System

Abstract

An object of a system according to an embodiment is to efficiently provide learning content for individually optimized reskilling.SOLUTION: A system according to an embodiment includes a generation AI, a learning history analysis unit, a schedule management unit, and a problem generation unit. The generative AI analyzes the user's learning history, interests, and skill level. The learning history analysis unit analyzes the user's learning history, interest, and skill level. The schedule management unit manages a schedule of the user on the basis of the information analyzed by the learning history analysis unit. The problem generation unit automatically generates problems, explanations, and specific examples suitable for the user based on the schedule managed by the schedule management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to efficiently provide individually optimized learning content for reskilling.

[0005] The system according to the embodiment aims to efficiently provide learning content for individually optimized reskilling. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a learning history analysis unit, a schedule management unit, and a question generation unit. The generation AI analyzes the user's learning history, interests, and skill level. The learning history analysis unit analyzes the user's learning history, interests, and skill level. The schedule management unit manages the user's schedule based on the information analyzed by the learning history analysis unit. The question generation unit automatically creates questions, explanations, and specific examples suitable for the user based on the schedule managed by the schedule management unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide learning content for individually optimized 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 reskilling system according to an embodiment of the present invention is a system that uses generative AI to provide individually optimized reskilling. This reskilling system analyzes a user's learning history, interests, and skill level, and provides individually optimized learning content based on the analysis. This allows the reskilling system to provide individually optimized learning content based on the user's learning history, interests, and skill level.

[0029] A reskilling system according to an embodiment includes a learning history analysis unit, a schedule management unit, and a question generation unit. The learning history analysis unit analyzes a user's learning history, interests, and skill level. For example, the learning history analysis unit collects the user's past learning data and evaluates their learning progress and level of understanding. The learning history analysis unit can also identify the user's interests and customize learning content based on the identified interests. The learning history analysis unit evaluates the user's skill level and selects appropriate learning materials and questions. The schedule management unit manages the user's schedule based on the information analyzed by the learning history analysis unit. For example, the schedule management unit optimally allocates the user's study time and break time to create an efficient learning schedule. The schedule management unit can also provide content that allows the user to study during their free time. The schedule management unit analyzes the user's daily rhythm and smart device usage patterns and suggests optimal study timing. The question generation unit automatically generates questions, explanations, and specific examples appropriate for the user based on the schedule managed by the schedule management unit. For example, the question generation unit provides questions and explanations individually tailored to the user's learning progress. The question generator can also analyze the user's answer patterns and provide focused explanations on points where the user is likely to make mistakes. Furthermore, the question generator can analyze the user's learning history and automatically generate review questions based on past mistakes. This allows the reskilling system according to the embodiment to provide individually optimized learning content based on the user's learning history, interests, and skill level. For example, if a user wants to learn programming, the generation AI evaluates the user's current skill level and automatically selects appropriate learning materials and questions. Furthermore, providing content that allows the user to study in their spare time allows for efficient learning. Furthermore, the system supports users in deepening their understanding by automatically creating individually tailored questions, explanations, and concrete examples according to their learning progress.

[0030] The learning history analysis unit can evaluate the user's current skill level and automatically select appropriate learning materials or problems. The learning history analysis unit, for example, analyzes the user's past learning data and evaluates the user's current skill level. For example, the learning history analysis unit determines the skill level based on the user's test results and self-assessment. The learning history analysis unit can also identify the user's interests and select appropriate learning materials and problems based on those interests. For example, if the user is interested in programming, the learning history analysis unit automatically selects learning materials and problems related to programming. This makes it possible to provide the optimal learning materials and problems according to the user's skill level.

[0031] The schedule management unit can provide learning content that allows users to study during their spare time. The schedule management unit, for example, analyzes the user's schedule and identifies spare time. For example, the schedule management unit provides content that allows users to study during their commute or break time. The schedule management unit also generates learning modules that can be completed in a short time, allowing users to study efficiently. For example, the schedule management unit provides learning modules that can be completed in about 5 to 10 minutes. This allows users to effectively utilize their spare time to study.

[0032] The question generator can automatically create questions, explanations, and specific examples that are individually tailored to the user's learning progress. For example, the question generator can analyze the user's learning progress in real time and create questions that are individually tailored. For example, if the user is struggling with a particular topic, the question generator can provide specific examples and explanations related to that topic. The question generator can also analyze the user's answer patterns and focus on points where the user is likely to make mistakes. For example, the question generator can provide detailed explanations and supplementary materials for questions on which the user frequently makes mistakes. Furthermore, the question generator can analyze the user's learning history and automatically generate review questions based on past mistakes. For example, the question generator can re-ask questions that the user made mistakes on in the past to deepen understanding. This makes it possible to provide optimal questions, explanations, and specific examples that are tailored to the user's learning progress.

[0033] The schedule management unit can compare the user's past learning data with their current performance and automatically adjust the optimal learning pace. For example, the schedule management unit analyzes the user's past learning data and compares it with their current performance. For example, the schedule management unit sets the optimal learning pace based on their past learning speed and level of understanding. The schedule management unit can also monitor the user's learning progress in real time and adjust the learning pace as needed. For example, if the user is taking a long time to understand, the pace can be slowed down. Furthermore, the schedule management unit can automatically generate an optimal learning schedule for the user based on their past learning data and current performance. For example, it can set weekly learning goals and adjust them according to progress. This optimizes the user's learning pace and supports efficient learning.

[0034] The question generator can analyze a user's learning style and provide optimal learning content based on that. For example, the question generator can analyze a user's learning style and provide visual content to a user who prefers visual learning. For example, the question generator can generate learning materials that make extensive use of diagrams and graphs. The question generator can also provide learning content in the form of audio commentary or podcasts to a user who prefers auditory learning. For example, the question generator can generate learning materials that include audio commentary. Furthermore, the question generator can provide interactive learning content to a user who prefers tactile learning. For example, the question generator can generate questions and simulations that can be operated by drag and drop. This makes it possible to provide optimal learning content according to the user's learning style.

[0035] The learning history analysis unit can provide a composite reskilling program that combines skills from different fields. The learning history analysis unit, for example, analyzes a user's interests and career goals and provides a reskilling program that combines skills from different fields. For example, the learning history analysis unit generates teaching materials that combine programming and design skills. The learning history analysis unit can also suggest new career paths to users by combining skills from different fields. For example, the learning history analysis unit provides a program that combines marketing and data analysis skills. Furthermore, the learning history analysis unit can analyze a user's skill set and provide learning content for cultivating composite skills. For example, the learning history analysis unit generates teaching materials that combine business skills and technical skills. In this way, by providing a reskilling program that combines skills from different fields, it is possible to cultivate a diverse skill set for the user.

[0036] The learning history analysis unit can analyze the user's work history and suggest the optimal skill set for the career path. The learning history analysis unit can, for example, analyze the user's work history and suggest the optimal skill set for the career path. For example, the learning history analysis unit can suggest the next skill to acquire based on the user's past work experience. The learning history analysis unit can also suggest a promising skill set for the future based on the user's work history and current market needs. For example, the learning history analysis unit can create a learning plan based on skills that are in high demand. Furthermore, the learning history analysis unit can analyze the user's career goals and suggest the optimal skill set for those goals. For example, the learning history analysis unit can list the skills required for a specific occupation and provide a learning plan. This can effectively support the user's career path by suggesting the optimal skill set based on the user's work history.

[0037] The schedule management unit can analyze the user's smart device usage patterns and suggest the optimal timing for studying. The schedule management unit, for example, analyzes the user's smart device usage patterns and suggests the optimal timing for studying. For example, the schedule management unit sets a study schedule based on the frequency of device use and time of day. The schedule management unit can also identify spare time based on the user's smart device usage history and provide study content during that time. For example, the schedule management unit generates learning materials that can be used in a short amount of time during commuting or breaks. Furthermore, the schedule management unit can analyze the user's device usage patterns and suggest the optimal timing for studying in real time. For example, the schedule management unit sends study notifications based on the device usage status. This can support efficient study by suggesting the optimal timing for studying based on the user's smart device usage patterns.

[0038] The schedule management unit can analyze the user's lifestyle and automatically incorporate the most effective study time periods into the schedule. The schedule management unit, for example, analyzes the user's lifestyle and identifies the most effective study time periods. For example, the schedule management unit sets a study schedule based on sleep patterns and meal times. The schedule management unit can also automatically incorporate study time periods into the schedule based on the user's lifestyle. For example, the schedule management unit provides study content during morning commute time or evening relaxation time. Furthermore, the schedule management unit can monitor the user's lifestyle in real time and dynamically adjust the optimal study time periods. For example, the schedule management unit sends study notifications based on the user's activity level. This can support efficient study by suggesting optimal study time periods based on the user's lifestyle.

[0039] The schedule management unit can analyze the user's travel route and provide content that can be used to study while traveling. The schedule management unit, for example, analyzes the user's travel route and provides content that can be used to study while traveling. For example, the schedule management unit generates learning materials that can be used to study in a short amount of time based on the user's commute route and travel time. The schedule management unit can also provide optimal learning content based on the user's travel route. For example, the schedule management unit provides learning materials in the form of audio commentaries or podcasts that can be listened to while traveling. Furthermore, the schedule management unit can monitor the user's travel route in real time and dynamically provide content that can be used to study while traveling. For example, the schedule management unit sends learning notifications based on travel time. This makes it possible to support efficient learning by providing optimal learning content based on the user's travel route.

[0040] The schedule management unit can analyze a user's hobbies or interests and provide related learning content in their spare time. The schedule management unit, for example, analyzes a user's hobbies and interests and provides related learning content. For example, the schedule management unit generates learning materials that can be learned in a short amount of time on topics that interest the user. The schedule management unit can also provide content that can be used to study in the user's spare time based on the user's hobbies and interests. For example, the schedule management unit provides learning materials in the form of quizzes or mini-games related to the hobbies. Furthermore, the schedule management unit can analyze a user's hobbies and interests in real time and dynamically provide related learning content. For example, the schedule management unit sends learning notifications based on the user's interests. This can support efficient learning by providing optimal learning content based on the user's hobbies and interests.

[0041] The schedule management unit can analyze the user's social media activity and suggest related learning content. The schedule management unit, for example, analyzes the user's social media activity and suggests related learning content. For example, the schedule management unit generates learning materials based on the accounts the user follows and the content of their posts. The schedule management unit can also provide related learning content based on the user's social media activity. For example, the schedule management unit suggests articles and videos related to topics that interest the user. Furthermore, the schedule management unit can analyze the user's social media activity in real time and dynamically provide related learning content. For example, the schedule management unit sends learning notifications based on the content of the user's posts. This can support efficient learning by suggesting optimal learning content based on the user's social media activity.

[0042] The question generation unit can analyze the user's answer patterns and provide explanations that emphasize points where the user is likely to make mistakes. The question generation unit, for example, analyzes the user's answer patterns and identifies points where the user is likely to make mistakes. For example, the question generation unit extracts questions where the user frequently makes mistakes based on the user's past answer history. The question generation unit can also provide explanations that emphasize points where the user is likely to make mistakes. For example, the question generation unit generates detailed explanations and supplementary materials for questions where the user made a mistake. Furthermore, the question generation unit can analyze the user's answer patterns in real time and dynamically identify points where the user is likely to make mistakes. For example, the question generation unit instantly analyzes mistakes made while answering and provides explanations. This can support efficient learning by providing explanations that emphasize points where the user is likely to make mistakes based on the user's answer patterns.

[0043] The question generation unit can analyze the user's learning history and automatically generate review questions based on past mistakes. The question generation unit, for example, analyzes the user's learning history and automatically generates review questions based on past mistakes. For example, the question generation unit may re-ask questions that the user got wrong in the past to deepen understanding. The question generation unit can also identify topics that require review based on the user's learning history and generate questions related to those topics. For example, the question generation unit provides review questions related to a specific unit. Furthermore, the question generation unit can analyze the user's learning history in real time and automatically generate questions when review is necessary. For example, the question generation unit may ask review questions after a certain period of time has passed. In this way, efficient learning can be supported by automatically generating review questions based on the user's learning history.

[0044] The question generator can analyze the user's level of understanding in real time and provide concrete examples that complement areas where the user's understanding is shallow. For example, the question generator can analyze the user's answers and learning progress in real time and identify areas where the user's understanding is shallow. For example, the question generator can evaluate the level of understanding based on the accuracy rate of answers and the answer time. The question generator can also provide concrete examples for areas where the user's understanding is shallow. For example, the question generator generates teaching materials that explain abstract concepts using concrete examples. Furthermore, the question generator can analyze the user's level of understanding in real time and dynamically provide concrete examples that complement areas where the user's understanding is shallow. For example, the question generator can present concrete examples that instantly complement areas where the user's understanding is lacking while answering. This makes it possible to support efficient learning by providing concrete examples based on the user's level of understanding.

[0045] The question generation unit can combine questions from different learning fields to create questions that cultivate multi-disciplinary skills. For example, the question generation unit combines questions from different learning fields to create questions that cultivate multi-disciplinary skills. For example, the question generation unit generates a composite question that combines mathematics and physics questions. The question generation unit can also create questions that provide the user with a new perspective by combining skills from different learning fields. For example, the question generation unit provides a question that combines programming and data analysis skills. Furthermore, the question generation unit can analyze the user's learning history and dynamically generate questions to cultivate multi-disciplinary skills. For example, the question generation unit poses questions that integrate knowledge from different fields. In this way, multi-disciplinary skills can be cultivated by combining questions from different learning fields.

[0046] The question generation unit can automatically generate practical questions related to the user's occupation and cultivate practical skills. The question generation unit, for example, automatically generates practical questions related to the user's occupation. For example, the question generation unit provides engineers with questions that simulate problems that arise in actual projects. The question generation unit can also generate questions for cultivating practical skills based on the user's occupation. For example, the question generation unit provides marketing personnel with questions on market analysis and strategy planning. Furthermore, the question generation unit can analyze the user's occupational history and dynamically generate questions for cultivating practical skills. For example, the question generation unit presents practical questions based on the user's past work experience. In this way, practical skills can be cultivated by automatically generating practical questions related to the user's occupation.

[0047] The learning history analysis unit can analyze the user's learning environment and suggest an optimal learning environment. The learning history analysis unit, for example, analyzes the user's learning environment and suggests an optimal learning environment. For example, the learning history analysis unit sets an optimal environment based on the learning location and time of day. The learning history analysis unit can also make suggestions to improve learning efficiency based on the user's learning environment. For example, the learning history analysis unit suggests a quiet place or appropriate lighting conditions. Furthermore, the learning history analysis unit can monitor the user's learning environment in real time and dynamically suggest an optimal learning environment. For example, the learning history analysis unit adjusts the learning environment based on environmental sounds and temperature. This makes it possible to support efficient learning by suggesting an optimal learning environment based on the user's learning environment.

[0048] The learning history analysis unit can visualize the user's learning progress based on the user's learning history and provide an interface that makes it easy for the user to feel a sense of accomplishment. The learning history analysis unit, for example, analyzes the user's learning history and visualizes the user's learning progress. For example, the learning history analysis unit displays the learning achievement level and progress status in graphs and charts. The learning history analysis unit can also provide an interface that makes it easy for the user to feel a sense of accomplishment. For example, the learning history analysis unit introduces a system that allows the user to earn badges or points when learning goals are achieved. Furthermore, the learning history analysis unit can analyze the user's learning history in real time and dynamically visualize the learning progress. For example, the learning history analysis unit customizes the interface according to the learning progress. In this way, the learning progress can be visualized based on the user's learning history and an interface that makes it easy for the user to feel a sense of accomplishment, thereby supporting efficient learning.

[0049] The learning history analysis unit can analyze the user's learning pace and automatically generate a reasonable learning schedule. The learning history analysis unit, for example, analyzes the user's learning pace and automatically generates a reasonable learning schedule. For example, the learning history analysis unit sets a schedule based on the user's past learning speed and level of understanding. The learning history analysis unit can also propose a reasonable learning schedule based on the user's learning pace. For example, the learning history analysis unit sets weekly learning goals and adjusts them according to progress. Furthermore, the learning history analysis unit can monitor the user's learning pace in real time and dynamically generate a reasonable learning schedule. For example, the learning history analysis unit adjusts the schedule according to learning progress. In this way, efficient learning can be supported by automatically generating a reasonable learning schedule based on the user's learning pace.

[0050] The learning history analysis unit can analyze a user's hobbies or interests and provide related learning content. The learning history analysis unit can, for example, analyze a user's hobbies or interests and provide related learning content. For example, the learning history analysis unit can generate learning materials related to topics that interest the user. The learning history analysis unit can also customize learning content based on the user's hobbies and interests. For example, the learning history analysis unit can provide learning materials in the form of quizzes or mini-games related to the hobbies. Furthermore, the learning history analysis unit can analyze a user's hobbies and interests in real time and dynamically provide related learning content. For example, the learning history analysis unit can send learning notifications based on the user's interests. This can support efficient learning by providing optimal learning content based on the user's hobbies and interests.

[0051] The learning history analysis unit can analyze the user's social media activity and suggest related learning content. The learning history analysis unit, for example, analyzes the user's social media activity and suggests related learning content. For example, the learning history analysis unit generates learning materials based on the accounts the user follows and the content of their posts. The learning history analysis unit can also provide related learning content based on the user's social media activity. For example, the learning history analysis unit suggests articles and videos related to topics that interest the user. Furthermore, the learning history analysis unit can analyze the user's social media activity in real time and dynamically provide related learning content. For example, the learning history analysis unit sends learning notifications based on the content of the user's posts. This can support efficient learning by suggesting optimal learning content based on the user's social media activity.

[0052] The learning history analysis unit can analyze the user's learning history and provide an individually optimized reward system. The learning history analysis unit, for example, analyzes the user's learning history and provides an individually optimized reward system. For example, the learning history analysis unit introduces a mechanism that allows the user to earn points or badges according to their learning progress. The learning history analysis unit can also customize the reward system based on the user's learning history. For example, the learning history analysis unit can provide special rewards when specific goals are achieved. Furthermore, the learning history analysis unit can analyze the user's learning history in real time and dynamically provide an individually optimized reward system. For example, the learning history analysis unit adjusts the type and timing of rewards according to their learning progress. This makes it possible to support efficient learning by providing an individually optimized reward system based on the user's learning history.

[0053] The learning history analysis unit can analyze the user's learning progress in real time and provide rewards at times when the user is likely to feel a sense of accomplishment. The learning history analysis unit, for example, analyzes the user's learning progress in real time and provides rewards at times when the user is likely to feel a sense of accomplishment. For example, the learning history analysis unit provides rewards immediately after a learning goal is achieved. The learning history analysis unit can also dynamically adjust the timing of providing rewards based on the user's learning progress. For example, the learning history analysis unit provides rewards to increase motivation when learning progress stagnates. Furthermore, the learning history analysis unit can monitor the user's learning progress in real time and provide rewards at times when the user is likely to feel a sense of accomplishment. For example, the learning history analysis unit adjusts the type and timing of rewards according to the user's learning progress. This makes it possible to support efficient learning by providing rewards at times when the user is likely to feel a sense of accomplishment based on the user's learning progress.

[0054] The learning history analysis unit can analyze a user's learning style and introduce game elements based on the analysis. The learning history analysis unit, for example, analyzes a user's learning style and introduces game elements based on the analysis. For example, the learning history analysis unit provides a game including visual content to a user who prefers visual learning. The learning history analysis unit can also customize game elements based on the user's learning style. For example, the learning history analysis unit provides a game including audio commentary to a user who prefers auditory learning. Furthermore, the learning history analysis unit can analyze a user's learning style in real time and dynamically provide game elements based on the analysis. For example, the learning history analysis unit provides an interactive game to a user who prefers tactile learning. In this way, efficient learning can be supported by introducing game elements based on the user's learning style.

[0055] The learning history analysis unit can combine game elements from different learning fields to create a game that cultivates composite skills. The learning history analysis unit, for example, combines game elements from different learning fields to create a game that cultivates composite skills. For example, the learning history analysis unit provides a game that combines elements of mathematics and physics. The learning history analysis unit can also create a game that provides the user with a new perspective by combining skills from different learning fields. For example, the learning history analysis unit provides a game that combines elements of programming and data analysis. Furthermore, the learning history analysis unit can analyze the user's learning history and dynamically generate a game for cultivating composite skills. For example, the learning history analysis unit provides a game that integrates knowledge from different fields. This makes it possible to cultivate composite skills by combining game elements from different learning fields.

[0056] The learning history analysis unit can automatically generate practical games related to the user's occupation, thereby cultivating practical skills. The learning history analysis unit, for example, automatically generates practical games related to the user's occupation. For example, the learning history analysis unit provides a game for engineers that simulates problems that arise in actual projects. The learning history analysis unit can also generate games for cultivating practical skills based on the user's occupation. For example, the learning history analysis unit provides games for market analysis and strategy planning for marketing personnel. Furthermore, the learning history analysis unit can analyze the user's occupational history and dynamically generate games for cultivating practical skills. For example, the learning history analysis unit provides practical games based on the user's past work experience. In this way, practical skills can be cultivated by automatically generating practical games related to the user's occupation.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reskilling system can further include a health management unit that monitors the user's health condition. The health management unit, for example, analyzes the user's heart rate and sleep patterns and suggests the optimal timing for studying. For example, the health management unit adjusts the schedule so that the user starts studying after getting enough sleep. The health management unit can also adjust the study load based on the user's health condition. For example, if the user is tired, it can provide lighter study content. Furthermore, the health management unit can suggest breaks to refresh based on the user's health data. For example, it can periodically send notifications encouraging stretching or light exercise. This makes it possible to provide an optimal study environment based on the user's health condition.

[0059] The reskilling system can further include a community management unit that forms a learning community for users. The community management unit, for example, promotes interaction between users and increases motivation to learn. For example, the community management unit allows users to join groups with common interests. The community management unit can also provide a forum where users can share questions and opinions about learning. For example, it can provide a platform where users can ask other users questions about things they are unsure about while learning. Furthermore, the community management unit can introduce a mechanism for users to share their learning progress and encourage each other. For example, when a learning goal is achieved, a user can receive congratulatory messages from other users. This can increase motivation to learn through interaction between users.

[0060] The reskilling system can further include an evaluation unit that evaluates the user's learning outcomes. The evaluation unit, for example, periodically evaluates the user's learning progress and level of understanding and provides feedback. For example, the evaluation unit may administer a test on the content the user has learned and provide feedback based on the results. The evaluation unit can also visualize the user's learning outcomes and provide an interface that makes it easy for the user to feel a sense of accomplishment. For example, the learning progress may be displayed in graphs or charts. Furthermore, the evaluation unit can suggest the next content to be studied based on the user's learning outcomes. For example, if a specific skill is lacking, the evaluation unit may provide learning materials to strengthen that skill. This makes it possible to evaluate the user's learning outcomes and support efficient learning.

[0061] The reskilling system may further include an environment setting unit that customizes the user's learning environment. The environment setting unit, for example, adjusts the learning environment according to the user's preferences. For example, the environment setting unit may set the user's preferred background music or screen brightness. The environment setting unit may also customize the way learning content is displayed based on the user's learning style. For example, a user who prefers visual learning may be provided with a display that makes heavy use of visual content. Furthermore, the environment setting unit may continuously improve the learning environment based on user feedback. For example, if the user prefers a particular setting, the setting may be saved as the default. This allows the system to provide an optimal learning environment according to the user's preferences.

[0062] The reskilling system can further include a career support unit that suggests future career paths based on the user's learning history. The career support unit, for example, analyzes the user's learning history and skill set and suggests optimal career paths. For example, the career support unit may list the skills required for a job in which the user is interested and provide a learning plan for acquiring those skills. The career support unit can also suggest specific action plans based on the user's career goals. For example, it may show the steps required to acquire the qualifications and experience required to enter a specific job. Furthermore, the career support unit can monitor the user's learning progress and provide feedback on the progress toward achieving the career path. For example, it may periodically report progress toward achieving goals. This makes it possible to suggest optimal career paths based on the user's learning history and support their career growth.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The learning history analysis unit analyzes the user's learning history, interests, and skill level. For example, the learning history analysis unit collects the user's past learning data and evaluates their learning progress and level of understanding. It can also identify the user's interests and customize learning content based on them. It also evaluates the user's skill level and selects appropriate learning materials and questions. Step 2: The schedule management unit manages the user's schedule based on the information analyzed by the learning history analysis unit. For example, the schedule management unit creates an efficient learning schedule by optimally allocating the user's study time and break time. It can also provide content that allows the user to study in their spare time. Furthermore, it analyzes the user's lifestyle and smart device usage patterns to suggest optimal study times. Step 3: The question generator automatically creates questions, explanations, and specific examples suited to the user based on the schedule managed by the schedule manager. For example, the question generator provides questions and explanations that are individually tailored to the user's learning progress. It can also analyze the user's answer patterns and provide focused explanations on points where mistakes are likely to be made. It can also analyze the user's learning history and automatically generate review questions based on past mistakes.

[0065] (Example 2) A reskilling system according to an embodiment of the present invention is a system that uses generative AI to provide individually optimized reskilling. This reskilling system analyzes a user's learning history, interests, and skill level, and provides individually optimized learning content based on the analysis. This allows the reskilling system to provide individually optimized learning content based on the user's learning history, interests, and skill level.

[0066] A reskilling system according to an embodiment includes a learning history analysis unit, a schedule management unit, and a question generation unit. The learning history analysis unit analyzes a user's learning history, interests, and skill level. For example, the learning history analysis unit collects the user's past learning data and evaluates their learning progress and level of understanding. The learning history analysis unit can also identify the user's interests and customize learning content based on the identified interests. The learning history analysis unit evaluates the user's skill level and selects appropriate learning materials and questions. The schedule management unit manages the user's schedule based on the information analyzed by the learning history analysis unit. For example, the schedule management unit optimally allocates the user's study time and break time to create an efficient learning schedule. The schedule management unit can also provide content that allows the user to study during their free time. The schedule management unit analyzes the user's daily rhythm and smart device usage patterns and suggests optimal study timing. The question generation unit automatically generates questions, explanations, and specific examples appropriate for the user based on the schedule managed by the schedule management unit. For example, the question generation unit provides questions and explanations individually tailored to the user's learning progress. The question generator can also analyze the user's answer patterns and provide focused explanations on points where the user is likely to make mistakes. Furthermore, the question generator can analyze the user's learning history and automatically generate review questions based on past mistakes. This allows the reskilling system according to the embodiment to provide individually optimized learning content based on the user's learning history, interests, and skill level. For example, if a user wants to learn programming, the generation AI evaluates the user's current skill level and automatically selects appropriate learning materials and questions. Furthermore, providing content that allows the user to study in their spare time allows for efficient learning. Furthermore, the system supports users in deepening their understanding by automatically creating individually tailored questions, explanations, and concrete examples according to their learning progress.

[0067] The learning history analysis unit can evaluate the user's current skill level and automatically select appropriate learning materials or problems. The learning history analysis unit, for example, analyzes the user's past learning data and evaluates the user's current skill level. For example, the learning history analysis unit determines the skill level based on the user's test results and self-assessment. The learning history analysis unit can also identify the user's interests and select appropriate learning materials and problems based on those interests. For example, if the user is interested in programming, the learning history analysis unit automatically selects learning materials and problems related to programming. This makes it possible to provide the optimal learning materials and problems according to the user's skill level.

[0068] The schedule management unit can provide learning content that allows users to study during their spare time. The schedule management unit, for example, analyzes the user's schedule and identifies spare time. For example, the schedule management unit provides content that allows users to study during their commute or break time. The schedule management unit also generates learning modules that can be completed in a short time, allowing users to study efficiently. For example, the schedule management unit provides learning modules that can be completed in about 5 to 10 minutes. This allows users to effectively utilize their spare time to study.

[0069] The question generator can automatically create questions, explanations, and specific examples that are individually tailored to the user's learning progress. For example, the question generator can analyze the user's learning progress in real time and create questions that are individually tailored. For example, if the user is struggling with a particular topic, the question generator can provide specific examples and explanations related to that topic. The question generator can also analyze the user's answer patterns and focus on points where the user is likely to make mistakes. For example, the question generator can provide detailed explanations and supplementary materials for questions on which the user frequently makes mistakes. Furthermore, the question generator can analyze the user's learning history and automatically generate review questions based on past mistakes. For example, the question generator can re-ask questions that the user made mistakes on in the past to deepen understanding. This makes it possible to provide optimal questions, explanations, and specific examples that are tailored to the user's learning progress.

[0070] The learning history analysis unit can analyze the user's emotional state in real time and optimize the learning content based on the user's emotions at that time. The learning history analysis unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotional state in real time. For example, if the user is tired, the learning history analysis unit provides learning content that helps the user relax. The learning history analysis unit can also dynamically adjust the learning content based on the user's emotional state. For example, if the user is feeling stressed, questions with a lower level of difficulty are provided. Furthermore, the learning history analysis unit can provide learning content at a time when the user can best concentrate based on the emotion analysis data. For example, questions with a higher level of difficulty are presented when the user is relaxed. This makes it possible to provide optimal learning content according to the user's emotional state.

[0071] The schedule management unit can compare the user's past learning data with their current performance and automatically adjust the optimal learning pace. For example, the schedule management unit analyzes the user's past learning data and compares it with their current performance. For example, the schedule management unit sets the optimal learning pace based on their past learning speed and level of understanding. The schedule management unit can also monitor the user's learning progress in real time and adjust the learning pace as needed. For example, if the user is taking a long time to understand, the pace can be slowed down. Furthermore, the schedule management unit can automatically generate an optimal learning schedule for the user based on their past learning data and current performance. For example, it can set weekly learning goals and adjust them according to progress. This optimizes the user's learning pace and supports efficient learning.

[0072] The question generator can analyze a user's learning style and provide optimal learning content based on that. For example, the question generator can analyze a user's learning style and provide visual content to a user who prefers visual learning. For example, the question generator can generate learning materials that make extensive use of diagrams and graphs. The question generator can also provide learning content in the form of audio commentary or podcasts to a user who prefers auditory learning. For example, the question generator can generate learning materials that include audio commentary. Furthermore, the question generator can provide interactive learning content to a user who prefers tactile learning. For example, the question generator can generate questions and simulations that can be operated by drag and drop. This makes it possible to provide optimal learning content according to the user's learning style.

[0073] The learning history analysis unit can provide a composite reskilling program that combines skills from different fields. The learning history analysis unit, for example, analyzes a user's interests and career goals and provides a reskilling program that combines skills from different fields. For example, the learning history analysis unit generates teaching materials that combine programming and design skills. The learning history analysis unit can also suggest new career paths to users by combining skills from different fields. For example, the learning history analysis unit provides a program that combines marketing and data analysis skills. Furthermore, the learning history analysis unit can analyze a user's skill set and provide learning content for cultivating composite skills. For example, the learning history analysis unit generates teaching materials that combine business skills and technical skills. In this way, by providing a reskilling program that combines skills from different fields, it is possible to cultivate a diverse skill set for the user.

[0074] The learning history analysis unit can analyze the user's work history and suggest the optimal skill set for the career path. The learning history analysis unit can, for example, analyze the user's work history and suggest the optimal skill set for the career path. For example, the learning history analysis unit can suggest the next skill to acquire based on the user's past work experience. The learning history analysis unit can also suggest a promising skill set for the future based on the user's work history and current market needs. For example, the learning history analysis unit can create a learning plan based on skills that are in high demand. Furthermore, the learning history analysis unit can analyze the user's career goals and suggest the optimal skill set for those goals. For example, the learning history analysis unit can list the skills required for a specific occupation and provide a learning plan. This can effectively support the user's career path by suggesting the optimal skill set based on the user's work history.

[0075] The learning history analysis unit can use the emotion estimation function to detect stress or fatigue felt by the user while studying and provide relaxing content. The learning history analysis unit, for example, uses the emotion estimation function to detect stress or fatigue felt by the user while studying in real time. For example, the learning history analysis unit analyzes facial expressions and voice to measure stress levels. Furthermore, if the learning history analysis unit detects stress or fatigue in the user, it can provide relaxing learning content. For example, the learning history analysis unit generates content including relaxing music and light exercises. Furthermore, the learning history analysis unit can provide learning content at a time when the user is able to relax based on the emotion estimation data. For example, the learning history analysis unit presents difficult questions when stress is low. In this way, it is possible to detect the user's stress or fatigue and provide relaxing content, thereby improving learning efficiency.

[0076] The schedule management unit can analyze the user's smart device usage patterns and suggest the optimal timing for studying. The schedule management unit, for example, analyzes the user's smart device usage patterns and suggests the optimal timing for studying. For example, the schedule management unit sets a study schedule based on the frequency of device use and time of day. The schedule management unit can also identify spare time based on the user's smart device usage history and provide study content during that time. For example, the schedule management unit generates learning materials that can be used in a short amount of time during commuting or breaks. Furthermore, the schedule management unit can analyze the user's device usage patterns and suggest the optimal timing for studying in real time. For example, the schedule management unit sends study notifications based on the device usage status. This can support efficient study by suggesting the optimal timing for studying based on the user's smart device usage patterns.

[0077] The schedule management unit can analyze the user's lifestyle and automatically incorporate the most effective study time periods into the schedule. The schedule management unit, for example, analyzes the user's lifestyle and identifies the most effective study time periods. For example, the schedule management unit sets a study schedule based on sleep patterns and meal times. The schedule management unit can also automatically incorporate study time periods into the schedule based on the user's lifestyle. For example, the schedule management unit provides study content during morning commute time or evening relaxation time. Furthermore, the schedule management unit can monitor the user's lifestyle in real time and dynamically adjust the optimal study time periods. For example, the schedule management unit sends study notifications based on the user's activity level. This can support efficient study by suggesting optimal study time periods based on the user's lifestyle.

[0078] The schedule management unit can analyze the user's travel route and provide content that can be used to study while traveling. The schedule management unit, for example, analyzes the user's travel route and provides content that can be used to study while traveling. For example, the schedule management unit generates learning materials that can be used to study in a short amount of time based on the user's commute route and travel time. The schedule management unit can also provide optimal learning content based on the user's travel route. For example, the schedule management unit provides learning materials in the form of audio commentaries or podcasts that can be listened to while traveling. Furthermore, the schedule management unit can monitor the user's travel route in real time and dynamically provide content that can be used to study while traveling. For example, the schedule management unit sends learning notifications based on travel time. This makes it possible to support efficient learning by providing optimal learning content based on the user's travel route.

[0079] The schedule management unit can analyze a user's hobbies or interests and provide related learning content in their spare time. The schedule management unit, for example, analyzes a user's hobbies and interests and provides related learning content. For example, the schedule management unit generates learning materials that can be learned in a short amount of time on topics that interest the user. The schedule management unit can also provide content that can be used to study in the user's spare time based on the user's hobbies and interests. For example, the schedule management unit provides learning materials in the form of quizzes or mini-games related to the hobbies. Furthermore, the schedule management unit can analyze a user's hobbies and interests in real time and dynamically provide related learning content. For example, the schedule management unit sends learning notifications based on the user's interests. This can support efficient learning by providing optimal learning content based on the user's hobbies and interests.

[0080] The schedule management unit can analyze the user's social media activity and suggest related learning content. The schedule management unit, for example, analyzes the user's social media activity and suggests related learning content. For example, the schedule management unit generates learning materials based on the accounts the user follows and the content of their posts. The schedule management unit can also provide related learning content based on the user's social media activity. For example, the schedule management unit suggests articles and videos related to topics that interest the user. Furthermore, the schedule management unit can analyze the user's social media activity in real time and dynamically provide related learning content. For example, the schedule management unit sends learning notifications based on the content of the user's posts. This can support efficient learning by suggesting optimal learning content based on the user's social media activity.

[0081] The schedule management unit can use the emotion estimation function to provide refreshing learning content based on the emotions the user feels during their spare time. The schedule management unit, for example, uses the emotion estimation function to analyze the emotions the user feels during their spare time in real time. For example, the schedule management unit analyzes facial expressions and voice to identify times when refreshing is necessary. The schedule management unit can also provide refreshing learning content based on the user's emotional state. For example, the schedule management unit generates learning materials including relaxing music and light exercises. Furthermore, the schedule management unit can provide learning content at times when the user can refresh themselves based on the emotion estimation data. For example, the schedule management unit presents refreshing content when stress is low. This can support efficient learning by providing refreshing learning content based on the user's emotions.

[0082] The question generation unit can analyze the user's answer patterns and provide explanations that emphasize points where the user is likely to make mistakes. The question generation unit, for example, analyzes the user's answer patterns and identifies points where the user is likely to make mistakes. For example, the question generation unit extracts questions where the user frequently makes mistakes based on the user's past answer history. The question generation unit can also provide explanations that emphasize points where the user is likely to make mistakes. For example, the question generation unit generates detailed explanations and supplementary materials for questions where the user made a mistake. Furthermore, the question generation unit can analyze the user's answer patterns in real time and dynamically identify points where the user is likely to make mistakes. For example, the question generation unit instantly analyzes mistakes made while answering and provides explanations. This can support efficient learning by providing explanations that emphasize points where the user is likely to make mistakes based on the user's answer patterns.

[0083] The question generation unit can analyze the user's learning history and automatically generate review questions based on past mistakes. The question generation unit, for example, analyzes the user's learning history and automatically generates review questions based on past mistakes. For example, the question generation unit may re-ask questions that the user got wrong in the past to deepen understanding. The question generation unit can also identify topics that require review based on the user's learning history and generate questions related to those topics. For example, the question generation unit provides review questions related to a specific unit. Furthermore, the question generation unit can analyze the user's learning history in real time and automatically generate questions when review is necessary. For example, the question generation unit may ask review questions after a certain period of time has passed. In this way, efficient learning can be supported by automatically generating review questions based on the user's learning history.

[0084] The question generator can analyze the user's level of understanding in real time and provide concrete examples that complement areas where the user's understanding is shallow. For example, the question generator can analyze the user's answers and learning progress in real time and identify areas where the user's understanding is shallow. For example, the question generator can evaluate the level of understanding based on the accuracy rate of answers and the answer time. The question generator can also provide concrete examples for areas where the user's understanding is shallow. For example, the question generator generates teaching materials that explain abstract concepts using concrete examples. Furthermore, the question generator can analyze the user's level of understanding in real time and dynamically provide concrete examples that complement areas where the user's understanding is shallow. For example, the question generator can present concrete examples that instantly complement areas where the user's understanding is lacking while answering. This makes it possible to support efficient learning by providing concrete examples based on the user's level of understanding.

[0085] The question generation unit can combine questions from different learning fields to create questions that cultivate multi-disciplinary skills. For example, the question generation unit combines questions from different learning fields to create questions that cultivate multi-disciplinary skills. For example, the question generation unit generates a composite question that combines mathematics and physics questions. The question generation unit can also create questions that provide the user with a new perspective by combining skills from different learning fields. For example, the question generation unit provides a question that combines programming and data analysis skills. Furthermore, the question generation unit can analyze the user's learning history and dynamically generate questions to cultivate multi-disciplinary skills. For example, the question generation unit poses questions that integrate knowledge from different fields. In this way, multi-disciplinary skills can be cultivated by combining questions from different learning fields.

[0086] The question generation unit can automatically generate practical questions related to the user's occupation and cultivate practical skills. The question generation unit, for example, automatically generates practical questions related to the user's occupation. For example, the question generation unit provides engineers with questions that simulate problems that arise in actual projects. The question generation unit can also generate questions for cultivating practical skills based on the user's occupation. For example, the question generation unit provides marketing personnel with questions on market analysis and strategy planning. Furthermore, the question generation unit can analyze the user's occupational history and dynamically generate questions for cultivating practical skills. For example, the question generation unit presents practical questions based on the user's past work experience. In this way, practical skills can be cultivated by automatically generating practical questions related to the user's occupation.

[0087] The question generation unit can use the emotion estimation function to analyze the emotions of the user when solving the problem and provide explanations that elicit positive emotions. The question generation unit, for example, uses the emotion estimation function to analyze the emotions of the user when solving the problem in real time. For example, the question generation unit analyzes facial expressions and voice to identify the timing to elicit positive emotions. The question generation unit can also provide explanations that elicit positive emotions based on the user's emotional state. For example, the question generation unit generates explanations that include encouraging messages and success stories. Furthermore, the question generation unit can provide explanations at times when the user is feeling positive emotions based on the emotion estimation data. For example, the question generation unit dynamically presents explanations that elicit positive emotions while the user is answering the question. This makes it possible to support efficient learning by providing explanations that elicit positive emotions based on the user's emotions.

[0088] The learning history analysis unit can analyze the user's learning environment and suggest an optimal learning environment. The learning history analysis unit, for example, analyzes the user's learning environment and suggests an optimal learning environment. For example, the learning history analysis unit sets an optimal environment based on the learning location and time of day. The learning history analysis unit can also make suggestions to improve learning efficiency based on the user's learning environment. For example, the learning history analysis unit suggests a quiet place or appropriate lighting conditions. Furthermore, the learning history analysis unit can monitor the user's learning environment in real time and dynamically suggest an optimal learning environment. For example, the learning history analysis unit adjusts the learning environment based on environmental sounds and temperature. This makes it possible to support efficient learning by suggesting an optimal learning environment based on the user's learning environment.

[0089] The learning history analysis unit can visualize the user's learning progress based on the user's learning history and provide an interface that makes it easy for the user to feel a sense of accomplishment. The learning history analysis unit, for example, analyzes the user's learning history and visualizes the user's learning progress. For example, the learning history analysis unit displays the learning achievement level and progress status in graphs and charts. The learning history analysis unit can also provide an interface that makes it easy for the user to feel a sense of accomplishment. For example, the learning history analysis unit introduces a system that allows the user to earn badges or points when learning goals are achieved. Furthermore, the learning history analysis unit can analyze the user's learning history in real time and dynamically visualize the learning progress. For example, the learning history analysis unit customizes the interface according to the learning progress. In this way, the learning progress can be visualized based on the user's learning history and an interface that makes it easy for the user to feel a sense of accomplishment, thereby supporting efficient learning.

[0090] The learning history analysis unit can analyze the user's learning pace and automatically generate a reasonable learning schedule. The learning history analysis unit, for example, analyzes the user's learning pace and automatically generates a reasonable learning schedule. For example, the learning history analysis unit sets a schedule based on the user's past learning speed and level of understanding. The learning history analysis unit can also propose a reasonable learning schedule based on the user's learning pace. For example, the learning history analysis unit sets weekly learning goals and adjusts them according to progress. Furthermore, the learning history analysis unit can monitor the user's learning pace in real time and dynamically generate a reasonable learning schedule. For example, the learning history analysis unit adjusts the schedule according to learning progress. In this way, efficient learning can be supported by automatically generating a reasonable learning schedule based on the user's learning pace.

[0091] The learning history analysis unit can analyze a user's hobbies or interests and provide related learning content. The learning history analysis unit can, for example, analyze a user's hobbies or interests and provide related learning content. For example, the learning history analysis unit can generate learning materials related to topics that interest the user. The learning history analysis unit can also customize learning content based on the user's hobbies and interests. For example, the learning history analysis unit can provide learning materials in the form of quizzes or mini-games related to the hobbies. Furthermore, the learning history analysis unit can analyze a user's hobbies and interests in real time and dynamically provide related learning content. For example, the learning history analysis unit can send learning notifications based on the user's interests. This can support efficient learning by providing optimal learning content based on the user's hobbies and interests.

[0092] The learning history analysis unit can analyze the user's social media activity and suggest related learning content. The learning history analysis unit, for example, analyzes the user's social media activity and suggests related learning content. For example, the learning history analysis unit generates learning materials based on the accounts the user follows and the content of their posts. The learning history analysis unit can also provide related learning content based on the user's social media activity. For example, the learning history analysis unit suggests articles and videos related to topics that interest the user. Furthermore, the learning history analysis unit can analyze the user's social media activity in real time and dynamically provide related learning content. For example, the learning history analysis unit sends learning notifications based on the content of the user's posts. This can support efficient learning by suggesting optimal learning content based on the user's social media activity.

[0093] The learning history analysis unit can use the emotion estimation function to detect stress or fatigue felt by the user while studying and provide relaxing content. The learning history analysis unit, for example, uses the emotion estimation function to detect stress or fatigue felt by the user while studying in real time. For example, the learning history analysis unit analyzes facial expressions and voice to measure stress levels. Furthermore, if the learning history analysis unit detects stress or fatigue in the user, it can provide relaxing learning content. For example, the learning history analysis unit generates content including relaxing music and light exercises. Furthermore, the learning history analysis unit can provide learning content at a time when the user is able to relax based on the emotion estimation data. For example, the learning history analysis unit presents difficult questions when stress is low. This allows the system to detect the user's stress or fatigue and provide relaxing content to support efficient learning.

[0094] The learning history analysis unit can analyze the user's learning history and provide an individually optimized reward system. The learning history analysis unit, for example, analyzes the user's learning history and provides an individually optimized reward system. For example, the learning history analysis unit introduces a mechanism that allows the user to earn points or badges according to their learning progress. The learning history analysis unit can also customize the reward system based on the user's learning history. For example, the learning history analysis unit can provide special rewards when specific goals are achieved. Furthermore, the learning history analysis unit can analyze the user's learning history in real time and dynamically provide an individually optimized reward system. For example, the learning history analysis unit adjusts the type and timing of rewards according to their learning progress. This makes it possible to support efficient learning by providing an individually optimized reward system based on the user's learning history.

[0095] The learning history analysis unit can analyze the user's learning progress in real time and provide rewards at times when the user is likely to feel a sense of accomplishment. The learning history analysis unit, for example, analyzes the user's learning progress in real time and provides rewards at times when the user is likely to feel a sense of accomplishment. For example, the learning history analysis unit provides rewards immediately after a learning goal is achieved. The learning history analysis unit can also dynamically adjust the timing of providing rewards based on the user's learning progress. For example, the learning history analysis unit provides rewards to increase motivation when learning progress stagnates. Furthermore, the learning history analysis unit can monitor the user's learning progress in real time and provide rewards at times when the user is likely to feel a sense of accomplishment. For example, the learning history analysis unit adjusts the type and timing of rewards according to the user's learning progress. This makes it possible to support efficient learning by providing rewards at times when the user is likely to feel a sense of accomplishment based on the user's learning progress.

[0096] The learning history analysis unit can analyze a user's learning style and introduce game elements based on the analysis. The learning history analysis unit, for example, analyzes a user's learning style and introduces game elements based on the analysis. For example, the learning history analysis unit provides a game including visual content to a user who prefers visual learning. The learning history analysis unit can also customize game elements based on the user's learning style. For example, the learning history analysis unit provides a game including audio commentary to a user who prefers auditory learning. Furthermore, the learning history analysis unit can analyze a user's learning style in real time and dynamically provide game elements based on the analysis. For example, the learning history analysis unit provides an interactive game to a user who prefers tactile learning. In this way, efficient learning can be supported by introducing game elements based on the user's learning style.

[0097] The learning history analysis unit can combine game elements from different learning fields to create a game that cultivates composite skills. The learning history analysis unit, for example, combines game elements from different learning fields to create a game that cultivates composite skills. For example, the learning history analysis unit provides a game that combines elements of mathematics and physics. The learning history analysis unit can also create a game that provides the user with a new perspective by combining skills from different learning fields. For example, the learning history analysis unit provides a game that combines elements of programming and data analysis. Furthermore, the learning history analysis unit can analyze the user's learning history and dynamically generate a game for cultivating composite skills. For example, the learning history analysis unit provides a game that integrates knowledge from different fields. This makes it possible to cultivate composite skills by combining game elements from different learning fields.

[0098] The learning history analysis unit can automatically generate practical games related to the user's occupation, thereby cultivating practical skills. The learning history analysis unit, for example, automatically generates practical games related to the user's occupation. For example, the learning history analysis unit provides a game for engineers that simulates problems that arise in actual projects. The learning history analysis unit can also generate games for cultivating practical skills based on the user's occupation. For example, the learning history analysis unit provides games for market analysis and strategy planning for marketing personnel. Furthermore, the learning history analysis unit can analyze the user's occupational history and dynamically generate games for cultivating practical skills. For example, the learning history analysis unit provides practical games based on the user's past work experience. In this way, practical skills can be cultivated by automatically generating practical games related to the user's occupation.

[0099] The learning history analysis unit can use the emotion estimation function to analyze the emotions felt by the user during the game and provide game elements that elicit positive emotions. The learning history analysis unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during the game in real time. For example, the learning history analysis unit analyzes facial expressions and voice to identify the timing to elicit positive emotions. The learning history analysis unit can also provide game elements that elicit positive emotions based on the user's emotional state. For example, the learning history analysis unit generates game elements that include encouraging messages and success stories. Furthermore, the learning history analysis unit can also provide game elements at the timing when the user is feeling positive emotions based on the emotion estimation data. For example, the learning history analysis unit dynamically presents elements that elicit positive emotions during the game. This makes it possible to support efficient learning by providing game elements that elicit positive emotions based on the user's emotions.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reskilling system can further include a health management unit that monitors the user's health condition. The health management unit, for example, analyzes the user's heart rate and sleep patterns and suggests the optimal timing for studying. For example, the health management unit adjusts the schedule so that the user starts studying after getting enough sleep. The health management unit can also adjust the study load based on the user's health condition. For example, if the user is tired, it can provide lighter study content. Furthermore, the health management unit can suggest breaks to refresh based on the user's health data. For example, it can periodically send notifications encouraging stretching or light exercise. This makes it possible to provide an optimal study environment based on the user's health condition.

[0102] The reskilling system can further include a community management unit that forms a learning community for users. The community management unit, for example, promotes interaction between users and increases motivation to learn. For example, the community management unit allows users to join groups with common interests. The community management unit can also provide a forum where users can share questions and opinions about learning. For example, it can provide a platform where users can ask other users questions about things they are unsure about while learning. Furthermore, the community management unit can introduce a mechanism for users to share their learning progress and encourage each other. For example, when a learning goal is achieved, a user can receive congratulatory messages from other users. This can increase motivation to learn through interaction between users.

[0103] The reskilling system can further include an evaluation unit that evaluates the user's learning outcomes. The evaluation unit, for example, periodically evaluates the user's learning progress and level of understanding and provides feedback. For example, the evaluation unit may administer a test on the content the user has learned and provide feedback based on the results. The evaluation unit can also visualize the user's learning outcomes and provide an interface that makes it easy for the user to feel a sense of accomplishment. For example, the learning progress may be displayed in graphs or charts. Furthermore, the evaluation unit can suggest the next content to be studied based on the user's learning outcomes. For example, if a specific skill is lacking, the evaluation unit may provide learning materials to strengthen that skill. This makes it possible to evaluate the user's learning outcomes and support efficient learning.

[0104] The reskilling system may further include an environment setting unit that customizes the user's learning environment. The environment setting unit, for example, adjusts the learning environment according to the user's preferences. For example, the environment setting unit may set the user's preferred background music or screen brightness. The environment setting unit may also customize the way learning content is displayed based on the user's learning style. For example, a user who prefers visual learning may be provided with a display that makes heavy use of visual content. Furthermore, the environment setting unit may continuously improve the learning environment based on user feedback. For example, if the user prefers a particular setting, the setting may be saved as the default. This allows the system to provide an optimal learning environment according to the user's preferences.

[0105] The reskilling system can further include a career support unit that suggests future career paths based on the user's learning history. The career support unit, for example, analyzes the user's learning history and skill set and suggests optimal career paths. For example, the career support unit may list the skills required for a job in which the user is interested and provide a learning plan for acquiring those skills. The career support unit can also suggest specific action plans based on the user's career goals. For example, it may show the steps required to acquire the qualifications and experience required to enter a specific job. Furthermore, the career support unit can monitor the user's learning progress and provide feedback on the progress toward achieving the career path. For example, it may periodically report progress toward achieving goals. This makes it possible to suggest optimal career paths based on the user's learning history and support their career growth.

[0106] Reskilling systems can also analyze users' emotions and provide feedback to increase their motivation to study. For example, they can analyze the emotions a user feels while studying in real time and provide positive feedback. For example, if the user feels anxious about studying, they can send an encouraging message. Also, if the user feels a sense of accomplishment, they can provide a praising message. Furthermore, they can visualize the user's progress based on the user's emotional state and provide an interface that makes it easier to feel a sense of accomplishment. For example, they can display the user's progress in graphs or charts and visually show achieved goals. This makes it possible to increase the user's motivation to study based on their emotions.

[0107] The reskilling system can also analyze the user's emotions and provide refreshing content to reduce learning stress. For example, it can analyze the stress the user feels while studying in real time and identify when they need to refresh themselves. For example, if the user is tired, it can provide relaxing music or light exercises. If the user is feeling stressed, it can also provide meditation or deep breathing guides to reduce stress. Furthermore, it can adjust the learning load and reduce stress based on the user's emotional state. For example, it can suggest taking appropriate breaks when solving difficult problems. This makes it possible to reduce learning stress based on the user's emotions and support efficient learning.

[0108] The reskilling system can also analyze the user's emotions and provide an environment to improve concentration during learning. For example, it can analyze the user's emotions in real time while studying and identify when their concentration is declining. For example, if the user is lacking in concentration, it can provide music or environmental sounds to improve their concentration. It can also provide content to help the user learn techniques to improve concentration. For example, it can provide guides for the Pomodoro technique or mindfulness. Furthermore, it can adjust the learning environment based on the user's emotional state to improve concentration. For example, it can suggest a quiet environment or appropriate lighting conditions. This makes it possible to provide an environment to improve concentration during learning based on the user's emotions.

[0109] The reskilling system can also analyze the user's emotions and introduce game elements to make learning more enjoyable. For example, it can analyze the emotions the user feels while learning in real time and identify when they feel enjoyment. For example, if the user is losing interest in learning, it can provide learning content that incorporates game elements. It can also introduce a reward system to make the user feel more enjoyable. For example, it can provide a system that allows points or badges to be earned according to the progress of learning. Furthermore, it can provide interactive content to make learning more enjoyable based on the user's emotional state. For example, it can provide learning materials in the form of quizzes or mini-games. In this way, it can improve the enjoyment of learning based on the user's emotions.

[0110] Reskilling systems can also analyze users' emotions and provide feedback to enhance their sense of accomplishment in learning. For example, they can analyze the emotions a user feels while studying in real time and identify the timing at which they feel a sense of accomplishment. For example, when a user achieves a study goal, they can provide a praising message or a visual effect that enhances the sense of accomplishment. They can also provide an interface that visualizes learning progress to help users feel a sense of accomplishment. For example, they can display learning progress in graphs or charts to visually indicate achieved goals. Furthermore, they can introduce a reward system to enhance the sense of accomplishment based on the user's emotional state. For example, they can provide a mechanism that allows users to earn points or badges according to their study progress. This can enhance the sense of accomplishment in learning based on the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The learning history analysis unit analyzes the user's learning history, interests, and skill level. For example, the learning history analysis unit collects the user's past learning data and evaluates their learning progress and level of understanding. It can also identify the user's interests and customize learning content based on them. It also evaluates the user's skill level and selects appropriate learning materials and questions. Step 2: The schedule management unit manages the user's schedule based on the information analyzed by the learning history analysis unit. For example, the schedule management unit creates an efficient learning schedule by optimally allocating the user's study time and break time. It can also provide content that allows the user to study in their spare time. Furthermore, it analyzes the user's lifestyle and smart device usage patterns to suggest optimal study times. Step 3: The question generator automatically creates questions, explanations, and specific examples suited to the user based on the schedule managed by the schedule manager. For example, the question generator provides questions and explanations that are individually tailored to the user's learning progress. It can also analyze the user's answer patterns and provide focused explanations on points where mistakes are likely to be made. It can also analyze the user's learning history and automatically generate review questions based on past mistakes.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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]

[0180] 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. Equipped with generative AI, The generated AI is a learning history analysis unit that analyzes a user's learning history, interests, and skill level; a schedule management unit that manages the user's schedule based on the information analyzed by the learning history analysis unit; a question generation unit that automatically generates questions, explanations, and specific examples suitable for the user based on the schedule managed by the schedule management unit. A system characterized by:

2. The learning history analysis unit Assessing the user's current skill level and automatically selecting appropriate learning materials or questions 2. The system of claim 1.

3. The schedule management unit Providing learning content that allows the user to study in their spare time 2. The system of claim 1.

4. The question generator Automatically create questions, explanations, and examples that are tailored to the user's learning progress.

2. The system of claim 1.

5. The learning history analysis unit Analyze the user's emotional state in real time and optimize learning content based on that emotion.

2. The system of claim 1.

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