System

Abstract

An object of a system according to an embodiment is to generate a quiz on the basis of a category or a difficulty level and support learning or reskilling of a user.SOLUTION: A system includes a quiz generation part, a provision part, and a gamification part. The quiz generation unit generates a quiz based on the category and the difficulty level. The provision unit provides the user with the quiz generated by the quiz generation unit. The gamification unit provides a point system and a ranking function.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] Conventional technologies have difficulty effectively providing quizzes for learning and reskilling, and there is room for improvement in encouraging users to continue using the service.

[0005] The system according to the embodiment aims to generate quizzes based on categories and difficulty levels, and to support users in their learning and reskilling. [Means for solving the problem]

[0006] The system according to the embodiment includes a quiz generation unit, a providing unit, and a gamification unit. The quiz generation unit generates quizzes based on categories and difficulty levels. The providing unit provides the quizzes generated by the quiz generation unit to users. The gamification unit provides a point system and ranking functions. [Effects of the Invention]

[0007] The system according to the embodiment can generate quizzes based on categories and difficulty levels to assist users in learning and reskilling. [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) A learning support system according to an embodiment of the present invention uses a generative AI to provide relevant quizzes by category and difficulty level, supporting the learning and reskilling of students and working adults. This system achieves low-cost and efficient learning by using a generative AI to generate quizzes and provide them to users. Furthermore, by incorporating gamification elements, the system provides an environment that is easy for even beginners to participate in and promotes continuous learning. This allows students and working adults to efficiently and continuously advance their learning. For example, students can use quizzes to supplement their school classes, and working adults can acquire new skills as part of reskilling. Furthermore, gamification elements make learning fun and make it easier to maintain motivation. This improves the effectiveness of learning and supports individual growth.

[0029] A learning support system according to an embodiment includes a quiz generation unit, a providing unit, and a gamification unit. The quiz generation unit generates a quiz based on a category and difficulty level. For example, the generation AI generates a quiz based on a category and difficulty level selected by a user. The generation AI generates a quiz using a text generation AI (e.g., LLM). The generation AI can also generate quiz content using a multimodal generation AI. The generation AI can also generate related questions based on a user's selection. For example, when a user selects a category such as "Basics of Mathematics" or "Intermediate History," the generation AI generates questions related to that category. The providing unit provides the quiz generated by the quiz generation unit to the user. For example, the providing unit provides the generated quiz to the user through a web application or a mobile application. The providing unit can also send the generated quiz to the user by email. The providing unit can also print and provide the generated quiz. The gamification unit provides a point system and a ranking function. For example, the gamification unit adds points each time a user answers a quiz correctly, and when a user reaches a certain number of points, their rank increases. The gamification section also introduces a ranking function, allowing users to enjoy competition with other users. The gamification section can also provide special rewards and badges according to the user's learning progress. This allows the learning support system to generate quizzes based on category and difficulty and provide them to users, thereby achieving efficient learning. The gamification element can also increase motivation for learning.

[0030] The quiz generation unit can analyze the user's past answer history and generate quizzes optimized for each individual user based on the correct answer rate and answer time. For example, the quiz generation unit uses a generation AI to analyze the user's past answer history in detail and generate quizzes optimized for each individual user based on data such as the correct answer rate and answer time. For example, the quiz generation unit provides a quiz that includes many questions in areas in which the user is weak. The quiz generation unit can also grasp the user's learning progress based on the user's answer history and suggest the next quiz to tackle. This makes it possible to support efficient learning by analyzing the user's past answer history and providing optimized quizzes.

[0031] The quiz generation unit can monitor the user's answering speed and correct answer rate in real time and dynamically adjust the difficulty level. For example, the quiz generation unit uses a generation AI to monitor the user's answering speed and correct answer rate in real time and dynamically adjust the difficulty level. For example, if the user answers quickly and accurately, the difficulty level of the next question can be increased. Also, if the user takes a long time to answer and has a low correct answer rate, the difficulty level of the next question can be lowered. The quiz generation unit can also provide a quiz of an appropriate level of difficulty based on the user's answering speed and correct answer rate. In this way, by monitoring the user's answering speed and correct answer rate in real time and dynamically adjusting the difficulty level, a quiz of an appropriate level of difficulty can be provided.

[0032] The quiz generation unit can diversify the quiz format and generate multiple choice questions, written questions, and image recognition questions. For example, the quiz generation unit generates quizzes in various formats, such as multiple choice questions, written questions, and image recognition questions, using a generation AI. For example, if a user gets bored with multiple choice questions, the quiz generation unit can provide written questions. Also, if a user is struggling with written questions, the quiz generation unit can provide multiple choice questions. The quiz generation unit can also provide a quiz in an appropriate format based on the user's selection. In this way, diversifying the quiz format can keep the user interested and improve the effectiveness of learning.

[0033] The quiz generation unit can generate quizzes in different languages ​​to provide a multilingual learning environment. For example, the generation AI generates quizzes in different languages ​​to provide a multilingual learning environment. For example, quizzes are generated in multiple languages, such as English, French, and Chinese. The quiz generation unit can also provide quizzes in an appropriate language based on a user's selection. In this way, by generating quizzes in different languages, a multilingual learning environment can be provided, and international users can be accommodated.

[0034] The gamification unit can provide special rewards and badges according to the user's learning progress, thereby enhancing the user's sense of accomplishment. The gamification unit, for example, builds a system that provides special rewards and badges according to the user's learning progress. For example, a special badge is awarded to a user who has reached a certain number of points. The gamification unit can also visualize the user's learning progress, enhancing the user's sense of accomplishment. The gamification unit can also provide special rewards according to the user's learning progress. In this way, by providing special rewards and badges according to the user's learning progress, the user's sense of accomplishment can be enhanced and motivation for learning can be maintained.

[0035] The gamification unit can set different types of points and rewards depending on the difficulty of the quiz, thereby encouraging the user's willingness to take on the challenge. The gamification unit, for example, builds a system that sets different types of points and rewards depending on the difficulty of the quiz. For example, if a user answers a difficult quiz correctly, more points can be earned. The gamification unit can also provide rewards depending on the difficulty to encourage the user's willingness to take on the challenge. The gamification unit can also set different types of points to encourage the user's willingness to take on the challenge. In this way, by setting different types of points and rewards depending on the difficulty of the quiz, the user's willingness to take on the challenge can be increased.

[0036] The gamification unit can introduce a team battle mode in which users cooperate with each other to take quizzes. The gamification unit, for example, builds a system that introduces a team battle mode in which users cooperate with each other to take quizzes. For example, it provides a format in which each team competes for points. The gamification unit can also increase motivation to learn by having users cooperate with each other to take quizzes. The gamification unit can also promote cooperation between users by introducing the team battle mode. Thus, by introducing the team battle mode in which users cooperate with each other to take quizzes, motivation to learn can be increased.

[0037] The gamification unit can add an element that allows a user to develop a virtual character based on the results of a quiz. The gamification unit, for example, builds a system that adds an element that allows a user to develop a virtual character based on the results of a quiz. For example, the character grows with each correct answer to a quiz. The gamification unit can also enhance the character's skills and abilities according to the user's learning progress. The gamification unit can also customize the character's appearance and equipment by having the user take the quiz. In this way, adding an element that allows a user to develop a virtual character based on the results of a quiz can make learning more enjoyable and increase motivation.

[0038] The quiz generation unit can analyze the user's learning history in detail and automatically generate an individual learning plan. For example, the quiz generation unit builds a system in which a generation AI analyzes the user's learning history in detail and automatically generates an individual learning plan. For example, the learning plan is customized based on the user's strengths and weaknesses. The quiz generation unit can also suggest appropriate learning goals and schedules based on the user's learning history. The quiz generation unit can also select optimal learning materials based on the user's learning history. In this way, efficient learning can be supported by analyzing the user's learning history in detail and automatically generating an individual learning plan.

[0039] The quiz generation unit can visualize the user's strengths and weaknesses based on the learning history and suggest specific improvement measures. The quiz generation unit, for example, builds a system that visualizes the user's strengths and weaknesses based on the learning history. For example, the quiz generation unit visualizes the user's learning status using graphs or charts. The quiz generation unit can also suggest specific improvement measures based on the user's strengths and weaknesses. The quiz generation unit can also provide appropriate feedback based on the user's learning history. In this way, efficient learning can be supported by visualizing the user's strengths and weaknesses based on the learning history and suggesting specific improvement measures.

[0040] The quiz generation unit can link the learning history with other learning platforms and provide comprehensive learning data. The quiz generation unit, for example, links the learning history with other learning platforms and builds a system that provides comprehensive learning data. For example, data from multiple platforms is integrated and centrally managed. The quiz generation unit can also provide comprehensive learning data based on the user's learning history. The quiz generation unit can also improve the user's learning efficiency by linking the learning history with other learning platforms. In this way, the user's learning efficiency can be improved by linking the learning history with other learning platforms and providing comprehensive learning data.

[0041] The quiz generation unit can recommend learning communities and forums that are suitable for the user based on the learning history. The quiz generation unit, for example, builds a system that recommends learning communities and forums that are suitable for the user based on the learning history. For example, it proposes a community where users with the same interests gather. The quiz generation unit can also recommend an appropriate forum based on the user's learning history. The quiz generation unit can also increase learning motivation by recommending learning communities and forums based on the user's learning history. In this way, learning motivation can be increased by recommending learning communities and forums that are suitable for the user based on the learning history.

[0042] The quiz generation unit can analyze the user's occupational history and skill set and propose an optimal reskilling plan. The quiz generation unit, for example, builds a system in which a generation AI analyzes the user's occupational history and skill set in detail and proposes an optimal reskilling plan. For example, it proposes new skills based on the user's past work experience. The quiz generation unit can also propose an appropriate reskilling plan based on the user's skill set. The quiz generation unit can also support efficient skill acquisition by proposing an optimal reskilling plan based on the user's occupational history. In this way, efficient skill acquisition can be supported by analyzing the user's occupational history and skill set and proposing an optimal reskilling plan.

[0043] The quiz generation unit can dynamically generate new quizzes and learning content according to the progress of reskilling. For example, the quiz generation unit builds a system in which a generation AI dynamically generates new quizzes and learning content according to the progress of reskilling. For example, the quiz generation unit provides the next task to be tackled according to the user's progress. The quiz generation unit can also provide appropriate learning content based on the user's progress in reskilling. The quiz generation unit can also generate new quizzes according to the user's progress. In this way, efficient skill acquisition can be supported by dynamically generating new quizzes and learning content according to the progress of reskilling.

[0044] The quiz generation unit can provide reskilling quizzes that are customized for each industry or occupation. The quiz generation unit, for example, builds a system that provides reskilling quizzes that are customized for each industry or occupation. For example, quizzes for the IT industry or the medical industry are provided. The quiz generation unit can also provide appropriate quizzes based on the user's occupation. The quiz generation unit can also provide appropriate quizzes based on the user's industry. In this way, by providing reskilling quizzes that are customized for each industry or occupation, efficient skill acquisition can be supported.

[0045] The quiz generation unit can provide support for certification exams and qualification acquisition to evaluate the results of reskilling. The quiz generation unit, for example, builds a system that provides support for certification exams and qualification acquisition to evaluate the results of reskilling. For example, a certification exam is administered to users who have mastered a specific skill. The quiz generation unit can also provide support for appropriate qualification acquisition based on the results of the user's reskilling. The quiz generation unit can also provide strategies for certification exams based on the user's progress in reskilling. In this way, the effectiveness of learning can be improved by providing support for certification exams and qualification acquisition to evaluate the results of reskilling.

[0046] The quiz generation unit can generate quizzes offline, making them usable in areas with unstable internet connections. The quiz generation unit, for example, builds a system that enables the generation AI to generate quizzes offline. For example, the quiz generation unit generates quizzes based on data downloaded in advance. The quiz generation unit can also provide a method for saving data offline so that the quiz can be used in areas with unstable internet connections. The quiz generation unit can also provide functions that are intended for offline use. This allows quizzes to be generated offline, allowing students to continue learning even in areas with unstable internet connections.

[0047] The quiz generation unit can further reduce costs by distributing the computational resources required for generating quizzes. The quiz generation unit, for example, builds a system for distributing the computational resources required for generating quizzes, thereby reducing costs. For example, cloud computing is used to distribute the computational resources. The quiz generation unit can also introduce distributed computing technology to improve the efficiency of the computational resources. The quiz generation unit can also use clustering technology to reduce the operating costs of the computational resources. In this way, by distributing the computational resources required for generating quizzes, costs can be reduced and efficient quiz generation can be achieved.

[0048] The quiz generation unit can provide a quiz service using the generation AI in partnership with educational institutions and companies, making it accessible to a wide range of users. The quiz generation unit, for example, builds a system that provides a quiz service using the generation AI in partnership with educational institutions and companies. For example, the quiz service is introduced into training programs at schools and companies. The quiz generation unit can also make the quiz service accessible to a wide range of users by partnering with educational institutions and companies. The quiz generation unit can also meet the needs of educational institutions and companies by providing a quiz service using the generation AI. In this way, the quiz service using the generation AI can be made accessible to a wide range of users by partnering with educational institutions and companies.

[0049] The quiz generation unit can make the generation and provision of quizzes available on various platforms as mobile apps or web apps. The quiz generation unit, for example, builds a system that makes the generation and provision of quizzes available on various platforms as mobile apps or web apps. For example, the quiz can be made available on smartphones or tablets. The quiz generation unit can also provide the quiz as a web app so that users can access the quiz through a browser. The quiz generation unit can also provide the quiz as a mobile app so that users can take the quiz anytime, anywhere. In this way, by making the generation and provision of quizzes available on various platforms as mobile apps or web apps, user access can be improved.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The quiz generation unit can generate quizzes specialized in a specific theme based on the user's interests and concerns. For example, if the user is interested in history, many quizzes related to history can be provided. Also, if the user is interested in science, quizzes related to science can be provided. Furthermore, the quiz generation unit can generate quizzes related to a specific theme based on the user's interests. This can enhance the effectiveness of learning by providing quizzes specialized in a specific theme based on the user's interests and concerns.

[0052] The quiz generation unit can customize the quiz format according to the user's learning style. For example, a quiz containing many images and diagrams can be provided to a visual learner. Also, a quiz containing audio can be provided to an auditory learner. Furthermore, the quiz generation unit can provide a quiz in an appropriate format based on the user's learning style. In this way, the effectiveness of learning can be improved by customizing the quiz format according to the user's learning style.

[0053] The quiz generation unit can visualize the learning progress based on the user's learning history. For example, the user's learning status can be displayed using graphs or charts. The quiz generation unit can also evaluate the learning progress based on the user's learning history. Furthermore, the quiz generation unit can also suggest the next quiz to tackle based on the user's learning history. In this way, efficient learning can be supported by visualizing the learning progress based on the user's learning history.

[0054] The quiz generation unit can provide a customized study plan according to the user's learning goals. For example, a study plan for passing a specific exam can be provided. A study plan for acquiring a new skill can also be provided. Furthermore, the quiz generation unit can provide an appropriate study plan based on the user's learning goals. This makes it possible to support efficient learning by providing a study plan customized according to the user's learning goals.

[0055] The quiz generation unit can evaluate the effectiveness of learning based on the user's learning history. For example, it analyzes the user's correct answer rate and answer time to evaluate the progress of learning. The quiz generation unit can also provide feedback on the effectiveness of learning based on the user's learning history. Furthermore, the quiz generation unit can also suggest the next quiz to tackle based on the user's learning history. In this way, efficient learning can be supported by evaluating the effectiveness of learning based on the user's learning history.

[0056] The quiz generation unit can compare the user's learning progress with that of other users based on the user's learning history. For example, the quiz generation unit can compare the user's learning progress with that of other users who have solved quizzes in the same category. The quiz generation unit can also display a ranking based on the user's learning history. Furthermore, the quiz generation unit can also promote competition with other users based on the user's learning history. This can increase motivation to study by comparing the user's learning progress with that of other users based on the user's learning history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The quiz generation unit generates a quiz based on the category and difficulty level. For example, the generation AI generates a quiz based on the category and difficulty level selected by the user. The generation AI generates a quiz using a text generation AI (e.g., LLM). The generation AI can also generate quiz content using a multimodal generation AI. The generation AI can also generate related questions based on the user's selection. For example, when the generation AI selects a category such as "Basics of Mathematics" or "Intermediate History," it generates questions related to that category. Step 2: The providing unit provides the quiz generated by the quiz generating unit to the user. For example, the providing unit provides the generated quiz to the user through a web application or a mobile application. The providing unit can also send the generated quiz to the user by email. The providing unit can also print the generated quiz using a printer and provide it to the user. Step 3: The gamification section provides a point system and ranking function. For example, the gamification section adds points for each correct answer to a quiz, and when a certain number of points are reached, the user's rank increases. The gamification section also introduces a ranking function, allowing users to enjoy competition with other users. The gamification section can also provide special rewards and badges according to the user's learning progress.

[0059] (Example 2) A learning support system according to an embodiment of the present invention uses a generative AI to provide relevant quizzes by category and difficulty level, supporting the learning and reskilling of students and working adults. This system achieves low-cost and efficient learning by using a generative AI to generate quizzes and provide them to users. Furthermore, by incorporating gamification elements, the system provides an environment that is easy for even beginners to participate in and promotes continuous learning. This allows students and working adults to efficiently and continuously advance their learning. For example, students can use quizzes to supplement their school classes, and working adults can acquire new skills as part of reskilling. Furthermore, gamification elements make learning fun and make it easier to maintain motivation. This improves the effectiveness of learning and supports individual growth.

[0060] A learning support system according to an embodiment includes a quiz generation unit, a providing unit, and a gamification unit. The quiz generation unit generates a quiz based on a category and difficulty level. For example, the generation AI generates a quiz based on a category and difficulty level selected by a user. The generation AI generates a quiz using a text generation AI (e.g., LLM). The generation AI can also generate quiz content using a multimodal generation AI. The generation AI can also generate related questions based on a user's selection. For example, when a user selects a category such as "Basics of Mathematics" or "Intermediate History," the generation AI generates questions related to that category. The providing unit provides the quiz generated by the quiz generation unit to the user. For example, the providing unit provides the generated quiz to the user through a web application or a mobile application. The providing unit can also send the generated quiz to the user by email. The providing unit can also print and provide the generated quiz. The gamification unit provides a point system and a ranking function. For example, the gamification unit adds points each time a user answers a quiz correctly, and when a user reaches a certain number of points, their rank increases. The gamification section also introduces a ranking function, allowing users to enjoy competition with other users. The gamification section can also provide special rewards and badges according to the user's learning progress. This allows the learning support system to generate quizzes based on category and difficulty and provide them to users, thereby achieving efficient learning. The gamification element can also increase motivation for learning.

[0061] The quiz generation unit can analyze the user's past answer history and generate quizzes optimized for each individual user based on the correct answer rate and answer time. For example, the quiz generation unit uses a generation AI to analyze the user's past answer history in detail and generate quizzes optimized for each individual user based on data such as the correct answer rate and answer time. For example, the quiz generation unit provides a quiz that includes many questions in areas in which the user is weak. The quiz generation unit can also grasp the user's learning progress based on the user's answer history and suggest the next quiz to tackle. This makes it possible to support efficient learning by analyzing the user's past answer history and providing optimized quizzes.

[0062] The quiz generation unit can monitor the user's answering speed and correct answer rate in real time and dynamically adjust the difficulty level. For example, the quiz generation unit uses a generation AI to monitor the user's answering speed and correct answer rate in real time and dynamically adjust the difficulty level. For example, if the user answers quickly and accurately, the difficulty level of the next question can be increased. Also, if the user takes a long time to answer and has a low correct answer rate, the difficulty level of the next question can be lowered. The quiz generation unit can also provide a quiz of an appropriate level of difficulty based on the user's answering speed and correct answer rate. In this way, by monitoring the user's answering speed and correct answer rate in real time and dynamically adjusting the difficulty level, a quiz of an appropriate level of difficulty can be provided.

[0063] The quiz generation unit uses the emotion estimation function to generate quizzes that correspond to the user's emotional state, thereby reducing stress. The quiz generation unit uses, for example, the emotion estimation function to analyze the user's facial expressions and voice and estimate the user's emotional state in real time. For example, if the user is feeling stressed, a quiz with a lower level of difficulty can be provided. Also, if the user is relaxed, a quiz with a higher level of difficulty can be provided. The quiz generation unit can also provide an appropriate quiz based on the user's emotional state. In this way, by using the emotion estimation function to provide a quiz that corresponds to the user's emotional state, stress can be reduced and learning effectiveness can be improved.

[0064] The quiz generation unit can diversify the quiz format and generate multiple choice questions, written questions, and image recognition questions. For example, the quiz generation unit generates quizzes in various formats, such as multiple choice questions, written questions, and image recognition questions, using a generation AI. For example, if a user gets bored with multiple choice questions, the quiz generation unit can provide written questions. Also, if a user is struggling with written questions, the quiz generation unit can provide multiple choice questions. The quiz generation unit can also provide a quiz in an appropriate format based on the user's selection. In this way, diversifying the quiz format can keep the user interested and improve the effectiveness of learning.

[0065] The quiz generation unit can generate quizzes in different languages ​​to provide a multilingual learning environment. For example, the generation AI generates quizzes in different languages ​​to provide a multilingual learning environment. For example, quizzes are generated in multiple languages, such as English, French, and Chinese. The quiz generation unit can also provide quizzes in an appropriate language based on a user's selection. In this way, by generating quizzes in different languages, a multilingual learning environment can be provided, and international users can be accommodated.

[0066] The quiz generation unit can use the emotion estimation function to generate a quiz based on a topic that the user is most interested in. For example, the quiz generation unit uses the emotion estimation function to identify a topic that the user is most interested in and generate a quiz based on that topic. For example, the quiz generation unit provides questions related to the topic that the user is interested in. The quiz generation unit can also select an appropriate topic based on the user's emotion data and provide a quiz based on that topic. This can improve the effectiveness of learning by providing a quiz based on a topic that the user is most interested in using the emotion estimation function.

[0067] The gamification unit can provide special rewards and badges according to the user's learning progress, thereby enhancing the user's sense of accomplishment. The gamification unit, for example, builds a system that provides special rewards and badges according to the user's learning progress. For example, a special badge is awarded to a user who has reached a certain number of points. The gamification unit can also visualize the user's learning progress, enhancing the user's sense of accomplishment. The gamification unit can also provide special rewards according to the user's learning progress. In this way, by providing special rewards and badges according to the user's learning progress, the user's sense of accomplishment can be enhanced and motivation for learning can be maintained.

[0068] The gamification unit can set different types of points and rewards depending on the difficulty of the quiz, thereby encouraging the user's willingness to take on the challenge. The gamification unit, for example, builds a system that sets different types of points and rewards depending on the difficulty of the quiz. For example, if a user answers a difficult quiz correctly, more points can be earned. The gamification unit can also provide rewards depending on the difficulty to encourage the user's willingness to take on the challenge. The gamification unit can also set different types of points to encourage the user's willingness to take on the challenge. In this way, by setting different types of points and rewards depending on the difficulty of the quiz, the user's willingness to take on the challenge can be increased.

[0069] The gamification unit can use the emotion estimation function to provide an encouraging message or a special reward when the user's motivation drops. The gamification unit, for example, uses the emotion estimation function to build a system that provides an encouraging message when the user's motivation drops. For example, an encouraging message is displayed when the user is feeling stressed. The gamification unit can also provide a special reward when the user's motivation drops. The gamification unit can also provide appropriate feedback based on the user's emotion data. This makes it possible to support continued learning by using the emotion estimation function to provide an encouraging message or a special reward when the user's motivation drops.

[0070] The gamification unit can introduce a team battle mode in which users cooperate with each other to take quizzes. The gamification unit, for example, builds a system that introduces a team battle mode in which users cooperate with each other to take quizzes. For example, it provides a format in which each team competes for points. The gamification unit can also increase motivation to learn by having users cooperate with each other to take quizzes. The gamification unit can also promote cooperation between users by introducing the team battle mode. Thus, by introducing the team battle mode in which users cooperate with each other to take quizzes, motivation to learn can be increased.

[0071] The gamification unit can add an element that allows a user to develop a virtual character based on the results of a quiz. The gamification unit, for example, builds a system that adds an element that allows a user to develop a virtual character based on the results of a quiz. For example, the character grows with each correct answer to a quiz. The gamification unit can also enhance the character's skills and abilities according to the user's learning progress. The gamification unit can also customize the character's appearance and equipment by having the user take the quiz. In this way, adding an element that allows a user to develop a virtual character based on the results of a quiz can make learning more enjoyable and increase motivation.

[0072] The gamification unit can use the emotion estimation function to identify game elements that the user enjoys most and incorporate them into a quiz. The gamification unit, for example, uses the emotion estimation function to identify game elements that the user enjoys most and builds a system that incorporates them into a quiz. For example, the gamification unit selects game elements based on emotion data of the user when they are enjoying themselves. The gamification unit can also incorporate appropriate game elements into a quiz based on the user's emotion data. The gamification unit can also increase the enjoyment of learning and enhance motivation by identifying game elements that the user enjoys most and incorporating them into a quiz. In this way, the enjoyment of learning can be increased and motivation can be enhanced by using the emotion estimation function to identify game elements that the user enjoys most and incorporating them into a quiz.

[0073] The quiz generation unit can analyze the user's learning history in detail and automatically generate an individual learning plan. For example, the quiz generation unit builds a system in which a generation AI analyzes the user's learning history in detail and automatically generates an individual learning plan. For example, the learning plan is customized based on the user's strengths and weaknesses. The quiz generation unit can also suggest appropriate learning goals and schedules based on the user's learning history. The quiz generation unit can also select optimal learning materials based on the user's learning history. In this way, efficient learning can be supported by analyzing the user's learning history in detail and automatically generating an individual learning plan.

[0074] The quiz generation unit can visualize the user's strengths and weaknesses based on the learning history and suggest specific improvement measures. The quiz generation unit, for example, builds a system that visualizes the user's strengths and weaknesses based on the learning history. For example, the quiz generation unit visualizes the user's learning status using graphs or charts. The quiz generation unit can also suggest specific improvement measures based on the user's strengths and weaknesses. The quiz generation unit can also provide appropriate feedback based on the user's learning history. In this way, efficient learning can be supported by visualizing the user's strengths and weaknesses based on the learning history and suggesting specific improvement measures.

[0075] The quiz generation unit can use the emotion estimation function to analyze emotional fluctuations from the user's learning history and provide feedback to maintain learning motivation. The quiz generation unit, for example, uses the emotion estimation function to build a system that analyzes emotional fluctuations from the user's learning history. For example, emotional data during learning is collected and motivation fluctuations are visualized. The quiz generation unit can also provide appropriate feedback based on the user's emotional data. The quiz generation unit can also provide advice or words of encouragement to maintain learning motivation based on the user's emotional fluctuations. In this way, by using the emotion estimation function to analyze emotional fluctuations from the user's learning history and providing feedback to maintain learning motivation, the effectiveness of learning can be improved.

[0076] The quiz generation unit can link the learning history with other learning platforms and provide comprehensive learning data. The quiz generation unit, for example, links the learning history with other learning platforms and builds a system that provides comprehensive learning data. For example, data from multiple platforms is integrated and centrally managed. The quiz generation unit can also provide comprehensive learning data based on the user's learning history. The quiz generation unit can also improve the user's learning efficiency by linking the learning history with other learning platforms. In this way, the user's learning efficiency can be improved by linking the learning history with other learning platforms and providing comprehensive learning data.

[0077] The quiz generation unit can recommend learning communities and forums that are suitable for the user based on the learning history. The quiz generation unit, for example, builds a system that recommends learning communities and forums that are suitable for the user based on the learning history. For example, it proposes a community where users with the same interests gather. The quiz generation unit can also recommend an appropriate forum based on the user's learning history. The quiz generation unit can also increase learning motivation by recommending learning communities and forums based on the user's learning history. In this way, learning motivation can be increased by recommending learning communities and forums that are suitable for the user based on the learning history.

[0078] The quiz generation unit can use the emotion estimation function to provide emotional support based on the user's learning history. The quiz generation unit, for example, uses the emotion estimation function to build a system that provides emotional support based on the user's learning history. For example, an encouraging message is displayed based on emotion data during learning. The quiz generation unit can also provide appropriate support based on the user's emotion data. The quiz generation unit can also maintain learning motivation by providing emotional support based on the user's learning history. In this way, learning motivation can be maintained by using the emotion estimation function to provide emotional support based on the user's learning history.

[0079] The quiz generation unit can analyze the user's occupational history and skill set and propose an optimal reskilling plan. The quiz generation unit, for example, builds a system in which a generation AI analyzes the user's occupational history and skill set in detail and proposes an optimal reskilling plan. For example, it proposes new skills based on the user's past work experience. The quiz generation unit can also propose an appropriate reskilling plan based on the user's skill set. The quiz generation unit can also support efficient skill acquisition by proposing an optimal reskilling plan based on the user's occupational history. In this way, efficient skill acquisition can be supported by analyzing the user's occupational history and skill set and proposing an optimal reskilling plan.

[0080] The quiz generation unit can dynamically generate new quizzes and learning content according to the progress of reskilling. For example, the quiz generation unit builds a system in which a generation AI dynamically generates new quizzes and learning content according to the progress of reskilling. For example, the quiz generation unit provides the next task to be tackled according to the user's progress. The quiz generation unit can also provide appropriate learning content based on the user's progress in reskilling. The quiz generation unit can also generate new quizzes according to the user's progress. In this way, efficient skill acquisition can be supported by dynamically generating new quizzes and learning content according to the progress of reskilling.

[0081] The quiz generation unit can use the emotion estimation function to monitor the stress level of a user during reskilling and provide appropriate support. The quiz generation unit, for example, uses the emotion estimation function to build a system that monitors the stress level of a user during reskilling. For example, the quiz generation unit analyzes the user's facial expressions and voice to estimate the stress level in real time. The quiz generation unit can also provide appropriate support based on the user's stress level. The quiz generation unit can also provide advice or words of encouragement to reduce stress based on the user's emotion data. In this way, the effectiveness of learning can be improved by monitoring the user's stress level during reskilling using the emotion estimation function and providing appropriate support.

[0082] The quiz generation unit can provide reskilling quizzes that are customized for each industry or occupation. The quiz generation unit, for example, builds a system that provides reskilling quizzes that are customized for each industry or occupation. For example, quizzes for the IT industry or the medical industry are provided. The quiz generation unit can also provide appropriate quizzes based on the user's occupation. The quiz generation unit can also provide appropriate quizzes based on the user's industry. In this way, by providing reskilling quizzes that are customized for each industry or occupation, efficient skill acquisition can be supported.

[0083] The quiz generation unit can provide support for certification exams and qualification acquisition to evaluate the results of reskilling. The quiz generation unit, for example, builds a system that provides support for certification exams and qualification acquisition to evaluate the results of reskilling. For example, a certification exam is administered to users who have mastered a specific skill. The quiz generation unit can also provide support for appropriate qualification acquisition based on the results of the user's reskilling. The quiz generation unit can also provide strategies for certification exams based on the user's progress in reskilling. In this way, the effectiveness of learning can be improved by providing support for certification exams and qualification acquisition to evaluate the results of reskilling.

[0084] The quiz generation unit can use the emotion estimation function to identify the area in which the user is most interested during the reskilling process and provide quizzes specialized in that area. The quiz generation unit, for example, uses the emotion estimation function to build a system for identifying the area in which the user is most interested during the reskilling process. For example, the quiz generation unit identifies an area of ​​interest based on the user's emotion data. The quiz generation unit can also provide quizzes specialized in an appropriate area based on the user's emotion data. The quiz generation unit can also provide quizzes appropriate during the reskilling process based on the user's interests. In this way, the learning effect can be improved by using the emotion estimation function to identify the area in which the user is most interested during the reskilling process and providing quizzes specialized in that area.

[0085] The quiz generation unit can generate quizzes offline, making them usable in areas with unstable internet connections. The quiz generation unit, for example, builds a system that enables the generation AI to generate quizzes offline. For example, the quiz generation unit generates quizzes based on data downloaded in advance. The quiz generation unit can also provide a method for saving data offline so that the quiz can be used in areas with unstable internet connections. The quiz generation unit can also provide functions that are intended for offline use. This allows quizzes to be generated offline, allowing students to continue learning even in areas with unstable internet connections.

[0086] The quiz generation unit can further reduce costs by distributing the computational resources required for generating quizzes. The quiz generation unit, for example, builds a system for distributing the computational resources required for generating quizzes, thereby reducing costs. For example, cloud computing is used to distribute the computational resources. The quiz generation unit can also introduce distributed computing technology to improve the efficiency of the computational resources. The quiz generation unit can also use clustering technology to reduce the operating costs of the computational resources. In this way, by distributing the computational resources required for generating quizzes, costs can be reduced and efficient quiz generation can be achieved.

[0087] The quiz generation unit uses the emotion estimation function to provide a study plan that matches the user's financial situation, thereby optimizing cost performance. The quiz generation unit, for example, uses the emotion estimation function to build a system that provides a study plan that matches the user's financial situation. For example, the quiz generation unit proposes a plan that reduces the financial burden based on the user's emotional data. The quiz generation unit can also provide an appropriate study plan based on the user's financial situation. The quiz generation unit can also provide a plan that optimizes cost performance based on the user's financial situation. In this way, cost performance can be optimized by using the emotion estimation function to provide a study plan that matches the user's financial situation.

[0088] The quiz generation unit can provide a quiz service using the generation AI in partnership with educational institutions and companies, making it accessible to a wide range of users. The quiz generation unit, for example, builds a system that provides a quiz service using the generation AI in partnership with educational institutions and companies. For example, the quiz service is introduced into training programs at schools and companies. The quiz generation unit can also make the quiz service accessible to a wide range of users by partnering with educational institutions and companies. The quiz generation unit can also meet the needs of educational institutions and companies by providing a quiz service using the generation AI. In this way, the quiz service using the generation AI can be made accessible to a wide range of users by partnering with educational institutions and companies.

[0089] The quiz generation unit can make the generation and provision of quizzes available on various platforms as mobile apps or web apps. The quiz generation unit, for example, builds a system that makes the generation and provision of quizzes available on various platforms as mobile apps or web apps. For example, the quiz can be made available on smartphones or tablets. The quiz generation unit can also provide the quiz as a web app so that users can access the quiz through a browser. The quiz generation unit can also provide the quiz as a mobile app so that users can take the quiz anytime, anywhere. In this way, by making the generation and provision of quizzes available on various platforms as mobile apps or web apps, user access can be improved.

[0090] The quiz generation unit can use the emotion estimation function to propose an optimal pricing plan based on the user's usage status. The quiz generation unit, for example, uses the emotion estimation function to build a system that proposes an optimal pricing plan based on the user's usage status. For example, the pricing plan is customized based on the user's emotion data. The quiz generation unit can also propose an appropriate pricing plan based on the user's usage status. The quiz generation unit can also provide a pricing plan that improves cost performance according to the user's usage status. In this way, cost performance can be improved by using the emotion estimation function to propose an optimal pricing plan based on the user's usage status.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The quiz generation unit can generate quizzes specialized in a specific theme based on the user's interests and concerns. For example, if the user is interested in history, many quizzes related to history can be provided. Also, if the user is interested in science, quizzes related to science can be provided. Furthermore, the quiz generation unit can generate quizzes related to a specific theme based on the user's interests. This can enhance the effectiveness of learning by providing quizzes specialized in a specific theme based on the user's interests and concerns.

[0093] The quiz generation unit can customize the quiz format according to the user's learning style. For example, a quiz containing many images and diagrams can be provided to a visual learner. Also, a quiz containing audio can be provided to an auditory learner. Furthermore, the quiz generation unit can provide a quiz in an appropriate format based on the user's learning style. In this way, the effectiveness of learning can be improved by customizing the quiz format according to the user's learning style.

[0094] The quiz generation unit can use the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user is feeling anxious about the quiz, an encouraging message can be displayed. Alternatively, if the user is confident about the quiz, a praising message can be displayed. Furthermore, the quiz generation unit can provide appropriate feedback based on the user's emotional state. In this way, by using the emotion estimation function to provide feedback according to the user's emotional state, it is possible to increase motivation for learning.

[0095] The quiz generation unit can visualize the learning progress based on the user's learning history. For example, the user's learning status can be displayed using graphs or charts. The quiz generation unit can also evaluate the learning progress based on the user's learning history. Furthermore, the quiz generation unit can also suggest the next quiz to tackle based on the user's learning history. In this way, efficient learning can be supported by visualizing the learning progress based on the user's learning history.

[0096] The quiz generation unit can use the emotion estimation function to provide a quiz that corresponds to the user's stress level. For example, if the user is feeling high stress, an easy quiz that will help the user relax can be provided. Alternatively, if the user is relaxed, a challenging quiz can be provided. Furthermore, the quiz generation unit can provide an appropriate quiz based on the user's stress level. In this way, by using the emotion estimation function to provide a quiz that corresponds to the user's stress level, the effectiveness of learning can be improved.

[0097] The quiz generation unit can provide a customized study plan according to the user's learning goals. For example, a study plan for passing a specific exam can be provided. A study plan for acquiring a new skill can also be provided. Furthermore, the quiz generation unit can provide an appropriate study plan based on the user's learning goals. This makes it possible to support efficient learning by providing a study plan customized according to the user's learning goals.

[0098] The quiz generator can use the emotion estimation function to provide special rewards or incentives when the user's motivation drops. For example, if the user loses motivation, it can provide special badges or points. Also, if the user maintains motivation, it can provide additional rewards. Furthermore, the quiz generator can provide appropriate incentives based on the user's emotional state. In this way, by using the emotion estimation function to provide special rewards or incentives when the user's motivation drops, it is possible to support continued learning.

[0099] The quiz generation unit can evaluate the effectiveness of learning based on the user's learning history. For example, it analyzes the user's correct answer rate and answer time to evaluate the progress of learning. The quiz generation unit can also provide feedback on the effectiveness of learning based on the user's learning history. Furthermore, the quiz generation unit can also suggest the next quiz to tackle based on the user's learning history. In this way, efficient learning can be supported by evaluating the effectiveness of learning based on the user's learning history.

[0100] The quiz generation unit can use the emotion estimation function to monitor changes in the user's emotions while studying and suggest breaks at appropriate times. For example, if the user is tired, a message suggesting a break can be displayed. Also, if the user is losing concentration, a short break can be suggested. Furthermore, the quiz generation unit can suggest breaks at appropriate times based on the user's emotional state. In this way, by using the emotion estimation function to monitor changes in the user's emotions while studying and suggesting breaks at appropriate times, the effectiveness of learning can be improved.

[0101] The quiz generation unit can compare the user's learning progress with that of other users based on the user's learning history. For example, the quiz generation unit can compare the user's learning progress with that of other users who have solved quizzes in the same category. The quiz generation unit can also display a ranking based on the user's learning history. Furthermore, the quiz generation unit can also promote competition with other users based on the user's learning history. This can increase motivation to study by comparing the user's learning progress with that of other users based on the user's learning history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The quiz generation unit generates a quiz based on the category and difficulty level. For example, the generation AI generates a quiz based on the category and difficulty level selected by the user. The generation AI generates a quiz using a text generation AI (e.g., LLM). The generation AI can also generate quiz content using a multimodal generation AI. The generation AI can also generate related questions based on the user's selection. For example, when the generation AI selects a category such as "Basics of Mathematics" or "Intermediate History," it generates questions related to that category. Step 2: The providing unit provides the quiz generated by the quiz generating unit to the user. For example, the providing unit provides the generated quiz to the user through a web application or a mobile application. The providing unit can also send the generated quiz to the user by email. The providing unit can also print the generated quiz using a printer and provide it to the user. Step 3: The gamification section provides a point system and ranking function. For example, the gamification section adds points for each correct answer to a quiz, and when a certain number of points are reached, the user's rank increases. The gamification section also introduces a ranking function, allowing users to enjoy competition with other users. The gamification section can also provide special rewards and badges according to the user's learning progress.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 quiz generation unit that generates quizzes based on categories and difficulty levels; a providing unit that provides the quiz generated by the quiz generating unit to a user; It has a gamification section that provides a point system and ranking functions. A system characterized by:

2. The quiz generation unit Generate a quiz according to the user's emotional state to reduce stress.

2. The system of claim 1.

3. The quiz generation unit Generate quizzes in different languages ​​to provide a multilingual learning environment 2. The system of claim 1.

4. The gamification unit Providing special rewards and badges according to the user's learning progress, enhancing the sense of accomplishment 2. The system of claim 1.

5. The quiz generation unit Analyze the user's learning history in detail and automatically generate an individual learning plan.

2. The system of claim 1.

6. The quiz generation unit Monitor the stress level of the user during reskilling and provide appropriate support 2. The system of claim 1.

7. The quiz generation unit The quiz can be generated offline, making it usable in areas with poor internet connectivity.

2. The system of claim 1.

8. The quiz generation unit Propose the most suitable pricing plan based on the user's usage status 2. The system of claim 1.

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