. 【0745】 The "recommendation list" is a list of programs that are numerically evaluated and suggested as the most suitable learning course for the user based on analyzed data. 【0746】 "Visual information" refers to data obtained from a user's visual experience and environment. 【0747】 "Learning resources" refer to the information, learning materials, and support tools that users need when learning. 【0748】 This invention is a comprehensive system for receiving information from users and providing an optimal learning environment. The server plays a central role in this system, generating user characteristic information based on user input. For example, users can input their learning goals and career goals through voice input devices or keyboards. The server stores this information in a database and uses it to create user profiles. 【0749】 Next, the server collects information from educational and public databases and automatically analyzes it. This analysis uses natural language processing techniques and machine learning algorithms. For example, it utilizes Google Cloud Natural Language and TensorFlow as APIs. Using these technologies, it evaluates the content, difficulty level, and suitability of educational course information to select appropriate learning courses. 【0750】 The server scores the selected learning courses for the user, generates a suggestion list, and presents it through the device. The courses and resources selected by the user are displayed on the smart glasses' screen. To utilize visual information and provide resources in real time, the device monitors the user's environment using a combination of a camera and microphone. This helps the user to learn efficiently on the spot. 【0751】 For example, if a user who wants to learn a new language while sightseeing voice-inputs "How do you say this phrase in the local language?", the server will immediately search for relevant learning resources and display them on the smart glasses. Using such prompts, users can learn while leveraging their immediate experience. 【0752】 An example of an input prompt for a generative AI model might be, "Describe the process of analyzing a user's voice-based question and providing relevant learning information in real time." This prompt allows the system to respond quickly, further enriching the user's learning experience. 【0753】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0754】 Step 1: 【0755】 Users input learning and career goals using a voice input device or keyboard. This information is sent to the server as input data. The server generates user characteristics information and stores it in a database. Generating user characteristics information involves data processing that analyzes the input information and integrates it into existing user profiles. 【0756】 Step 2: 【0757】 The server collects educational course information from educational databases and public databases. This collected data is automatically analyzed using natural language processing technology (e.g., Google Cloud Natural Language). As a result of the analysis, metadata such as the content, suitability, and difficulty level of the educational courses is generated. Based on this, the educational course information is stored as data on the server. 【0758】 Step 3: 【0759】 The server uses analyzed educational course information and user characteristics information to select the most suitable learning course. This selection process utilizes machine learning algorithms (e.g., TensorFlow). The algorithm matches user needs with the characteristics of the educational courses and performs a numerical evaluation. Courses deemed optimal are scored and output as a recommendation list. 【0760】 Step 4: 【0761】 The server sends the generated recommendation list to the terminal and presents it to the user. Detailed information about the educational course selected by the user (e.g., course content, duration, cost, etc.) is visually displayed on the terminal's screen. Furthermore, if audio guidance is needed, the terminal provides it. The user can then begin learning based on this information. 【0762】 Step 5: 【0763】 The device's camera captures visual information generated by the user during learning in real time. The acquired video information is sent to a server for visual information analysis. Through visual information analysis, learning resources adapted to the user's current learning environment and situation are supported in real time. 【0764】 Step 6: 【0765】 When a user completes a learning program, they input feedback information into the server via their device. This feedback data is stored in a database to improve the program. The server analyzes the collected feedback and uses it to improve algorithms and suggest new learning programs, thereby enabling continuous system improvement. 【0766】 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. 【0767】 The system of this invention incorporates an emotion engine that recognizes user emotions, aiming to make individual learning experiences more interactive and personalized. This system functions by integrating the processing of user input information, program information collected from public databases, automatic analysis, and the user's emotional state. 【0768】 Users use their devices to input learning and career goals, creating individual profiles. In addition, an emotion engine analyzes voice input and text data from the user to infer their emotional state. This information is stored as part of the user profile and used in subsequent processes. 【0769】 The server automatically analyzes educational program information collected from educational and public databases. This analysis uses natural language processing technology to gain a detailed understanding of the program's content, suitability, and target skills. 【0770】 The analysis results are matched with the user's profile and emotional state to generate optimized suggestions tailored to their emotions. Specifically, for example, if the user is showing signs of stress, simpler and shorter programs will be prioritized. 【0771】 The terminal presents the user with a list of suggestions retrieved from the server and displays detailed information about each program. The user selects a program from the options optimized based on their emotional state and begins studying. 【0772】 During learning, the server continuously monitors the user's emotional state through the emotion engine and adjusts the learning portal accordingly. This adjustment includes pacing the learning content and enhancing engagement. 【0773】 After completing the program, the user provides feedback via their device. The emotion engine analyzes this feedback, detects the user's emotional response, and incorporates it into future learning experiences. 【0774】 For example, if a user wants to learn a new programming language but is likely to become discouraged by the initial difficulty level, the system will sense the user's feelings and recommend more beginner-friendly exercises or supplementary lectures. This adaptive support allows users to continue learning effectively and sustainably. 【0775】 Thus, the system of the present invention enables the provision of individually optimized learning that utilizes the user's emotion recognition, thereby improving the user's skills and increasing their satisfaction. 【0776】 The following describes the processing flow. 【0777】 Step 1: 【0778】 Users create a profile by entering their learning and career goals through their device. The device displays options and input forms, allowing users to intuitively enter the necessary information. 【0779】 Step 2: 【0780】 The server builds a user profile based on the input information and activates the emotion engine. It analyzes the user's voice and text data in real time to recognize their emotional state. This analysis utilizes natural language processing and speech recognition technologies. 【0781】 Step 3: 【0782】 The server collects program information from educational and public databases. This includes information on the latest course content, difficulty level, and target skills. APIs and web scraping techniques are used to access the databases. 【0783】 Step 4: 【0784】 The server analyzes the collected data using natural language processing techniques to gain a detailed understanding of the content and characteristics of each program. This analysis is then compared with the user's profile information and emotional state. 【0785】 Step 5: 【0786】 The server uses machine learning algorithms to select the most suitable educational program. This selection process scores the program's suitability and personalizes the recommendations based on the user's emotional state. 【0787】 Step 6: 【0788】 The terminal presents the user with a list of suggestions sent from the server. This list prioritizes the educational program best suited to the user's situation and includes detailed information about each program. 【0789】 Step 7: 【0790】 When a user selects a program, they access a learning portal through their device. The portal provides video lectures and practice problems, allowing users to learn at their own pace. 【0791】 Step 8: 【0792】 The server continuously monitors the user's emotional changes during learning using an emotion engine. Based on the user's emotional state, feedback is displayed in real time to adjust the difficulty of the learning content and enhance engagement. 【0793】 Step 9: 【0794】 After the user completes the program, they can provide feedback from their device. This includes evaluations of the program, suggestions for improvement, and their overall impressions, as well as emotional feedback. 【0795】 Step 10: 【0796】 The server analyzes the collected feedback using an emotion engine and incorporates the user's emotion-based responses into new learning suggestions. These analysis results will be used to improve and customize the system in the future. 【0797】 (Example 2) 【0798】 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". 【0799】 Traditional learning systems often fail to adequately optimize the learning experience by considering the user's learning progress and emotional state, leading to user stress and inefficient learning. Furthermore, the lack of personalized learning tasks makes it difficult to improve user satisfaction and sustained motivation. 【0800】 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. 【0801】 In this invention, the server includes means for receiving input from the user and generating user identification information including individual goals; means for collecting data from publicly available information and automatically analyzing it; and means for selecting and evaluating optimized information according to the emotional state based on the analyzed data and user identification information, and generating a list. This enables users to learn efficiently, improve satisfaction, and provide appropriate learning content according to their emotions. 【0802】 "User identification information" refers to information generated based on user input, including individual learning goals and career goals. 【0803】 "Publicly available information" refers to educational programs and related information collected from external sources such as educational databases. 【0804】 "Automatically analyzing" refers to the process of analyzing collected information using machine learning algorithms and natural language processing. 【0805】 "Emotional state" refers to the user's mental and emotional condition as inferred by the emotion engine through the user's voice input and text data. 【0806】 "Optimized information" refers to the content of a learning program that has been adjusted based on analyzed data to best suit the user's emotional state and identification information. 【0807】 "Generating a list" refers to creating a list of selected educational programs to evaluate and recommend to users. 【0808】 In order to implement the system based on this invention, the user, server, and terminal each need to play specific roles. The user accesses the learning system and inputs their learning goals and career goals through the terminal. The terminal has an emotion engine built in that analyzes voice and text input to estimate the user's emotional state and stores it as user identification information. 【0809】 The server collects educational program information from educational databases and publicly available information sources. Using natural language processing technology, it automatically analyzes the collected program content to gain a detailed understanding of the program's overview, target audience, and level. Furthermore, by comparing the analysis results with the user's emotional state through an emotion engine, it selects the most suitable educational program for the user, evaluates the information, and creates a list of recommendations for the user. 【0810】 The device presents this list of suggestions to the user and provides a learning portal based on the program selected by the user. During learning, the server uses an emotion engine to continuously monitor the user's emotional state and optimize the learning experience individually by adjusting the pacing and engagement of the learning content. 【0811】 As a concrete example, consider a scenario where a user is motivated to learn a new programming language but is likely to become discouraged in the early stages of learning. This system effectively supports learning by sensing the user's emotions and suggesting beginner-friendly materials and additional lectures. Through this approach, users can learn continuously and efficiently towards their goals. 【0812】 An example of a prompt for a generative AI model would be: "Please generate program suggestions that take user emotions into consideration and reduce stress. For example, if a user is likely to get discouraged in the early stages of learning, please select beginner-friendly resources." 【0813】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0814】 Step 1: 【0815】 Users access the system using a terminal and input their learning and career goals. This process inputs the user's basic learning motivation and desired skills as digital data. The terminal receives this information and stores it as user identification information. 【0816】 Step 2: 【0817】 The device sends voice input and text data to an emotion engine to analyze the user's emotional state. Specifically, the device analyzes voice tone and text keywords to estimate emotions such as stress and joy. This analysis result is added to the user identification information. 【0818】 Step 3: 【0819】 The server accesses external educational databases and publicly available information sources to collect educational program information. The user's learning objectives are considered as input. The server uses natural language processing techniques to analyze the program content, level, target audience, etc., and outputs this information as structured data. 【0820】 Step 4: 【0821】 The server matches the obtained program information with the user's emotional state and identification information. The server uses an emotion engine to filter the information and select a program optimized for the user. The selected programs are generated as a suggestion list. 【0822】 Step 5: 【0823】 The terminal displays a list of suggestions received from the server to the user. Its function is to present information such as an overview of each program, estimated time required, and difficulty level, making it easy for the user to select. The user chooses a program that matches their emotional state and provides input to begin learning. 【0824】 Step 6: 【0825】 As learning progresses, the server continuously monitors the user's emotional state through the emotion engine. Real-time responses from the user are taken in as input. The server understands the progress of the learning content and adjusts pacing and engagement as needed, thereby dynamically adjusting the learning portal. 【0826】 Step 7: 【0827】 After completing the program, the user provides feedback via the device. The device analyzes the feedback using an emotion engine, obtaining new data on the user's emotional response. This information is used to improve future learning programs. 【0828】 (Application Example 2) 【0829】 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". 【0830】 While traditional educational support systems offer personalization based on users' learning and career goals, they lack consideration for users' emotional states, limiting their ability to optimize individual learning experiences. In situations where users are prone to stress or frustration, support and program adjustments are not provided in response to their emotions, making it difficult to maintain effective learning. This results in challenges such as decreased learning efficiency and reduced user satisfaction. 【0831】 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. 【0832】 This invention includes a server that receives input information and emotional data from the user and generates a user profile including individual learning goals, career goals, and emotional states; a server that collects and automatically analyzes program information from an educational database and a public database; and a server that, based on the analyzed data and the user profile, selects and scores an optimized educational program that takes emotional states into account and generates a list of suggestions. This enables individual adjustments based on emotional analysis, making the user's learning experience more interactive and personalized. 【0833】 A "user profile" is a collection of information related to an individual user, including learning goals, career goals, and emotional state. 【0834】 "Emotional state" refers to data that indicates the emotions a user is feeling at a particular moment, and is detected through emotion analysis. 【0835】 An "educational database" is a database that collects information related to education and is used to gather program information. 【0836】 A "public database" is a database that is accessible to the general public and provides information related to education and learning. 【0837】 "Program information" refers to information that includes details about educational programs tailored to the user's learning objectives. 【0838】 "Sentiment analysis" is a process that detects a user's current emotional state based on their text and voice data. 【0839】 The "suggestion list" is a list of optimal educational programs generated based on analyzed data and user profiles. 【0840】 To realize this invention, the user first provides input information using a terminal, generating a user profile that includes learning goals, career goals, and emotional state. The terminal is equipped with a camera and microphone, which can acquire data for emotion analysis. For emotion analysis, deep learning frameworks such as TensorFlow and PyTorch are used to automatically detect the emotional state from audio and video data. 【0841】 The server collects relevant program information from educational and public databases based on profile data obtained from the user. In this process, it analyzes the program content using natural language processing technologies such as SpaCy and NLTK, and optimizes the educational programs suggested to the user using machine learning algorithms. 【0842】 Furthermore, the server performs scoring that takes the user's emotional state into account, generates an optimized list of suggestions, and presents it to the user via the terminal. The user then selects their learning activities based on this list, and the server provides a learning portal based on the selected program. This portal is adjusted in real time according to the user's emotional state to improve learning efficiency. 【0843】 After learning progresses, users provide feedback again from their devices. The server collects this feedback and analyzes the data using sentiment analysis to improve the program. Based on this feedback, future learning programs will be more personalized. 【0844】 For example, if a user finds learning a new language difficult and experiences stress, the server analyzes the user's emotions and suggests a modified learning plan that includes easier tasks and practice exercises, making it easier for the user to continue learning. 【0845】 To support this process, it is possible to use a generative AI model and prompt it with the following statements: 【0846】 "If a user shows signs of stress while learning English vocabulary, how can you adjust the learning menu? Please provide three specific suggestions." 【0847】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0848】 Step 1: 【0849】 The device receives audio and video data from the user as input. This data is stored in internal memory for sentiment analysis. 【0850】 Step 2: 【0851】 The audio and video data acquired by the device are analyzed using TensorFlow to obtain the user's emotional state as output. In this process, a deep learning model analyzes features such as voice tone and facial expressions to detect emotions. 【0852】 Step 3: 【0853】 The device receives learning and career goals directly from the user and generates a user profile along with their emotional state. The generated profile is then sent to the server. 【0854】 Step 4: 【0855】 The server collects program information from educational and public databases. During this process, it uses SpaCy or NLTK to analyze the data using natural language processing and extract educational programs suitable for the user profile. 【0856】 Step 5: 【0857】 The server considers the user's emotional state and scores the extracted programs. This generates an optimized list of suggestions tailored to the user's emotions and outputs it to the terminal. 【0858】 Step 6: 【0859】 The terminal presents the user with a list of suggestions received from the server. The user selects a learning program from the presented list, and the selection is sent from the terminal to the server. 【0860】 Step 7: 【0861】 The server builds a learning portal based on the selected program and provides it to the user via the terminal. This portal has the functionality to manage progress while monitoring the user's emotional state. 【0862】 Step 8: 【0863】 After completing the training, the user provides feedback through their device. The device sends the feedback to a server, which analyzes it to help optimize future programs. 【0864】 Step 9: 【0865】 The server stores the analysis results based on feedback in an internal database, which are then used to select the next learning program. This enables continuous personalization for individual users. 【0866】 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. 【0867】 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. 【0868】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0869】 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. 【0870】 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. 【0871】 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. 【0872】 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. 【0873】 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. 【0874】 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." 【0875】 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. 【0876】 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. 【0877】 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. 【0878】 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. 【0879】 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. 【0880】 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. 【0881】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0882】 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. 【0883】 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. 【0884】 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. 【0885】 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. 【0886】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0887】 The following is further disclosed regarding the embodiments described above. 【0888】 (Claim 1) 【0889】 A means for receiving input information from a user and generating a user profile that includes individual learning goals and career goals, 【0890】 A means for collecting and automatically analyzing program information from educational databases and public databases, 【0891】 A method for selecting and scoring optimized educational programs based on analyzed data and user profiles to generate a list of suggestions, 【0892】 A means of presenting a list of suggestions to the user, providing a learning portal based on the selected program, and managing progress, 【0893】 A means of collecting feedback data after the program is completed and analyzing it for future program improvements, 【0894】 A system that includes this. 【0895】 (Claim 2) 【0896】 The system according to claim 1, wherein the analysis means includes a process of analyzing the program content using natural language processing technology and optimizing suggestions to the user using a machine learning algorithm. 【0897】 (Claim 3) 【0898】 The system according to claim 1, comprising means for periodically recording user learning progress data and generating progress reports. 【0899】 "Example 1" 【0900】 (Claim 1) 【0901】 A means for receiving input information from a user and generating a user profile that includes individual learning goals and career goals, 【0902】 A means for collecting and automatically analyzing program information from information sets including educational information and publicly available information sources, 【0903】 A means for selecting and scoring optimized educational programs based on analyzed data and user profiles to generate a set of proposals, 【0904】 A means of presenting a set of suggestions to the user, providing learning media based on the selected program, and managing progress, 【0905】 A means of collecting evaluation information after the program is completed and analyzing it for future program improvements, 【0906】 A means of monitoring progress and sending reminders, 【0907】 A system that includes this. 【0908】 (Claim 2) 【0909】 The system according to claim 1, wherein the analysis means includes a process of analyzing the program content using natural language processing technology and optimizing suggestions to the user using a computational learning algorithm. 【0910】 (Claim 3) 【0911】 The system according to claim 1, comprising means for periodically recording user learning progress data and generating progress reports. 【0912】 "Application Example 1" 【0913】 (Claim 1) 【0914】 A means for receiving information from users and generating user characteristics information including individual learning goals and career goals, 【0915】 A means of collecting educational course information from a database and automatically analyzing it, 【0916】 A method for selecting optimized learning courses based on analyzed data and user characteristics information, numerically evaluating them, and generating a recommendation list. 【0917】 A means of presenting a recommendation list to the user, providing a learning environment based on the selected course, and monitoring progress, 【0918】 A means of collecting feedback information after the course is completed and analyzing it for future course improvements, 【0919】 A means of analyzing visual information and providing learning resources in real time, 【0920】 A system that includes this. 【0921】 (Claim 2) 【0922】 The system according to claim 1, wherein the analysis means includes a process of analyzing course content using natural language processing technology and optimizing recommendations to the user using a machine learning algorithm. 【0923】 (Claim 3) 【0924】 The system according to claim 1, comprising means for periodically recording user learning progress data and generating progress reports. 【0925】 "Example 2 of combining an emotion engine" 【0926】 (Claim 1) 【0927】 A means for receiving input from a user and generating user identification information including individual goals, 【0928】 A means of collecting data from publicly available information and automatically analyzing it, 【0929】 A means for selecting and evaluating optimized information according to emotional state based on analyzed data and user identification information, and generating a list. 【0930】 A means of presenting a list to the user, providing and managing a learning environment based on the selected information, 【0931】 A means of collecting feedback after data completion and analyzing it for future information improvement, 【0932】 A system that includes this. 【0933】 (Claim 2) 【0934】 The system according to claim 1, wherein the analysis means includes a process of analyzing content using natural language processing technology, optimizing suggestions to the user using a machine learning algorithm, and further performing adjustments based on the user's emotional state using an emotion engine. 【0935】 (Claim 3) 【0936】 The system according to claim 1, comprising means for periodically recording the user's progress, generating progress information, continuously monitoring emotions during learning, and adjusting the environment as necessary. 【0937】 "Application example 2 when combining with an emotional engine" 【0938】 (Claim 1) 【0939】 A means for receiving user input information and emotional data, and generating a user profile that includes individual learning goals, career goals, and emotional states, 【0940】 A means for collecting and automatically analyzing program information from educational databases and public databases, 【0941】 A means for selecting and scoring an optimized educational program that takes emotional states into account, based on analyzed data and user profiles, and generating a list of suggestions. 【0942】 A means of presenting a list of suggestions to the user, providing a learning portal based on the selected program while making individual adjustments based on sentiment analysis, and managing progress, 【0943】 A means of collecting feedback data after the program is completed and analyzing it for future program improvements, 【0944】 A system that includes this. 【0945】 (Claim 2) 【0946】 The system according to claim 1, wherein the analysis means includes a process of analyzing the program content using natural language processing technology, optimizing suggestions to the user using machine learning algorithms, and further performing individual adjustments based on sentiment analysis. 【0947】 (Claim 3) 【0948】 The system according to claim 1, comprising means for periodically recording user learning progress data and emotional data, and generating progress reports and emotional state analysis reports. [Explanation of symbols] 【0949】 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 receiving input information from a user and generating a user profile that includes individual learning goals and career goals, A means for collecting and automatically analyzing program information from educational databases and public databases, A method for selecting and scoring optimized educational programs based on analyzed data and user profiles to generate a list of suggestions, A means of presenting a list of suggestions to the user, providing a learning portal based on the selected program, and managing progress, A means of collecting feedback data after the program is completed and analyzing it for future program improvements, A system that includes this. [Claim 2] The system according to claim 1, wherein the analysis means includes a process of analyzing the program content using natural language processing technology and optimizing suggestions to the user using a machine learning algorithm. [Claim 3] The system according to claim 1, comprising means for periodically recording user learning progress data and generating progress reports.

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