System

Abstract

An object of the system according to the embodiment is to select an optimal teaching material based on the content that the user wants to learn, customize a learning plan, and immediately respond to questions during learning.SOLUTION: A system includes a content input part, a teaching material selection part, a learning plan customization part, and a question support part. The content input unit inputs content that the user wants to learn. The teaching material selection section selects an optimum teaching material based on the content input by the content input section. The learning plan customizing section customizes the learning plan based on the learning material selected by the learning material selecting section. The question support unit provides immediate support for questions that the user faces when proceeding with learning based on the learning plan customized by the learning plan customization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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 Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technologies had the problem of not being able to adequately select the most appropriate learning materials based on the content the user wanted to learn, customize their learning plan, or immediately respond to questions they had while studying.

[0005] The system according to the embodiment aims to select the most suitable learning materials based on the content that the user wants to learn, customize the learning plan, and immediately respond to questions that arise during the learning process. [Means for solving the problem]

[0006] The system according to the embodiment includes a content input unit, a learning material selection unit, a study plan customization unit, and a question support unit. The content input unit inputs content that the user wants to learn. The learning material selection unit selects optimal learning materials based on the content input by the content input unit. The study plan customization unit customizes the study plan based on the learning materials selected by the learning material selection unit. The question support unit provides immediate support for questions that the user may encounter when studying based on the study plan customized by the study plan customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can select the most suitable learning materials based on the content that the user wants to learn, customize the learning plan, and immediately respond to questions that arise during the learning process. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The personalized learning platform according to an embodiment of the present invention is a system in which, when a user inputs what they want to learn, a generative AI selects the most suitable learning materials, customizes a learning plan in real time according to their progress, and provides immediate support for any questions, thereby enabling users to efficiently acquire skills and enrich their careers.

[0029] A personalized learning platform according to an embodiment includes a content input unit, a learning material selection unit, a learning plan customization unit, and a question support unit. The content input unit inputs the content the user wants to learn. For example, the user inputs "I want to learn how to use pivot tables in Excel." The content input unit can also analyze the user's past learning history and performance data to select learning materials optimal for each individual learning style. For example, it prioritizes the selection of learning materials that have received high ratings in the past. Furthermore, the content input unit can automatically evaluate the user's current skill level and select learning materials based on the evaluation results. For example, it can measure the skill level using online tests or quizzes. The learning material selection unit selects optimal learning materials based on the content input by the content input unit. For example, a generation AI understands "how to use pivot tables in Excel" and selects related learning materials. The learning plan customization unit customizes the learning plan based on the learning materials selected by the learning material selection unit. For example, once the user has learned basic Excel operations, it automatically suggests intermediate-level learning materials to advance to. The learning plan customization unit can also customize learning plans taking into account not only the user's learning progress but also their daily schedule and lifestyle. For example, it can work with a calendar app to optimize study time. The question support unit provides instant support for questions the user may encounter while studying based on the learning plan customized by the learning plan customization unit. For example, if a user asks, "I don't know how to use this function," the generation AI analyzes the question and provides an appropriate answer. This allows the personalized learning platform according to the embodiment to enable users to efficiently acquire skills and enrich their careers. For example, an engineer learning a new programming language can broaden the scope of their projects and lead to career advancement. Similarly, a businessperson learning advanced Excel functions can improve their work efficiency and enhance their reputation.

[0030] The content input unit can analyze the user's past learning history and performance data to select learning materials that are optimal for each individual learning style. The content input unit, for example, analyzes the user's past learning history to identify which learning materials were most effective. For example, it prioritizes the selection of learning materials that have received high marks in the past. The content input unit also analyzes the user's performance data to select learning materials that are optimal for each individual learning style. For example, it selects learning materials based on test results and task completion levels. Furthermore, the content input unit can select learning materials according to the user's learning speed and level of understanding. For example, it suggests more difficult learning materials to users who learn quickly. In this way, by analyzing the user's past learning history and performance data, it is possible to select learning materials that are optimal for each individual learning style.

[0031] The content input unit can automatically evaluate the user's current skill level and select learning materials based on the evaluation results. The content input unit, for example, builds a system that automatically evaluates the user's current skill level. For example, the skill level is measured using an online test or quiz. The content input unit can also evaluate the skill level based on the user's self-evaluation or third-party evaluation. For example, the user inputs their self-evaluation, and learning materials are selected based on the result. Furthermore, the content input unit can also evaluate the skill level based on the user's past learning history and performance data. For example, the skill level is evaluated based on past test results and the degree of completion of tasks. This makes it possible to automatically evaluate the user's current skill level and select optimal learning materials based on the evaluation results.

[0032] The content input unit can use voice input or image recognition to allow the user to input content they want to learn more intuitively. For example, the content input unit can add a voice input function to allow the user to input content they want to learn by voice. For example, a microphone can be used to recognize voice commands. The content input unit can also add an image recognition function to allow the user to input content they want to learn by image. For example, a camera can be used to recognize handwritten notes and drawings. Furthermore, the content input unit can improve the user interface to allow the user to input content intuitively. For example, drag-and-drop and gesture operations can be supported. This allows the user to input content they want to learn more intuitively using voice input or image recognition.

[0033] The content input unit can select learning materials from different learning platforms and online courses to provide a wide range of options to users. For example, the content input unit builds a system that collects learning materials from different learning platforms and provides them to users. For example, it integrates online courses such as Coursera and Udemy. The content input unit can also select learning materials from educational apps and learning management systems. For example, it uses platforms such as Khan Academy and edX. Furthermore, the content input unit can suggest the optimal learning platform based on the user's learning needs. For example, it can select a platform specialized for a specific skill or knowledge area. This allows the content input unit to select learning materials from different learning platforms and online courses to provide users with a wide range of options.

[0034] The study plan customization unit can customize the study plan taking into consideration not only the user's study progress but also their daily schedule and lifestyle. The study plan customization unit, for example, analyzes the user's daily schedule and builds a system that customizes the study plan. For example, it may work in conjunction with a calendar app to optimize study time. The study plan customization unit can also adjust the study plan taking into consideration the user's lifestyle. For example, it may set study time based on sleep patterns and activity times. Furthermore, the study plan customization unit can customize the study plan according to the user's priorities. For example, it may adjust the study schedule to coincide with important tasks and events. This allows for more effective study by customizing the study plan taking into consideration not only the user's study progress but also their daily schedule and lifestyle.

[0035] The study plan customization unit can reflect user feedback in real time and make flexible adjustments. The study plan customization unit, for example, builds a system that collects user feedback in real time and reflects it in the study plan. For example, the plan is adjusted based on evaluations and comments on the study content. The study plan customization unit can also adjust the study plan according to the user's learning progress and level of understanding. For example, it can suggest more difficult learning materials to users who learn quickly. Furthermore, the study plan customization unit can customize the study plan according to the user's learning style. For example, it can provide visual learning materials to visual users. This allows the study plan to be adjusted flexibly based on user feedback in real time, enabling more effective learning.

[0036] The learning plan customization unit can customize the learning plan by referring to the success stories and best practices of other users. For example, the learning plan customization unit builds a system that collects success stories of other users and reflects them in the learning plan. For example, it refers to the learning plans of users who have acquired the same skills. The learning plan customization unit can also customize the learning plan based on best practices. For example, it can incorporate effective learning methods and successful learning plans. Furthermore, the learning plan customization unit can improve the learning plan based on user feedback. For example, it can adjust the plan by referring to the user's ratings and comments. In this way, more effective learning can be achieved by customizing the learning plan by referring to the success stories and best practices of other users.

[0037] The learning plan customization unit can incorporate gamification elements into the learning plan to increase motivation for learning. The learning plan customization unit, for example, builds a system that incorporates gamification elements into the learning plan. For example, it allows users to earn points and badges according to their learning progress. The learning plan customization unit can also introduce rankings and leaderboards to encourage competition between users. For example, it can display rankings based on learning outcomes. Furthermore, the learning plan customization unit can provide rewards for achieving learning goals. For example, it can provide benefits to users who have mastered a specific skill. In this way, incorporating gamification elements into the learning plan can increase motivation for learning.

[0038] The question support unit can simultaneously present related additional information and reference materials when providing an answer to a user's question. For example, the question support unit builds a system that automatically collects related additional information and simultaneously presents it when providing an answer to a user's question. For example, it displays related articles and videos. The question support unit can also deepen the user's understanding by providing reference materials. For example, it can present links to papers and books. Furthermore, the question support unit can also provide information that supplements the answer to the user's question. For example, it can present detailed explanations and specific examples. In this way, by simultaneously presenting related additional information and reference materials when providing an answer to a user's question, a deeper understanding can be achieved.

[0039] The question support unit can evaluate the quality of answers to user questions and build a feedback loop for continuous improvement. The question support unit, for example, builds a system for evaluating the quality of answers to user questions. For example, it measures the quality of answers based on user feedback and evaluation scores. The question support unit can also collect user feedback and build a feedback loop for continuous improvement of answer quality. For example, it can revise the content of answers based on user comments and evaluations. Furthermore, the question support unit can provide training and resources for improving the quality of answers. For example, it can provide reviews and guidelines by experts. This allows for the evaluation of the quality of answers to user questions and build a feedback loop for continuous improvement, thereby providing higher quality support.

[0040] The question support unit can provide answers to users' questions not only in text format but also in video and audio formats. For example, the question support unit builds a system that provides answers to users' questions in video format. For example, it displays explanatory videos or tutorials. The question support unit can also provide answers in audio format. For example, it uses audio messages or podcasts. Furthermore, the question support unit can allow the user to select between text, video, and audio. For example, the answer format can be selected according to the user's preference. This allows answers to users' questions to be provided not only in text format but also in video and audio format, making it possible to provide support in a wider variety of formats.

[0041] The question support unit displays answers to questions from users in association with questions from other users, thereby solving common questions. The question support unit, for example, builds a system that displays answers to questions from users in association with questions from other users. For example, it displays questions from users who have the same question together. The question support unit can also provide FAQs to solve common questions. For example, it displays a list of frequently asked questions and their answers. Furthermore, the question support unit can suggest related questions based on the user's question history. For example, it displays questions similar to past questions. This allows answers to the user's questions to be displayed in association with questions from other users, solving common questions and providing efficient support.

[0042] The system can automatically analyze the prerequisite knowledge and related skills required to acquire each skill and incorporate them into a study plan. For example, a system can be constructed that automatically analyzes the prerequisite knowledge required to acquire each skill. For example, the system analyzes the mathematical knowledge required to learn a programming language. The system can also automatically analyze related skills and incorporate them into a study plan. For example, the system analyzes the statistical knowledge required to learn data analysis. Furthermore, the system can evaluate prerequisite knowledge and related skills based on the user's learning history and performance data. For example, the system evaluates prerequisite knowledge based on past learning content and test results. In this way, the system can automatically analyze the prerequisite knowledge and related skills required to acquire each skill and incorporate them into a study plan, enabling more effective learning.

[0043] The system can propose a learning plan that combines skills from different fields and promote the acquisition of cross-skills. For example, the system is constructed to propose a learning plan that combines skills from different fields. For example, a plan that combines programming and design skills is provided. The system can also propose an optimal combination of cross-skills according to the user's learning needs. For example, a plan that combines data analysis and business strategy skills is provided. Furthermore, the system can provide feedback to promote the acquisition of cross-skills based on the user's learning progress and level of understanding. For example, additional learning materials are provided according to the user's learning progress. In this way, a learning plan that combines skills from different fields is proposed and the acquisition of cross-skills is promoted, allowing the user to acquire a wider variety of skills.

[0044] The system can provide users with opportunities to apply the skills they have acquired in real projects and tasks. For example, the system can be constructed to provide users with opportunities to apply the skills they have acquired in real projects and tasks. For example, the system can introduce internships and freelance jobs. The system can also provide simulation projects and practical tasks. For example, the system can apply skills through virtual projects. Furthermore, the system can suggest optimal projects and tasks based on the user's skill level and interests. For example, the system can introduce projects that can utilize specific skills. This allows users to acquire practical skills by providing them with opportunities to apply the skills they have acquired in real projects and tasks.

[0045] The system can provide a plan that allows the user to systematically learn the necessary skills and knowledge based on the user's career goals. For example, the system constructs a system that provides a plan that allows the user to systematically learn the necessary skills and knowledge based on the user's career goals. For example, the system creates a learning curriculum according to the career goals. The system can also provide learning steps and progress management based on the user's career goals. For example, the system provides a learning plan according to short-term and long-term goals. Furthermore, the system can provide evaluation methods and feedback based on the user's career goals. For example, the system evaluates learning outcomes and provides feedback. In this way, the system can support career advancement by providing a plan that allows the user to systematically learn the necessary skills and knowledge based on the user's career goals.

[0046] The system can periodically evaluate a user's career progress and suggest skills necessary for career advancement. For example, the system builds a system that periodically evaluates a user's career progress. For example, it measures progress toward career goals. The system can also suggest skills necessary for career advancement based on the user's career progress. For example, it can suggest skills necessary for a specific position or job. Furthermore, the system can adjust skills for career advancement based on user feedback. For example, it can adjust the skill suggestions based on the user's comments and ratings. In this way, the system can support career growth by periodically evaluating a user's career progress and suggesting skills necessary for career advancement.

[0047] The system can provide networking opportunities with experts in different industries and occupations according to the user's career goals. For example, the system builds a system that provides networking opportunities with experts in different industries and occupations according to the user's career goals. For example, the system holds online meetings and events. The system can also provide a mentoring program with experts based on the user's career goals. For example, the system can provide mentoring with industry leaders and experienced professionals. Furthermore, the system can also hold networking events and forums according to the user's career goals. For example, the system can hold events specialized in specific industries and occupations. This makes it possible to support the user's career growth by providing networking opportunities with experts in different industries and occupations according to the user's career goals.

[0048] The system can provide information on seminars and workshops that are useful for the user's career advancement. For example, the system builds a system that provides information on seminars and workshops that are useful for the user's career advancement. For example, it displays schedules for online seminars and workshops. The system can also suggest specific seminars and workshops according to the user's career goals. For example, it can suggest seminars specialized in specific skills or knowledge. Furthermore, the system can adjust the content of seminars and workshops based on user feedback. For example, it can improve the seminar content based on user comments and ratings. In this way, it is possible to support the user's career growth by providing information on seminars and workshops that are useful for the user's career advancement.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] To motivate users to learn, personalized learning platforms can implement a reward system based on learning progress. For example, users can earn badges or points when they achieve certain learning goals. They can also provide rewards and coupons based on their learning progress. Furthermore, they can visualize learning progress so that users can see their own growth. For example, they can display learning progress in graphs and charts. This can motivate users to learn and maintain their learning motivation.

[0051] A personalized learning platform can customize the format of learning content according to a user's learning style. For example, it can provide visual learning materials to visual users and audio learning materials to auditory users. It can also introduce interactive content to allow users to actively participate in learning. For example, it can provide learning content using quizzes and simulations. It can also continuously improve learning content based on user feedback. This allows it to provide content that is best suited to a user's learning style and improve learning effectiveness.

[0052] A personalized learning platform can provide a function that allows users to compare their learning progress with that of other users. For example, learning progress can be displayed in a ranking format to encourage competition among users. A community function for sharing learning results can also be introduced. For example, users can share their learning results and questions through a forum or chat function. Furthermore, learning motivation can be increased by referring to the success stories of other users. This can increase users' motivation to learn and improve the effectiveness of their learning.

[0053] A personalized learning platform can provide a dashboard function to visualize a user's learning progress. For example, it can display learning progress and achievement levels in graphs and charts. It can also allow users to check progress toward learning goals in real time. It can also analyze learning trends and patterns based on the user's learning history and performance data. This allows users to understand their own learning progress and increase their motivation to study.

[0054] A personalized learning platform can provide social functions that allow users to share their learning progress with other users. For example, a function to share learning results on social media can be added. It can also create study groups so that users can study together. Furthermore, users can be motivated to learn by referring to the learning progress of other users. This can increase users' motivation to learn and improve the effectiveness of their learning.

[0055] A personalized learning platform can provide a function that allows users to compare their learning progress with that of other users. For example, learning progress can be displayed in a ranking format to encourage competition among users. A community function for sharing learning results can also be introduced. For example, users can share their learning results and questions through a forum or chat function. Furthermore, learning motivation can be increased by referring to the success stories of other users. This can increase users' motivation to learn and improve the effectiveness of their learning.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The content input unit inputs the content the user wants to learn. For example, the user might input, "I want to learn how to use pivot tables in Excel." The content input unit can also analyze the user's past learning history and performance data to select learning materials that are best suited to each individual learning style. For example, it can prioritize the selection of learning materials that have received high ratings in the past. Furthermore, the content input unit can automatically evaluate the user's current skill level and select learning materials based on the evaluation results. For example, it can measure skill level using online tests or quizzes. Step 2: The teaching material selection unit selects the most appropriate teaching materials based on the content entered by the content input unit. For example, the generation AI understands "how to use pivot tables in Excel" and selects related teaching materials. Step 3: The learning plan customization unit customizes the learning plan based on the learning materials selected by the learning material selection unit. For example, once the user has completed learning the basic operations of Excel, it will automatically suggest intermediate-level learning materials that the user should move on to. The learning plan customization unit can also customize the learning plan taking into account not only the user's learning progress, but also their daily schedule and lifestyle. For example, it can optimize study time by linking with a calendar app. Step 4: The question support unit provides immediate support for questions the user may encounter while studying based on the learning plan customized by the learning plan customization unit. For example, if the user asks, "I don't know how to use this function," the generation AI analyzes the question and provides an appropriate answer.

[0058] (Example 2) The personalized learning platform according to an embodiment of the present invention is a system in which, when a user inputs what they want to learn, a generative AI selects the most suitable learning materials, customizes a learning plan in real time according to their progress, and provides immediate support for any questions, thereby enabling users to efficiently acquire skills and enrich their careers.

[0059] A personalized learning platform according to an embodiment includes a content input unit, a learning material selection unit, a learning plan customization unit, and a question support unit. The content input unit inputs the content the user wants to learn. For example, the user inputs "I want to learn how to use pivot tables in Excel." The content input unit can also analyze the user's past learning history and performance data to select learning materials optimal for each individual learning style. For example, it prioritizes the selection of learning materials that have received high ratings in the past. Furthermore, the content input unit can automatically evaluate the user's current skill level and select learning materials based on the evaluation results. For example, it can measure the skill level using online tests or quizzes. The learning material selection unit selects optimal learning materials based on the content input by the content input unit. For example, a generation AI understands "how to use pivot tables in Excel" and selects related learning materials. The learning plan customization unit customizes the learning plan based on the learning materials selected by the learning material selection unit. For example, once the user has learned basic Excel operations, it automatically suggests intermediate-level learning materials to advance to. The learning plan customization unit can also customize learning plans taking into account not only the user's learning progress but also their daily schedule and lifestyle. For example, it can work with a calendar app to optimize study time. The question support unit provides instant support for questions the user may encounter while studying based on the learning plan customized by the learning plan customization unit. For example, if a user asks, "I don't know how to use this function," the generation AI analyzes the question and provides an appropriate answer. This allows the personalized learning platform according to the embodiment to enable users to efficiently acquire skills and enrich their careers. For example, an engineer learning a new programming language can broaden the scope of their projects and lead to career advancement. Similarly, a businessperson learning advanced Excel functions can improve their work efficiency and enhance their reputation.

[0060] The content input unit can analyze the user's past learning history and performance data to select learning materials that are optimal for each individual learning style. The content input unit, for example, analyzes the user's past learning history to identify which learning materials were most effective. For example, it prioritizes the selection of learning materials that have received high marks in the past. The content input unit also analyzes the user's performance data to select learning materials that are optimal for each individual learning style. For example, it selects learning materials based on test results and task completion levels. Furthermore, the content input unit can select learning materials according to the user's learning speed and level of understanding. For example, it suggests more difficult learning materials to users who learn quickly. In this way, by analyzing the user's past learning history and performance data, it is possible to select learning materials that are optimal for each individual learning style.

[0061] The content input unit can automatically evaluate the user's current skill level and select learning materials based on the evaluation results. The content input unit, for example, builds a system that automatically evaluates the user's current skill level. For example, the skill level is measured using an online test or quiz. The content input unit can also evaluate the skill level based on the user's self-evaluation or third-party evaluation. For example, the user inputs their self-evaluation, and learning materials are selected based on the result. Furthermore, the content input unit can also evaluate the skill level based on the user's past learning history and performance data. For example, the skill level is evaluated based on past test results and the degree of completion of tasks. This makes it possible to automatically evaluate the user's current skill level and select optimal learning materials based on the evaluation results.

[0062] The content input unit can use the emotion estimation function to estimate the user's motivation and willingness to learn and select learning materials accordingly. The content input unit, for example, uses the emotion estimation function to analyze the user's motivation to learn in real time. For example, it analyzes facial expressions and voice tone to quantify the motivation to learn. The content input unit can also analyze the user's behavioral data to estimate the motivation to learn. For example, it can evaluate the motivation to learn based on study frequency and study time. Furthermore, the content input unit can estimate the motivation to learn based on the user's self-report. For example, it can evaluate the motivation to learn based on questionnaires and feedback. In this way, the emotion estimation function can be used to estimate the user's motivation and willingness to learn and select learning materials accordingly.

[0063] The content input unit can use voice input or image recognition to allow the user to input content they want to learn more intuitively. For example, the content input unit can add a voice input function to allow the user to input content they want to learn by voice. For example, a microphone can be used to recognize voice commands. The content input unit can also add an image recognition function to allow the user to input content they want to learn by image. For example, a camera can be used to recognize handwritten notes and drawings. Furthermore, the content input unit can improve the user interface to allow the user to input content intuitively. For example, drag-and-drop and gesture operations can be supported. This allows the user to input content they want to learn more intuitively using voice input or image recognition.

[0064] The content input unit can select learning materials from different learning platforms and online courses to provide a wide range of options to users. For example, the content input unit builds a system that collects learning materials from different learning platforms and provides them to users. For example, it integrates online courses such as Coursera and Udemy. The content input unit can also select learning materials from educational apps and learning management systems. For example, it uses platforms such as Khan Academy and edX. Furthermore, the content input unit can suggest the optimal learning platform based on the user's learning needs. For example, it can select a platform specialized for a specific skill or knowledge area. This allows the content input unit to select learning materials from different learning platforms and online courses to provide users with a wide range of options.

[0065] The content input unit can use the emotion estimation function to analyze the emotions of the user when entering text in real time and make suggestions to elicit positive emotions. The content input unit, for example, uses the emotion estimation function to analyze the emotions of the user when entering text in real time. For example, emotions are estimated using a camera or microphone. The content input unit can also make suggestions to elicit positive emotions in accordance with the user's emotions. For example, it can send encouraging messages or share successful experiences. Furthermore, the content input unit can also adjust a study plan based on the user's emotion data. For example, it can suggest taking a break to reduce stress and fatigue. In this way, the emotion estimation function can be used to analyze the emotions of the user when entering text in real time and make suggestions to elicit positive emotions, thereby increasing motivation to study.

[0066] The study plan customization unit can customize the study plan taking into consideration not only the user's study progress but also their daily schedule and lifestyle. The study plan customization unit, for example, analyzes the user's daily schedule and builds a system that customizes the study plan. For example, it may work in conjunction with a calendar app to optimize study time. The study plan customization unit can also adjust the study plan taking into consideration the user's lifestyle. For example, it may set study time based on sleep patterns and activity times. Furthermore, the study plan customization unit can customize the study plan according to the user's priorities. For example, it may adjust the study schedule to coincide with important tasks and events. This allows for more effective study by customizing the study plan taking into consideration not only the user's study progress but also their daily schedule and lifestyle.

[0067] The study plan customization unit can reflect user feedback in real time and make flexible adjustments. The study plan customization unit, for example, builds a system that collects user feedback in real time and reflects it in the study plan. For example, the plan is adjusted based on evaluations and comments on the study content. The study plan customization unit can also adjust the study plan according to the user's learning progress and level of understanding. For example, it can suggest more difficult learning materials to users who learn quickly. Furthermore, the study plan customization unit can customize the study plan according to the user's learning style. For example, it can provide visual learning materials to visual users. This allows the study plan to be adjusted flexibly based on user feedback in real time, enabling more effective learning.

[0068] The learning plan customization unit can customize the learning plan by referring to the success stories and best practices of other users. For example, the learning plan customization unit builds a system that collects success stories of other users and reflects them in the learning plan. For example, it refers to the learning plans of users who have acquired the same skills. The learning plan customization unit can also customize the learning plan based on best practices. For example, it can incorporate effective learning methods and successful learning plans. Furthermore, the learning plan customization unit can improve the learning plan based on user feedback. For example, it can adjust the plan by referring to the user's ratings and comments. In this way, more effective learning can be achieved by customizing the learning plan by referring to the success stories and best practices of other users.

[0069] The learning plan customization unit can incorporate gamification elements into the learning plan to increase motivation for learning. The learning plan customization unit, for example, builds a system that incorporates gamification elements into the learning plan. For example, it allows users to earn points and badges according to their learning progress. The learning plan customization unit can also introduce rankings and leaderboards to encourage competition between users. For example, it can display rankings based on learning outcomes. Furthermore, the learning plan customization unit can provide rewards for achieving learning goals. For example, it can provide benefits to users who have mastered a specific skill. In this way, incorporating gamification elements into the learning plan can increase motivation for learning.

[0070] The study plan customization unit can use the emotion estimation function to automatically generate positive feedback to increase the user's motivation to study. The study plan customization unit, for example, uses the emotion estimation function to build a system that automatically generates positive feedback to increase the user's motivation to study. For example, it presents encouraging messages and success stories. The study plan customization unit can also provide positive feedback based on the user's emotion data. For example, it can send praise and encouraging messages according to the user's progress in studying. Furthermore, the study plan customization unit can visualize the user's study results and give the user a sense of accomplishment. For example, it can display the user's progress in a graph or chart. In this way, the emotion estimation function can be used to automatically generate positive feedback to increase the user's motivation to study, thereby maintaining motivation to study.

[0071] The question support unit can simultaneously present related additional information and reference materials when providing an answer to a user's question. For example, the question support unit builds a system that automatically collects related additional information and simultaneously presents it when providing an answer to a user's question. For example, it displays related articles and videos. The question support unit can also deepen the user's understanding by providing reference materials. For example, it can present links to papers and books. Furthermore, the question support unit can also provide information that supplements the answer to the user's question. For example, it can present detailed explanations and specific examples. In this way, by simultaneously presenting related additional information and reference materials when providing an answer to a user's question, a deeper understanding can be achieved.

[0072] The question support unit can evaluate the quality of answers to user questions and build a feedback loop for continuous improvement. The question support unit, for example, builds a system for evaluating the quality of answers to user questions. For example, it measures the quality of answers based on user feedback and evaluation scores. The question support unit can also collect user feedback and build a feedback loop for continuous improvement of answer quality. For example, it can revise the content of answers based on user comments and evaluations. Furthermore, the question support unit can provide training and resources for improving the quality of answers. For example, it can provide reviews and guidelines by experts. This allows for the evaluation of the quality of answers to user questions and build a feedback loop for continuous improvement, thereby providing higher quality support.

[0073] The question support unit can use the emotion estimation function to evaluate whether an answer to a user's question is emotionally satisfying and improve it. For example, the question support unit uses the emotion estimation function to build a system that evaluates whether an answer to a user's question is emotionally satisfying. For example, the question support unit analyzes the user's facial expressions and voice. The question support unit can also evaluate the emotional satisfaction of an answer based on user feedback. For example, it measures emotional satisfaction based on the user's comments and evaluation score. Furthermore, the question support unit can provide improvement measures to increase emotional satisfaction. For example, it can modify the content of the answer and use more positive expressions. In this way, better support can be provided by using the emotion estimation function to evaluate whether an answer to a user's question is emotionally satisfying and improve it.

[0074] The question support unit can provide answers to users' questions not only in text format but also in video and audio formats. For example, the question support unit builds a system that provides answers to users' questions in video format. For example, it displays explanatory videos or tutorials. The question support unit can also provide answers in audio format. For example, it uses audio messages or podcasts. Furthermore, the question support unit can allow the user to select between text, video, and audio. For example, the answer format can be selected according to the user's preference. This allows answers to users' questions to be provided not only in text format but also in video and audio format, making it possible to provide support in a wider variety of formats.

[0075] The question support unit displays answers to questions from users in association with questions from other users, thereby solving common questions. The question support unit, for example, builds a system that displays answers to questions from users in association with questions from other users. For example, it displays questions from users who have the same question together. The question support unit can also provide FAQs to solve common questions. For example, it displays a list of frequently asked questions and their answers. Furthermore, the question support unit can suggest related questions based on the user's question history. For example, it displays questions similar to past questions. This allows answers to the user's questions to be displayed in association with questions from other users, solving common questions and providing efficient support.

[0076] The question support unit can use the emotion estimation function to adjust answers to user questions so that they evoke positive emotions. For example, the question support unit uses the emotion estimation function to build a system that adjusts answers to user questions so that they evoke positive emotions. For example, the question support unit changes the content of the answer to a more positive expression. The question support unit can also adjust the tone and style of the answer based on the user's emotion data. For example, it can add words of encouragement or expressions of gratitude. Furthermore, the question support unit can provide improvements to improve the emotional satisfaction of the answer based on user feedback. For example, it can modify the content of the answer based on the user's comments and ratings. In this way, better support can be provided by using the emotion estimation function to adjust answers to user questions so that they evoke positive emotions.

[0077] The system can automatically analyze the prerequisite knowledge and related skills required to acquire each skill and incorporate them into a study plan. For example, a system can be constructed that automatically analyzes the prerequisite knowledge required to acquire each skill. For example, the system analyzes the mathematical knowledge required to learn a programming language. The system can also automatically analyze related skills and incorporate them into a study plan. For example, the system analyzes the statistical knowledge required to learn data analysis. Furthermore, the system can evaluate prerequisite knowledge and related skills based on the user's learning history and performance data. For example, the system evaluates prerequisite knowledge based on past learning content and test results. In this way, the system can automatically analyze the prerequisite knowledge and related skills required to acquire each skill and incorporate them into a study plan, enabling more effective learning.

[0078] The system can use the emotion estimation function to provide support to maintain the user's motivation to acquire skills. For example, the system uses the emotion estimation function to build a system that provides support to maintain the user's motivation to acquire skills. For example, it presents encouraging messages and success stories. The system can also provide feedback to maintain motivation based on the user's emotion data. For example, it can send praise and encouraging messages according to the user's learning progress. Furthermore, the system can visualize the user's learning results and give the user a sense of accomplishment. For example, it can display the user's learning progress in a graph or chart. In this way, more effective learning is possible by using the emotion estimation function to provide support to maintain the user's motivation to acquire skills.

[0079] The system can propose a learning plan that combines skills from different fields and promote the acquisition of cross-skills. For example, the system is constructed to propose a learning plan that combines skills from different fields. For example, a plan that combines programming and design skills is provided. The system can also propose an optimal combination of cross-skills according to the user's learning needs. For example, a plan that combines data analysis and business strategy skills is provided. Furthermore, the system can provide feedback to promote the acquisition of cross-skills based on the user's learning progress and level of understanding. For example, additional learning materials are provided according to the user's learning progress. In this way, a learning plan that combines skills from different fields is proposed and the acquisition of cross-skills is promoted, allowing the user to acquire a wider variety of skills.

[0080] The system can provide users with opportunities to apply the skills they have acquired in real projects and tasks. For example, the system can be constructed to provide users with opportunities to apply the skills they have acquired in real projects and tasks. For example, the system can introduce internships and freelance jobs. The system can also provide simulation projects and practical tasks. For example, the system can apply skills through virtual projects. Furthermore, the system can suggest optimal projects and tasks based on the user's skill level and interests. For example, the system can introduce projects that can utilize specific skills. This allows users to acquire practical skills by providing them with opportunities to apply the skills they have acquired in real projects and tasks.

[0081] The system can use the emotion estimation function to support users in having positive emotions toward skill acquisition. For example, the system uses the emotion estimation function to build a system that supports users in having positive emotions toward skill acquisition. For example, it presents encouraging messages and success stories. The system can also provide feedback to elicit positive emotions based on the user's emotion data. For example, it can send praise and encouraging messages according to the user's learning progress. Furthermore, the system can visualize the user's learning results and give them a sense of accomplishment. For example, it can display the learning progress in graphs and charts. In this way, the emotion estimation function can be used to support users in having positive emotions toward skill acquisition, thereby increasing their motivation to learn.

[0082] The system can provide a plan that allows the user to systematically learn the necessary skills and knowledge based on the user's career goals. For example, the system constructs a system that provides a plan that allows the user to systematically learn the necessary skills and knowledge based on the user's career goals. For example, the system creates a learning curriculum according to the career goals. The system can also provide learning steps and progress management based on the user's career goals. For example, the system provides a learning plan according to short-term and long-term goals. Furthermore, the system can provide evaluation methods and feedback based on the user's career goals. For example, the system evaluates learning outcomes and provides feedback. In this way, the system can support career advancement by providing a plan that allows the user to systematically learn the necessary skills and knowledge based on the user's career goals.

[0083] The system can periodically evaluate a user's career progress and suggest skills necessary for career advancement. For example, the system builds a system that periodically evaluates a user's career progress. For example, it measures progress toward career goals. The system can also suggest skills necessary for career advancement based on the user's career progress. For example, it can suggest skills necessary for a specific position or job. Furthermore, the system can adjust skills for career advancement based on user feedback. For example, it can adjust the skill suggestions based on the user's comments and ratings. In this way, the system can support career growth by periodically evaluating a user's career progress and suggesting skills necessary for career advancement.

[0084] The system can use the emotion estimation function to monitor a user's career satisfaction and adjust the career plan. For example, the system uses the emotion estimation function to build a system that monitors a user's career satisfaction. For example, it analyzes the user's facial expressions and voice. The system can also adjust the career plan based on the user's emotion data. For example, it changes the career plan according to the user's satisfaction. Furthermore, the system can improve the career plan based on user feedback. For example, it adjusts the career plan based on the user's comments and ratings. In this way, it is possible to support career growth by using the emotion estimation function to monitor a user's career satisfaction and adjust the career plan.

[0085] The system can provide networking opportunities with experts in different industries and occupations according to the user's career goals. For example, the system builds a system that provides networking opportunities with experts in different industries and occupations according to the user's career goals. For example, the system holds online meetings and events. The system can also provide a mentoring program with experts based on the user's career goals. For example, the system can provide mentoring with industry leaders and experienced professionals. Furthermore, the system can also hold networking events and forums according to the user's career goals. For example, the system can hold events specialized in specific industries and occupations. This makes it possible to support the user's career growth by providing networking opportunities with experts in different industries and occupations according to the user's career goals.

[0086] The system can provide information on seminars and workshops that are useful for the user's career advancement. For example, the system builds a system that provides information on seminars and workshops that are useful for the user's career advancement. For example, it displays schedules for online seminars and workshops. The system can also suggest specific seminars and workshops according to the user's career goals. For example, it can suggest seminars specialized in specific skills or knowledge. Furthermore, the system can adjust the content of seminars and workshops based on user feedback. For example, it can improve the seminar content based on user comments and ratings. In this way, it is possible to support the user's career growth by providing information on seminars and workshops that are useful for the user's career advancement.

[0087] The system can use the emotion estimation function to provide support for eliciting positive emotions about a user's career. For example, a system can be constructed that uses the emotion estimation function to provide support for eliciting positive emotions about a user's career. For example, the system can present encouraging messages and success stories. The system can also provide feedback to elicit positive emotions based on the user's emotion data. For example, the system can send compliments and encouraging messages according to career progress. Furthermore, the system can visualize the user's career achievements to give the user a sense of accomplishment. For example, the system can display career progress in graphs and charts. In this way, the system can support career growth by using the emotion estimation function to provide support for eliciting positive emotions about a user's career.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] To motivate users to learn, personalized learning platforms can implement a reward system based on learning progress. For example, users can earn badges or points when they achieve certain learning goals. They can also provide rewards and coupons based on their learning progress. Furthermore, they can visualize learning progress so that users can see their own growth. For example, they can display learning progress in graphs and charts. This can motivate users to learn and maintain their learning motivation.

[0090] A personalized learning platform can customize the format of learning content according to a user's learning style. For example, it can provide visual learning materials to visual users and audio learning materials to auditory users. It can also introduce interactive content to allow users to actively participate in learning. For example, it can provide learning content using quizzes and simulations. It can also continuously improve learning content based on user feedback. This allows it to provide content that is best suited to a user's learning style and improve learning effectiveness.

[0091] The personalized learning platform can use the emotion estimation function to adjust the learning environment to increase the user's motivation to learn. For example, if the user is feeling stressed, it can play relaxing music. Also, if the user is lacking concentration, it can provide advice to improve concentration. Furthermore, it can adjust the learning plan based on the user's emotion data. For example, if the user is tired, it can suggest taking a break. In this way, it is possible to provide support to increase the user's motivation to learn using the emotion estimation function.

[0092] A personalized learning platform can provide a function that allows users to compare their learning progress with that of other users. For example, learning progress can be displayed in a ranking format to encourage competition among users. A community function for sharing learning results can also be introduced. For example, users can share their learning results and questions through a forum or chat function. Furthermore, learning motivation can be increased by referring to the success stories of other users. This can increase users' motivation to learn and improve the effectiveness of their learning.

[0093] The personalized learning platform can use the emotion estimation function to provide positive feedback to increase the user's motivation to study. For example, if the user has positive emotions toward studying, it can send an encouraging message. On the other hand, if the user has negative emotions toward studying, it can provide advice to increase motivation. Furthermore, it can adjust the study plan based on the user's emotion data. For example, if the user is tired, it can suggest taking a break. In this way, the emotion estimation function can be used to provide support to increase the user's motivation to study.

[0094] A personalized learning platform can provide a dashboard function to visualize a user's learning progress. For example, it can display learning progress and achievement levels in graphs and charts. It can also allow users to check progress toward learning goals in real time. It can also analyze learning trends and patterns based on the user's learning history and performance data. This allows users to understand their own learning progress and increase their motivation to study.

[0095] The personalized learning platform can use the emotion estimation function to automatically generate positive feedback to increase the user's motivation to learn. For example, if the user has positive feelings about learning, it can present encouraging messages and success stories. On the other hand, if the user has negative feelings about learning, it can provide advice to increase motivation. Furthermore, it can adjust the learning plan based on the user's emotion data. For example, if the user is tired, it can suggest taking a break. In this way, the emotion estimation function can be used to automatically generate positive feedback to increase the user's motivation to learn, thereby maintaining motivation to learn.

[0096] A personalized learning platform can provide social functions that allow users to share their learning progress with other users. For example, a function to share learning results on social media can be added. It can also create study groups so that users can study together. Furthermore, users can be motivated to learn by referring to the learning progress of other users. This can increase users' motivation to learn and improve the effectiveness of their learning.

[0097] The personalized learning platform can use the emotion estimation function to provide positive feedback to increase the user's motivation to study. For example, if the user has positive emotions toward studying, it can send an encouraging message. On the other hand, if the user has negative emotions toward studying, it can provide advice to increase motivation. Furthermore, it can adjust the study plan based on the user's emotion data. For example, if the user is tired, it can suggest taking a break. In this way, the emotion estimation function can be used to provide support to increase the user's motivation to study.

[0098] A personalized learning platform can provide a function that allows users to compare their learning progress with that of other users. For example, learning progress can be displayed in a ranking format to encourage competition among users. A community function for sharing learning results can also be introduced. For example, users can share their learning results and questions through a forum or chat function. Furthermore, learning motivation can be increased by referring to the success stories of other users. This can increase users' motivation to learn and improve the effectiveness of their learning.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The content input unit inputs the content the user wants to learn. For example, the user might input, "I want to learn how to use pivot tables in Excel." The content input unit can also analyze the user's past learning history and performance data to select learning materials that are best suited to each individual learning style. For example, it can prioritize the selection of learning materials that have received high ratings in the past. Furthermore, the content input unit can automatically evaluate the user's current skill level and select learning materials based on the evaluation results. For example, it can measure skill level using online tests or quizzes. Step 2: The teaching material selection unit selects the most appropriate teaching materials based on the content entered by the content input unit. For example, the generation AI understands "how to use pivot tables in Excel" and selects related teaching materials. Step 3: The learning plan customization unit customizes the learning plan based on the learning materials selected by the learning material selection unit. For example, once the user has completed learning the basic operations of Excel, it will automatically suggest intermediate-level learning materials that the user should move on to. The learning plan customization unit can also customize the learning plan taking into account not only the user's learning progress, but also their daily schedule and lifestyle. For example, it can optimize study time by linking with a calendar app. Step 4: The question support unit provides immediate support for questions the user may encounter while studying based on the learning plan customized by the learning plan customization unit. For example, if the user asks, "I don't know how to use this function," the generation AI analyzes the question and provides an appropriate answer.

[0101] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a content input section for inputting content that the user wants to learn; a teaching material selection unit that selects optimal teaching materials based on the content input by the content input unit; a learning plan customization unit that customizes a learning plan based on the learning materials selected by the learning material selection unit; a question support unit that provides immediate support for questions that the user faces when proceeding with learning based on the learning plan customized by the learning plan customization unit. A system characterized by:

2. The content input unit Automatically assess the user's current skill level and select learning materials based on the assessment results The system of claim 1 .

3. The content input unit When users input what they want to learn, they can do so more intuitively using voice input and image recognition. The system of claim 1 .

4. The learning plan customization unit Customize your learning plan by taking into account not only your learning progress but also your daily schedule and lifestyle. The system of claim 1 .

5. The question support unit Answering users' questions while also providing additional relevant information and references The system of claim 1 .

6. The system comprises: Automatically analyzes the prerequisite knowledge and related skills required to acquire each skill and incorporates them into a learning plan The system of claim 1 .

7. The system comprises: Provide a systematic learning plan for the necessary skills and knowledge based on the user's career goals The system of claim 1 .

8. The content input unit Using emotion estimation function, we estimate the user's willingness and motivation to learn and select learning materials accordingly. The system of claim 1 .

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