System

Abstract

An object of the system according to the embodiment is to make an effective learning plan based on a learning situation of a student and to propose an appropriate support method to a protector.SOLUTION: A system includes a collection part, a planning part, a support part, and a monitoring part. The collection unit collects a learning status of a student. The planning unit makes a learning plan based on the data collected by the collection unit. The support unit proposes a support method to the protector on the basis of the study plan made by the planning unit. The monitoring unit monitors the progress status of the student based on the study plan made by the planning unit, and modifies the study plan as necessary.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 technology has the drawback of making it difficult to properly grasp students' learning situations and create effective learning plans.

[0005] The system according to the embodiment aims to create an effective learning plan based on the student's learning situation and to propose appropriate support methods to parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a planning unit, a support unit, and a monitoring unit. The collection unit collects data on students' learning situations. The planning unit creates a learning plan based on the data collected by the collection unit. The support unit suggests support methods to parents based on the learning plan created by the planning unit. The monitoring unit monitors students' progress based on the learning plan created by the planning unit and modifies the learning plan as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can create effective learning plans based on the student's learning situation and can suggest appropriate support methods to parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A planning system according to an embodiment of the present invention automatically collects data on students' learning status, creates a learning plan using a generation AI, suggests support methods to parents, monitors the student's progress, and modifies the learning plan accordingly. The planning system collects data on students' learning status, creates a learning plan using a generation AI, suggests support methods to parents, monitors the student's progress, and modifies the learning plan accordingly. For example, the planning system collects data such as the student's past grades, mock test results, and current learning progress. The generation AI then analyzes the student's test results to identify the student's strong and weak subjects. The generation AI then sets goals for the student, including the passing grades for the school of their choice and target mock test scores. The generation AI then proposes appropriate support methods to the parents. For example, the generation AI provides specific advice on how parents can support the student. The generation AI then monitors the student's progress and modifies the learning plan as needed. For example, if the student is not progressing as planned, the generation AI reviews the learning plan and makes appropriate modifications. This allows the planning system to provide effective learning support even when parental involvement is difficult. This allows the planning system to provide effective learning support by gaining a detailed understanding of students' learning situations, creating individual learning plans, proposing appropriate support methods to parents, and monitoring students' progress to revise the learning plans. For example, even if parents have no experience preparing for junior high school entrance exams, the generative AI can support students' learning by proposing appropriate support methods. In addition, the generative AI can monitor students' progress and revise the learning plans as necessary to maximize the effectiveness of students' learning. This reduces the burden on parents and provides effective support for students' learning.

[0029] The planning system according to the embodiment includes a collection unit, a planning unit, a support unit, and a monitoring unit. The collection unit collects information about a student's learning status. The student's learning status includes, but is not limited to, past grades, mock test results, and current learning progress. For example, the collection unit acquires the student's past grades from a database. The collection unit can also analyze mock test results to determine the student's current learning progress. The collection unit can also collect data such as the student's learning attitude and the number of times the student speaks during class. For example, the collection unit acquires the student's past grades from a database, analyzes mock test results, and determines the student's current learning progress. The planning unit creates a study plan based on the data collected by the collection unit. The study plan includes, but is not limited to, the pass mark for the desired school and the target score for the mock test. For example, the planning unit sets the pass mark for the desired school and the target score for the mock test. The planning unit can also create a specific study plan. For example, the planning unit plans in detail the daily study time, study content, and review timing. The support unit suggests support methods to parents based on the study plan drawn up by the planning unit. Support methods include, but are not limited to, support for study at home, improving the study environment, and methods to increase motivation. For example, the support unit provides specific action plans on how parents can support their students. The support unit can also suggest effective support methods even if the parents have no experience preparing for junior high school entrance exams. The monitoring unit monitors the student's progress based on the study plan drawn up by the planning unit and revise the study plan as necessary. For example, if the student is not progressing as planned, the monitoring unit reviews the study plan and makes appropriate revisions. The monitoring unit can also monitor the student's progress in real time and revise the study plan as appropriate. For example, the monitoring unit monitors the student's study progress in real time and, if the student is not progressing as planned, reviews the study plan and makes appropriate revisions.As a result, the planning system according to the embodiment can provide effective learning support by gaining a detailed understanding of students' learning situations, creating individual learning plans, proposing appropriate support methods to parents, and monitoring students' progress to revise their learning plans.

[0030] The collection unit can collect a student's past grades, mock test results, current learning progress, and other related data. For example, the collection unit obtains a student's past grades from a database. The collection unit can also analyze mock test results to understand the student's current learning progress. The collection unit can also collect data such as the student's learning attitude and the number of times the student speaks during class. For example, the collection unit obtains a student's past grades from a database, analyzes mock test results, and understands the student's current learning progress. The collection unit can also observe the student's learning attitude and record the number of times the student speaks during class. This allows the collection unit to collect data to understand the student's learning situation in detail. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input a student's past grade data into a generation AI, which can analyze the data to understand the student's learning situation.

[0031] The planning unit can set the passing grade for the desired school and the target score for mock exams based on the collected data, and create a specific study plan. For example, the planning unit sets the passing grade for the desired school and the target score for mock exams. The planning unit can also create a specific study plan. For example, the planning unit plans in detail the daily study time, study content, review timing, etc. The planning unit can also create a study plan taking into account the student's strong and weak subjects. For example, the planning unit creates a plan to focus on the student's strong subjects and strengthen their weak subjects. The planning unit can also create a study plan taking into account the student's learning pace and concentration. For example, the planning unit creates a study plan based on the time period when the student's concentration is sustained. This allows the planning unit to create a specific study plan according to the student's goals. Some or all of the above-mentioned processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit can input the collected data into a generation AI, which can then create a study plan.

[0032] The support unit can provide parents with specific methods for supporting their students and specific action plans. For example, the support unit provides a specific action plan on how parents should support their students. The support unit can also suggest effective support methods even if parents have no experience preparing for junior high school entrance exams. For example, the support unit suggests ways to ensure study time at home, improve the learning environment, and increase motivation. The support unit can also provide specific action plans for parents to support their students' learning. For example, the support unit provides a specific action plan for parents to support their students' learning. This allows the support unit to provide a specific action plan for parents to effectively support their students. Some or all of the above-mentioned processing in the support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the support unit can input parents' support methods into a generation AI, which then suggests an optimal action plan.

[0033] The monitoring unit monitors the student's progress, and if the student is not progressing in his / her studies according to the plan, the monitoring unit can review the study plan and make appropriate corrections. For example, if the student is not progressing in his / her studies according to the plan, the monitoring unit can review the study plan and make appropriate corrections. The monitoring unit can also monitor the student's progress in real time and revise the study plan as appropriate. For example, the monitoring unit monitors the student's study progress in real time, and if the student is not progressing in his / her studies according to the plan, the monitoring unit can review the study plan and make appropriate corrections. In this way, the monitoring unit can monitor the student's progress and revise the study plan as necessary to maximize the learning effect. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the student's progress into the generation AI, which can analyze the progress and revise the study plan.

[0034] The collection unit can analyze the student's learning environment and select an appropriate data collection method. For example, if the student is studying in a quiet environment, the generation AI can collect data using voice input. Furthermore, if the student is studying in a noisy environment, the collection unit can also collect data using text input. Furthermore, if the student is studying while on the move, the collection unit can also collect data using a mobile device. For example, if the student is studying in a quiet environment, the collection unit can collect data using voice input. Furthermore, if the student is studying in a noisy environment, the collection unit can also collect data using text input. Furthermore, if the student is studying while on the move, the collection unit can also collect data using a mobile device. This allows the collection unit to select the optimal data collection method according to the student's learning environment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's learning environment data into the generation AI, which can then select the optimal data collection method.

[0035] The collection unit can adjust the data collection means according to the student's learning style. For example, in the case of a visual learner, the generation AI can collect data using images or videos. In the case of an auditory learner, the collection unit can also collect data using audio data. In the case of a tactile learner, the collection unit can also collect data using interactive tools. For example, in the case of a visual learner, the collection unit can collect data using images or videos. In the case of an auditory learner, the collection unit can also collect data using audio data. In the case of a tactile learner, the collection unit can also collect data using interactive tools. This allows the collection unit to provide an optimal data collection means according to the student's learning style. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's learning style data into the generation AI, which can then provide the optimal data collection means.

[0036] The collection unit can analyze the student's learning history in real time and automatically collect necessary data. For example, when a student is studying a new unit, the generation AI collects data related to that unit in real time. Furthermore, when a student is reviewing, the collection unit can also have the generation AI collect data based on the student's past learning history. Furthermore, when a student is taking a mock test, the collection unit can also have the generation AI collect mock test results in real time. For example, when a student is studying a new unit, the collection unit can collect data related to that unit in real time. Furthermore, when a student is reviewing, the collection unit can also have the generation AI collect data based on the student's past learning history. Furthermore, when a student is taking a mock test, the collection unit can also have the generation AI collect mock test results in real time. This allows the collection unit to analyze the student's learning history in real time and automatically collect necessary data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input students' learning history data into the generation AI, which can then analyze it in real time and automatically collect the necessary data.

[0037] The collection unit can set an optimal data collection schedule based on the student's lifestyle rhythm. For example, if the student is a morning person, the collection unit causes the generation AI to collect data in the morning. Furthermore, if the student is a night owl, the collection unit can also cause the generation AI to collect data in the evening. Furthermore, if the student has an irregular lifestyle rhythm, the collection unit can also cause the generation AI to collect data on a flexible schedule. For example, if the student is a morning person, the collection unit causes the generation AI to collect data in the morning. Furthermore, if the student is a night owl, the collection unit can also cause the generation AI to collect data in the evening. Furthermore, if the student has an irregular lifestyle rhythm, the collection unit can also cause the generation AI to collect data on a flexible schedule. This allows the collection unit to set an optimal data collection schedule according to the student's lifestyle rhythm. Some or all of the above-described processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's lifestyle rhythm data into the generation AI, which can then set an optimal data collection schedule.

[0038] The collection unit can analyze the students' social media activities and collect data related to learning. For example, the collection unit causes the generation AI to collect learning content shared by the students on social media. The collection unit can also cause the generation AI to collect information on education-related accounts followed by the students on social media. The collection unit can also cause the generation AI to collect activities of learning groups in which the students participate on social media. For example, the collection unit causes the generation AI to collect learning content shared by the students on social media. The collection unit can also cause the generation AI to collect information on education-related accounts followed by the students on social media. The collection unit can also cause the generation AI to collect activities of learning groups in which the students participate on social media. This allows the collection unit to collect data related to learning based on the students' social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the students' social media activity data into the generation AI, which then collects data related to learning.

[0039] The collection unit can customize the data collection method by reflecting the student's past feedback. For example, the collection unit causes the generation AI to prioritize use of study methods that the student previously preferred. The collection unit can also cause the generation AI to eliminate study methods that the student previously avoided. The collection unit can also cause the generation AI to propose an optimal data collection method based on the student's past feedback. For example, the collection unit causes the generation AI to prioritize use of study methods that the student previously preferred. The collection unit can also cause the generation AI to eliminate study methods that the student previously avoided. The collection unit can also cause the generation AI to propose an optimal data collection method based on the student's past feedback. This allows the collection unit to provide an optimal data collection method based on the student's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the student's past feedback data into the generation AI, and the generation AI can propose an optimal data collection method.

[0040] When creating a study plan, the planning unit can optimize the plan based on the student's learning pace and concentration. For example, the planning unit creates a study plan based on a time period during which the student can maintain concentration. The planning unit can also create a study plan that appropriately includes break times according to the student's learning pace. The planning unit can also create a study plan that avoids time periods during which the student's concentration is low. For example, the planning unit creates a study plan based on a time period during which the student can maintain concentration. The planning unit can also create a study plan that appropriately includes break times according to the student's learning pace. The planning unit can also create a study plan that avoids time periods during which the student's concentration is low. This allows the planning unit to provide an optimal study plan based on the student's learning pace and concentration. Some or all of the above-described processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit inputs the student's learning pace data into the generation AI, which can then create an optimal study plan.

[0041] The planning unit can balance the student's strong and weak subjects when formulating a study plan. For example, the planning unit creates a study plan that alternates between the student's strong and weak subjects. The planning unit can also create a plan to have the student study the student's strong subjects first, increase their motivation, and then study the weak subjects. The planning unit can also create a plan to focus on the student's weak subjects and refresh them with the strong subjects. For example, the planning unit creates a study plan that alternates between the student's strong and weak subjects. The planning unit can also create a plan to have the student study the student's strong subjects first, increase their motivation, and then study the weak subjects. The planning unit can also create a plan to focus on the student's weak subjects and refresh them with the strong subjects. In this way, the planning unit can provide a study plan that balances the student's strong and weak subjects. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the planning department can input data on a student's strong and weak subjects into the generation AI, which can then create an optimal study plan.

[0042] When creating a study plan, the planning unit can improve the accuracy of the plan by referring to the student's past learning results. The planning unit, for example, creates a study plan based on the student's past test results. The planning unit can also create a study plan based on the student's past mock test results. The planning unit can also create a study plan based on the student's past learning history. For example, the planning unit creates a study plan based on the student's past test results. The planning unit can also create a study plan based on the student's past mock test results. The planning unit can also create a study plan based on the student's past learning history. This allows the planning unit to improve the accuracy of the study plan by referring to the student's past learning results. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the planning unit inputs the student's past learning result data into the generation AI, which can then create an optimal study plan.

[0043] When creating a study plan, the planning unit can adjust study times based on the student's lifestyle rhythm. For example, if the student is a morning person, the planning unit can create a plan to concentrate study in the morning. Furthermore, if the student is a night owl, the planning unit can create a plan to concentrate study in the evening. Furthermore, the planning unit can create a flexible study plan if the student's lifestyle rhythm is irregular. For example, if the student is a morning person, the planning unit can create a plan to concentrate study in the morning. Furthermore, if the student is a night owl, the planning unit can create a plan to concentrate study in the evening. Furthermore, the planning unit can create a flexible study plan if the student's lifestyle rhythm is irregular. This allows the planning unit to provide optimal study times according to the student's lifestyle rhythm. Some or all of the above-described processing in the planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the planning unit can input the student's lifestyle rhythm data into the generation AI, which can then provide optimal study times.

[0044] The planning unit can customize the learning content based on the student's interests when creating a learning plan. For example, the planning unit creates a plan to prioritize learning of subjects that the student is interested in. The planning unit can also create a plan to incorporate topics that the student is interested in into the learning content. The planning unit can also create a plan to customize the learning content based on the student's interests. For example, the planning unit creates a plan to prioritize learning of subjects that the student is interested in. The planning unit can also create a plan to incorporate topics that the student is interested in into the learning content. The planning unit can also create a plan to customize the learning content based on the student's interests. This allows the planning unit to provide learning content based on the student's interests. Some or all of the above-described processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit can input data on the student's interests into the generation AI, which can then provide optimal learning content.

[0045] When creating a study plan, the planning unit can adjust the plan according to the student's future goals. For example, the planning unit creates a study plan that is tailored to the student's preferred school. The planning unit can also create a study plan that is tailored to the student's future career goals. The planning unit can also adjust the study plan according to the student's future goals. For example, the planning unit creates a study plan that is tailored to the student's preferred school. The planning unit can also create a study plan that is tailored to the student's future career goals. The planning unit can also adjust the study plan according to the student's future goals. This allows the planning unit to provide a study plan that is tailored to the student's future goals. Some or all of the above-described processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit can input data on the student's future goals into the generation AI, which can then provide an optimal study plan.

[0046] The support unit can analyze the parent's past support history and propose an optimal support method. For example, the support unit allows the generation AI to propose an optimal support method based on the parent's past support methods. The support unit can also analyze the parent's past support history and allow the generation AI to propose an effective support method. The support unit can also propose a customized support method based on the parent's past support history. For example, the support unit allows the generation AI to propose an optimal support method based on the parent's past support methods. The support unit can also analyze the parent's past support history and allow the generation AI to propose an effective support method. The support unit can also propose a customized support method based on the parent's past support history. In this way, the support unit can provide an optimal support method based on the parent's past support history. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input parent's past support history data into the generation AI, and the generation AI can propose an optimal support method.

[0047] The support unit can customize the support plan based on the guardian's lifestyle and work schedule. For example, if the guardian is busy, the generation AI can propose a simple support plan. Furthermore, if the guardian has time, the generation AI can also propose a detailed support plan. Furthermore, the support unit can also propose a flexible support plan that matches the guardian's lifestyle and work schedule. For example, if the guardian is busy, the generation AI can propose a simple support plan. Furthermore, if the guardian has time, the generation AI can also propose a detailed support plan. Furthermore, the support unit can also propose a flexible support plan that matches the guardian's lifestyle and work schedule. This allows the support unit to provide an optimal support plan that matches the guardian's lifestyle and work schedule. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input the guardian's lifestyle data and work schedule data into the generation AI, which can then propose an optimal support plan.

[0048] The support unit can improve the support method by reflecting the parent's feedback. For example, the support unit causes the generation AI to improve the support method based on the parent's feedback. The support unit can also analyze the parent's past feedback and cause the generation AI to suggest an effective support method. The support unit can also reflect the parent's feedback and cause the generation AI to provide a customized support method. For example, the support unit causes the generation AI to improve the support method based on the parent's feedback. The support unit can also analyze the parent's past feedback and cause the generation AI to suggest an effective support method. The support unit can also reflect the parent's feedback and cause the generation AI to provide a customized support method. This allows the support unit to provide an optimal support method based on the parent's feedback. Some or all of the above-mentioned processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input parent's feedback data into the generation AI, and the generation AI can suggest an optimal support method.

[0049] The support unit can propose an optimal support method taking into account the geographical location information of the guardian. For example, when the guardian is at home, the support unit can have the generation AI propose a support method that can be performed at home. Furthermore, when the guardian is out, the support unit can also propose a support method that the generation AI can perform while away from home. Furthermore, when the guardian is at work, the support unit can also propose a support method that the generation AI can perform at work. For example, when the guardian is at home, the support unit can propose a support method that the generation AI can perform at home. Furthermore, when the guardian is out, the support unit can also propose a support method that the generation AI can perform while away from home. Furthermore, when the guardian is at work, the support unit can also propose a support method that the generation AI can perform at work. This allows the support unit to provide an optimal support method based on the geographical location information of the guardian. Some or all of the above-mentioned processing in the support unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the support unit can input the guardian's geographical location information data into the generation AI, which can then propose an optimal support method.

[0050] The support unit can analyze the parent's social media activity and suggest relevant support methods. For example, the support unit can have the generation AI suggest support methods based on information shared by the parent on social media. The support unit can also have the generation AI suggest support methods based on information about education-related accounts followed by the parent on social media. The support unit can also have the generation AI suggest support methods based on the activities of groups the parent participates in on social media. For example, the support unit can have the generation AI suggest support methods based on information shared by the parent on social media. The support unit can also have the generation AI suggest support methods based on information about education-related accounts followed by the parent on social media. The support unit can also have the generation AI suggest support methods based on the activities of groups the parent participates in on social media. This allows the support unit to provide optimal support methods based on the parent's social media activity. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input parent's social media activity data into the generation AI, which then suggests optimal support methods.

[0051] The support unit can customize the support plan by reflecting the parent's past feedback. In the support unit, for example, the generation AI customizes the support plan based on the parent's past feedback. The support unit can also analyze the parent's feedback and have the generation AI propose an effective support plan. The support unit can also reflect the parent's feedback and have the generation AI provide an optimal support plan. For example, the support unit customizes the support plan based on the parent's past feedback. The support unit can also analyze the parent's feedback and have the generation AI propose an effective support plan. The support unit can also reflect the parent's feedback and have the generation AI provide an optimal support plan. In this way, the support unit can provide an optimal support plan based on the parent's past feedback. Some or all of the above-described processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the parent's past feedback data into the generation AI, and the generation AI can propose an optimal support plan.

[0052] The monitoring unit can analyze the student's learning data in real time and provide immediate feedback on the progress status. For example, when the student is studying a new unit, the monitoring unit can provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is taking a mock test, the monitoring unit can also provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is reviewing, the monitoring unit can also provide the generation AI with real-time feedback on the progress status. For example, when the student is studying a new unit, the monitoring unit can provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is taking a mock test, the monitoring unit can also provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is reviewing, the monitoring unit can analyze the student's learning data in real time and provide immediate feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the student's learning data to the generation AI, which can analyze the data in real time and provide immediate feedback on the progress status.

[0053] The monitoring unit monitors changes in the student's learning environment and can revise the learning plan as necessary. For example, if the student's learning environment changes, the generation AI revise the learning plan. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment is noisy. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment becomes quiet. For example, if the student's learning environment changes, the monitoring unit can cause the generation AI to revise the learning plan. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment is noisy. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment becomes quiet. In this way, the monitoring unit can revise the learning plan in accordance with changes in the student's learning environment. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input student's learning environment data into the generation AI, and the generation AI can revise the learning plan based on changes in the learning environment.

[0054] The monitoring unit can customize the monitoring method according to the student's learning style. For example, in the case of a visual learner, the generation AI can provide a visual monitoring method. Furthermore, in the case of an auditory learner, the monitoring unit can also provide an audio monitoring method. Furthermore, in the case of a tactile learner, the generation AI can also provide an interactive monitoring method. For example, in the case of a visual learner, the generation AI can provide a visual monitoring method. Furthermore, in the case of an auditory learner, the monitoring unit can also provide an audio monitoring method. Furthermore, in the case of a tactile learner, the generation AI can also provide an interactive monitoring method. This allows the monitoring unit to provide an optimal monitoring method according to the student's learning style. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the student's learning style data into the generation AI, which can then provide the optimal monitoring method.

[0055] The monitoring unit can set an optimal monitoring schedule based on the student's lifestyle rhythm. For example, if the student is a morning person, the generation AI can perform monitoring in the morning. Also, if the student is a night owl, the monitoring unit can have the generation AI perform monitoring in the evening. Also, if the student's lifestyle rhythm is irregular, the monitoring unit can have the generation AI set a flexible monitoring schedule. For example, if the student is a morning person, the monitoring unit can have the generation AI perform monitoring in the morning. Also, if the student is a night owl, the monitoring unit can have the generation AI perform monitoring in the evening. Also, if the student's lifestyle rhythm is irregular, the monitoring unit can have the generation AI set a flexible monitoring schedule. This allows the monitoring unit to provide an optimal monitoring schedule according to the student's lifestyle rhythm. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the student's lifestyle rhythm data into the generation AI, which can set an optimal monitoring schedule.

[0056] The monitoring unit can analyze the student's social media activity and monitor progress data related to learning. For example, the monitoring unit uses the generation AI to monitor the learning content shared by the student on social media. The monitoring unit can also use the generation AI to monitor information about education-related accounts followed by the student on social media. The monitoring unit can also use the generation AI to monitor the activities of learning groups in which the student participates on social media. For example, the monitoring unit uses the generation AI to monitor the learning content shared by the student on social media. The monitoring unit can also use the generation AI to monitor information about education-related accounts followed by the student on social media. The monitoring unit can also use the generation AI to monitor the activities of learning groups in which the student participates on social media. This allows the monitoring unit to provide optimal progress data based on the student's social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the student's social media activity data into the generation AI, which then monitors the progress data related to learning.

[0057] The monitoring unit can customize the monitoring method by reflecting the student's past feedback. In the monitoring unit, for example, the generation AI customizes the monitoring method based on the student's past feedback. The monitoring unit can also analyze the student's feedback and have the generation AI suggest an effective monitoring method. The monitoring unit can also reflect the student's feedback and have the generation AI provide an optimal monitoring method. For example, the monitoring unit customizes the monitoring method based on the student's past feedback. The monitoring unit can also analyze the student's feedback and have the generation AI suggest an effective monitoring method. The monitoring unit can also reflect the student's feedback and have the generation AI provide an optimal monitoring method. In this way, the monitoring unit can provide an optimal monitoring method based on the student's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the student's past feedback data into the generation AI, and the generation AI can suggest an optimal monitoring method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can collect physiological data such as health status and sleep patterns in addition to students' learning status. For example, the collection unit can monitor students' heart rates and sleep times to provide data to maximize learning efficiency. The collection unit can also record students' dietary habits and exercise amounts and reflect this data in their learning plans. Furthermore, the collection unit can measure students' stress levels and adjust the learning load to reduce it if stress levels are high. This allows the collection unit to collect data that takes into account the students' overall health status.

[0060] The planning department can incorporate game elements into students' learning plans. For example, the planning department can allow students to earn points or badges when they achieve certain goals. The planning department can also provide learning content in the form of quests, allowing students to progress through their studies while having fun. Furthermore, the planning department can introduce a ranking system that allows students to compete with their friends, increasing students' motivation. This allows the planning department to incorporate ideas to increase students' motivation to learn.

[0061] The monitoring unit can monitor not only students' learning progress, but also their concentration and attention while studying. For example, the monitoring unit measures how focused a student is while studying and suggests taking a break if their concentration level drops. The monitoring unit can also issue an alert to bring a student back to attention if their attention becomes distracted while studying. Furthermore, the monitoring unit can adjust the study plan based on the student's concentration and attention data. In this way, the monitoring unit can provide support to maximize students' learning efficiency.

[0062] The planning department can incorporate reflection time into students' learning plans. For example, the planning department can set aside time at the end of each day for students to reflect on what they learned that day. The planning department can also set up a weekly session for students to self-evaluate their learning progress. Furthermore, the planning department can provide reflection time for students to summarize their learning results each month and set goals for the next month. In this way, the planning department can provide support for students to improve the quality of their learning through self-evaluation.

[0063] The monitoring unit can provide a dashboard for visually displaying the student's learning progress. For example, the monitoring unit can display the student's learning progress in graphs and charts to make it visually easy to understand. The monitoring unit can also display the student's degree of achievement of learning goals in a color-coded manner. Furthermore, the monitoring unit can update the student's learning progress in real time to provide the latest information. In this way, the monitoring unit can enable the student to grasp their learning progress at a glance.

[0064] The planning department can incorporate reflection time into students' learning plans. For example, the planning department can set aside time at the end of each day for students to reflect on what they learned that day. The planning department can also set up a weekly session for students to self-evaluate their learning progress. Furthermore, the planning department can provide reflection time for students to summarize their learning results each month and set goals for the next month. In this way, the planning department can provide support for students to improve the quality of their learning through self-evaluation.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data on students' learning status. Specifically, it collects data such as past grades, mock test results, current learning progress, learning attitude, and number of comments made during class. For example, the collection unit retrieves past grades from a database and analyzes mock test results to understand current learning progress. Step 2: The planning department creates a study plan based on the data collected by the collection department. The study plan includes the pass mark for the desired school, the target score for mock exams, the daily study time and content, and the timing for review. For example, the planning department sets the pass mark for the desired school and the target score for mock exams. Step 3: The Support Department proposes support methods to parents based on the learning plan drawn up by the Planning Department. Support methods include support for learning at home, creating a learning environment, and methods to increase motivation. For example, the Support Department provides parents with a specific action plan and proposes effective support methods even if the parents have no experience preparing for junior high school entrance exams. Step 4: The monitoring department monitors students' progress based on the learning plan prepared by the planning department and modifies the learning plan as necessary. For example, if a student is not progressing as planned, the learning plan will be reviewed and appropriate modifications will be made. The monitoring department also monitors students' progress in real time and modifies the learning plan as appropriate.

[0067] (Example 2) A planning system according to an embodiment of the present invention automatically collects data on students' learning status, creates a learning plan using a generation AI, suggests support methods to parents, monitors the student's progress, and modifies the learning plan accordingly. The planning system collects data on students' learning status, creates a learning plan using a generation AI, suggests support methods to parents, monitors the student's progress, and modifies the learning plan accordingly. For example, the planning system collects data such as the student's past grades, mock test results, and current learning progress. The generation AI then analyzes the student's test results to identify the student's strong and weak subjects. The generation AI then sets goals for the student, including the passing grades for the school of their choice and target mock test scores. The generation AI then proposes appropriate support methods to the parents. For example, the generation AI provides specific advice on how parents can support the student. The generation AI then monitors the student's progress and modifies the learning plan as needed. For example, if the student is not progressing as planned, the generation AI reviews the learning plan and makes appropriate modifications. This allows the planning system to provide effective learning support even when parental involvement is difficult. This allows the planning system to provide effective learning support by gaining a detailed understanding of students' learning situations, creating individual learning plans, proposing appropriate support methods to parents, and monitoring students' progress to revise the learning plans. For example, even if parents have no experience preparing for junior high school entrance exams, the generative AI can support students' learning by proposing appropriate support methods. In addition, the generative AI can monitor students' progress and revise the learning plans as necessary to maximize the effectiveness of students' learning. This reduces the burden on parents and provides effective support for students' learning.

[0068] The planning system according to the embodiment includes a collection unit, a planning unit, a support unit, and a monitoring unit. The collection unit collects information about a student's learning status. The student's learning status includes, but is not limited to, past grades, mock test results, and current learning progress. For example, the collection unit acquires the student's past grades from a database. The collection unit can also analyze mock test results to determine the student's current learning progress. The collection unit can also collect data such as the student's learning attitude and the number of times the student speaks during class. For example, the collection unit acquires the student's past grades from a database, analyzes mock test results, and determines the student's current learning progress. The planning unit creates a study plan based on the data collected by the collection unit. The study plan includes, but is not limited to, the pass mark for the desired school and the target score for the mock test. For example, the planning unit sets the pass mark for the desired school and the target score for the mock test. The planning unit can also create a specific study plan. For example, the planning unit plans in detail the daily study time, study content, and review timing. The support unit suggests support methods to parents based on the study plan drawn up by the planning unit. Support methods include, but are not limited to, support for study at home, improving the study environment, and methods to increase motivation. For example, the support unit provides specific action plans on how parents can support their students. The support unit can also suggest effective support methods even if the parents have no experience preparing for junior high school entrance exams. The monitoring unit monitors the student's progress based on the study plan drawn up by the planning unit and revise the study plan as necessary. For example, if the student is not progressing as planned, the monitoring unit reviews the study plan and makes appropriate revisions. The monitoring unit can also monitor the student's progress in real time and revise the study plan as appropriate. For example, the monitoring unit monitors the student's study progress in real time and, if the student is not progressing as planned, reviews the study plan and makes appropriate revisions.As a result, the planning system according to the embodiment can provide effective learning support by gaining a detailed understanding of students' learning situations, creating individual learning plans, proposing appropriate support methods to parents, and monitoring students' progress to revise their learning plans.

[0069] The collection unit can collect a student's past grades, mock test results, current learning progress, and other related data. For example, the collection unit obtains a student's past grades from a database. The collection unit can also analyze mock test results to understand the student's current learning progress. The collection unit can also collect data such as the student's learning attitude and the number of times the student speaks during class. For example, the collection unit obtains a student's past grades from a database, analyzes mock test results, and understands the student's current learning progress. The collection unit can also observe the student's learning attitude and record the number of times the student speaks during class. This allows the collection unit to collect data to understand the student's learning situation in detail. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input a student's past grade data into a generation AI, which can analyze the data to understand the student's learning situation.

[0070] The planning unit can set the passing grade for the desired school and the target score for mock exams based on the collected data, and create a specific study plan. For example, the planning unit sets the passing grade for the desired school and the target score for mock exams. The planning unit can also create a specific study plan. For example, the planning unit plans in detail the daily study time, study content, review timing, etc. The planning unit can also create a study plan taking into account the student's strong and weak subjects. For example, the planning unit creates a plan to focus on the student's strong subjects and strengthen their weak subjects. The planning unit can also create a study plan taking into account the student's learning pace and concentration. For example, the planning unit creates a study plan based on the time period when the student's concentration is sustained. This allows the planning unit to create a specific study plan according to the student's goals. Some or all of the above-mentioned processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit can input the collected data into a generation AI, which can then create a study plan.

[0071] The support unit can provide parents with specific methods for supporting their students and specific action plans. For example, the support unit provides a specific action plan on how parents should support their students. The support unit can also suggest effective support methods even if parents have no experience preparing for junior high school entrance exams. For example, the support unit suggests ways to ensure study time at home, improve the learning environment, and increase motivation. The support unit can also provide specific action plans for parents to support their students' learning. For example, the support unit provides a specific action plan for parents to support their students' learning. This allows the support unit to provide a specific action plan for parents to effectively support their students. Some or all of the above-mentioned processing in the support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the support unit can input parents' support methods into a generation AI, which then suggests an optimal action plan.

[0072] The monitoring unit monitors the student's progress, and if the student is not progressing in his / her studies according to the plan, the monitoring unit can review the study plan and make appropriate corrections. For example, if the student is not progressing in his / her studies according to the plan, the monitoring unit can review the study plan and make appropriate corrections. The monitoring unit can also monitor the student's progress in real time and revise the study plan as appropriate. For example, the monitoring unit monitors the student's study progress in real time, and if the student is not progressing in his / her studies according to the plan, the monitoring unit can review the study plan and make appropriate corrections. In this way, the monitoring unit can monitor the student's progress and revise the study plan as necessary to maximize the learning effect. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the student's progress into the generation AI, which can analyze the progress and revise the study plan.

[0073] The collection unit can estimate the student's emotions and adjust the timing of collecting learning data based on the estimated student's emotions. For example, if the student is feeling stressed, the collection unit collects learning data during a time when the generation AI can relax. Furthermore, if the student is concentrating, the collection unit can also have the generation AI collect detailed learning data at that time. Furthermore, if the student is tired, the collection unit can also have the generation AI collect learning data after a break. For example, if the student is feeling stressed, the collection unit collects learning data during a time when the generation AI can relax. Furthermore, if the student is concentrating, the collection unit can also have the generation AI collect detailed learning data at that time. Furthermore, if the student is tired, the collection unit can also have the generation AI collect learning data after a break. This allows the collection unit to collect learning data at the optimal time depending on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input emotional data of students into the generation AI, and the generation AI may adjust the timing of collecting learning data based on the emotions.

[0074] The collection unit can analyze the student's learning environment and select an appropriate data collection method. For example, if the student is studying in a quiet environment, the generation AI can collect data using voice input. Furthermore, if the student is studying in a noisy environment, the collection unit can also collect data using text input. Furthermore, if the student is studying while on the move, the collection unit can also collect data using a mobile device. For example, if the student is studying in a quiet environment, the collection unit can collect data using voice input. Furthermore, if the student is studying in a noisy environment, the collection unit can also collect data using text input. Furthermore, if the student is studying while on the move, the collection unit can also collect data using a mobile device. This allows the collection unit to select the optimal data collection method according to the student's learning environment. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's learning environment data into the generation AI, which can then select the optimal data collection method.

[0075] The collection unit can adjust the data collection means according to the student's learning style. For example, in the case of a visual learner, the generation AI can collect data using images or videos. In the case of an auditory learner, the collection unit can also collect data using audio data. In the case of a tactile learner, the collection unit can also collect data using interactive tools. For example, in the case of a visual learner, the collection unit can collect data using images or videos. In the case of an auditory learner, the collection unit can also collect data using audio data. In the case of a tactile learner, the collection unit can also collect data using interactive tools. This allows the collection unit to provide an optimal data collection means according to the student's learning style. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's learning style data into the generation AI, which can then provide the optimal data collection means.

[0076] The collection unit can analyze the student's learning history in real time and automatically collect necessary data. For example, when a student is studying a new unit, the generation AI collects data related to that unit in real time. Furthermore, when a student is reviewing, the collection unit can also have the generation AI collect data based on the student's past learning history. Furthermore, when a student is taking a mock test, the collection unit can also have the generation AI collect mock test results in real time. For example, when a student is studying a new unit, the collection unit can collect data related to that unit in real time. Furthermore, when a student is reviewing, the collection unit can also have the generation AI collect data based on the student's past learning history. Furthermore, when a student is taking a mock test, the collection unit can also have the generation AI collect mock test results in real time. This allows the collection unit to analyze the student's learning history in real time and automatically collect necessary data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input students' learning history data into the generation AI, which can then analyze it in real time and automatically collect the necessary data.

[0077] The collection unit can estimate the student's emotions and determine the priority of data to be collected based on the estimated student's emotions. For example, if the student is feeling anxious, the collection unit can prioritize collecting data that causes the generation AI to feel reassured. Furthermore, if the student is excited, the collection unit can prioritize collecting data that causes the generation AI to increase concentration. Furthermore, if the student is relaxed, the collection unit can prioritize collecting data that causes the generation AI to maximize learning effectiveness. For example, if the student is feeling anxious, the collection unit can prioritize collecting data that causes the generation AI to feel reassured. Furthermore, if the student is excited, the collection unit can prioritize collecting data that causes the generation AI to increase concentration. Furthermore, if the student is relaxed, the collection unit can prioritize collecting data that causes the generation AI to maximize learning effectiveness. This allows the collection unit to prioritize data to be collected according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input student emotion data into the generation AI, and the generation AI may determine the priority of data to be collected based on the emotion.

[0078] The collection unit can set an optimal data collection schedule based on the student's lifestyle rhythm. For example, if the student is a morning person, the collection unit causes the generation AI to collect data in the morning. Furthermore, if the student is a night owl, the collection unit can also cause the generation AI to collect data in the evening. Furthermore, if the student has an irregular lifestyle rhythm, the collection unit can also cause the generation AI to collect data on a flexible schedule. For example, if the student is a morning person, the collection unit causes the generation AI to collect data in the morning. Furthermore, if the student is a night owl, the collection unit can also cause the generation AI to collect data in the evening. Furthermore, if the student has an irregular lifestyle rhythm, the collection unit can also cause the generation AI to collect data on a flexible schedule. This allows the collection unit to set an optimal data collection schedule according to the student's lifestyle rhythm. Some or all of the above-described processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the student's lifestyle rhythm data into the generation AI, which can then set an optimal data collection schedule.

[0079] The collection unit can analyze the students' social media activities and collect data related to learning. For example, the collection unit causes the generation AI to collect learning content shared by the students on social media. The collection unit can also cause the generation AI to collect information on education-related accounts followed by the students on social media. The collection unit can also cause the generation AI to collect activities of learning groups in which the students participate on social media. For example, the collection unit causes the generation AI to collect learning content shared by the students on social media. The collection unit can also cause the generation AI to collect information on education-related accounts followed by the students on social media. The collection unit can also cause the generation AI to collect activities of learning groups in which the students participate on social media. This allows the collection unit to collect data related to learning based on the students' social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the students' social media activity data into the generation AI, which then collects data related to learning.

[0080] The collection unit can customize the data collection method by reflecting the student's past feedback. For example, the collection unit causes the generation AI to prioritize use of study methods that the student previously preferred. The collection unit can also cause the generation AI to eliminate study methods that the student previously avoided. The collection unit can also cause the generation AI to propose an optimal data collection method based on the student's past feedback. For example, the collection unit causes the generation AI to prioritize use of study methods that the student previously preferred. The collection unit can also cause the generation AI to eliminate study methods that the student previously avoided. The collection unit can also cause the generation AI to propose an optimal data collection method based on the student's past feedback. This allows the collection unit to provide an optimal data collection method based on the student's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the student's past feedback data into the generation AI, and the generation AI can propose an optimal data collection method.

[0081] The planning unit can estimate the student's emotions and adjust the way the lesson plan is presented based on the estimated student's emotions. For example, if the student is nervous, the generation AI can provide a simple, highly visible lesson plan. Furthermore, if the student is relaxed, the generation AI can provide a detailed lesson plan. Furthermore, if the student is excited, the generation AI can provide a visually stimulating lesson plan. For example, if the student is nervous, the generation AI can provide a simple, highly visible lesson plan. Furthermore, if the student is relaxed, the generation AI can provide a detailed lesson plan. Furthermore, if the student is excited, the generation AI can provide a visually stimulating lesson plan. This allows the planning unit to adjust the way the lesson plan is presented based on the student's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the planning unit may be performed using, or without, the generation AI. For example, the planning unit may input emotional data of the student into the generation AI, and the generation AI may adjust the way the lesson plan is presented based on the emotion.

[0082] When creating a study plan, the planning unit can optimize the plan based on the student's learning pace and concentration. For example, the planning unit creates a study plan based on a time period during which the student can maintain concentration. The planning unit can also create a study plan that appropriately includes break times according to the student's learning pace. The planning unit can also create a study plan that avoids time periods during which the student's concentration is low. For example, the planning unit creates a study plan based on a time period during which the student can maintain concentration. The planning unit can also create a study plan that appropriately includes break times according to the student's learning pace. The planning unit can also create a study plan that avoids time periods during which the student's concentration is low. This allows the planning unit to provide an optimal study plan based on the student's learning pace and concentration. Some or all of the above-described processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit inputs the student's learning pace data into the generation AI, which can then create an optimal study plan.

[0083] The planning unit can balance the student's strong and weak subjects when formulating a study plan. For example, the planning unit creates a study plan that alternates between the student's strong and weak subjects. The planning unit can also create a plan to have the student study the student's strong subjects first, increase their motivation, and then study the weak subjects. The planning unit can also create a plan to focus on the student's weak subjects and refresh them with the strong subjects. For example, the planning unit creates a study plan that alternates between the student's strong and weak subjects. The planning unit can also create a plan to have the student study the student's strong subjects first, increase their motivation, and then study the weak subjects. The planning unit can also create a plan to focus on the student's weak subjects and refresh them with the strong subjects. In this way, the planning unit can provide a study plan that balances the student's strong and weak subjects. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the planning department can input data on a student's strong and weak subjects into the generation AI, which can then create an optimal study plan.

[0084] When creating a study plan, the planning unit can improve the accuracy of the plan by referring to the student's past learning results. The planning unit, for example, creates a study plan based on the student's past test results. The planning unit can also create a study plan based on the student's past mock test results. The planning unit can also create a study plan based on the student's past learning history. For example, the planning unit creates a study plan based on the student's past test results. The planning unit can also create a study plan based on the student's past mock test results. The planning unit can also create a study plan based on the student's past learning history. This allows the planning unit to improve the accuracy of the study plan by referring to the student's past learning results. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the planning unit inputs the student's past learning result data into the generation AI, which can then create an optimal study plan.

[0085] The planning unit can estimate the student's emotions and adjust the length of the lesson plan based on the estimated student's emotions. For example, if the student is tired, the planning unit can create a shorter lesson plan. Also, if the student is relaxed, the planning unit can create a detailed lesson plan. Also, if the student is excited, the planning unit can create a visually stimulating lesson plan. For example, if the student is tired, the planning unit can create a shorter lesson plan. Also, if the student is relaxed, the planning unit can create a detailed lesson plan. Also, if the student is excited, the planning unit can create a visually stimulating lesson plan. This allows the planning unit to adjust the length of the lesson plan according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using the generative AI, or may be performed without using the generative AI. For example, the planning department can input students' emotional data into the generation AI, which can then adjust the length of the lesson plan based on the emotions.

[0086] When creating a study plan, the planning unit can adjust study times based on the student's lifestyle rhythm. For example, if the student is a morning person, the planning unit can create a plan to concentrate study in the morning. Furthermore, if the student is a night owl, the planning unit can create a plan to concentrate study in the evening. Furthermore, the planning unit can create a flexible study plan if the student's lifestyle rhythm is irregular. For example, if the student is a morning person, the planning unit can create a plan to concentrate study in the morning. Furthermore, if the student is a night owl, the planning unit can create a plan to concentrate study in the evening. Furthermore, the planning unit can create a flexible study plan if the student's lifestyle rhythm is irregular. This allows the planning unit to provide optimal study times according to the student's lifestyle rhythm. Some or all of the above-described processing in the planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the planning unit can input the student's lifestyle rhythm data into the generation AI, which can then provide optimal study times.

[0087] The planning unit can customize the learning content based on the student's interests when creating a learning plan. For example, the planning unit creates a plan to prioritize learning of subjects that the student is interested in. The planning unit can also create a plan to incorporate topics that the student is interested in into the learning content. The planning unit can also create a plan to customize the learning content based on the student's interests. For example, the planning unit creates a plan to prioritize learning of subjects that the student is interested in. The planning unit can also create a plan to incorporate topics that the student is interested in into the learning content. The planning unit can also create a plan to customize the learning content based on the student's interests. This allows the planning unit to provide learning content based on the student's interests. Some or all of the above-described processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit can input data on the student's interests into the generation AI, which can then provide optimal learning content.

[0088] When creating a study plan, the planning unit can adjust the plan according to the student's future goals. For example, the planning unit creates a study plan that is tailored to the student's preferred school. The planning unit can also create a study plan that is tailored to the student's future career goals. The planning unit can also adjust the study plan according to the student's future goals. For example, the planning unit creates a study plan that is tailored to the student's preferred school. The planning unit can also create a study plan that is tailored to the student's future career goals. The planning unit can also adjust the study plan according to the student's future goals. This allows the planning unit to provide a study plan that is tailored to the student's future goals. Some or all of the above-described processing in the planning unit may be performed using, or without, a generation AI. For example, the planning unit can input data on the student's future goals into the generation AI, which can then provide an optimal study plan.

[0089] The support unit can estimate the guardian's emotions and adjust the support method based on the estimated guardian's emotions. For example, if the guardian is feeling anxious, the generation AI can suggest a support method that gives a sense of security. Furthermore, if the guardian is relaxed, the support unit can suggest a detailed support method. Furthermore, if the guardian is busy, the support unit can suggest a concise support method. For example, if the guardian is feeling anxious, the generation AI can suggest a support method that gives a sense of security. Furthermore, if the guardian is relaxed, the generation AI can suggest a detailed support method. Furthermore, if the guardian is busy, the generation AI can suggest a concise support method. This allows the support unit to provide an optimal support method according to the guardian's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support department can input the parent's emotional data into the generation AI, which can then adjust the support method based on the emotion.

[0090] The support unit can analyze the parent's past support history and propose an optimal support method. For example, the support unit allows the generation AI to propose an optimal support method based on the parent's past support methods. The support unit can also analyze the parent's past support history and allow the generation AI to propose an effective support method. The support unit can also propose a customized support method based on the parent's past support history. For example, the support unit allows the generation AI to propose an optimal support method based on the parent's past support methods. The support unit can also analyze the parent's past support history and allow the generation AI to propose an effective support method. The support unit can also propose a customized support method based on the parent's past support history. In this way, the support unit can provide an optimal support method based on the parent's past support history. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input parent's past support history data into the generation AI, and the generation AI can propose an optimal support method.

[0091] The support unit can customize the support plan based on the guardian's lifestyle and work schedule. For example, if the guardian is busy, the generation AI can propose a simple support plan. Furthermore, if the guardian has time, the generation AI can also propose a detailed support plan. Furthermore, the support unit can also propose a flexible support plan that matches the guardian's lifestyle and work schedule. For example, if the guardian is busy, the generation AI can propose a simple support plan. Furthermore, if the guardian has time, the generation AI can also propose a detailed support plan. Furthermore, the support unit can also propose a flexible support plan that matches the guardian's lifestyle and work schedule. This allows the support unit to provide an optimal support plan that matches the guardian's lifestyle and work schedule. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input the guardian's lifestyle data and work schedule data into the generation AI, which can then propose an optimal support plan.

[0092] The support unit can improve the support method by reflecting the parent's feedback. For example, the support unit causes the generation AI to improve the support method based on the parent's feedback. The support unit can also analyze the parent's past feedback and cause the generation AI to suggest an effective support method. The support unit can also reflect the parent's feedback and cause the generation AI to provide a customized support method. For example, the support unit causes the generation AI to improve the support method based on the parent's feedback. The support unit can also analyze the parent's past feedback and cause the generation AI to suggest an effective support method. The support unit can also reflect the parent's feedback and cause the generation AI to provide a customized support method. This allows the support unit to provide an optimal support method based on the parent's feedback. Some or all of the above-mentioned processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input parent's feedback data into the generation AI, and the generation AI can suggest an optimal support method.

[0093] The support unit can estimate the guardian's emotions and determine the priority of support based on the estimated guardian's emotions. For example, if the guardian is feeling anxious, the generation AI can prioritize support that provides a sense of security. Furthermore, if the guardian is relaxed, the support unit can also prioritize detailed support. Furthermore, if the guardian is busy, the support unit can also prioritize concise support. For example, if the guardian is feeling anxious, the support unit can prioritize support that provides a sense of security. Furthermore, if the guardian is relaxed, the support unit can also prioritize detailed support. Furthermore, if the guardian is busy, the support unit can also prioritize concise support. This allows the support unit to provide optimal support priorities according to the guardian's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, a generation AI. For example, the support unit may input emotional data of the guardian into the generation AI, and the generation AI may determine support priorities based on the emotions.

[0094] The support unit can propose an optimal support method taking into account the geographical location information of the guardian. For example, when the guardian is at home, the support unit can have the generation AI propose a support method that can be performed at home. Furthermore, when the guardian is out, the support unit can also propose a support method that the generation AI can perform while away from home. Furthermore, when the guardian is at work, the support unit can also propose a support method that the generation AI can perform at work. For example, when the guardian is at home, the support unit can propose a support method that the generation AI can perform at home. Furthermore, when the guardian is out, the support unit can also propose a support method that the generation AI can perform while away from home. Furthermore, when the guardian is at work, the support unit can also propose a support method that the generation AI can perform at work. This allows the support unit to provide an optimal support method based on the geographical location information of the guardian. Some or all of the above-mentioned processing in the support unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the support unit can input the guardian's geographical location information data into the generation AI, which can then propose an optimal support method.

[0095] The support unit can analyze the parent's social media activity and suggest relevant support methods. For example, the support unit can have the generation AI suggest support methods based on information shared by the parent on social media. The support unit can also have the generation AI suggest support methods based on information about education-related accounts followed by the parent on social media. The support unit can also have the generation AI suggest support methods based on the activities of groups the parent participates in on social media. For example, the support unit can have the generation AI suggest support methods based on information shared by the parent on social media. The support unit can also have the generation AI suggest support methods based on information about education-related accounts followed by the parent on social media. The support unit can also have the generation AI suggest support methods based on the activities of groups the parent participates in on social media. This allows the support unit to provide optimal support methods based on the parent's social media activity. Some or all of the above-described processing in the support unit may be performed using, or without, the generation AI. For example, the support unit can input parent's social media activity data into the generation AI, which then suggests optimal support methods.

[0096] The support unit can customize the support plan by reflecting the parent's past feedback. In the support unit, for example, the generation AI customizes the support plan based on the parent's past feedback. The support unit can also analyze the parent's feedback and have the generation AI propose an effective support plan. The support unit can also reflect the parent's feedback and have the generation AI provide an optimal support plan. For example, the support unit customizes the support plan based on the parent's past feedback. The support unit can also analyze the parent's feedback and have the generation AI propose an effective support plan. The support unit can also reflect the parent's feedback and have the generation AI provide an optimal support plan. In this way, the support unit can provide an optimal support plan based on the parent's past feedback. Some or all of the above-described processing in the support unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the support unit can input the parent's past feedback data into the generation AI, and the generation AI can propose an optimal support plan.

[0097] The monitoring unit can estimate the student's emotions and adjust the progress monitoring method based on the estimated student's emotions. For example, if the student is feeling anxious, the generation AI can provide a monitoring method that provides a sense of security. Furthermore, if the student is relaxed, the monitoring unit can provide a detailed monitoring method. Furthermore, if the student is excited, the monitoring unit can provide a visually stimulating monitoring method. For example, if the student is feeling anxious, the generation AI can provide a monitoring method that provides a sense of security. Furthermore, if the student is relaxed, the generation AI can provide a detailed monitoring method. Furthermore, if the student is excited, the monitoring unit can provide a visually stimulating monitoring method. This allows the monitoring unit to provide an optimal monitoring method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input emotional data of students into the generation AI, and the generation AI may adjust the monitoring method based on the emotions.

[0098] The monitoring unit can analyze the student's learning data in real time and provide immediate feedback on the progress status. For example, when the student is studying a new unit, the monitoring unit can provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is taking a mock test, the monitoring unit can also provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is reviewing, the monitoring unit can also provide the generation AI with real-time feedback on the progress status. For example, when the student is studying a new unit, the monitoring unit can provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is taking a mock test, the monitoring unit can also provide the generation AI with real-time feedback on the progress status. Furthermore, when the student is reviewing, the monitoring unit can analyze the student's learning data in real time and provide immediate feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the student's learning data to the generation AI, which can analyze the data in real time and provide immediate feedback on the progress status.

[0099] The monitoring unit monitors changes in the student's learning environment and can revise the learning plan as necessary. For example, if the student's learning environment changes, the generation AI revise the learning plan. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment is noisy. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment becomes quiet. For example, if the student's learning environment changes, the monitoring unit can cause the generation AI to revise the learning plan. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment is noisy. The monitoring unit can also cause the generation AI to revise the learning plan if the student's learning environment becomes quiet. In this way, the monitoring unit can revise the learning plan in accordance with changes in the student's learning environment. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input student's learning environment data into the generation AI, and the generation AI can revise the learning plan based on changes in the learning environment.

[0100] The monitoring unit can customize the monitoring method according to the student's learning style. For example, in the case of a visual learner, the generation AI can provide a visual monitoring method. Furthermore, in the case of an auditory learner, the monitoring unit can also provide an audio monitoring method. Furthermore, in the case of a tactile learner, the generation AI can also provide an interactive monitoring method. For example, in the case of a visual learner, the generation AI can provide a visual monitoring method. Furthermore, in the case of an auditory learner, the monitoring unit can also provide an audio monitoring method. Furthermore, in the case of a tactile learner, the generation AI can also provide an interactive monitoring method. This allows the monitoring unit to provide an optimal monitoring method according to the student's learning style. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the student's learning style data into the generation AI, which can then provide the optimal monitoring method.

[0101] The monitoring unit can estimate the student's emotions and adjust the progress display method based on the estimated student's emotions. For example, if the student is feeling anxious, the generation AI can provide a display method that gives a sense of security. Furthermore, if the student is relaxed, the monitoring unit can provide a detailed display method. Furthermore, if the student is excited, the monitoring unit can provide a visually stimulating display method. For example, if the student is feeling anxious, the generation AI can provide a display method that gives a sense of security. Furthermore, if the student is relaxed, the monitoring unit can provide a detailed display method. Furthermore, if the student is excited, the monitoring unit can provide a visually stimulating display method. In this way, the monitoring unit can provide an optimal progress display method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit may input emotional data of the student into the generation AI, and the generation AI may adjust the display method of the progress status based on the emotion.

[0102] The monitoring unit can set an optimal monitoring schedule based on the student's lifestyle rhythm. For example, if the student is a morning person, the generation AI can perform monitoring in the morning. Also, if the student is a night owl, the monitoring unit can have the generation AI perform monitoring in the evening. Also, if the student's lifestyle rhythm is irregular, the monitoring unit can have the generation AI set a flexible monitoring schedule. For example, if the student is a morning person, the monitoring unit can have the generation AI perform monitoring in the morning. Also, if the student is a night owl, the monitoring unit can have the generation AI perform monitoring in the evening. Also, if the student's lifestyle rhythm is irregular, the monitoring unit can have the generation AI set a flexible monitoring schedule. This allows the monitoring unit to provide an optimal monitoring schedule according to the student's lifestyle rhythm. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the student's lifestyle rhythm data into the generation AI, which can set an optimal monitoring schedule.

[0103] The monitoring unit can analyze the student's social media activity and monitor progress data related to learning. For example, the monitoring unit uses the generation AI to monitor the learning content shared by the student on social media. The monitoring unit can also use the generation AI to monitor information about education-related accounts followed by the student on social media. The monitoring unit can also use the generation AI to monitor the activities of learning groups in which the student participates on social media. For example, the monitoring unit uses the generation AI to monitor the learning content shared by the student on social media. The monitoring unit can also use the generation AI to monitor information about education-related accounts followed by the student on social media. The monitoring unit can also use the generation AI to monitor the activities of learning groups in which the student participates on social media. This allows the monitoring unit to provide optimal progress data based on the student's social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, or without, the generation AI. For example, the monitoring unit can input the student's social media activity data into the generation AI, which then monitors the progress data related to learning.

[0104] The monitoring unit can customize the monitoring method by reflecting the student's past feedback. In the monitoring unit, for example, the generation AI customizes the monitoring method based on the student's past feedback. The monitoring unit can also analyze the student's feedback and have the generation AI suggest an effective monitoring method. The monitoring unit can also reflect the student's feedback and have the generation AI provide an optimal monitoring method. For example, the monitoring unit customizes the monitoring method based on the student's past feedback. The monitoring unit can also analyze the student's feedback and have the generation AI suggest an effective monitoring method. The monitoring unit can also reflect the student's feedback and have the generation AI provide an optimal monitoring method. In this way, the monitoring unit can provide an optimal monitoring method based on the student's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the student's past feedback data into the generation AI, and the generation AI can suggest an optimal monitoring method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, planning unit, support unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the student's learning status using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a learning plan based on the collected data. The support unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests appropriate support methods to parents. The monitoring unit, realized, for example, by the control unit 46A of the smart device 14, monitors the student's progress in real time and modifies the learning plan as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, planning unit, support unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the student's learning status using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The planning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a learning plan based on the collected data. The support unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests appropriate support methods to parents. The monitoring unit, realized, for example, by the control unit 46A of the smart glasses 214, monitors the student's progress in real time and modifies the learning plan as necessary. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, planning unit, support unit, and monitoring unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the student's learning status using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a learning plan based on the collected data. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate support methods to parents. The monitoring unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and monitors the student's progress in real time and modifies the learning plan as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, planning unit, support unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the student's learning status using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The planning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a learning plan based on the collected data. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate support methods to parents. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the student's progress in real time and modifies the learning plan as necessary.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can collect physiological data such as health status and sleep patterns in addition to students' learning status. For example, the collection unit can monitor students' heart rates and sleep times to provide data to maximize learning efficiency. The collection unit can also record students' dietary habits and exercise amounts and reflect this data in their learning plans. Furthermore, the collection unit can measure students' stress levels and adjust the learning load to reduce it if stress levels are high. This allows the collection unit to collect data that takes into account the students' overall health status.

[0107] The planning department can incorporate game elements into students' learning plans. For example, the planning department can allow students to earn points or badges when they achieve certain goals. The planning department can also provide learning content in the form of quests, allowing students to progress through their studies while having fun. Furthermore, the planning department can introduce a ranking system that allows students to compete with their friends, increasing students' motivation. This allows the planning department to incorporate ideas to increase students' motivation to learn.

[0108] The support department can suggest ways to provide psychological support to parents regarding their students' studies. For example, the support department can teach parents relaxation techniques to help them reduce stress. The support department can also suggest communication methods to help parents increase their students' self-esteem. Furthermore, the support department can provide counseling techniques to help parents reduce their students' anxiety regarding their studies. In this way, the support department can help parents support their students from a psychological perspective.

[0109] The monitoring unit can monitor not only students' learning progress, but also their concentration and attention while studying. For example, the monitoring unit measures how focused a student is while studying and suggests taking a break if their concentration level drops. The monitoring unit can also issue an alert to bring a student back to attention if their attention becomes distracted while studying. Furthermore, the monitoring unit can adjust the study plan based on the student's concentration and attention data. In this way, the monitoring unit can provide support to maximize students' learning efficiency.

[0110] The collection unit can estimate the student's emotions and adjust the method of collecting learning data based on the estimated student's emotions. For example, if the student is feeling stressed, the collection unit can have the generation AI collect data in an environment where the student can relax. Also, if the student is concentrating, the collection unit can have the generation AI collect detailed learning data at that time. Furthermore, if the student is tired, the collection unit can have the generation AI collect learning data after a break. This allows the collection unit to provide the optimal data collection method according to the student's emotions.

[0111] The planning department can incorporate reflection time into students' learning plans. For example, the planning department can set aside time at the end of each day for students to reflect on what they learned that day. The planning department can also set up a weekly session for students to self-evaluate their learning progress. Furthermore, the planning department can provide reflection time for students to summarize their learning results each month and set goals for the next month. In this way, the planning department can provide support for students to improve the quality of their learning through self-evaluation.

[0112] The support department can suggest methods for providing feedback to parents regarding learning. For example, the support department can teach parents specific feedback methods for acknowledging students' efforts. The support department can also suggest communication techniques for parents to constructively point out areas for improvement in their students. Furthermore, the support department can provide effective methods for parents to praise their students' learning achievements. In this way, the support department can support parents in providing appropriate feedback to their students.

[0113] The monitoring unit can provide a dashboard for visually displaying the student's learning progress. For example, the monitoring unit can display the student's learning progress in graphs and charts to make it visually easy to understand. The monitoring unit can also display the student's degree of achievement of learning goals in a color-coded manner. Furthermore, the monitoring unit can update the student's learning progress in real time to provide the latest information. In this way, the monitoring unit can enable the student to grasp their learning progress at a glance.

[0114] The collection unit can estimate the student's emotions and determine the priority of data to be collected based on the estimated student's emotions. For example, if the student is feeling anxious, the collection unit can prioritize collecting data that causes the generation AI to feel reassured. In addition, if the student is excited, the collection unit can also prioritize collecting data that causes the generation AI to increase concentration. Furthermore, if the student is relaxed, the collection unit can also prioritize collecting data that maximizes learning effectiveness. In this way, the collection unit can determine the priority of data to be collected according to the student's emotions.

[0115] The planning department can incorporate reflection time into students' learning plans. For example, the planning department can set aside time at the end of each day for students to reflect on what they learned that day. The planning department can also set up a weekly session for students to self-evaluate their learning progress. Furthermore, the planning department can provide reflection time for students to summarize their learning results each month and set goals for the next month. In this way, the planning department can provide support for students to improve the quality of their learning through self-evaluation.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects data on students' learning status. Specifically, it collects data such as past grades, mock test results, current learning progress, learning attitude, and number of comments made during class. For example, the collection unit retrieves past grades from a database and analyzes mock test results to understand current learning progress. Step 2: The planning department creates a study plan based on the data collected by the collection department. The study plan includes the pass mark for the desired school, the target score for mock exams, the daily study time and content, and the timing for review. For example, the planning department sets the pass mark for the desired school and the target score for mock exams. Step 3: The Support Department proposes support methods to parents based on the learning plan drawn up by the Planning Department. Support methods include support for learning at home, creating a learning environment, and methods to increase motivation. For example, the Support Department provides parents with a specific action plan and proposes effective support methods even if the parents have no experience preparing for junior high school entrance exams. Step 4: The monitoring department monitors students' progress based on the learning plan prepared by the planning department and modifies the learning plan as necessary. For example, if a student is not progressing as planned, the learning plan will be reviewed and appropriate modifications will be made. The monitoring department also monitors students' progress in real time and modifies the learning plan as appropriate.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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 AI 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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, in order to avoid confusion and to 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 collection unit that collects information on students' learning status; a planning unit that creates a study plan based on the data collected by the collecting unit; a support unit that proposes support methods to parents based on the learning plan established by the planning unit; a monitoring unit that monitors the progress of students based on the learning plan created by the planning unit and modifies the learning plan as necessary. A system characterized by:

2. The collecting unit Collecting students' past grades, mock exam results, current learning progress and other relevant data 2. The system of claim 1.

3. The planning unit Based on the collected data, students will set the passing grade for their desired school and target scores for mock exams, and create a specific study plan.

2. The system of claim 1.

4. The support portion is Providing parents with concrete action plans and ways to support their students 2. The system of claim 1.

5. The monitoring unit Monitor student progress and, if students are not progressing according to plan, review their learning plans and make appropriate adjustments.

2. The system of claim 1.

6. The collecting unit Estimate student emotions and adjust the timing of learning data collection based on the estimated student emotions.

2. The system of claim 1.

7. The collecting unit Analyze students' learning environments and select appropriate data collection methods 2. The system of claim 1.

8. The collecting unit Adapt data collection methods to students' learning styles 2. The system of claim 1.

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