System

Abstract

An object of a system according to an embodiment is to provide an optimal learning method and information on a cram school and a preparatory school based on a learning status of a student.SOLUTION: A system according to an embodiment includes a learning state analysis unit, a learning method suggestion unit, and a cram school information providing unit. The learning condition analysis unit analyzes a learning condition of the student. The learning method proposal unit proposes an optimal learning method based on the result analyzed by the learning state analysis unit. The cram school information provision unit provides information on an optimal cram school or preparatory school on the basis of the learning method proposed by the learning method proposal 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 do not adequately provide optimal learning methods or information on cram schools and preparatory schools based on students' learning situations, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal learning methods and information on cram schools and preparatory schools based on the student's learning situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning situation analysis unit, a learning method suggestion unit, and a cram school information provision unit. The learning situation analysis unit analyzes the learning situation of a student. The learning method suggestion unit suggests an optimal learning method based on the results of the analysis by the learning situation analysis unit. The cram school information provision unit provides information on optimal cram schools and preparatory schools based on the learning method suggested by the learning method suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal learning methods and information on cram schools and preparatory schools based on the student's learning situation. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The learning support system according to an embodiment of the present invention uses generative AI to provide optimal learning methods to elementary, junior high, and high school students, as well as high school graduates, and also provides information on optimal cram schools and preparatory schools. This allows the learning support system to maximize the learning effect of each student and support them in achieving their goals.

[0029] A learning support system according to an embodiment includes a learning status analysis unit, a learning method suggestion unit, and a cram school information provision unit. The learning status analysis unit analyzes a student's learning status. For example, the learning status analysis unit receives as input the student's grade data, learning history, and information about the student's target school and exams, and analyzes the student's learning status based on the data. The learning status analysis unit can also evaluate the student's study time and level of understanding to monitor the student's learning progress. The learning method suggestion unit proposes an optimal learning method based on the results of the analysis by the learning status analysis unit. For example, the learning method suggestion unit proposes methods such as individual instruction, group learning, and online learning. The learning method suggestion unit can also analyze a student's learning style (visual, auditory, tactile) and propose an optimal learning method based on the results. Furthermore, the learning method suggestion unit can propose optimal study times and break times based on the student's daily rhythm and health data. The cram school information provision unit provides information on optimal cram schools and preparatory schools based on the learning method proposed by the learning method suggestion unit. For example, the cram school information provision unit references a database of local cram schools and preparatory schools to recommend cram schools and preparatory schools that meet the student's needs. The cram school information providing unit can also provide information such as the evaluations of instructors at cram schools and preparatory schools, curriculum, and fees. This allows the learning support system according to the embodiment to provide optimal learning methods and information on cram schools and preparatory schools based on the student's learning situation. For example, the learning support system can manage the student's learning progress and provide feedback as appropriate. The learning support system can also recommend resources such as online learning materials, reference books, and video lectures. Furthermore, the learning support system can also introduce learning communities that students can join.

[0030] The learning status analysis unit receives grade data, learning history, target schools, and exam information as input, and can analyze the learning status based on this. The learning status analysis unit, for example, collects student grade data and analyzes the learning status. For example, it grasps learning progress based on test scores and assignment evaluations. The learning status analysis unit also analyzes learning history and identifies learning trends and patterns. For example, it evaluates learning progress based on study time and learning content. The learning status analysis unit also sets learning goals based on target schools and exam information, and analyzes the learning status based on these. For example, it adjusts the learning plan taking into account entrance exam subjects and passing criteria. This makes it possible to analyze the learning status based on the student's grade data, learning history, and goals.

[0031] The learning method suggestion unit can analyze learning styles and suggest learning methods based on the results. For example, the learning method suggestion unit analyzes students' learning styles through questionnaires and tests to identify whether they have strengths in visual, auditory, or tactile learning. For example, the learning method suggestion unit suggests learning materials that make extensive use of diagrams and graphs to visual students. Also, it suggests audio learning materials and lecture videos to auditory students. And it suggests experiments and hands-on learning activities to tactile students. In this way, it is possible to suggest optimal learning methods based on students' learning styles.

[0032] The study method suggestion unit can suggest study times and break times taking into account the student's lifestyle rhythm and health data. The study method suggestion unit, for example, collects and analyzes the student's lifestyle rhythm and health data from a wearable device. For example, it identifies the optimal study time based on sleep patterns and heart rate. The study method suggestion unit also suggests break times based on the health data. For example, it may advise the student to take a break when their concentration is low. The study method suggestion unit can also suggest a study schedule that matches the student's lifestyle rhythm. For example, it may recommend studying early in the morning for a student who is a morning person. This makes it possible to suggest optimal study times and break times based on the student's lifestyle rhythm and health data.

[0033] The learning method suggestion unit can incorporate feedback from parents and teachers and dynamically adjust the learning plan. For example, the learning method suggestion unit builds a system that collects feedback from parents and teachers and reflects it in the learning plan. For example, parents and teachers evaluate the student's learning situation and adjust the learning plan based on that evaluation. The learning method suggestion unit can also incorporate the opinions of parents and teachers to set learning goals. For example, it creates a learning plan taking into account the learning content and progress desired by parents and teachers. This makes it possible to dynamically adjust the learning plan by incorporating feedback from parents and teachers.

[0034] The learning method suggestion unit can incorporate hobbies and interests into learning plans to improve motivation. The learning method suggestion unit, for example, collects information about students' hobbies and interests through questionnaires and interviews and reflects this information in learning plans. For example, it can set sports-related questions for students who like sports, or provide music-related learning materials for students who like music. The learning method suggestion unit can also set learning goals based on hobbies and interests. For example, it can incorporate projects related to hobbies as part of learning. In this way, by incorporating students' hobbies and interests into learning plans, it is possible to improve their motivation to learn.

[0035] The cram school information provider can match curriculums with learning goals and suggest the most suitable program. For example, the cram school information provider can collect curriculum data from cram schools and preparatory schools and build a system that matches it with students' learning goals. For example, it can recommend cram schools with curricula specialized in preparing for specific exams. The cram school information provider can also suggest programs taking into account the progress and content of the curriculum. For example, it can suggest programs that involve intensive study over a short period of time. The cram school information provider can also suggest curricula that suit students' learning styles. For example, it can suggest programs that combine online learning and face-to-face classes. This makes it possible to match curriculums with learning goals and suggest the most suitable program.

[0036] The cram school information providing unit can analyze the pass rate data and provide options with a high success rate. The cram school information providing unit, for example, collects data on the past pass rates of cram schools and preparatory schools and builds a system that provides options with a high success rate. For example, it recommends cram schools with a high success rate in getting students into specific schools. The cram school information providing unit can also evaluate cram schools and preparatory schools based on the pass rate data and provide highly reliable information. For example, it evaluates cram schools based on the number of students who passed and the pass rate. The cram school information providing unit can also adjust students' study plans based on the pass rate data. For example, it can propose a study plan that focuses on subjects with a high pass rate. This makes it possible to provide options with a high success rate based on the past pass rate data.

[0037] The cram school information providing unit can analyze word-of-mouth and reviews and provide highly reliable information. The cram school information providing unit, for example, builds a system that collects and analyzes word-of-mouth and reviews of cram schools and preparatory schools. For example, it collects word-of-mouth data from online platforms and provides highly reliable information. The cram school information providing unit can also evaluate the content of word-of-mouth and reviews and select highly reliable information. For example, it provides information based on actual user ratings and certification by third-party organizations. The cram school information providing unit can also evaluate cram schools and preparatory schools based on word-of-mouth and reviews and create rankings. For example, it creates a ranking of cram schools based on word-of-mouth evaluation scores. This makes it possible to analyze word-of-mouth and reviews and provide highly reliable information.

[0038] The learning plan progress management unit can analyze learning progress data in real time and provide immediate feedback. The learning plan progress management unit, for example, builds a system that collects and analyzes learning progress data in real time. For example, it collects learning progress data through an online platform and provides immediate feedback. The learning plan progress management unit can also evaluate learning progress based on the data analyzed in real time and provide appropriate advice. For example, if learning progress is lagging behind, it can advise adjusting the learning pace. The learning plan progress management unit can also set learning goals and manage progress based on the data collected in real time. For example, it can adjust goals according to learning progress and evaluate the degree of achievement. In this way, learning progress data can be analyzed in real time and feedback can be provided immediately.

[0039] The learning plan progress management unit can track learning history over the long term and provide feedback based on past data. The learning plan progress management unit, for example, builds a system that tracks a student's learning history over the long term and provides feedback based on past data. For example, it adjusts the current learning plan based on past grades and learning progress. The learning plan progress management unit can also identify learning trends and patterns based on long-term learning history and provide appropriate advice. For example, it can identify learning strengths and weaknesses from past data and adjust the learning plan based on that. The learning plan progress management unit can also set learning goals based on long-term learning history and manage progress. For example, it can set long-term goals and evaluate the degree of achievement. This makes it possible to track a student's learning history over the long term and provide feedback based on past data.

[0040] The learning plan progress management unit can build a system for sharing learning progress with parents and teachers and providing collaborative feedback. The learning plan progress management unit, for example, can build a system for sharing learning progress data with parents and teachers and providing collaborative feedback. For example, learning progress can be shared through an online platform and parents and teachers can add comments. The learning plan progress management unit can also set learning goals and manage progress in collaboration with parents and teachers. For example, it can create a learning plan taking into account the learning content and progress desired by parents and teachers. The learning plan progress management unit can also adjust the learning plan based on feedback from parents and teachers. For example, it can change the learning pace and content by incorporating the opinions of parents and teachers. In this way, a system for sharing learning progress with parents and teachers and providing collaborative feedback can be built.

[0041] The learning plan progress management unit can introduce a reward system according to learning progress, thereby maintaining student motivation. The learning plan progress management unit, for example, introduces a reward system according to learning progress. For example, points are awarded when a specific goal is achieved, and the points can be used to earn rewards. The learning plan progress management unit can also evaluate learning progress based on the reward system and provide appropriate feedback. For example, learning progress is evaluated based on the point acquisition status, and the degree of achievement is evaluated. The learning plan progress management unit can also set learning goals based on the reward system and provide advice to maintain motivation. For example, short-term goals can be set, and rewards are given each time they are achieved. In this way, a reward system according to learning progress can be introduced, thereby maintaining student motivation.

[0042] The learning resource providing unit can dynamically adjust the difficulty level of the learning resources according to the student's level of comprehension. The learning resource providing unit, for example, builds a system that dynamically adjusts the difficulty level of the learning resources according to the student's level of comprehension. For example, if the level of comprehension is high, it recommends difficult learning materials, and if the level of comprehension is low, it recommends easy learning materials. The learning resource providing unit can also adjust the content of the learning resources according to the level of comprehension. For example, if the level of comprehension is high, it provides applied problems, and if the level of comprehension is low, it provides basic problems. The learning resource providing unit can also adjust the learning progress according to the level of comprehension. For example, if the level of comprehension is high, it speeds up the learning progress, and if the level of comprehension is low, it slows down the learning progress. In this way, the difficulty level of the learning resources can be dynamically adjusted according to the student's level of comprehension.

[0043] The learning resource providing unit can share learning resources with parents and teachers and build a system to collaboratively support learning. The learning resource providing unit, for example, shares learning resources with parents and teachers and builds a system to collaboratively support learning. For example, learning resources can be shared through an online platform and parents and teachers can add comments. The learning resource providing unit can also set learning goals and manage progress in collaboration with parents and teachers. For example, it can create a learning plan taking into account the learning content and progress desired by parents and teachers. The learning resource providing unit can also adjust learning resources based on feedback from parents and teachers. For example, it can change the learning pace and content by incorporating the opinions of parents and teachers. In this way, a system can be built to share learning resources with parents and teachers and collaboratively support learning.

[0044] The learning resource providing unit can collect students' feedback on learning resources and improve the quality of the resources. The learning resource providing unit, for example, builds a system for collecting students' feedback on learning resources and improving the quality of the resources. For example, the learning resource providing unit collects feedback through an online platform and improves the resources. The learning resource providing unit can also adjust the content of the resources based on students' feedback. For example, the content of the teaching materials can be updated based on the feedback. The learning resource providing unit can also set learning goals based on the feedback and provide advice for improving the quality of the resources. For example, the learning plan can be adjusted based on the feedback and high-quality resources can be provided. In this way, it is possible to collect students' feedback on learning resources and improve the quality of the resources.

[0045] The learning community introduction unit can analyze the activity history of learning communities and suggest the most active communities. The learning community introduction unit, for example, builds a system that collects and analyzes the activity history of learning communities. For example, the most active community is identified based on the number of posts in online forums and the activity of participants. The learning community introduction unit can also adjust the content of the community based on the activity history. For example, it can provide a community during times when activity is most active. The learning community introduction unit can also set learning goals based on the activity history and provide advice for providing an active community. For example, it can recommend communities where active members gather. In this way, the activity history of learning communities can be analyzed and the most active community can be suggested.

[0046] The learning community introduction unit can introduce specialized communities according to learning goals. The learning community introduction unit, for example, builds a system to introduce specialized communities based on the student's learning goals. For example, it recommends communities specialized in specific subjects or exams. The learning community introduction unit can also adjust the content of the community based on the learning goals. For example, it can provide a community where members with specialized knowledge according to the learning goals gather. The learning community introduction unit can also set learning goals based on the learning goals and provide advice for providing specialized communities. For example, it can recommend communities specialized in a specific academic field. This makes it possible to introduce specialized communities according to the student's learning goals.

[0047] The learning community introduction department can build a feedback system between members of the learning community and promote mutual learning. The learning community introduction department, for example, builds a feedback system between members of the learning community. For example, it adds a function that allows members to send comments and ratings to each other through an online platform. The learning community introduction department can also adjust the content of the community based on the feedback system. For example, it can change the content of the community activities based on the feedback. The learning community introduction department can also set learning goals based on the feedback system and provide advice to promote mutual learning. For example, it can adjust a learning plan based on feedback between members. In this way, a feedback system can be built between members of the learning community and promote mutual learning.

[0048] The learning community introduction section can propose online and offline hybrid communities and provide flexible methods of participation. For example, the learning community introduction section builds a system that proposes online and offline hybrid communities. For example, it proposes a community that combines online forums and offline study sessions. The learning community introduction section can also provide an integrated set of online and offline activities. For example, it proposes a community that combines online discussions and offline practical training. The learning community introduction section can also provide a system that allows for flexible switching between online and offline activities. For example, it recommends offline participation when online participation is difficult. This makes it possible to propose online and offline hybrid communities and provide flexible methods of participation.

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

[0050] The learning support system further includes a learning progress notification unit. The learning progress notification unit can notify parents and teachers of students' learning progress in real time. For example, the learning progress notification unit collects learning progress data through an online platform and sends emails or app notifications to parents and teachers. The learning progress notification unit can also send alerts to parents and teachers if students' learning progress is falling behind. This allows parents and teachers to understand the students' learning status and provide appropriate support.

[0051] The learning support system further includes a learning goal setting unit. The learning goal setting unit allows students, parents, and teachers to jointly set learning goals. For example, the learning goal setting unit provides an interface for goal setting through an online platform, allowing students, parents, and teachers to jointly set goals. The learning goal setting unit can also automatically generate a learning plan based on the set goals. This makes it possible to provide an effective learning plan tailored to the student's learning goals.

[0052] The learning support system further includes a learning resource customization unit. The learning resource customization unit can customize learning resources according to the student's learning style and level of comprehension. For example, the learning resource customization unit can provide visually-oriented students with learning materials that make extensive use of diagrams and graphs, and provide auditory-oriented students with audio learning materials and lecture videos. It can also adjust the difficulty level of the learning materials according to the student's level of comprehension. This makes it possible to provide optimal learning resources that match the student's learning style and level of comprehension.

[0053] The learning support system further includes a learning progress reporting unit. The learning progress reporting unit can periodically report the student's learning progress to parents and teachers. For example, the learning progress reporting unit can generate learning progress reports on a weekly or monthly basis and send them to parents and teachers via email or app notification. The learning progress reporting unit can also send alerts to parents and teachers if the student's learning progress is falling behind. This allows parents and teachers to understand the student's learning situation and provide appropriate support.

[0054] The learning assistance system further includes a learning progress assessment unit. The learning progress assessment unit can analyze the student's learning progress in real time and provide immediate feedback. For example, the learning progress assessment unit collects learning progress data through an online platform and provides immediate feedback. The learning progress assessment unit can also evaluate learning progress based on the data analyzed in real time and provide appropriate advice. This makes it possible to analyze learning progress data in real time and provide immediate feedback.

[0055] The learning support system further includes a learning resource sharing unit. The learning resource sharing unit can share learning resources with parents and teachers, thereby building a system for collaboratively supporting learning. For example, the learning resource sharing unit can share learning resources through an online platform and allow parents and teachers to add comments. The learning resource sharing unit can also set learning goals and manage progress in collaboration with parents and teachers. This makes it possible to share learning resources with parents and teachers, thereby building a system for collaboratively supporting learning.

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

[0057] Step 1: The learning status analysis unit analyzes the student's learning status. For example, the learning status analysis unit receives as input the student's grade data, learning history, and information about the target school and exam, and analyzes the learning status based on this. The learning status analysis unit can also evaluate the student's study time and level of understanding, and grasp the student's learning progress. Step 2: The learning method suggestion unit suggests the optimal learning method based on the results of the analysis by the learning situation analysis unit. For example, the learning method suggestion unit suggests methods such as individual instruction, group learning, or online learning. The learning method suggestion unit can also analyze the student's learning style (visual, auditory, tactile) and suggest the optimal learning method based on that. Furthermore, the learning method suggestion unit can also suggest the optimal study times and break times, taking into account the student's daily rhythm and health data. Step 3: The cram school information provider provides information on the most suitable cram schools and preparatory schools based on the study methods proposed by the study method suggestor. For example, the cram school information provider refers to a database of local cram schools and preparatory schools and recommends cram schools and preparatory schools that meet the student's needs. The cram school information provider can also provide information such as the evaluations of instructors at cram schools and preparatory schools, curriculum, and fees.

[0058] (Example 2) The learning support system according to an embodiment of the present invention uses generative AI to provide optimal learning methods to elementary, junior high, and high school students, as well as high school graduates, and also provides information on optimal cram schools and preparatory schools. This allows the learning support system to maximize the learning effect of each student and support them in achieving their goals.

[0059] A learning support system according to an embodiment includes a learning status analysis unit, a learning method suggestion unit, and a cram school information provision unit. The learning status analysis unit analyzes a student's learning status. For example, the learning status analysis unit receives as input the student's grade data, learning history, and information about the student's target school and exams, and analyzes the student's learning status based on the data. The learning status analysis unit can also evaluate the student's study time and level of understanding to monitor the student's learning progress. The learning method suggestion unit proposes an optimal learning method based on the results of the analysis by the learning status analysis unit. For example, the learning method suggestion unit proposes methods such as individual instruction, group learning, and online learning. The learning method suggestion unit can also analyze a student's learning style (visual, auditory, tactile) and propose an optimal learning method based on the results. Furthermore, the learning method suggestion unit can propose optimal study times and break times based on the student's daily rhythm and health data. The cram school information provision unit provides information on optimal cram schools and preparatory schools based on the learning method proposed by the learning method suggestion unit. For example, the cram school information provision unit references a database of local cram schools and preparatory schools to recommend cram schools and preparatory schools that meet the student's needs. The cram school information providing unit can also provide information such as the evaluations of instructors at cram schools and preparatory schools, curriculum, and fees. This allows the learning support system according to the embodiment to provide optimal learning methods and information on cram schools and preparatory schools based on the student's learning situation. For example, the learning support system can manage the student's learning progress and provide feedback as appropriate. The learning support system can also recommend resources such as online learning materials, reference books, and video lectures. Furthermore, the learning support system can also introduce learning communities that students can join.

[0060] The learning status analysis unit receives grade data, learning history, target schools, and exam information as input, and can analyze the learning status based on this. The learning status analysis unit, for example, collects student grade data and analyzes the learning status. For example, it grasps learning progress based on test scores and assignment evaluations. The learning status analysis unit also analyzes learning history and identifies learning trends and patterns. For example, it evaluates learning progress based on study time and learning content. The learning status analysis unit also sets learning goals based on target schools and exam information, and analyzes the learning status based on these. For example, it adjusts the learning plan taking into account entrance exam subjects and passing criteria. This makes it possible to analyze the learning status based on the student's grade data, learning history, and goals.

[0061] The learning method suggestion unit can collect emotional data and dynamically adjust the learning plan based on emotional fluctuations. The learning method suggestion unit, for example, analyzes the student's facial expressions and voice while studying to collect emotional data. For example, it uses a camera or microphone to monitor emotional fluctuations in real time and detects stress and fatigue. The learning method suggestion unit also dynamically adjusts the learning plan based on the emotional data. For example, it changes the pace or content of learning according to emotional fluctuations. The learning method suggestion unit can also provide advice to improve learning motivation based on the emotional data. For example, it sends an encouraging message if emotions are low. In this way, the learning plan can be dynamically adjusted based on the student's emotional data.

[0062] The learning method suggestion unit can analyze learning styles and suggest learning methods based on the results. For example, the learning method suggestion unit analyzes students' learning styles through questionnaires and tests to identify whether they have strengths in visual, auditory, or tactile learning. For example, the learning method suggestion unit suggests learning materials that make extensive use of diagrams and graphs to visual students. Also, it suggests audio learning materials and lecture videos to auditory students. And it suggests experiments and hands-on learning activities to tactile students. In this way, it is possible to suggest optimal learning methods based on students' learning styles.

[0063] The study method suggestion unit can suggest study times and break times taking into account the student's lifestyle rhythm and health data. The study method suggestion unit, for example, collects and analyzes the student's lifestyle rhythm and health data from a wearable device. For example, it identifies the optimal study time based on sleep patterns and heart rate. The study method suggestion unit also suggests break times based on the health data. For example, it may advise the student to take a break when their concentration is low. The study method suggestion unit can also suggest a study schedule that matches the student's lifestyle rhythm. For example, it may recommend studying early in the morning for a student who is a morning person. This makes it possible to suggest optimal study times and break times based on the student's lifestyle rhythm and health data.

[0064] The learning method suggestion unit can incorporate feedback from parents and teachers and dynamically adjust the learning plan. For example, the learning method suggestion unit builds a system that collects feedback from parents and teachers and reflects it in the learning plan. For example, parents and teachers evaluate the student's learning situation and adjust the learning plan based on that evaluation. The learning method suggestion unit can also incorporate the opinions of parents and teachers to set learning goals. For example, it creates a learning plan taking into account the learning content and progress desired by parents and teachers. This makes it possible to dynamically adjust the learning plan by incorporating feedback from parents and teachers.

[0065] The learning method suggestion unit can incorporate hobbies and interests into learning plans to improve motivation. The learning method suggestion unit, for example, collects information about students' hobbies and interests through questionnaires and interviews and reflects this information in learning plans. For example, it can set sports-related questions for students who like sports, or provide music-related learning materials for students who like music. The learning method suggestion unit can also set learning goals based on hobbies and interests. For example, it can incorporate projects related to hobbies as part of learning. In this way, by incorporating students' hobbies and interests into learning plans, it is possible to improve their motivation to learn.

[0066] The learning method suggestion unit can use the emotion estimation function to suggest an environment in which the student can best concentrate. For example, the learning method suggestion unit can use the emotion estimation function to identify an environment in which the student can best concentrate. For example, it can suggest a quiet environment or an environment in which the student can study while listening to music. The learning method suggestion unit can also adjust the learning environment based on the emotion data. For example, it can advise the student to study during times when their emotions are stable. The learning method suggestion unit can also suggest environmental settings to improve concentration. For example, it can set appropriate lighting and temperature. In this way, the learning effect can be maximized by suggesting an environment in which the student can best concentrate.

[0067] The cram school information providing unit can recommend the most suitable instructor based on instructor evaluations and student emotional data. The cram school information providing unit, for example, collects instructor evaluation data from cram schools and preparatory schools and combines it with student emotional data to recommend the most suitable instructor. For example, it prioritizes the recommendation of instructors who are highly rated and well-liked based on student emotional data. The cram school information providing unit can also make recommendations taking into account the instructor's field of expertise and teaching experience. For example, it can recommend instructors who are strong in specific subjects. The cram school information providing unit can also recommend instructors who suit the student's learning style. For example, it can recommend instructors who make extensive use of visual teaching materials to visually-oriented students. This makes it possible to recommend the most suitable instructor based on instructor evaluations and student emotional data.

[0068] The cram school information provider can match curriculums with learning goals and suggest the most suitable program. For example, the cram school information provider can collect curriculum data from cram schools and preparatory schools and build a system that matches it with students' learning goals. For example, it can recommend cram schools with curricula specialized in preparing for specific exams. The cram school information provider can also suggest programs taking into account the progress and content of the curriculum. For example, it can suggest programs that involve intensive study over a short period of time. The cram school information provider can also suggest curricula that suit students' learning styles. For example, it can suggest programs that combine online learning and face-to-face classes. This makes it possible to match curriculums with learning goals and suggest the most suitable program.

[0069] The cram school information providing unit can analyze the pass rate data and provide options with a high success rate. The cram school information providing unit, for example, collects data on the past pass rates of cram schools and preparatory schools and builds a system that provides options with a high success rate. For example, it recommends cram schools with a high success rate in getting students into specific schools. The cram school information providing unit can also evaluate cram schools and preparatory schools based on the pass rate data and provide highly reliable information. For example, it evaluates cram schools based on the number of students who passed and the pass rate. The cram school information providing unit can also adjust students' study plans based on the pass rate data. For example, it can propose a study plan that focuses on subjects with a high pass rate. This makes it possible to provide options with a high success rate based on the past pass rate data.

[0070] The cram school information providing unit can analyze word-of-mouth and reviews and provide highly reliable information. The cram school information providing unit, for example, builds a system that collects and analyzes word-of-mouth and reviews of cram schools and preparatory schools. For example, it collects word-of-mouth data from online platforms and provides highly reliable information. The cram school information providing unit can also evaluate the content of word-of-mouth and reviews and select highly reliable information. For example, it provides information based on actual user ratings and certification by third-party organizations. The cram school information providing unit can also evaluate cram schools and preparatory schools based on word-of-mouth and reviews and create rankings. For example, it creates a ranking of cram schools based on word-of-mouth evaluation scores. This makes it possible to analyze word-of-mouth and reviews and provide highly reliable information.

[0071] The cram school information providing unit can use the emotion estimation function to recommend cram schools and prep schools that offer the most relaxing learning environments for students. For example, the cram school information providing unit uses the emotion estimation function to identify cram schools and prep schools that offer the most relaxing learning environments for students. For example, it recommends cram schools with environments that have a high level of relaxation. The cram school information providing unit can also evaluate the environments of cram schools and prep schools based on the emotion data. For example, it can identify time periods and locations that offer a high level of relaxation from the emotion data and recommend cram schools based on that. The cram school information providing unit can also provide advice for providing a relaxing learning environment. For example, it can recommend cram schools that offer appropriate lighting, temperature, and quiet locations. In this way, it is possible to recommend cram schools and prep schools that offer a relaxing learning environment using the emotion estimation function.

[0072] The learning plan progress management unit can propose motivation improvement measures according to the progress of learning based on the emotional data. The learning plan progress management unit, for example, builds a system that proposes motivation improvement measures according to the progress of learning based on the student's emotional data. For example, it can send encouraging messages when motivation is declining based on the emotional data. The learning plan progress management unit can also evaluate the progress of learning based on the emotional data and provide appropriate feedback. For example, it can send praise or comments that give a sense of accomplishment when emotions are stable. The learning plan progress management unit can also set learning goals based on the emotional data and provide advice to improve motivation. For example, it can set short-term goals and give rewards each time they are achieved. In this way, it is possible to propose motivation improvement measures according to the progress of learning based on the student's emotional data.

[0073] The learning plan progress management unit can analyze learning progress data in real time and provide immediate feedback. The learning plan progress management unit, for example, builds a system that collects and analyzes learning progress data in real time. For example, it collects learning progress data through an online platform and provides immediate feedback. The learning plan progress management unit can also evaluate learning progress based on the data analyzed in real time and provide appropriate advice. For example, if learning progress is lagging behind, it can advise adjusting the learning pace. The learning plan progress management unit can also set learning goals and manage progress based on the data collected in real time. For example, it can adjust goals according to learning progress and evaluate the degree of achievement. In this way, learning progress data can be analyzed in real time and feedback can be provided immediately.

[0074] The learning plan progress management unit can track learning history over the long term and provide feedback based on past data. The learning plan progress management unit, for example, builds a system that tracks a student's learning history over the long term and provides feedback based on past data. For example, it adjusts the current learning plan based on past grades and learning progress. The learning plan progress management unit can also identify learning trends and patterns based on long-term learning history and provide appropriate advice. For example, it can identify learning strengths and weaknesses from past data and adjust the learning plan based on that. The learning plan progress management unit can also set learning goals based on long-term learning history and manage progress. For example, it can set long-term goals and evaluate the degree of achievement. This makes it possible to track a student's learning history over the long term and provide feedback based on past data.

[0075] The learning plan progress management unit can build a system for sharing learning progress with parents and teachers and providing collaborative feedback. The learning plan progress management unit, for example, can build a system for sharing learning progress data with parents and teachers and providing collaborative feedback. For example, learning progress can be shared through an online platform and parents and teachers can add comments. The learning plan progress management unit can also set learning goals and manage progress in collaboration with parents and teachers. For example, it can create a learning plan taking into account the learning content and progress desired by parents and teachers. The learning plan progress management unit can also adjust the learning plan based on feedback from parents and teachers. For example, it can change the learning pace and content by incorporating the opinions of parents and teachers. In this way, a system for sharing learning progress with parents and teachers and providing collaborative feedback can be built.

[0076] The learning plan progress management unit can introduce a reward system according to learning progress, thereby maintaining student motivation. The learning plan progress management unit, for example, introduces a reward system according to learning progress. For example, points are awarded when a specific goal is achieved, and the points can be used to earn rewards. The learning plan progress management unit can also evaluate learning progress based on the reward system and provide appropriate feedback. For example, learning progress is evaluated based on the point acquisition status, and the degree of achievement is evaluated. The learning plan progress management unit can also set learning goals based on the reward system and provide advice to maintain motivation. For example, short-term goals can be set, and rewards are given each time they are achieved. In this way, a reward system according to learning progress can be introduced, thereby maintaining student motivation.

[0077] The learning plan progress management unit can use the emotion estimation function to suggest the timing when a student can receive the most positive feedback. The learning plan progress management unit, for example, uses the emotion estimation function to identify the timing when a student can receive the most positive feedback. For example, feedback is sent during times when positive emotions are most prevalent based on emotion data. The learning plan progress management unit can also adjust the content of the feedback based on the emotion data. For example, it can send praise or comments that give a sense of accomplishment during times when emotions are high. The learning plan progress management unit can also set learning goals based on the emotion data and provide advice for providing positive feedback. For example, it can set short-term goals and send positive feedback each time they are achieved. In this way, the emotion estimation function can be used to suggest the timing when a student can receive the most positive feedback.

[0078] The learning resource providing unit can recommend the most interesting learning materials based on the emotional data. The learning resource providing unit, for example, builds a system that recommends the most interesting learning materials based on the emotional data of students. For example, it identifies and recommends learning materials that are likely to increase interest from the emotional data. The learning resource providing unit can also adjust the content of the learning materials based on the emotional data. For example, it can provide learning materials that are interesting when emotions are high. The learning resource providing unit can also set learning goals based on the emotional data and provide advice on providing learning materials that are interesting. For example, it can set short-term goals and provide learning materials that are interesting each time a goal is achieved. In this way, it is possible to recommend the most interesting learning materials based on the emotional data of students.

[0079] The learning resource providing unit can dynamically adjust the difficulty level of the learning resources according to the student's level of comprehension. The learning resource providing unit, for example, builds a system that dynamically adjusts the difficulty level of the learning resources according to the student's level of comprehension. For example, if the level of comprehension is high, it recommends difficult learning materials, and if the level of comprehension is low, it recommends easy learning materials. The learning resource providing unit can also adjust the content of the learning resources according to the level of comprehension. For example, if the level of comprehension is high, it provides applied problems, and if the level of comprehension is low, it provides basic problems. The learning resource providing unit can also adjust the learning progress according to the level of comprehension. For example, if the level of comprehension is high, it speeds up the learning progress, and if the level of comprehension is low, it slows down the learning progress. In this way, the difficulty level of the learning resources can be dynamically adjusted according to the student's level of comprehension.

[0080] The learning resource providing unit can share learning resources with parents and teachers and build a system to collaboratively support learning. The learning resource providing unit, for example, shares learning resources with parents and teachers and builds a system to collaboratively support learning. For example, learning resources can be shared through an online platform and parents and teachers can add comments. The learning resource providing unit can also set learning goals and manage progress in collaboration with parents and teachers. For example, it can create a learning plan taking into account the learning content and progress desired by parents and teachers. The learning resource providing unit can also adjust learning resources based on feedback from parents and teachers. For example, it can change the learning pace and content by incorporating the opinions of parents and teachers. In this way, a system can be built to share learning resources with parents and teachers and collaboratively support learning.

[0081] The learning resource providing unit can collect students' feedback on learning resources and improve the quality of the resources. The learning resource providing unit, for example, builds a system for collecting students' feedback on learning resources and improving the quality of the resources. For example, the learning resource providing unit collects feedback through an online platform and improves the resources. The learning resource providing unit can also adjust the content of the resources based on students' feedback. For example, the content of the teaching materials can be updated based on the feedback. The learning resource providing unit can also set learning goals based on the feedback and provide advice for improving the quality of the resources. For example, the learning plan can be adjusted based on the feedback and high-quality resources can be provided. In this way, it is possible to collect students' feedback on learning resources and improve the quality of the resources.

[0082] The learning resource providing unit can use the emotion estimation function to recommend resources that allow students to study in the most relaxed manner. For example, the learning resource providing unit uses the emotion estimation function to identify resources that allow students to study in the most relaxed manner. For example, it recommends learning materials that provide a high level of relaxation. The learning resource providing unit can also adjust the content of resources based on the emotion data. For example, it can provide relaxing learning materials during times when students are most likely to relax. The learning resource providing unit can also set learning goals based on the emotion data and provide advice for providing relaxing resources. For example, it can provide relaxing music or a meditation guide. In this way, the emotion estimation function can be used to recommend resources that allow students to study in the most relaxed manner.

[0083] The learning community introduction unit can recommend the most relatable community based on the emotional data. The learning community introduction unit, for example, identifies the most relatable community based on the student's emotional data. For example, it recommends a community with a high degree of relatability based on the emotional data. The learning community introduction unit can also adjust the content of the community based on the emotional data. For example, it can provide a relatable community during a time period when relatability is high. The learning community introduction unit can also set learning goals based on the emotional data and provide advice for providing a relatable community. For example, it can recommend a community where members share common hobbies or goals gather. In this way, it can recommend the most relatable community based on the student's emotional data.

[0084] The learning community introduction unit can analyze the activity history of learning communities and suggest the most active communities. The learning community introduction unit, for example, builds a system that collects and analyzes the activity history of learning communities. For example, the most active community is identified based on the number of posts in online forums and the activity of participants. The learning community introduction unit can also adjust the content of the community based on the activity history. For example, it can provide a community during times when activity is most active. The learning community introduction unit can also set learning goals based on the activity history and provide advice for providing an active community. For example, it can recommend communities where active members gather. In this way, the activity history of learning communities can be analyzed and the most active community can be suggested.

[0085] The learning community introduction unit can introduce specialized communities according to learning goals. The learning community introduction unit, for example, builds a system to introduce specialized communities based on the student's learning goals. For example, it recommends communities specialized in specific subjects or exams. The learning community introduction unit can also adjust the content of the community based on the learning goals. For example, it can provide a community where members with specialized knowledge according to the learning goals gather. The learning community introduction unit can also set learning goals based on the learning goals and provide advice for providing specialized communities. For example, it can recommend communities specialized in a specific academic field. This makes it possible to introduce specialized communities according to the student's learning goals.

[0086] The learning community introduction department can build a feedback system between members of the learning community and promote mutual learning. The learning community introduction department, for example, builds a feedback system between members of the learning community. For example, it adds a function that allows members to send comments and ratings to each other through an online platform. The learning community introduction department can also adjust the content of the community based on the feedback system. For example, it can change the content of the community activities based on the feedback. The learning community introduction department can also set learning goals based on the feedback system and provide advice to promote mutual learning. For example, it can adjust a learning plan based on feedback between members. In this way, a feedback system can be built between members of the learning community and promote mutual learning.

[0087] The learning community introduction section can propose online and offline hybrid communities and provide flexible methods of participation. For example, the learning community introduction section builds a system that proposes online and offline hybrid communities. For example, it proposes a community that combines online forums and offline study sessions. The learning community introduction section can also provide an integrated set of online and offline activities. For example, it proposes a community that combines online discussions and offline practical training. The learning community introduction section can also provide a system that allows for flexible switching between online and offline activities. For example, it recommends offline participation when online participation is difficult. This makes it possible to propose online and offline hybrid communities and provide flexible methods of participation.

[0088] The learning community introduction unit can use the emotion estimation function to recommend a community in which students can participate most relaxedly. The learning community introduction unit, for example, uses the emotion estimation function to identify a community in which students can participate most relaxedly. For example, it recommends a community with a high level of relaxation. The learning community introduction unit can also adjust the content of the community based on the emotion data. For example, it can provide a community during times when students are most relaxed. The learning community introduction unit can also set learning goals based on the emotion data and provide advice to provide a relaxing community. For example, it can recommend a community that provides a stress-free environment. In this way, it is possible to use the emotion estimation function to recommend a community in which students can participate most relaxedly.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The learning support system further includes a learning progress notification unit. The learning progress notification unit can notify parents and teachers of students' learning progress in real time. For example, the learning progress notification unit collects learning progress data through an online platform and sends emails or app notifications to parents and teachers. The learning progress notification unit can also send alerts to parents and teachers if students' learning progress is falling behind. This allows parents and teachers to understand the students' learning status and provide appropriate support.

[0091] The learning support system further includes a learning goal setting unit. The learning goal setting unit allows students, parents, and teachers to jointly set learning goals. For example, the learning goal setting unit provides an interface for goal setting through an online platform, allowing students, parents, and teachers to jointly set goals. The learning goal setting unit can also automatically generate a learning plan based on the set goals. This makes it possible to provide an effective learning plan tailored to the student's learning goals.

[0092] The learning support system further includes a learning motivation improvement unit. The learning motivation improvement unit can provide advice to improve a student's motivation to learn based on the student's emotional data. For example, the learning motivation improvement unit can send encouraging messages when the student's motivation is declining based on the emotional data. The learning motivation improvement unit can also set learning goals based on the emotional data and provide rewards each time they are achieved. This makes it possible to improve a student's motivation to learn based on the student's emotional data.

[0093] The learning support system further includes a learning environment optimization unit. The learning environment optimization unit can propose an optimal learning environment based on the student's emotional data. For example, the learning environment optimization unit can identify an environment in which the student can best concentrate from the emotional data and propose a quiet environment or an environment where the student can study while listening to music. The learning environment optimization unit can also adjust the learning environment based on the emotional data. This makes it possible to maximize learning effectiveness by providing an environment in which the student can best concentrate.

[0094] The learning support system also includes a learning community collaboration unit. The learning community collaboration unit introduces learning communities that students can join, thereby improving their motivation to learn. For example, the learning community collaboration unit provides information on online forums and study groups, providing a place where students can interact with other students. The learning community collaboration unit can also recommend the community that students most empathize with based on emotional data. This allows students to participate in learning communities, thereby improving their motivation to learn.

[0095] The learning support system further includes a learning resource customization unit. The learning resource customization unit can customize learning resources according to the student's learning style and level of comprehension. For example, the learning resource customization unit can provide visually-oriented students with learning materials that make extensive use of diagrams and graphs, and provide auditory-oriented students with audio learning materials and lecture videos. It can also adjust the difficulty level of the learning materials according to the student's level of comprehension. This makes it possible to provide optimal learning resources that match the student's learning style and level of comprehension.

[0096] The learning support system further includes a learning progress reporting unit. The learning progress reporting unit can periodically report the student's learning progress to parents and teachers. For example, the learning progress reporting unit can generate learning progress reports on a weekly or monthly basis and send them to parents and teachers via email or app notification. The learning progress reporting unit can also send alerts to parents and teachers if the student's learning progress is falling behind. This allows parents and teachers to understand the student's learning situation and provide appropriate support.

[0097] The learning support system further includes a learning resource recommendation unit. The learning resource recommendation unit can recommend the most interesting learning materials based on the student's emotional data. For example, the learning resource recommendation unit identifies and recommends learning materials that are likely to pique the student's interest from the emotional data. The learning resource recommendation unit can also adjust the content of the learning materials based on the emotional data. This makes it possible to recommend the most interesting learning materials based on the student's emotional data.

[0098] The learning assistance system further includes a learning progress assessment unit. The learning progress assessment unit can analyze the student's learning progress in real time and provide immediate feedback. For example, the learning progress assessment unit collects learning progress data through an online platform and provides immediate feedback. The learning progress assessment unit can also evaluate learning progress based on the data analyzed in real time and provide appropriate advice. This makes it possible to analyze learning progress data in real time and provide immediate feedback.

[0099] The learning support system further includes a learning resource sharing unit. The learning resource sharing unit can share learning resources with parents and teachers, thereby building a system for collaboratively supporting learning. For example, the learning resource sharing unit can share learning resources through an online platform and allow parents and teachers to add comments. The learning resource sharing unit can also set learning goals and manage progress in collaboration with parents and teachers. This makes it possible to share learning resources with parents and teachers, thereby building a system for collaboratively supporting learning.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The learning status analysis unit analyzes the student's learning status. For example, the learning status analysis unit receives as input the student's grade data, learning history, and information about the target school and exam, and analyzes the learning status based on this. The learning status analysis unit can also evaluate the student's study time and level of understanding, and grasp the student's learning progress. Step 2: The learning method suggestion unit suggests the optimal learning method based on the results of the analysis by the learning situation analysis unit. For example, the learning method suggestion unit suggests methods such as individual instruction, group learning, or online learning. The learning method suggestion unit can also analyze the student's learning style (visual, auditory, tactile) and suggest the optimal learning method based on that. Furthermore, the learning method suggestion unit can also suggest the optimal study times and break times, taking into account the student's daily rhythm and health data. Step 3: The cram school information provider provides information on the most suitable cram schools and preparatory schools based on the study methods proposed by the study method suggestor. For example, the cram school information provider refers to a database of local cram schools and preparatory schools and recommends cram schools and preparatory schools that meet the student's needs. The cram school information provider can also provide information such as the evaluations of instructors at cram schools and preparatory schools, curriculum, and fees.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a learning situation analysis unit that analyzes the learning situation of students; a learning method suggestion unit that suggests an optimal learning method based on the results of the analysis by the learning situation analysis unit; and a cram school information providing unit that provides information on optimal cram schools and preparatory schools based on the study methods proposed by the study method suggesting unit. A system characterized by:

2. The learning method suggestion unit Collect emotional data and dynamically adjust learning plans based on emotional fluctuations 2. The system of claim 1.

3. The cram school information providing unit Recommending the most suitable instructor based on the instructor's evaluation and the student's emotional data 2. The system of claim 1.

4. The learning plan progress management section Based on emotional data, we propose measures to improve motivation according to learning progress.

2. The system of claim 1.

5. The Learning Resources Department Recommend the most interesting learning materials based on emotional data 2. The system of claim 1.

6. The Learning Community Introduction Department Recommending the most relatable community based on emotional data 2. The system of claim 1.

7. The learning method suggestion unit Using emotion estimation, we suggest an environment where the student can concentrate best.

2. The system of claim 1.

8. The cram school information providing unit Using an emotion estimation function, the cram school or preparatory school that provides the most relaxing learning environment for the student is recommended.

2. The system of claim 1.

Images