system

Abstract

The system according to this embodiment aims to construct educational programs tailored to specific objectives and to manage their progress. [Solution] The system according to the embodiment comprises a reception unit, a construction unit, a feedback unit, a review unit, a recovery unit, and an advice unit. The reception unit receives goal settings. The construction unit constructs an educational program based on the goals received by the reception unit. The feedback unit provides feedback on mock tests and assignments based on the educational program constructed by the construction unit. The review unit reviews the daily learning plan based on the results obtained by the feedback unit. The recovery unit implements a recovery plan based on the plan reviewed by the review unit. The advice unit provides educational advice to parents based on the recovery plan implemented by the recovery unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the construction and progress management of an educational program according to a goal have not been sufficiently performed, and there is room for improvement.

[0005] The system according to an embodiment aims to construct an educational program according to a goal and perform progress management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a construction unit, a feedback unit, a review unit, a recovery unit, and an advice unit. The reception unit receives goal settings. The construction unit constructs an educational program based on the goals received by the reception unit. The feedback unit provides feedback on mock tests and assignments based on the educational program constructed by the construction unit. The review unit reviews the daily learning plan based on the results obtained by the feedback unit. The recovery unit implements a recovery plan based on the plan reviewed by the review unit. The advice unit provides educational advice to parents based on the recovery plan implemented by the recovery unit. [Effects of the Invention]

[0007] The system according to this embodiment can construct educational programs tailored to specific goals and manage their progress. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system in which an agent constructs an educational program based on the student's desired university level and provides support until graduation. This educational support system is designed to solve the problem of parents who want their children to have a good life but don't know how to proceed with their education. The target age range is from 5 to 18 years old, and examples of goal settings include: a course for admission to a top national university, a course for admission to one of the six private universities, and a course for admission to the MARCH universities. First, the parent specifies the university level they want their child to aim for. Next, the AI ​​agent constructs an educational program based on that goal. For example, if the course for admission to a top national university is selected, the AI ​​agent provides a learning plan toward that goal and provides feedback on mock exams and assignments. It also reviews the daily learning plan taking into account school test results and provides support for entrance exams. Furthermore, the AI ​​agent follows the child's progress and implements recovery measures if there is a deviation from the goal. For example, if the results of a mock exam do not meet the goal, the AI ​​agent provides an additional learning plan and supports the child in achieving the goal. It also provides educational advice for parents to support the improvement of the child's academic ability. This system targets families in the child-rearing generation and solves the problem of not knowing the right educational plan for their child. The AI ​​agent creates personalized educational plans for each individual and provides thorough support until they are achieved. For example, it provides a basic learning plan for a 5-year-old child and a learning plan focused on exam preparation for an 18-year-old child. In this way, the AI ​​agent creates educational plans based on goal setting and provides thorough support until achievement, thereby supporting the child's growth and alleviating parental anxiety. By utilizing generative AI, it provides the optimal educational plan for each child, supporting the child's academic improvement and goal achievement. In this way, the educational support system can support the child's growth and alleviate parental anxiety.

[0029] The educational support system according to this embodiment comprises a reception unit, a construction unit, a feedback unit, a review unit, a recovery unit, and an advice unit. The reception unit receives goal settings. For example, the reception unit can allow parents to specify the university level they wish to aim for. The construction unit constructs an educational program based on the goals received by the reception unit. For example, if a course for admission to a prestigious national university is selected, the construction unit provides a learning plan aimed at that goal. The feedback unit provides feedback on mock exams and assignments based on the educational program constructed by the construction unit. For example, the feedback unit evaluates the results of mock exams and the progress of assignments and provides feedback. The review unit reviews the daily learning plan based on the results obtained by the feedback unit. For example, the review unit adjusts the learning plan by taking into account the results of school tests. The recovery unit implements a recovery plan based on the plan reviewed by the review unit. For example, if the results of a mock exam do not meet the goal, the recovery unit provides an additional learning plan. The advice unit provides educational advice to parents based on the recovery plan implemented by the recovery unit. For example, the advice unit provides advice to support the improvement of the child's academic ability. As a result, the educational support system according to this embodiment can provide consistent support from goal setting to the construction of educational programs, feedback, review, recovery, and advice.

[0030] The reception desk accepts goal setting requests. For example, parents can specify the university level their child is aiming for. Specifically, the reception desk provides an interface for parents and students to log into the system and input their target university, faculty, and specific academic goals. When setting goals on the system, parents and students can also input past grades and current academic levels. This allows the system to set goals tailored to each student's situation. Furthermore, the reception desk also has a function to provide appropriate advice to parents and students when setting goals. For example, it supports realistic goal setting based on entrance examination information for target universities and data on past successful applicants. The reception desk also saves the goal setting information in a database, making it accessible to subsequent development and feedback departments. This allows the reception desk to efficiently and effectively support the goal setting process and maintain consistency throughout the entire system.

[0031] The Development Department constructs educational programs based on the goals received by the Reception Department. For example, if a student selects the course for admission to a prestigious national university, the Development Department will provide a learning plan tailored to that goal. Specifically, the Development Department analyzes the entrance examination subjects and question trends of the target university and faculty, and creates a learning plan based on that analysis. The learning plan includes a detailed schedule for managing daily learning content and progress. For example, it will specify daily study time, learning content for each subject, and weekly goals. The Development Department also considers the student's current academic level and past performance when creating the learning plan. This allows them to provide a learning plan optimized for each individual student. Furthermore, the Development Department uses AI to optimize the learning plan. The AI ​​analyzes the student's learning history and mock exam results and suggests improvements to the learning plan. For example, if a student is struggling in a particular subject, the AI ​​will suggest a learning plan that focuses on that subject. The Development Department also monitors the progress of the learning plan in real time and revises the plan as needed. This allows the Development Department to provide an effective learning plan for students to achieve their goals and maximize learning efficiency.

[0032] The Feedback Department provides feedback on mock exams and assignments based on the educational programs built by the Development Department. For example, the Feedback Department evaluates mock exam results and assignment progress, and provides feedback. Specifically, the Feedback Department analyzes the results of mock exams and submitted assignments in detail, evaluating performance and understanding. The evaluation results are presented clearly to students and parents. For example, mock exam results show scores, standard deviations, and national rankings for each subject, while assignment evaluations include accuracy rates and understanding. The Feedback Department also provides specific areas for improvement and learning advice based on the evaluation results. For example, if a student has a weak area in a particular subject, the Feedback Department suggests learning methods and reference books that focus on that area. Furthermore, the Feedback Department utilizes AI to improve the accuracy of feedback. The AI ​​analyzes students' past performance and learning history, providing personalized feedback. For example, based on past mock exam results, it identifies the cause of stagnation in performance and proposes specific improvement measures. The Feedback Department also stores evaluation results in a database, making them accessible to the Review and Recovery Departments. This allows the feedback department to accurately understand students' learning progress and provide effective feedback.

[0033] The Review Department revises daily learning plans based on the results obtained by the Feedback Department. For example, the Review Department adjusts learning plans by taking into account school test results. Specifically, the Review Department analyzes in detail the evaluation results and school test results provided by the Feedback Department to identify areas for improvement in the learning plan. For example, if a student's performance in a particular subject is declining, the Review Department restructures the learning plan to focus on that subject. The Review Department also monitors students' learning status and progress in real time and revises learning plans as needed. For example, if a student is falling behind in their learning, the Review Department reviews the allocation of study time and suggests more efficient learning methods. Furthermore, the Review Department uses AI to optimize learning plans. The AI ​​analyzes students' learning history and evaluation results and suggests areas for improvement in the learning plan. For example, if a student's performance in a particular subject is stagnating, the AI ​​suggests a learning plan that focuses on that subject. The Review Department also saves the results of the learning plan revisions to a database, making it accessible to the Recovery Department and the Advice Department. This allows the Review Department to accurately understand students' learning situations and revise learning plans effectively.

[0034] The Recovery Department implements the recovery plan based on the plan revised by the Review Department. For example, if a student's mock exam results do not meet the target, the Recovery Department provides an additional learning plan. Specifically, the Recovery Department creates an additional learning plan based on improvements made to the learning plan provided by the Review Department. For example, if a student's performance in a particular subject is declining, the Recovery Department provides an additional learning plan that focuses on that subject. The Recovery Department also monitors the student's learning status and progress in real time and checks the implementation status of the recovery plan as needed. For example, it checks whether the additional learning plan is being implemented appropriately and provides support as needed. Furthermore, the Recovery Department uses AI to optimize the recovery plan. The AI ​​analyzes the student's learning history and evaluation results and suggests improvements to the recovery plan. For example, if a student's performance in a particular subject is stagnating, the AI ​​suggests a recovery plan that focuses on that subject. The Recovery Department also saves the results of the recovery plan's implementation in a database, making it accessible to the Advice Department. This allows the Recovery Department to accurately understand the student's learning situation and implement effective recovery plans.

[0035] The Advice Department provides educational advice to parents based on the recovery plan implemented by the Recovery Department. For example, the Advice Department provides advice to support the improvement of children's academic performance. Specifically, the Advice Department provides concrete educational advice to parents based on the results of implementing the recovery plan provided by the Recovery Department. For example, it provides detailed explanations of the child's learning situation and fluctuations in grades, and advises on methods of supporting learning at home and creating an effective learning environment. The Advice Department also provides advice on how to increase children's motivation and maintain their motivation through communication with parents. Furthermore, the Advice Department utilizes AI to improve the accuracy of its advice. The AI ​​analyzes the child's learning history and evaluation results to provide parents with optimal advice. For example, it identifies the child's strengths and weaknesses based on past grades and learning history, and provides specific advice accordingly. The Advice Department also collects feedback from parents and uses it to improve the advice. This enables the Advice Department to provide effective educational advice to parents and support the improvement of children's academic performance.

[0036] The presentation section can provide examples of goal setting. For example, it could present goal setting examples such as a course for admission to a top national university, a course for admission to one of the six private universities, or a course for admission to universities in the MARCH group (Meiji, Aoyama Gakuin, Rikkyo, Chuo, Hosei). The presentation section can provide specific goal examples to make it easier for parents to set goals. This makes it easier for parents to set goals.

[0037] The analysis unit can analyze the results of mock exams. For example, the analysis unit analyzes the results of mock exams to understand the progress of learning. By analyzing the results of mock exams, the analysis unit makes it easier to understand the progress of learning. This allows the analysis unit to easily understand the progress of learning.

[0038] The Additional Plans section can provide additional learning plans. For example, if the results of a practice test do not meet the target, the Additional Plans section will provide additional learning plans. The Additional Plans section will provide additional learning plans to strengthen support toward achieving the goal. In this way, the Additional Plans section can strengthen support toward achieving the goal.

[0039] The monitoring unit can monitor the child's growth. For example, the monitoring unit can monitor the child's growth and provide support at the appropriate time. By monitoring the child's growth, the monitoring unit can provide support at the appropriate time. This allows the monitoring unit to provide support at the right time.

[0040] The reception desk can analyze past goal-setting history and select the most suitable goal-setting method. For example, the reception desk can prioritize suggesting goal-setting methods that have been successful in the past. The reception desk can also suggest avoiding goal-setting methods that have failed in the past. Based on past goal-setting history, the reception desk can also suggest goal-setting methods that parents prefer. In this way, the reception desk can select the most suitable goal-setting method by analyzing past goal-setting history.

[0041] The reception desk can filter goals based on the parents' current living situation and areas of interest when setting goals. For example, if the parents are busy, the reception desk can suggest goals that can be set in a short amount of time. If the parents are highly interested in education, the reception desk can also suggest more detailed goal setting. If the parents are interested in a particular field, the reception desk can also suggest goals related to that field. In this way, the reception desk can filter goals based on the parents' living situation and areas of interest, enabling more appropriate goal setting.

[0042] The reception desk can prioritize highly relevant goals when setting goals, taking into account the parents' geographical location. For example, the reception desk can prioritize goals related to educational institutions in the area where the parents live. It can also prioritize goals related to the area where the parents commute. It can also prioritize goals related to areas that the parents frequently visit. This allows the reception desk to set more appropriate goals by taking the parents' geographical location into consideration.

[0043] The reception desk can analyze parents' social media activity and set relevant goals when setting targets. For example, the reception desk can set targets related to educational institutions that parents show interest in on social media. The reception desk can also set targets based on the opinions of educational experts that parents follow on social media. The reception desk can also set targets based on educational information that parents share on social media. This allows the reception desk to set more appropriate targets by analyzing parents' social media activity.

[0044] The program development unit can adjust the level of detail in the educational program based on the importance of the objectives. For example, the unit can provide a detailed educational program for high-priority objectives, and a concise educational program for low-priority objectives. The unit can also customize the content of the educational program according to the importance of the objectives. This allows the unit to provide a more appropriate educational program by adjusting the level of detail based on the importance of the objectives.

[0045] The program development unit can apply different development algorithms depending on the category of the objective when developing educational programs. For example, it can apply an academic algorithm to academic objectives, a sports-related algorithm to sports-related objectives, and an artistic algorithm to artistic objectives. By applying different development algorithms depending on the category of the objectives, the program development unit can provide more appropriate educational programs.

[0046] The program development unit can prioritize educational programs based on the timing of goal setting when developing them. For example, if the goal setting is early, the development unit will prioritize providing the educational program. If the goal setting is late, the development unit can also postpone providing the educational program. The development unit can also customize the content of the educational program according to the timing of goal setting. As a result, the development unit can provide more appropriate educational programs by prioritizing them based on the timing of goal setting.

[0047] The program development unit can adjust the order of educational programs based on the relevance of their objectives when developing them. For example, the unit can prioritize providing educational programs where the objectives are highly relevant. It can also postpone providing educational programs where the objectives are less relevant. The unit can also customize the content of the educational programs according to the relevance of their objectives. This allows the unit to provide more appropriate educational programs by adjusting the order of educational programs based on the relevance of their objectives.

[0048] The feedback unit can adjust the level of detail in the feedback based on the importance of the practice test or assignment. For example, it can provide detailed feedback for high-importance practice tests or assignments, and concise feedback for low-importance ones. The feedback unit can also customize the content of the feedback according to the importance of the practice test or assignment. This allows the feedback unit to provide more appropriate feedback by adjusting the level of detail based on the importance of the practice test or assignment.

[0049] The feedback unit can apply different feedback algorithms depending on the category of the mock exam or assignment. For example, the feedback unit can apply an academic feedback algorithm to academic mock exams or assignments. It can also apply a sports-related feedback algorithm to sports-related mock exams or assignments. It can also apply an artistic feedback algorithm to artistic mock exams or assignments. By applying different feedback algorithms depending on the category of the mock exam or assignment, the feedback unit can provide more appropriate feedback.

[0050] The feedback department can prioritize feedback based on the submission timing of mock exams and assignments. For example, it can prioritize feedback on mock exams and assignments submitted early, and postpone feedback on mock exams and assignments submitted late. The feedback department can also customize the content of the feedback depending on the submission timing. This allows the feedback department to provide more appropriate feedback by prioritizing feedback based on the submission timing of mock exams and assignments.

[0051] The feedback unit can adjust the order of feedback based on the relevance of the practice tests and assignments. For example, it can prioritize providing feedback on practice tests and assignments that are highly relevant. It can also postpone providing feedback on practice tests and assignments that are less relevant. The feedback unit can also customize the content of the feedback according to its relevance. As a result, the feedback unit can provide more appropriate feedback by adjusting the order of feedback based on the relevance of the practice tests and assignments.

[0052] The review unit can select the optimal review method by referring to past learning data when reviewing a learning plan. For example, the review unit can review by referring to successful learning plans from the past. The review unit can also review in a way that avoids learning plans that have failed in the past. The review unit can also select the optimal review method from past learning data. In this way, the review unit can select the optimal review method by referring to past learning data.

[0053] The review unit can customize the review process based on the current learning situation when reviewing the learning plan. For example, if the current learning situation is good, the review unit can perform a detailed review. If the current learning situation is poor, the review unit can also perform a brief review. The review unit can also customize the review process according to the current learning situation. This allows the review unit to perform more appropriate reviews by customizing the review process based on the current learning situation.

[0054] The review department can select the most appropriate review method when reviewing learning plans, taking geographical location information into consideration. For example, the review department can prioritize reviews related to educational institutions in the area where the child lives. The review department can also prioritize reviews related to the area the child attends school in. The review department can also prioritize reviews related to areas the child frequently visits. This allows the review department to select the most appropriate review method by taking geographical location information into consideration, enabling more appropriate reviews.

[0055] The review department can analyze social media activity and propose methods for revision when reviewing learning plans. For example, the review department can make revisions by referring to educational information that the child shows interest in on social media. The review department can also make revisions by referring to the opinions of educational experts that the child follows on social media. The review department can also make revisions based on educational information that the child shares on social media. In this way, the review department can make more appropriate revisions by analyzing social media activity and proposing methods for revision.

[0056] The recovery unit can select the optimal execution method by referring to past recovery data when executing a recovery plan. For example, the recovery unit can execute a recovery plan by referring to a previously successful recovery plan. The recovery unit can also execute a recovery plan while avoiding a previously failed one. The recovery unit can also select the optimal execution method from past recovery data. In this way, the recovery unit can select the optimal execution method by referring to past recovery data.

[0057] The recovery unit can customize the recovery method based on the current learning status when executing a recovery plan. For example, if the current learning status is good, the recovery unit provides a detailed recovery plan. If the current learning status is poor, the recovery unit can also provide a concise recovery plan. The recovery unit can also customize the recovery method according to the current learning status. This allows the recovery unit to perform more appropriate recovery by customizing the recovery method based on the current learning status.

[0058] The recovery unit can select the optimal recovery method by considering geographical location information when implementing a recovery plan. For example, the recovery unit can prioritize implementing recovery plans related to educational institutions in the area where the child lives. The recovery unit can also prioritize implementing recovery plans related to the area the child attends school in. The recovery unit can also prioritize implementing recovery plans related to areas the child frequently visits. By selecting the optimal recovery method by considering geographical location information, the recovery unit can achieve more appropriate recovery.

[0059] The recovery department can analyze social media activity and propose recovery measures when implementing a recovery plan. For example, the recovery department can implement a recovery plan by referring to educational information that the child is interested in on social media. The recovery department can also implement a recovery plan by referring to the opinions of educational experts that the child follows on social media. The recovery department can also implement a recovery plan based on educational information that the child shares on social media. In this way, the recovery department can enable more appropriate recovery by analyzing social media activity and proposing recovery measures.

[0060] The advisory unit can select the optimal advice method by referring to past advice data when providing educational advice. For example, the advisory unit can provide advice based on past successful educational advice. It can also provide advice that avoids past unsuccessful educational advice. The advisory unit can select the optimal advice method from past advice data. Thus, the advisory unit can select the optimal advice method by referring to past advice data.

[0061] The advisory department can customize the means of providing educational advice based on the current educational situation. For example, if the current educational situation is good, the advisory department will provide detailed educational advice. If the current educational situation is poor, the advisory department can also provide concise educational advice. The advisory department can also customize the means of providing advice according to the current educational situation. This allows the advisory department to provide more appropriate advice by customizing the means of advice based on the current educational situation.

[0062] The advisory department can select the most appropriate advice method when providing educational advice, taking geographical location information into consideration. For example, the advisory department can prioritize advice related to educational institutions in the area where the parents live. The advisory department can also prioritize advice related to the area where the parents commute. The advisory department can also prioritize advice related to areas that the parents frequently visit. In this way, the advisory department can provide more appropriate advice by selecting the most appropriate advice method, taking geographical location information into consideration.

[0063] The advisory department can analyze social media activity and propose methods for providing educational advice. For example, the advisory department can provide advice based on educational information that parents are interested in on social media. The advisory department can also provide advice based on the opinions of educational experts that parents follow on social media. The advisory department can also provide advice based on educational information that parents share on social media. In this way, the advisory department can provide more appropriate advice by analyzing social media activity and proposing methods for providing advice.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The educational support system can also include a progress management unit. This unit manages the child's learning progress and provides timely feedback. For example, if a child achieves a certain learning goal, the unit can record that achievement and set the next goal. If a child falls behind in their studies, the unit can identify the delay and provide a recovery plan. Furthermore, the unit can report the child's learning progress to the parents and encourage appropriate support. This allows the progress management unit to effectively manage the child's learning progress and support their continued learning.

[0066] The educational support system can also include a communication department. This department supports communication between parents and children. For example, when a parent asks a child a question about their studies, the communication department can format the question appropriately and present it in a way that the child can easily understand. Similarly, when a child provides feedback on their studies to their parent, the communication department can organize that feedback and present it in a way that the parent can easily understand. Furthermore, the communication department can provide advice to facilitate smooth communication between parents and children. In this way, the communication department can support communication between parents and children and enhance the effectiveness of learning.

[0067] The educational support system can also include a learning environment department. This department supports children in learning in an optimal environment. For example, to provide children with an environment where they can concentrate, the department can suggest appropriate study spaces. It can also provide children with necessary learning materials and tools. Furthermore, the department can provide parents with advice on creating a suitable learning environment for their children. In this way, the learning environment department can support children in learning in an optimal environment and enhance the effectiveness of their learning.

[0068] The educational support system can also include a learning style section. This section provides learning methods tailored to each child's learning style. For example, if a child prefers visual learning, the learning style section can provide visual learning materials. If a child prefers auditory learning, the learning style section can provide audio learning materials. Furthermore, if a child prefers experiential learning, the learning style section can provide hands-on learning activities. This allows the learning style section to provide learning methods suited to each child's learning style, thereby enhancing the effectiveness of their learning.

[0069] The educational support system can also include a learning objectives unit. This unit manages the learning objectives set by the child and provides appropriate support. For example, when a child sets a learning objective, the unit can evaluate whether that objective is realistic and achievable. It can also provide specific steps for the child to achieve the learning objective and manage their progress. Furthermore, the unit can report the child's learning objectives to the parents and encourage appropriate support. This allows the unit to effectively manage the learning objectives set by the child and support their continued learning.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk accepts goal setting. For example, parents can specify the level of university they want their child to attend. Step 2: The Development Department constructs the educational program based on the goals received by the Reception Department. For example, if a student selects the course for admission to a prestigious national university, the Development Department will provide a learning plan to achieve that goal. Step 3: The Feedback Department provides feedback on mock exams and assignments based on the educational program built by the Development Department. For example, they evaluate the results of mock exams and the progress of assignments and provide feedback. Step 4: The review team revises the daily study plan based on the results obtained from the feedback team. For example, they adjust the study plan to take into account school test results. Step 5: The recovery team implements the recovery plan based on the plan revised by the review team. For example, if the mock exam results do not meet the target, they provide an additional study plan. Step 6: The Advice Department provides educational advice to parents based on the recovery plan implemented by the Recovery Department. For example, they provide advice to help improve the child's academic performance.

[0072] (Example of form 2) The educational support system according to an embodiment of the present invention is a system in which an agent constructs an educational program based on the student's desired university level and provides support until graduation. This educational support system is designed to solve the problem of parents who want their children to have a good life but don't know how to proceed with their education. The target age range is from 5 to 18 years old, and examples of goal settings include: a course for admission to a top national university, a course for admission to one of the six private universities, and a course for admission to the MARCH universities. First, the parent specifies the university level they want their child to aim for. Next, the AI ​​agent constructs an educational program based on that goal. For example, if the course for admission to a top national university is selected, the AI ​​agent provides a learning plan toward that goal and provides feedback on mock exams and assignments. It also reviews the daily learning plan taking into account school test results and provides support for entrance exams. Furthermore, the AI ​​agent follows the child's progress and implements recovery measures if there is a deviation from the goal. For example, if the results of a mock exam do not meet the goal, the AI ​​agent provides an additional learning plan and supports the child in achieving the goal. It also provides educational advice for parents to support the improvement of the child's academic ability. This system targets families in the child-rearing generation and solves the problem of not knowing the right educational plan for their child. The AI ​​agent creates personalized educational plans for each individual and provides thorough support until they are achieved. For example, it provides a basic learning plan for a 5-year-old child and a learning plan focused on exam preparation for an 18-year-old child. In this way, the AI ​​agent creates educational plans based on goal setting and provides thorough support until achievement, thereby supporting the child's growth and alleviating parental anxiety. By utilizing generative AI, it provides the optimal educational plan for each child, supporting the child's academic improvement and goal achievement. In this way, the educational support system can support the child's growth and alleviate parental anxiety.

[0073] The educational support system according to this embodiment comprises a reception unit, a construction unit, a feedback unit, a review unit, a recovery unit, and an advice unit. The reception unit receives goal settings. For example, the reception unit can allow parents to specify the university level they wish to aim for. The construction unit constructs an educational program based on the goals received by the reception unit. For example, if a course for admission to a prestigious national university is selected, the construction unit provides a learning plan aimed at that goal. The feedback unit provides feedback on mock exams and assignments based on the educational program constructed by the construction unit. For example, the feedback unit evaluates the results of mock exams and the progress of assignments and provides feedback. The review unit reviews the daily learning plan based on the results obtained by the feedback unit. For example, the review unit adjusts the learning plan by taking into account the results of school tests. The recovery unit implements a recovery plan based on the plan reviewed by the review unit. For example, if the results of a mock exam do not meet the goal, the recovery unit provides an additional learning plan. The advice unit provides educational advice to parents based on the recovery plan implemented by the recovery unit. For example, the advice unit provides advice to support the improvement of the child's academic ability. As a result, the educational support system according to this embodiment can provide consistent support from goal setting to the construction of educational programs, feedback, review, recovery, and advice.

[0074] The reception desk accepts goal setting requests. For example, parents can specify the university level their child is aiming for. Specifically, the reception desk provides an interface for parents and students to log into the system and input their target university, faculty, and specific academic goals. When setting goals on the system, parents and students can also input past grades and current academic levels. This allows the system to set goals tailored to each student's situation. Furthermore, the reception desk also has a function to provide appropriate advice to parents and students when setting goals. For example, it supports realistic goal setting based on entrance examination information for target universities and data on past successful applicants. The reception desk also saves the goal setting information in a database, making it accessible to subsequent development and feedback departments. This allows the reception desk to efficiently and effectively support the goal setting process and maintain consistency throughout the entire system.

[0075] The Development Department constructs educational programs based on the goals received by the Reception Department. For example, if a student selects the course for admission to a prestigious national university, the Development Department will provide a learning plan tailored to that goal. Specifically, the Development Department analyzes the entrance examination subjects and question trends of the target university and faculty, and creates a learning plan based on that analysis. The learning plan includes a detailed schedule for managing daily learning content and progress. For example, it will specify daily study time, learning content for each subject, and weekly goals. The Development Department also considers the student's current academic level and past performance when creating the learning plan. This allows them to provide a learning plan optimized for each individual student. Furthermore, the Development Department uses AI to optimize the learning plan. The AI ​​analyzes the student's learning history and mock exam results and suggests improvements to the learning plan. For example, if a student is struggling in a particular subject, the AI ​​will suggest a learning plan that focuses on that subject. The Development Department also monitors the progress of the learning plan in real time and revises the plan as needed. This allows the Development Department to provide an effective learning plan for students to achieve their goals and maximize learning efficiency.

[0076] The Feedback Department provides feedback on mock exams and assignments based on the educational programs built by the Development Department. For example, the Feedback Department evaluates mock exam results and assignment progress, and provides feedback. Specifically, the Feedback Department analyzes the results of mock exams and submitted assignments in detail, evaluating performance and understanding. The evaluation results are presented clearly to students and parents. For example, mock exam results show scores, standard deviations, and national rankings for each subject, while assignment evaluations include accuracy rates and understanding. The Feedback Department also provides specific areas for improvement and learning advice based on the evaluation results. For example, if a student has a weak area in a particular subject, the Feedback Department suggests learning methods and reference books that focus on that area. Furthermore, the Feedback Department utilizes AI to improve the accuracy of feedback. The AI ​​analyzes students' past performance and learning history, providing personalized feedback. For example, based on past mock exam results, it identifies the cause of stagnation in performance and proposes specific improvement measures. The Feedback Department also stores evaluation results in a database, making them accessible to the Review and Recovery Departments. This allows the feedback department to accurately understand students' learning progress and provide effective feedback.

[0077] The Review Department revises daily learning plans based on the results obtained by the Feedback Department. For example, the Review Department adjusts learning plans by taking into account school test results. Specifically, the Review Department analyzes in detail the evaluation results and school test results provided by the Feedback Department to identify areas for improvement in the learning plan. For example, if a student's performance in a particular subject is declining, the Review Department restructures the learning plan to focus on that subject. The Review Department also monitors students' learning status and progress in real time and revises learning plans as needed. For example, if a student is falling behind in their learning, the Review Department reviews the allocation of study time and suggests more efficient learning methods. Furthermore, the Review Department uses AI to optimize learning plans. The AI ​​analyzes students' learning history and evaluation results and suggests areas for improvement in the learning plan. For example, if a student's performance in a particular subject is stagnating, the AI ​​suggests a learning plan that focuses on that subject. The Review Department also saves the results of the learning plan revisions to a database, making it accessible to the Recovery Department and the Advice Department. This allows the Review Department to accurately understand students' learning situations and revise learning plans effectively.

[0078] The Recovery Department implements the recovery plan based on the plan revised by the Review Department. For example, if a student's mock exam results do not meet the target, the Recovery Department provides an additional learning plan. Specifically, the Recovery Department creates an additional learning plan based on improvements made to the learning plan provided by the Review Department. For example, if a student's performance in a particular subject is declining, the Recovery Department provides an additional learning plan that focuses on that subject. The Recovery Department also monitors the student's learning status and progress in real time and checks the implementation status of the recovery plan as needed. For example, it checks whether the additional learning plan is being implemented appropriately and provides support as needed. Furthermore, the Recovery Department uses AI to optimize the recovery plan. The AI ​​analyzes the student's learning history and evaluation results and suggests improvements to the recovery plan. For example, if a student's performance in a particular subject is stagnating, the AI ​​suggests a recovery plan that focuses on that subject. The Recovery Department also saves the results of the recovery plan's implementation in a database, making it accessible to the Advice Department. This allows the Recovery Department to accurately understand the student's learning situation and implement effective recovery plans.

[0079] The Advice Department provides educational advice to parents based on the recovery plan implemented by the Recovery Department. For example, the Advice Department provides advice to support the improvement of children's academic performance. Specifically, the Advice Department provides concrete educational advice to parents based on the results of implementing the recovery plan provided by the Recovery Department. For example, it provides detailed explanations of the child's learning situation and fluctuations in grades, and advises on methods of supporting learning at home and creating an effective learning environment. The Advice Department also provides advice on how to increase children's motivation and maintain their motivation through communication with parents. Furthermore, the Advice Department utilizes AI to improve the accuracy of its advice. The AI ​​analyzes the child's learning history and evaluation results to provide parents with optimal advice. For example, it identifies the child's strengths and weaknesses based on past grades and learning history, and provides specific advice accordingly. The Advice Department also collects feedback from parents and uses it to improve the advice. This enables the Advice Department to provide effective educational advice to parents and support the improvement of children's academic performance.

[0080] The presentation section can provide examples of goal setting. For example, it could present goal setting examples such as a course for admission to a top national university, a course for admission to one of the six private universities, or a course for admission to universities in the MARCH group (Meiji, Aoyama Gakuin, Rikkyo, Chuo, Hosei). The presentation section can provide specific goal examples to make it easier for parents to set goals. This makes it easier for parents to set goals.

[0081] The analysis unit can analyze the results of mock exams. For example, the analysis unit analyzes the results of mock exams to understand the progress of learning. By analyzing the results of mock exams, the analysis unit makes it easier to understand the progress of learning. This allows the analysis unit to easily understand the progress of learning.

[0082] The Additional Plans section can provide additional learning plans. For example, if the results of a practice test do not meet the target, the Additional Plans section will provide additional learning plans. The Additional Plans section will provide additional learning plans to strengthen support toward achieving the goal. In this way, the Additional Plans section can strengthen support toward achieving the goal.

[0083] The monitoring unit can monitor the child's growth. For example, the monitoring unit can monitor the child's growth and provide support at the appropriate time. By monitoring the child's growth, the monitoring unit can provide support at the appropriate time. This allows the monitoring unit to provide support at the right time.

[0084] The reception desk can estimate the parent's emotions and adjust the timing of goal setting based on the estimated emotions. For example, if the parent is stressed, the reception desk will prompt goal setting at a time when the parent can relax. If the parent is busy, the reception desk can prompt goal setting at a time when the parent has more free time. If the parent is agitated, the reception desk can prompt goal setting at a time when the parent can calm down. In this way, the reception desk can adjust the timing of goal setting according to the parent's emotions, enabling more effective goal setting. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception desk can analyze past goal-setting history and select the most suitable goal-setting method. For example, the reception desk can prioritize suggesting goal-setting methods that have been successful in the past. The reception desk can also suggest avoiding goal-setting methods that have failed in the past. Based on past goal-setting history, the reception desk can also suggest goal-setting methods that parents prefer. In this way, the reception desk can select the most suitable goal-setting method by analyzing past goal-setting history.

[0086] The reception desk can filter goals based on the parents' current living situation and areas of interest when setting goals. For example, if the parents are busy, the reception desk can suggest goals that can be set in a short amount of time. If the parents are highly interested in education, the reception desk can also suggest more detailed goal setting. If the parents are interested in a particular field, the reception desk can also suggest goals related to that field. In this way, the reception desk can filter goals based on the parents' living situation and areas of interest, enabling more appropriate goal setting.

[0087] The reception system can estimate the parent's emotions and determine the priority of goal setting based on those estimated emotions. For example, if the parent is stressed, the reception system will postpone less important goals. If the parent is relaxed, the reception system may prioritize high-importance goals. If the parent is busy, the reception system may prioritize short-term goals. This allows the reception system to set more effective goals by prioritizing goals according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk can prioritize highly relevant goals when setting goals, taking into account the parents' geographical location. For example, the reception desk can prioritize goals related to educational institutions in the area where the parents live. It can also prioritize goals related to the area where the parents commute. It can also prioritize goals related to areas that the parents frequently visit. This allows the reception desk to set more appropriate goals by taking the parents' geographical location into consideration.

[0089] The reception desk can analyze parents' social media activity and set relevant goals when setting targets. For example, the reception desk can set targets related to educational institutions that parents show interest in on social media. The reception desk can also set targets based on the opinions of educational experts that parents follow on social media. The reception desk can also set targets based on educational information that parents share on social media. This allows the reception desk to set more appropriate targets by analyzing parents' social media activity.

[0090] The program builder can estimate the parent's emotions and adjust the presentation of the educational program based on the estimated emotions. For example, if the parent is relaxed, the program builder can provide an educational program with detailed explanations. If the parent is busy, the program builder can also provide an educational program with concise explanations. If the parent is excited, the program builder can also provide a visually appealing educational program. In this way, the program builder can provide a more effective educational program by adjusting the presentation of the educational program according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The program development unit can adjust the level of detail in the educational program based on the importance of the objectives. For example, the unit can provide a detailed educational program for high-priority objectives, and a concise educational program for low-priority objectives. The unit can also customize the content of the educational program according to the importance of the objectives. This allows the unit to provide a more appropriate educational program by adjusting the level of detail based on the importance of the objectives.

[0092] The program development unit can apply different development algorithms depending on the category of the objective when developing educational programs. For example, it can apply an academic algorithm to academic objectives, a sports-related algorithm to sports-related objectives, and an artistic algorithm to artistic objectives. By applying different development algorithms depending on the category of the objectives, the program development unit can provide more appropriate educational programs.

[0093] The program builder can estimate the parent's emotions and adjust the length of the educational program based on those emotions. For example, if the parent is relaxed, the program builder can provide a longer program. If the parent is busy, it can provide a shorter program. If the parent is excited, it can provide a visually engaging program. This allows the program builder to provide a more effective educational program by adjusting its length according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The program development unit can prioritize educational programs based on the timing of goal setting when developing them. For example, if the goal setting is early, the development unit will prioritize providing the educational program. If the goal setting is late, the development unit can also postpone providing the educational program. The development unit can also customize the content of the educational program according to the timing of goal setting. As a result, the development unit can provide more appropriate educational programs by prioritizing them based on the timing of goal setting.

[0095] The program development unit can adjust the order of educational programs based on the relevance of their objectives when developing them. For example, the unit can prioritize providing educational programs where the objectives are highly relevant. It can also postpone providing educational programs where the objectives are less relevant. The unit can also customize the content of the educational programs according to the relevance of their objectives. This allows the unit to provide more appropriate educational programs by adjusting the order of educational programs based on the relevance of their objectives.

[0096] The feedback unit can estimate the child's emotions and adjust the way it presents feedback based on those emotions. For example, if the child is relaxed, the feedback unit can provide detailed feedback. If the child is tense, the feedback unit can provide concise feedback. If the child is excited, the feedback unit can provide visually appealing feedback. This allows the feedback unit to provide more effective feedback by adjusting its presentation according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The feedback unit can adjust the level of detail in the feedback based on the importance of the practice test or assignment. For example, it can provide detailed feedback for high-importance practice tests or assignments, and concise feedback for low-importance ones. The feedback unit can also customize the content of the feedback according to the importance of the practice test or assignment. This allows the feedback unit to provide more appropriate feedback by adjusting the level of detail based on the importance of the practice test or assignment.

[0098] The feedback unit can apply different feedback algorithms depending on the category of the mock exam or assignment. For example, the feedback unit can apply an academic feedback algorithm to academic mock exams or assignments. It can also apply a sports-related feedback algorithm to sports-related mock exams or assignments. It can also apply an artistic feedback algorithm to artistic mock exams or assignments. By applying different feedback algorithms depending on the category of the mock exam or assignment, the feedback unit can provide more appropriate feedback.

[0099] The feedback unit can estimate the child's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the child is relaxed, the feedback unit will provide longer feedback. If the child is tense, the feedback unit may also provide shorter feedback. If the child is excited, the feedback unit may also provide visually appealing feedback. This allows the feedback unit to provide more effective feedback by adjusting the length of the feedback according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The feedback department can prioritize feedback based on the submission timing of mock exams and assignments. For example, it can prioritize feedback on mock exams and assignments submitted early, and postpone feedback on mock exams and assignments submitted late. The feedback department can also customize the content of the feedback depending on the submission timing. This allows the feedback department to provide more appropriate feedback by prioritizing feedback based on the submission timing of mock exams and assignments.

[0101] The feedback unit can adjust the order of feedback based on the relevance of the practice tests and assignments. For example, it can prioritize providing feedback on practice tests and assignments that are highly relevant. It can also postpone providing feedback on practice tests and assignments that are less relevant. The feedback unit can also customize the content of the feedback according to its relevance. As a result, the feedback unit can provide more appropriate feedback by adjusting the order of feedback based on the relevance of the practice tests and assignments.

[0102] The review unit can estimate the child's emotions and adjust the review method of the learning plan based on the estimated emotions. For example, if the child is relaxed, the review unit can perform a detailed review. If the child is tense, the review unit can perform a concise review. If the child is excited, the review unit can perform a visually engaging review. This allows the review unit to perform more effective reviews by adjusting the review method of the learning plan according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The review unit can select the optimal review method by referring to past learning data when reviewing a learning plan. For example, the review unit can review by referring to successful learning plans from the past. The review unit can also review in a way that avoids learning plans that have failed in the past. The review unit can also select the optimal review method from past learning data. In this way, the review unit can select the optimal review method by referring to past learning data.

[0104] The review unit can customize the review process based on the current learning situation when reviewing the learning plan. For example, if the current learning situation is good, the review unit can perform a detailed review. If the current learning situation is poor, the review unit can also perform a brief review. The review unit can also customize the review process according to the current learning situation. This allows the review unit to perform more appropriate reviews by customizing the review process based on the current learning situation.

[0105] The review unit can estimate a child's emotions and prioritize revisions to the learning plan based on those emotions. For example, if the child is relaxed, the review unit will prioritize high-priority revisions. If the child is stressed, the review unit may postpone lower-priority revisions. If the child is excited, the review unit may prioritize visually appealing revisions. This allows the review unit to prioritize revisions to the learning plan according to the child's emotions, enabling more effective revisions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The review department can select the most appropriate review method when reviewing learning plans, taking geographical location information into consideration. For example, the review department can prioritize reviews related to educational institutions in the area where the child lives. The review department can also prioritize reviews related to the area the child attends school in. The review department can also prioritize reviews related to areas the child frequently visits. This allows the review department to select the most appropriate review method by taking geographical location information into consideration, enabling more appropriate reviews.

[0107] The review department can analyze social media activity and propose methods for revision when reviewing learning plans. For example, the review department can make revisions by referring to educational information that the child shows interest in on social media. The review department can also make revisions by referring to the opinions of educational experts that the child follows on social media. The review department can also make revisions based on educational information that the child shares on social media. In this way, the review department can make more appropriate revisions by analyzing social media activity and proposing methods for revision.

[0108] The recovery unit can estimate the child's emotions and adjust the implementation of the recovery plan based on the estimated emotions. For example, if the child is relaxed, the recovery unit can provide a detailed recovery plan. If the child is tense, the recovery unit can also provide a concise recovery plan. If the child is excited, the recovery unit can also provide a visually appealing recovery plan. This allows the recovery unit to enable more effective recovery by adjusting the implementation of the recovery plan according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The recovery unit can select the optimal execution method by referring to past recovery data when executing a recovery plan. For example, the recovery unit can execute a recovery plan by referring to a previously successful recovery plan. The recovery unit can also execute a recovery plan while avoiding a previously failed one. The recovery unit can also select the optimal execution method from past recovery data. In this way, the recovery unit can select the optimal execution method by referring to past recovery data.

[0110] The recovery unit can customize the recovery method based on the current learning status when executing a recovery plan. For example, if the current learning status is good, the recovery unit provides a detailed recovery plan. If the current learning status is poor, the recovery unit can also provide a concise recovery plan. The recovery unit can also customize the recovery method according to the current learning status. This allows the recovery unit to perform more appropriate recovery by customizing the recovery method based on the current learning status.

[0111] The recovery unit can estimate the child's emotions and prioritize recovery options based on those emotions. For example, if the child is relaxed, the recovery unit will prioritize high-priority recovery options. If the child is tense, the recovery unit may postpone lower-priority recovery options. If the child is excited, the recovery unit may prioritize visually appealing recovery options. This allows the recovery unit to prioritize recovery options according to the child's emotions, enabling more effective recovery. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The recovery unit can select the optimal recovery method by considering geographical location information when implementing a recovery plan. For example, the recovery unit can prioritize implementing recovery plans related to educational institutions in the area where the child lives. The recovery unit can also prioritize implementing recovery plans related to the area the child attends school in. The recovery unit can also prioritize implementing recovery plans related to areas the child frequently visits. By selecting the optimal recovery method by considering geographical location information, the recovery unit can achieve more appropriate recovery.

[0113] The recovery department can analyze social media activity and propose recovery measures when implementing a recovery plan. For example, the recovery department can implement a recovery plan by referring to educational information that the child is interested in on social media. The recovery department can also implement a recovery plan by referring to the opinions of educational experts that the child follows on social media. The recovery department can also implement a recovery plan based on educational information that the child shares on social media. In this way, the recovery department can enable more appropriate recovery by analyzing social media activity and proposing recovery measures.

[0114] The advice unit can estimate the parent's emotions and adjust the way educational advice is presented based on those estimated emotions. For example, if the parent is relaxed, the advice unit can provide detailed educational advice. If the parent is busy, it can provide concise educational advice. If the parent is agitated, it can provide visually appealing educational advice. In this way, the advice unit can provide more effective advice by adjusting the way educational advice is presented according to the parent's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The advisory unit can select the optimal advice method by referring to past advice data when providing educational advice. For example, the advisory unit can provide advice based on past successful educational advice. It can also provide advice that avoids past unsuccessful educational advice. The advisory unit can select the optimal advice method from past advice data. Thus, the advisory unit can select the optimal advice method by referring to past advice data.

[0116] The advisory department can customize the means of providing educational advice based on the current educational situation. For example, if the current educational situation is good, the advisory department will provide detailed educational advice. If the current educational situation is poor, the advisory department can also provide concise educational advice. The advisory department can also customize the means of providing advice according to the current educational situation. This allows the advisory department to provide more appropriate advice by customizing the means of advice based on the current educational situation.

[0117] The advice unit can estimate the parent's emotions and prioritize educational advice based on those emotions. For example, if the parent is relaxed, the advice unit will prioritize providing high-priority educational advice. If the parent is busy, the advice unit may postpone less important educational advice. If the parent is agitated, the advice unit may prioritize providing visually appealing educational advice. This allows the advice unit to provide more effective advice by prioritizing educational advice according to the parent's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The advisory department can select the most appropriate advice method when providing educational advice, taking geographical location information into consideration. For example, the advisory department can prioritize advice related to educational institutions in the area where the parents live. The advisory department can also prioritize advice related to the area where the parents commute. The advisory department can also prioritize advice related to areas that the parents frequently visit. In this way, the advisory department can provide more appropriate advice by selecting the most appropriate advice method, taking geographical location information into consideration.

[0119] The advisory department can analyze social media activity and propose methods for providing educational advice. For example, the advisory department can provide advice based on educational information that parents are interested in on social media. The advisory department can also provide advice based on the opinions of educational experts that parents follow on social media. The advisory department can also provide advice based on educational information that parents share on social media. In this way, the advisory department can provide more appropriate advice by analyzing social media activity and proposing methods for providing advice.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The educational support system can also include a motivational component. This component provides motivation to enhance children's desire to learn. For example, it can send praise messages when children achieve their goals. It can also provide engaging learning content if a child has lost interest in learning. Furthermore, it can send reassuring messages if a child feels anxious about learning. In this way, the motivational component can increase children's motivation to learn and support their continued learning.

[0122] The educational support system can also include a progress management unit. This unit manages the child's learning progress and provides timely feedback. For example, if a child achieves a certain learning goal, the unit can record that achievement and set the next goal. If a child falls behind in their studies, the unit can identify the delay and provide a recovery plan. Furthermore, the unit can report the child's learning progress to the parents and encourage appropriate support. This allows the progress management unit to effectively manage the child's learning progress and support their continued learning.

[0123] The educational support system can also include a communication department. This department supports communication between parents and children. For example, when a parent asks a child a question about their studies, the communication department can format the question appropriately and present it in a way that the child can easily understand. Similarly, when a child provides feedback on their studies to their parent, the communication department can organize that feedback and present it in a way that the parent can easily understand. Furthermore, the communication department can provide advice to facilitate smooth communication between parents and children. In this way, the communication department can support communication between parents and children and enhance the effectiveness of learning.

[0124] The educational support system can also include a refreshment section. This section provides methods for refreshing children to alleviate learning fatigue. For example, if a child studies for extended periods, the refreshment section can suggest appropriate break times and provide refreshing methods. Furthermore, if a child is feeling stressed about studying, the refreshment section can suggest ways to relax. In addition, the refreshment section can also suggest refreshing methods for parents, providing an environment where both parents and children can refresh. This allows the refreshment section to reduce learning fatigue in children and enhance learning effectiveness.

[0125] The educational support system can also include a goal achievement unit. This unit provides support for children in achieving their goals. For example, it can provide specific steps for children to achieve their goals and manage their progress. Furthermore, if a child is making efforts toward achieving their goals, the unit can evaluate their efforts and send motivational messages. Additionally, if a child is experiencing difficulties in achieving their goals, the unit can provide advice to help them overcome those difficulties. In this way, the goal achievement unit can support children in achieving their goals and enhance the effectiveness of their learning.

[0126] The educational support system can also include a learning environment department. This department supports children in learning in an optimal environment. For example, to provide children with an environment where they can concentrate, the department can suggest appropriate study spaces. It can also provide children with necessary learning materials and tools. Furthermore, the department can provide parents with advice on creating a suitable learning environment for their children. In this way, the learning environment department can support children in learning in an optimal environment and enhance the effectiveness of their learning.

[0127] The educational support system can also include a learning style section. This section provides learning methods tailored to each child's learning style. For example, if a child prefers visual learning, the learning style section can provide visual learning materials. If a child prefers auditory learning, the learning style section can provide audio learning materials. Furthermore, if a child prefers experiential learning, the learning style section can provide hands-on learning activities. This allows the learning style section to provide learning methods suited to each child's learning style, thereby enhancing the effectiveness of their learning.

[0128] The educational support system can also include a learning record section. This section manages the child's learning records and provides timely feedback. For example, it can record what the child has learned and track their progress. It can also record the child's feelings about learning and provide feedback based on those feelings. Furthermore, the learning record section can report the child's learning records to parents and encourage appropriate support. This allows the learning record section to effectively manage the child's learning records and support their continued learning.

[0129] The educational support system can also include a learning assessment unit. This unit evaluates children's learning outcomes and provides appropriate feedback. For example, it can test children on what they have learned and evaluate the results. It can also assess children's feelings towards learning and provide feedback based on those feelings. Furthermore, the learning assessment unit can report children's learning outcomes to parents and encourage appropriate support. This allows the learning assessment unit to effectively evaluate children's learning outcomes and support their continued learning.

[0130] The educational support system can also include a learning objectives unit. This unit manages the learning objectives set by the child and provides appropriate support. For example, when a child sets a learning objective, the unit can evaluate whether that objective is realistic and achievable. It can also provide specific steps for the child to achieve the learning objective and manage their progress. Furthermore, the unit can report the child's learning objectives to the parents and encourage appropriate support. This allows the unit to effectively manage the learning objectives set by the child and support their continued learning.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk accepts goal setting. For example, parents can specify the level of university they want their child to attend. Step 2: The Development Department constructs the educational program based on the goals received by the Reception Department. For example, if a student selects the course for admission to a prestigious national university, the Development Department will provide a learning plan to achieve that goal. Step 3: The Feedback Department provides feedback on mock exams and assignments based on the educational program built by the Development Department. For example, they evaluate the results of mock exams and the progress of assignments and provide feedback. Step 4: The review team revises the daily study plan based on the results obtained from the feedback team. For example, they adjust the study plan to take into account school test results. Step 5: The recovery team implements the recovery plan based on the plan revised by the review team. For example, if the mock exam results do not meet the target, they provide an additional study plan. Step 6: The Advice Department provides educational advice to parents based on the recovery plan implemented by the Recovery Department. For example, they provide advice to help improve the child's academic performance.

[0133] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0136] Each of the multiple elements described above, including the reception unit, construction unit, feedback unit, review unit, recovery unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which allows parents to specify the university level they wish to aim for. The construction unit is implemented by the specific processing unit 290 of the data processing unit 12, which constructs an educational program based on the goal. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides feedback on mock exams and assignments. The review unit is implemented by the specific processing unit 290 of the data processing unit 12, which reviews the daily learning plan. The recovery unit is implemented by the specific processing unit 290 of the data processing unit 12, which executes a recovery plan. The advice unit is implemented by the control unit 46A of the smart device 14, which provides educational advice to parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, construction unit, feedback unit, review unit, recovery unit, and advice unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which allows parents to specify the university level they wish to aim for. The construction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which constructs an educational program based on the goal. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides feedback on mock exams and assignments. The review unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which reviews the daily learning plan. The recovery unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which executes a recovery plan. The advice unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides educational advice to parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the reception unit, construction unit, feedback unit, review unit, recovery unit, and advice unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which allows parents to specify the university level they wish to aim for. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which constructs an educational program based on the goal. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides feedback on mock exams and assignments. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which reviews the daily learning plan. The recovery unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which executes a recovery plan. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides educational advice to parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the reception unit, construction unit, feedback unit, review unit, recovery unit, and advice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which allows parents to specify the university level they wish to aim for. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which constructs an educational program based on the goal. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides feedback on mock exams and assignments. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which reviews the daily learning plan. The recovery unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which executes a recovery plan. The advice unit is implemented by, for example, the control unit 46A of the robot 414, which provides educational advice to parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0186] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) The reception desk for setting goals, A development department constructs an educational program based on the objectives received by the aforementioned reception department, A feedback unit provides feedback on mock exams and assignments based on the educational program constructed by the aforementioned construction unit, A review unit that reviews the daily learning plan based on the results obtained by the aforementioned feedback unit, A recovery unit executes a recovery plan based on the plan revised by the aforementioned review unit, The system includes an advice unit that provides educational advice to parents based on the recovery plan implemented by the recovery unit. A system characterized by the following features. (Note 2) It includes a section that presents examples of goal setting. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an analysis unit for analyzing the results of mock exams. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an additional plans section that provides additional learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with a monitoring unit to monitor the child's growth. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Estimate the parents' emotions and adjust the timing of goal setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze past goal-setting history and select the optimal goal-setting method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When setting goals, filter them based on the parents' current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Estimate the parents' emotions and determine the priority of goal setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When setting goals, prioritize highly relevant goals by considering the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When setting goals, analyze your parents' social media activity and set relevant goals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned construction unit is The system estimates parental emotions and adjusts the presentation of the educational program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned construction unit is When designing an educational program, adjust the level of detail based on the importance of the objectives. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned construction unit is When building an educational program, apply different building algorithms depending on the target category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned construction unit is The program estimates the parents' emotions and adjusts the length of the educational program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned construction unit is When developing an educational program, prioritize the program based on the timing of goal setting. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned construction unit is When designing an educational program, adjust the sequence of programs based on the relevance of the objectives. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is The system estimates the child's emotions and adjusts the way feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the practice test or assignment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of the mock exam or assignment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is The system estimates the child's emotions and adjusts the length of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on the submission timing of mock exams and assignments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of the practice tests and assignments. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned review unit is, We estimate the child's emotions and adjust the learning plan based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned review unit is, When reviewing your study plan, refer to past study data to select the most suitable review method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned review unit is, When reviewing your study plan, customize the review process based on your current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned review unit is, Estimate the child's emotions and prioritize revisions to the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned review unit is, When reviewing a study plan, select the most suitable review method by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned review unit is, When reviewing your learning plan, we will analyze your social media activity and propose ways to revise it. The system described in Appendix 1, characterized by the features described herein. (Note 30) The recovery unit is Estimate the child's emotions and adjust the implementation of the recovery plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recovery unit is When implementing a recovery plan, the optimal method is selected by referring to past recovery data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recovery unit is When implementing a recovery plan, customize the recovery method based on the current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recovery unit is Estimate the child's emotions and determine the priority of recovery plans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recovery unit is When implementing a recovery plan, the optimal recovery method will be selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recovery unit is When implementing a recovery plan, we will analyze social media activity and propose recovery methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advice section, It estimates the parents' emotions and adjusts the way educational advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advice section, When providing educational advice, we select the most suitable advice method by referring to past advice data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned advice section, When providing educational advice, customize the methods of advice based on the current educational situation. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned advice section, It estimates the parents' emotions and prioritizes educational advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned advice section, When providing educational advice, the most suitable advice method will be selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned advice section, When providing educational advice, we analyze social media activity and propose methods for providing advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk accepts goal setting requests, A development department constructs an educational program based on the objectives received by the aforementioned reception department, A feedback unit provides feedback on mock exams and assignments based on the educational program constructed by the aforementioned construction unit, A review unit that reviews the daily learning plan based on the results obtained by the aforementioned feedback unit, A recovery unit executes a recovery plan based on the plan revised by the aforementioned review unit, The system includes an advice unit that provides educational advice to parents based on the recovery plan implemented by the recovery unit. A system characterized by the following features.

2. It includes a section that presents examples of goal setting. The system according to feature 1.

3. It includes an analysis unit for analyzing the results of mock exams. The system according to feature 1.

4. It includes an additional plans section that provides additional learning plans. The system according to feature 1.

5. It is equipped with a monitoring unit to monitor the child's growth. The system according to feature 1.

6. The aforementioned reception unit is Estimate the parents' emotions and adjust the timing of goal setting based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze past goal-setting history and select the optimal goal-setting method. The system according to feature 1.

8. The aforementioned reception unit is When setting goals, filter them based on the parents' current living situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is Estimate the parents' emotions and determine the priority of goal setting based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When setting goals, prioritize highly relevant goals by considering the parents' geographical location. The system according to feature 1.

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