system

Abstract

The system according to this embodiment aims to provide individual learners with an optimal learning plan based on their MBTI diagnostic results, thereby supporting their learning motivation and self-management. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives MBTI diagnostic results. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The monitoring unit monitors learning progress based on the reminders delivered by the reminder unit. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in 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, there is a problem that it is difficult to find an optimal learning method for each individual learner, and it is difficult to maintain learning motivation and self-management.

[0005] The system according to the embodiment aims to provide an optimal learning plan for each individual learner based on the MBTI diagnosis result and support learning motivation and self-management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives MBTI diagnostic results. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The monitoring unit monitors learning progress based on the reminders delivered by the reminder unit. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal learning plan for each learner based on MBTI diagnostic results, and can support learning motivation and self-management. [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 learning support system according to an embodiment of the present invention is a system that proposes optimal learning methods and materials based on MBTI type. This learning support system provides a personalized learning plan by having the user take an MBTI assessment and having the AI ​​analyze the results. Furthermore, it maintains the user's learning motivation through a regular reminder delivery function. It also makes it easier to find optimal learning resources by allowing interaction and information sharing with other users. Finally, it supports the user's self-management by setting clear learning goals and providing regular feedback. This promotes efficient and effective learning. For example, the user takes an MBTI assessment and the AI ​​analyzes the results. In this process, the user answers several questions and is classified into one of 16 personality types based on their answers. For example, the user may be classified as an "extroverted," "intuitive," "thinking," or "judging" type. This information is input into the AI. Next, the AI ​​provides a personalized learning plan based on the analysis results. The AI ​​proposes optimal learning methods and materials based on the user's MBTI type. For example, for an "extroverted" user, it proposes a learning method that includes group discussions, and for an "introverted" user, it proposes a method that allows for focused individual learning. Furthermore, the regular reminder delivery function helps maintain user motivation for learning. The AI ​​monitors the user's learning progress and delivers reminders at appropriate times. For example, if a user is slacking off on their studies, the AI ​​will send a reminder to encourage them to study. It also makes it easier to find optimal learning resources by allowing users to interact and share information with other users. The AI ​​supports interaction among users and promotes the sharing of learning resources. For example, by sharing their learning methods and materials with other users, users can find optimal learning resources. Finally, it supports user self-management by setting clear learning goals and providing regular feedback. The AI ​​sets learning goals for users and monitors their progress. It provides regular feedback to make self-management easier for users. For example, the AI ​​evaluates the progress toward the learning goals set by the user and provides necessary advice.This allows the learning support system to promote efficient and effective learning and support users in improving their skills.

[0029] The learning support system according to this embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives the MBTI diagnostic results from the user. For example, the user takes the MBTI diagnostic test through an online platform and inputs the results into the reception unit. The reception unit can also analyze the user's MBTI diagnostic results using AI. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. For example, the analysis unit suggests the optimal learning method and materials based on the user's MBTI type. For example, it suggests a learning method that includes group discussions for an extroverted user, and a method that allows for focused individual learning for an introverted user. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. For example, the reminder unit monitors the user's learning progress and delivers reminders at appropriate times. For example, if the user is slacking off on their studies, the reminder unit sends a reminder to encourage them to study. The monitoring unit monitors learning progress based on reminders delivered by the reminder unit. The monitoring unit, for example, monitors the user's learning progress in real time and records the progress status. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit. The feedback unit, for example, evaluates the user's progress toward their learning goals and provides necessary advice. As a result, the learning support system according to this embodiment can promote efficient and effective learning based on MBTI diagnostic results.

[0030] The reception desk receives MBTI assessment results from users. Users take the MBTI assessment, for example, through an online platform, and input the results into the reception desk. Specifically, users take the MBTI assessment using a dedicated website or application and input the results into the system. The assessment results indicate the user's personality type, which is classified into 16 types. The reception desk stores these assessment results in a database and uses them for subsequent analysis and the creation of learning plans. Furthermore, the reception desk can also analyze the user's MBTI assessment results using AI. The AI ​​analyzes patterns in the assessment results and gains a detailed understanding of the user's personality traits. For example, the AI ​​analyzes the user's response patterns and extracts specific personality traits and behavioral tendencies. This information is useful for subsequent processing in the analysis and reminder departments. The reception desk also provides an interface for users to input their assessment results, making it easy for them to enter their results. For example, it provides forms and options for inputting assessment results, allowing users to operate intuitively. This enables the reception desk to efficiently receive users' MBTI assessment results and provide the data necessary for subsequent processing.

[0031] The analysis department analyzes the MBTI diagnostic results received by the reception department and provides a personalized learning plan. Specifically, the analysis department proposes the optimal learning methods and materials based on the user's MBTI type. For example, it suggests learning methods that include group discussions for extroverted users and methods that allow introverted users to concentrate on learning alone. The analysis department uses AI to analyze the user's learning style and preferences in detail and create the optimal learning plan. The AI ​​continuously improves the learning plan based on past learning data and user feedback. For example, the AI ​​analyzes what learning methods the user has succeeded with in the past and suggests similar methods. The analysis department also flexibly adjusts the learning plan considering the user's learning goals and progress. For example, it clarifies the steps necessary for the user to achieve a specific goal and provides a plan that allows them to progress through learning step by step. In this way, the analysis department can provide the user with the optimal learning plan and support efficient and effective learning.

[0032] The Reminders Unit delivers regular reminders based on the learning plan provided by the Analytics Unit. Specifically, the Reminders Unit monitors the user's learning progress and delivers reminders at the appropriate time. For example, if a user is slacking off on their studies, the Reminders Unit will send a reminder to encourage them to study. Reminders are customized according to the user's preferences. For example, reminders are delivered using the method most easily received by the user, such as email, SMS, or in-app notifications. The Reminders Unit uses AI to analyze the user's learning patterns and send reminders at the optimal time. For example, if a user tends to study during a specific time slot, a reminder will be sent during that time. The Reminders Unit also adjusts the content of reminders according to the user's learning progress. For example, if a user is approaching their goal, an encouraging message will be sent to boost their motivation. In this way, the Reminders Unit can provide an environment that makes it easier for users to continue learning and maximize the effectiveness of their learning.

[0033] The monitoring unit monitors learning progress based on reminders delivered by the reminder unit. Specifically, the monitoring unit monitors the user's learning progress in real time and records the progress status. The monitoring unit verifies whether the user is progressing according to the learning plan and saves the progress status to a database. For example, it checks whether the user has completed a specific learning task and records the information if completed. The monitoring unit uses AI to analyze the user's learning data and detect abnormal patterns or delays in progress. For example, if a user's learning pace is slower than usual, the monitoring unit notifies the reminder unit and feedback unit of this information. This allows the monitoring unit to accurately grasp the user's learning progress and take necessary actions quickly. Furthermore, the monitoring unit accumulates user learning data over the long term and uses it for trend analysis and improvement of future learning plans. This allows the monitoring unit to continuously monitor the user's learning progress and improve the overall effectiveness of the system.

[0034] The feedback unit provides feedback based on learning progress monitored by the monitoring unit. Specifically, the feedback unit evaluates the user's progress toward their learning goals and provides necessary advice. For example, it evaluates the extent to which the user has achieved their set learning goals and provides feedback according to their level of achievement. The feedback unit uses AI to analyze the user's learning data and generate personalized advice. For example, if a user has difficulty in a particular area, it suggests specific learning methods and materials for that area. The feedback unit also provides feedback tailored to the user's learning style and preferences. For example, it provides feedback using graphs and charts for users who prefer visual information, and detailed written feedback for users who prefer text-based information. This allows the feedback unit to provide specific advice to help users learn effectively and improve the quality of their learning. Furthermore, the feedback unit improves the entire system based on user feedback. For example, it collects user feedback and reviews the content of learning plans and reminders to provide more effective learning support. This allows the feedback unit to continuously improve the user's learning experience.

[0035] The analysis unit can suggest optimal learning methods and materials based on the user's MBTI type. For example, based on the user's MBTI type, the analysis unit can suggest learning methods that include group discussions for extroverted users. It can also suggest methods that allow introverted users to concentrate on learning alone. Furthermore, the analysis unit can suggest optimal learning materials such as video materials, text materials, and interactive exercises based on the user's MBTI type. In this way, it can provide the optimal learning methods and materials based on the user's MBTI type.

[0036] The reminder function can monitor the user's learning progress and deliver reminders at appropriate times. For example, the reminder function can monitor the user's learning progress in real time and deliver reminders according to the progress. For instance, if a user is slacking off on their studies, the reminder function will send a reminder to encourage them to study. The reminder function can also deliver reminders at appropriate times based on the user's learning progress. This allows for the delivery of reminders at appropriate times according to the user's learning progress.

[0037] The monitoring system can support interaction among users and promote the sharing of learning resources. For example, it can provide forums and chat rooms to support user interaction. It can also facilitate the sharing of users' learning methods and materials with other users. For instance, it can provide a platform for users to share their learning methods and materials. This supports user interaction and promotes the sharing of learning resources.

[0038] The feedback unit can evaluate the user's progress toward their learning goals and provide necessary advice. For example, the feedback unit can evaluate the user's progress toward their learning goals and provide necessary advice. Furthermore, the feedback unit can provide necessary advice based on the user's learning progress. This allows the system to evaluate the user's progress toward their learning goals and provide necessary advice.

[0039] The reception desk can refer to the user's past MBTI assessment results and generate additional questions to improve the accuracy of the assessment. For example, the reception desk can analyze trends in questions the user has answered in the past and generate additional questions for questions that received many ambiguous answers. The reception desk can also check the degree of agreement between the user's past assessment results and current answers, and generate additional questions if there is a discrepancy. Furthermore, the reception desk can generate detailed questions about specific personality types based on the user's past assessment results. This allows for improved assessment accuracy by referring to past assessment results.

[0040] The reception desk can customize the questions based on the user's current learning status and goals when receiving diagnostic results. For example, if the user enters their current learning status, the reception desk will generate questions appropriate to that status. The reception desk can also generate questions about the skills necessary to achieve the learning goals set by the user. Furthermore, the reception desk can consider the user's learning progress and generate questions appropriate to that progress. By customizing the questions according to the user's learning status and goals, a more appropriate diagnosis can be made.

[0041] The reception desk can prioritize presenting highly relevant questions based on the user's geographical location when receiving diagnostic results. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. It can also prioritize travel-related questions if the user is traveling. Furthermore, if the user is at home, it can prioritize questions that promote relaxation. This allows for more accurate diagnoses by presenting highly relevant questions based on the user's geographical location.

[0042] The reception desk can analyze the user's social media activity and add relevant questions when receiving diagnostic results. For example, the reception desk can generate questions based on the user's interests and passions shared on social media. It can also generate questions based on the user's social media activity times. Furthermore, it can generate questions based on the user's social media friendships. This allows for a more accurate diagnosis by adding relevant questions based on the user's social media activity.

[0043] The analysis unit can suggest the optimal learning methods and materials by referring to the user's past learning history during analysis. For example, the analysis unit can analyze the effectiveness of materials the user has used in the past and suggest the most suitable materials. It can also analyze the effectiveness of the user's past learning methods and suggest the most suitable learning methods. Furthermore, the analysis unit can suggest materials tailored to the user's learning progress based on their past learning history. In this way, by referring to past learning history, it can suggest the optimal learning methods and materials.

[0044] The analysis unit can customize the learning plan based on the user's current learning goals during analysis. For example, the analysis unit provides a learning plan necessary to achieve the learning goals set by the user. It can also provide a learning plan to acquire the necessary skills according to the user's learning goals. Furthermore, the analysis unit can adjust the learning plan while monitoring progress based on the user's learning goals. This allows for effective learning towards goal achievement by customizing the learning plan based on the user's learning goals.

[0045] The analysis unit can suggest optimal learning resources by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can suggest learning resources related to that region. Furthermore, if the user is traveling, the analysis unit can suggest learning resources related to travel. Additionally, if the user is at home, the analysis unit can suggest resources that can be used for learning at home. This allows for more effective learning by suggesting optimal learning resources based on the user's geographical location.

[0046] The analysis unit can analyze a user's social media activity during analysis and suggest relevant learning resources. For example, the analysis unit can suggest learning resources based on the user's interests and passions shared on social media. It can also suggest learning resources based on the user's social media activity times. Furthermore, the analysis unit can suggest learning resources based on the user's social media friendships. This allows for more effective learning support by suggesting relevant learning resources based on the user's social media activity.

[0047] The reminder function can send reminders at the optimal time by referring to the user's past learning progress when sending reminders. For example, the reminder function can send reminders during times when the user has previously neglected their studies. It can also send reminders during times when the user has previously studied. Furthermore, the reminder function can send reminders at the optimal time based on the user's past learning progress. This allows the system to send reminders at the optimal time by referring to past learning progress.

[0048] The reminder function can adjust the frequency of reminders based on the user's current learning status when sending reminders. For example, if the user inputs their current learning status, the reminder function will adjust the frequency of reminders accordingly. The reminder function can also increase the frequency of reminders if the user is behind schedule, based on their learning progress. Furthermore, the reminder function can decrease the frequency of reminders if the user is progressing well, based on their learning progress. By adjusting the frequency of reminders according to the user's learning status, it is possible to support more effective learning.

[0049] The reminder function can send reminders at the optimal time, taking into account the user's geographical location. For example, if the user is in a specific region, the reminder function will send reminders related to that region. It can also send travel-related reminders if the user is traveling. Furthermore, if the user is at home, the reminder function can send reminders for home study. This allows for more effective learning by sending reminders at the optimal time based on the user's geographical location.

[0050] The reminder function can analyze a user's social media activity when sending reminders and send relevant reminders. For example, the reminder function can send reminders based on the user's interests and passions shared on social media. It can also send reminders based on the user's social media activity times. Furthermore, the reminder function can send reminders based on the user's social media friendships. This allows for more effective learning by sending relevant reminders based on the user's social media activity.

[0051] The monitoring unit can select the optimal monitoring method by referring to the user's past learning history during monitoring. For example, the monitoring unit can analyze the effectiveness of monitoring methods previously used by the user and select the most suitable method. Furthermore, the monitoring unit can select a monitoring method based on the user's learning progress, derived from their past learning history. In addition, the monitoring unit can analyze the user's past learning history and select the most effective monitoring method. This allows the optimal monitoring method to be selected by referring to past learning history.

[0052] The monitoring unit can adjust the monitoring frequency based on the user's current learning goals during monitoring. For example, the monitoring unit can provide the monitoring frequency necessary to achieve the learning goals set by the user. It can also provide the monitoring frequency necessary to acquire the required skills according to the user's learning goals. Furthermore, the monitoring unit can adjust the monitoring frequency while checking the user's progress based on their learning goals. This allows for more effective learning support by adjusting the monitoring frequency based on the user's learning goals.

[0053] The monitoring unit can select the optimal monitoring method while considering the user's geographical location. For example, if the user is in a specific region, the monitoring unit will select a monitoring method relevant to that region. Furthermore, if the user is traveling, the monitoring unit can select a monitoring method relevant to their travel. Additionally, if the user is at home, the monitoring unit can select a monitoring method suitable for home learning. This allows for more effective learning support by selecting the optimal monitoring method based on the user's geographical location.

[0054] The monitoring unit can analyze the user's social media activity during monitoring and provide relevant monitoring results. For example, the monitoring unit can provide monitoring results based on the interests and passions the user shares on social media. It can also provide monitoring results based on the time of day the user is active on social media. Furthermore, the monitoring unit can provide monitoring results based on the user's social media friendships. This allows for more effective learning support by providing relevant monitoring results based on the user's social media activity.

[0055] The feedback unit can provide optimal advice by referring to the user's past learning progress when providing feedback. For example, the feedback unit can provide advice based on the learning goals the user has achieved in the past. It can also provide advice if the user is falling behind, based on their past learning progress. Furthermore, it can provide advice if the user is progressing well, based on their past learning progress. This allows the system to provide optimal advice by referring to past learning progress.

[0056] The feedback unit can adjust the frequency of feedback based on the user's current learning goals when providing feedback. For example, the feedback unit can provide the feedback frequency necessary to achieve the learning goals set by the user. It can also provide the feedback frequency necessary to acquire the required skills according to the user's learning goals. Furthermore, the feedback unit can adjust the feedback frequency while monitoring progress based on the user's learning goals. This allows for more effective learning support by adjusting the feedback frequency based on the user's learning goals.

[0057] The feedback system can provide optimal advice by considering the user's geographical location when providing feedback. For example, if the user is in a specific region, the feedback system can provide advice relevant to that region. It can also provide travel-related advice if the user is traveling. Furthermore, if the user is at home, the feedback system can provide advice that can be used for learning at home. This allows for more effective learning by providing optimal advice based on the user's geographical location.

[0058] The feedback system can analyze a user's social media activity and provide relevant advice when providing feedback. For example, it can provide advice based on the user's interests and passions shared on social media. It can also provide advice based on the user's social media activity schedule. Furthermore, it can provide advice based on the user's social media friendships. This allows for more effective learning by providing relevant advice based on the user's social media activity.

[0059] The feedback system can provide optimal advice by considering the user's health condition when providing feedback. For example, if the user is tired, the feedback system can advise them to rest. It can also suggest learning methods that incorporate exercise if the user is seeking healthy exercise. Furthermore, if the user is feeling unwell, the feedback system can suggest learning methods that don't overexert them. This allows for more effective learning by providing optimal advice based on the user's health condition.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can estimate the user's learning style and adjust the learning plan based on that estimated style. For example, users with a visual learning style can be provided with learning materials that make extensive use of visual aids. Users with an auditory learning style can be provided with a learning plan centered on audio materials. Furthermore, users with an experiential learning style can be provided with a learning plan that includes practical exercises and simulations. In this way, by providing an optimal learning plan tailored to the user's learning style, it is possible to support more effective learning.

[0062] The monitoring unit can monitor the user's learning environment and provide advice to ensure an optimal learning environment. For example, if a user is studying in a noisy environment, it can recommend studying in a quiet place. It can also advise the user to use appropriate lighting if they are studying in a dark place. Furthermore, if a user is studying in the same posture for extended periods, it can advise them to take regular breaks. By optimizing the user's learning environment, it can support more effective learning.

[0063] The analysis unit can analyze a user's learning history and predict future learning plans based on past learning patterns. For example, it can identify learning methods that have been successful for the user in the past and provide a learning plan based on them. It can also identify learning content that the user has struggled with in the past and provide a complementary learning plan for that area. Furthermore, it can identify learning methods that were effective at a specific time from the user's learning history and provide a learning plan tailored to that period. In this way, by providing an optimal learning plan based on the user's past learning history, it can support more effective learning.

[0064] The monitoring unit can monitor users' learning progress in real time and send special alerts if progress is behind schedule. For example, if a user is behind on their set learning goals, it can send an alert to encourage them to continue learning. It can also send alerts suggesting supplementary learning resources if a user is behind on a particular learning topic. Furthermore, if a user is slacking off on their studies, it can send an alert to encourage them to resume learning. In this way, by providing appropriate alerts according to the user's learning progress, it can support continued learning.

[0065] The analysis unit can analyze a user's learning history and predict future learning plans based on past learning patterns. For example, it can identify learning methods that have been successful for the user in the past and provide a learning plan based on them. It can also identify learning content that the user has struggled with in the past and provide a complementary learning plan for that area. Furthermore, it can identify learning methods that were effective at a specific time from the user's learning history and provide a learning plan tailored to that period. In this way, by providing an optimal learning plan based on the user's past learning history, it can support more effective learning.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the user's MBTI assessment results. For example, the user takes the MBTI assessment through an online platform and enters the results into the reception desk. The reception desk can also use AI to analyze the user's MBTI assessment results. Step 2: The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. For example, the analysis unit suggests optimal learning methods and materials based on the user's MBTI type. For instance, it might suggest learning methods that include group discussions for extroverted users, and methods that allow introverted users to concentrate on learning alone. Step 3: The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The reminder unit, for example, monitors the user's learning progress and delivers reminders at the appropriate time. For example, if a user is slacking off on their studies, the reminder unit will send a reminder to encourage them to study. Step 4: The monitoring unit monitors learning progress based on reminders delivered by the reminder unit. For example, the monitoring unit monitors the user's learning progress in real time and records the progress status. Step 5: The feedback unit provides feedback based on the learning progress monitored by the monitoring unit. For example, the feedback unit evaluates the user's progress toward their learning goals and provides necessary advice.

[0068] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that proposes optimal learning methods and materials based on MBTI type. This learning support system provides a personalized learning plan by having the user take an MBTI assessment and having the AI ​​analyze the results. Furthermore, it maintains the user's learning motivation through a regular reminder delivery function. It also makes it easier to find optimal learning resources by allowing interaction and information sharing with other users. Finally, it supports the user's self-management by setting clear learning goals and providing regular feedback. This promotes efficient and effective learning. For example, the user takes an MBTI assessment and the AI ​​analyzes the results. In this process, the user answers several questions and is classified into one of 16 personality types based on their answers. For example, the user may be classified as an "extroverted," "intuitive," "thinking," or "judging" type. This information is input into the AI. Next, the AI ​​provides a personalized learning plan based on the analysis results. The AI ​​proposes optimal learning methods and materials based on the user's MBTI type. For example, for an "extroverted" user, it proposes a learning method that includes group discussions, and for an "introverted" user, it proposes a method that allows for focused individual learning. Furthermore, the regular reminder delivery function helps maintain user motivation for learning. The AI ​​monitors the user's learning progress and delivers reminders at appropriate times. For example, if a user is slacking off on their studies, the AI ​​will send a reminder to encourage them to study. It also makes it easier to find optimal learning resources by allowing users to interact and share information with other users. The AI ​​supports interaction among users and promotes the sharing of learning resources. For example, by sharing their learning methods and materials with other users, users can find optimal learning resources. Finally, it supports user self-management by setting clear learning goals and providing regular feedback. The AI ​​sets learning goals for users and monitors their progress. It provides regular feedback to make self-management easier for users. For example, the AI ​​evaluates the progress toward the learning goals set by the user and provides necessary advice.This allows the learning support system to promote efficient and effective learning and support users in improving their skills.

[0069] The learning support system according to this embodiment comprises a reception unit, an analysis unit, a reminder unit, a monitoring unit, and a feedback unit. The reception unit receives the MBTI diagnostic results from the user. For example, the user takes the MBTI diagnostic test through an online platform and inputs the results into the reception unit. The reception unit can also analyze the user's MBTI diagnostic results using AI. The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. For example, the analysis unit suggests the optimal learning method and materials based on the user's MBTI type. For example, it suggests a learning method that includes group discussions for an extroverted user, and a method that allows for focused individual learning for an introverted user. The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. For example, the reminder unit monitors the user's learning progress and delivers reminders at appropriate times. For example, if the user is slacking off on their studies, the reminder unit sends a reminder to encourage them to study. The monitoring unit monitors learning progress based on reminders delivered by the reminder unit. The monitoring unit, for example, monitors the user's learning progress in real time and records the progress status. The feedback unit provides feedback based on the learning progress monitored by the monitoring unit. The feedback unit, for example, evaluates the user's progress toward their learning goals and provides necessary advice. As a result, the learning support system according to this embodiment can promote efficient and effective learning based on MBTI diagnostic results.

[0070] The reception desk receives MBTI assessment results from users. Users take the MBTI assessment, for example, through an online platform, and input the results into the reception desk. Specifically, users take the MBTI assessment using a dedicated website or application and input the results into the system. The assessment results indicate the user's personality type, which is classified into 16 types. The reception desk stores these assessment results in a database and uses them for subsequent analysis and the creation of learning plans. Furthermore, the reception desk can also analyze the user's MBTI assessment results using AI. The AI ​​analyzes patterns in the assessment results and gains a detailed understanding of the user's personality traits. For example, the AI ​​analyzes the user's response patterns and extracts specific personality traits and behavioral tendencies. This information is useful for subsequent processing in the analysis and reminder departments. The reception desk also provides an interface for users to input their assessment results, making it easy for them to enter their results. For example, it provides forms and options for inputting assessment results, allowing users to operate intuitively. This enables the reception desk to efficiently receive users' MBTI assessment results and provide the data necessary for subsequent processing.

[0071] The analysis department analyzes the MBTI diagnostic results received by the reception department and provides a personalized learning plan. Specifically, the analysis department proposes the optimal learning methods and materials based on the user's MBTI type. For example, it suggests learning methods that include group discussions for extroverted users and methods that allow introverted users to concentrate on learning alone. The analysis department uses AI to analyze the user's learning style and preferences in detail and create the optimal learning plan. The AI ​​continuously improves the learning plan based on past learning data and user feedback. For example, the AI ​​analyzes what learning methods the user has succeeded with in the past and suggests similar methods. The analysis department also flexibly adjusts the learning plan considering the user's learning goals and progress. For example, it clarifies the steps necessary for the user to achieve a specific goal and provides a plan that allows them to progress through learning step by step. In this way, the analysis department can provide the user with the optimal learning plan and support efficient and effective learning.

[0072] The Reminders Unit delivers regular reminders based on the learning plan provided by the Analytics Unit. Specifically, the Reminders Unit monitors the user's learning progress and delivers reminders at the appropriate time. For example, if a user is slacking off on their studies, the Reminders Unit will send a reminder to encourage them to study. Reminders are customized according to the user's preferences. For example, reminders are delivered using the method most easily received by the user, such as email, SMS, or in-app notifications. The Reminders Unit uses AI to analyze the user's learning patterns and send reminders at the optimal time. For example, if a user tends to study during a specific time slot, a reminder will be sent during that time. The Reminders Unit also adjusts the content of reminders according to the user's learning progress. For example, if a user is approaching their goal, an encouraging message will be sent to boost their motivation. In this way, the Reminders Unit can provide an environment that makes it easier for users to continue learning and maximize the effectiveness of their learning.

[0073] The monitoring unit monitors learning progress based on reminders delivered by the reminder unit. Specifically, the monitoring unit monitors the user's learning progress in real time and records the progress status. The monitoring unit verifies whether the user is progressing according to the learning plan and saves the progress status to a database. For example, it checks whether the user has completed a specific learning task and records the information if completed. The monitoring unit uses AI to analyze the user's learning data and detect abnormal patterns or delays in progress. For example, if a user's learning pace is slower than usual, the monitoring unit notifies the reminder unit and feedback unit of this information. This allows the monitoring unit to accurately grasp the user's learning progress and take necessary actions quickly. Furthermore, the monitoring unit accumulates user learning data over the long term and uses it for trend analysis and improvement of future learning plans. This allows the monitoring unit to continuously monitor the user's learning progress and improve the overall effectiveness of the system.

[0074] The feedback unit provides feedback based on learning progress monitored by the monitoring unit. Specifically, the feedback unit evaluates the user's progress toward their learning goals and provides necessary advice. For example, it evaluates the extent to which the user has achieved their set learning goals and provides feedback according to their level of achievement. The feedback unit uses AI to analyze the user's learning data and generate personalized advice. For example, if a user has difficulty in a particular area, it suggests specific learning methods and materials for that area. The feedback unit also provides feedback tailored to the user's learning style and preferences. For example, it provides feedback using graphs and charts for users who prefer visual information, and detailed written feedback for users who prefer text-based information. This allows the feedback unit to provide specific advice to help users learn effectively and improve the quality of their learning. Furthermore, the feedback unit improves the entire system based on user feedback. For example, it collects user feedback and reviews the content of learning plans and reminders to provide more effective learning support. This allows the feedback unit to continuously improve the user's learning experience.

[0075] The analysis unit can suggest optimal learning methods and materials based on the user's MBTI type. For example, based on the user's MBTI type, the analysis unit can suggest learning methods that include group discussions for extroverted users. It can also suggest methods that allow introverted users to concentrate on learning alone. Furthermore, the analysis unit can suggest optimal learning materials such as video materials, text materials, and interactive exercises based on the user's MBTI type. In this way, it can provide the optimal learning methods and materials based on the user's MBTI type.

[0076] The reminder function can monitor the user's learning progress and deliver reminders at appropriate times. For example, the reminder function can monitor the user's learning progress in real time and deliver reminders according to the progress. For instance, if a user is slacking off on their studies, the reminder function will send a reminder to encourage them to study. The reminder function can also deliver reminders at appropriate times based on the user's learning progress. This allows for the delivery of reminders at appropriate times according to the user's learning progress.

[0077] The monitoring system can support interaction among users and promote the sharing of learning resources. For example, it can provide forums and chat rooms to support user interaction. It can also facilitate the sharing of users' learning methods and materials with other users. For instance, it can provide a platform for users to share their learning methods and materials. This supports user interaction and promotes the sharing of learning resources.

[0078] The feedback unit can evaluate the user's progress toward their learning goals and provide necessary advice. For example, the feedback unit can evaluate the user's progress toward their learning goals and provide necessary advice. Furthermore, the feedback unit can provide necessary advice based on the user's learning progress. This allows the system to evaluate the user's progress toward their learning goals and provide necessary advice.

[0079] The reception system can estimate the user's emotions and dynamically adjust the MBTI assessment questions based on the estimated emotions. For example, if the user is tense, the reception system will prioritize questions that help them relax. It can also present questions to improve concentration if the user is excited. Furthermore, if the user is tired, it can prioritize simple and short questions. This allows for more accurate assessment results by dynamically adjusting the MBTI assessment questions according to the user'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.

[0080] The reception desk can refer to the user's past MBTI assessment results and generate additional questions to improve the accuracy of the assessment. For example, the reception desk can analyze trends in questions the user has answered in the past and generate additional questions for questions that received many ambiguous answers. The reception desk can also check the degree of agreement between the user's past assessment results and current answers, and generate additional questions if there is a discrepancy. Furthermore, the reception desk can generate detailed questions about specific personality types based on the user's past assessment results. This allows for improved assessment accuracy by referring to past assessment results.

[0081] The reception desk can customize the questions based on the user's current learning status and goals when receiving diagnostic results. For example, if the user enters their current learning status, the reception desk will generate questions appropriate to that status. The reception desk can also generate questions about the skills necessary to achieve the learning goals set by the user. Furthermore, the reception desk can consider the user's learning progress and generate questions appropriate to that progress. By customizing the questions according to the user's learning status and goals, a more appropriate diagnosis can be made.

[0082] The reception desk can estimate the user's emotions and adjust the timing of receiving the diagnostic results based on the estimated emotions. For example, if the user is relaxed, the reception desk can immediately receive the diagnostic results. If the user is stressed, the reception desk can also receive the diagnostic results at a time when the user can relax. Furthermore, if the user is tired, the reception desk can also receive the diagnostic results after the user has rested. By adjusting the timing of receiving the diagnostic results according to the user's emotions, the results can be received at a more appropriate time. 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.

[0083] The reception desk can prioritize presenting highly relevant questions based on the user's geographical location when receiving diagnostic results. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. It can also prioritize travel-related questions if the user is traveling. Furthermore, if the user is at home, it can prioritize questions that promote relaxation. This allows for more accurate diagnoses by presenting highly relevant questions based on the user's geographical location.

[0084] The reception desk can analyze the user's social media activity and add relevant questions when receiving diagnostic results. For example, the reception desk can generate questions based on the user's interests and passions shared on social media. It can also generate questions based on the user's social media activity times. Furthermore, it can generate questions based on the user's social media friendships. This allows for a more accurate diagnosis by adding relevant questions based on the user's social media activity.

[0085] The analysis unit can estimate the user's emotions and adjust the content of the personalized learning plan based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide a learning plan that allows for learning in a relaxed state. It can also provide a learning plan to alleviate tension if the user is stressed. Furthermore, if the user is excited, it can provide a learning plan to enhance concentration. This allows for more effective learning by adjusting the learning plan content according to the user'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.

[0086] The analysis unit can suggest the optimal learning methods and materials by referring to the user's past learning history during analysis. For example, the analysis unit can analyze the effectiveness of materials the user has used in the past and suggest the most suitable materials. It can also analyze the effectiveness of the user's past learning methods and suggest the most suitable learning methods. Furthermore, the analysis unit can suggest materials tailored to the user's learning progress based on their past learning history. In this way, by referring to past learning history, it can suggest the optimal learning methods and materials.

[0087] The analysis unit can customize the learning plan based on the user's current learning goals during analysis. For example, the analysis unit provides a learning plan necessary to achieve the learning goals set by the user. It can also provide a learning plan to acquire the necessary skills according to the user's learning goals. Furthermore, the analysis unit can adjust the learning plan while monitoring progress based on the user's learning goals. This allows for effective learning towards goal achievement by customizing the learning plan based on the user's learning goals.

[0088] The analysis unit can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is relaxed, the analysis unit will prioritize learning plans that promote relaxation. If the user is tense, the analysis unit can also prioritize learning plans designed to alleviate tension. Furthermore, if the user is excited, the analysis unit can prioritize learning plans designed to enhance concentration. This allows for more effective learning by prioritizing learning plans according to the user'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.

[0089] The analysis unit can suggest optimal learning resources by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can suggest learning resources related to that region. Furthermore, if the user is traveling, the analysis unit can suggest learning resources related to travel. Additionally, if the user is at home, the analysis unit can suggest resources that can be used for learning at home. This allows for more effective learning by suggesting optimal learning resources based on the user's geographical location.

[0090] The analysis unit can analyze a user's social media activity during analysis and suggest relevant learning resources. For example, the analysis unit can suggest learning resources based on the user's interests and passions shared on social media. It can also suggest learning resources based on the user's social media activity times. Furthermore, the analysis unit can suggest learning resources based on the user's social media friendships. This allows for more effective learning support by suggesting relevant learning resources based on the user's social media activity.

[0091] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is relaxed, the reminder unit can send a reminder to encourage learning in a relaxed state. It can also send a reminder to alleviate tension if the user is stressed. Furthermore, if the user is excited, the reminder unit can send a reminder to improve concentration. By adjusting the content of reminders according to the user's emotions, more effective learning can be supported. 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.

[0092] The reminder function can send reminders at the optimal time by referring to the user's past learning progress when sending reminders. For example, the reminder function can send reminders during times when the user has previously neglected their studies. It can also send reminders during times when the user has previously studied. Furthermore, the reminder function can send reminders at the optimal time based on the user's past learning progress. This allows the system to send reminders at the optimal time by referring to past learning progress.

[0093] The reminder function can adjust the frequency of reminders based on the user's current learning status when sending reminders. For example, if the user inputs their current learning status, the reminder function will adjust the frequency of reminders accordingly. The reminder function can also increase the frequency of reminders if the user is behind schedule, based on their learning progress. Furthermore, the reminder function can decrease the frequency of reminders if the user is progressing well, based on their learning progress. By adjusting the frequency of reminders according to the user's learning status, it is possible to support more effective learning.

[0094] The reminder function can estimate the user's emotions and prioritize reminders based on those emotions. For example, if the user is relaxed, the reminder function will prioritize reminders that encourage learning in a relaxed state. It can also prioritize reminders that help alleviate tension if the user is stressed. Furthermore, if the user is excited, the reminder function can prioritize reminders that help improve concentration. This allows for more effective learning by prioritizing reminders according to the user's emotions. 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.

[0095] The reminder function can send reminders at the optimal time, taking into account the user's geographical location. For example, if the user is in a specific region, the reminder function will send reminders related to that region. It can also send travel-related reminders if the user is traveling. Furthermore, if the user is at home, the reminder function can send reminders for home study. This allows for more effective learning by sending reminders at the optimal time based on the user's geographical location.

[0096] The reminder function can analyze a user's social media activity when sending reminders and send relevant reminders. For example, the reminder function can send reminders based on the user's interests and passions shared on social media. It can also send reminders based on the user's social media activity times. Furthermore, the reminder function can send reminders based on the user's social media friendships. This allows for more effective learning by sending relevant reminders based on the user's social media activity.

[0097] The monitoring unit can estimate the user's emotions and adjust the method of monitoring learning progress based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit can provide a method of monitoring learning progress in a relaxed state. It can also provide a monitoring method to alleviate tension if the user is stressed. Furthermore, if the user is excited, it can provide a monitoring method to enhance concentration. This allows for more effective learning support by adjusting the learning progress monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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.

[0098] The monitoring unit can select the optimal monitoring method by referring to the user's past learning history during monitoring. For example, the monitoring unit can analyze the effectiveness of monitoring methods previously used by the user and select the most suitable method. Furthermore, the monitoring unit can select a monitoring method based on the user's learning progress, derived from their past learning history. In addition, the monitoring unit can analyze the user's past learning history and select the most effective monitoring method. This allows the optimal monitoring method to be selected by referring to past learning history.

[0099] The monitoring unit can adjust the monitoring frequency based on the user's current learning goals during monitoring. For example, the monitoring unit can provide the monitoring frequency necessary to achieve the learning goals set by the user. It can also provide the monitoring frequency necessary to acquire the required skills according to the user's learning goals. Furthermore, the monitoring unit can adjust the monitoring frequency while checking the user's progress based on their learning goals. This allows for more effective learning support by adjusting the monitoring frequency based on the user's learning goals.

[0100] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit can provide a method of displaying the monitoring results in a relaxed state. It can also provide a method of displaying the monitoring results to alleviate tension if the user is tense. Furthermore, if the user is excited, it can provide a method of displaying the monitoring results to enhance concentration. This allows for more effective learning by adjusting the display method of monitoring results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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.

[0101] The monitoring unit can select the optimal monitoring method while considering the user's geographical location. For example, if the user is in a specific region, the monitoring unit will select a monitoring method relevant to that region. Furthermore, if the user is traveling, the monitoring unit can select a monitoring method relevant to their travel. Additionally, if the user is at home, the monitoring unit can select a monitoring method suitable for home learning. This allows for more effective learning support by selecting the optimal monitoring method based on the user's geographical location.

[0102] The monitoring unit can analyze the user's social media activity during monitoring and provide relevant monitoring results. For example, the monitoring unit can provide monitoring results based on the interests and passions the user shares on social media. It can also provide monitoring results based on the time of day the user is active on social media. Furthermore, the monitoring unit can provide monitoring results based on the user's social media friendships. This allows for more effective learning support by providing relevant monitoring results based on the user's social media activity.

[0103] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit will provide feedback in a relaxed manner. It can also provide feedback to alleviate tension if the user is tense. Furthermore, if the user is excited, it can provide feedback to enhance concentration. This allows for more effective learning by adjusting the content of feedback according to the user's emotions. 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.

[0104] The feedback unit can provide optimal advice by referring to the user's past learning progress when providing feedback. For example, the feedback unit can provide advice based on the learning goals the user has achieved in the past. It can also provide advice if the user is falling behind, based on their past learning progress. Furthermore, it can provide advice if the user is progressing well, based on their past learning progress. This allows the system to provide optimal advice by referring to past learning progress.

[0105] The feedback unit can adjust the frequency of feedback based on the user's current learning goals when providing feedback. For example, the feedback unit can provide the feedback frequency necessary to achieve the learning goals set by the user. It can also provide the feedback frequency necessary to acquire the required skills according to the user's learning goals. Furthermore, the feedback unit can adjust the feedback frequency while monitoring progress based on the user's learning goals. This allows for more effective learning support by adjusting the feedback frequency based on the user's learning goals.

[0106] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is relaxed, the feedback unit will provide relaxed feedback. If the user is tense, the feedback unit can also provide feedback to alleviate that tension. Furthermore, if the user is excited, the feedback unit can provide feedback to enhance their concentration. This allows for more effective learning by prioritizing feedback according to the user's emotions. 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.

[0107] The feedback system can provide optimal advice by considering the user's geographical location when providing feedback. For example, if the user is in a specific region, the feedback system can provide advice relevant to that region. It can also provide travel-related advice if the user is traveling. Furthermore, if the user is at home, the feedback system can provide advice that can be used for learning at home. This allows for more effective learning by providing optimal advice based on the user's geographical location.

[0108] The feedback system can analyze a user's social media activity and provide relevant advice when providing feedback. For example, it can provide advice based on the user's interests and passions shared on social media. It can also provide advice based on the user's social media activity schedule. Furthermore, it can provide advice based on the user's social media friendships. This allows for more effective learning by providing relevant advice based on the user's social media activity.

[0109] The feedback system can provide optimal advice by considering the user's health condition when providing feedback. For example, if the user is tired, the feedback system can advise them to rest. It can also suggest learning methods that incorporate exercise if the user is seeking healthy exercise. Furthermore, if the user is feeling unwell, the feedback system can suggest learning methods that don't overexert them. This allows for more effective learning by providing optimal advice based on the user's health condition.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's learning style and adjust the learning plan based on that estimated style. For example, users with a visual learning style can be provided with learning materials that make extensive use of visual aids. Users with an auditory learning style can be provided with a learning plan centered on audio materials. Furthermore, users with an experiential learning style can be provided with a learning plan that includes practical exercises and simulations. In this way, by providing an optimal learning plan tailored to the user's learning style, it is possible to support more effective learning.

[0112] The reminder function can send special motivational messages based on the user's learning progress, especially if they are falling behind. For example, it can send an encouraging message if the user has not reached their goal. It can also send a message that helps the user feel a sense of accomplishment when they are approaching a specific learning goal. Furthermore, it can send a message that suggests a concrete action plan to help the user continue learning. This allows the system to support continued learning by providing appropriate motivational messages according to the user's learning progress.

[0113] The monitoring unit can monitor the user's learning environment and provide advice to ensure an optimal learning environment. For example, if a user is studying in a noisy environment, it can recommend studying in a quiet place. It can also advise the user to use appropriate lighting if they are studying in a dark place. Furthermore, if a user is studying in the same posture for extended periods, it can advise them to take regular breaks. By optimizing the user's learning environment, it can support more effective learning.

[0114] The feedback function can provide special feedback based on the user's learning progress, especially if the user is falling behind. For example, if the user has not reached their goal, it can provide feedback that points out specific areas for improvement. It can also provide feedback that gives the user a sense of accomplishment when they are approaching a specific learning goal. Furthermore, it can provide feedback that suggests a concrete action plan to help the user continue learning. In this way, by providing appropriate feedback according to the user's learning progress, it can support continued learning.

[0115] The analysis unit can analyze a user's learning history and predict future learning plans based on past learning patterns. For example, it can identify learning methods that have been successful for the user in the past and provide a learning plan based on them. It can also identify learning content that the user has struggled with in the past and provide a complementary learning plan for that area. Furthermore, it can identify learning methods that were effective at a specific time from the user's learning history and provide a learning plan tailored to that period. In this way, by providing an optimal learning plan based on the user's past learning history, it can support more effective learning.

[0116] The reminder function can estimate the user's emotions and adjust the content of reminders based on those emotions. For example, if the user is relaxed, it can send reminders that encourage learning in a relaxed state. If the user is stressed, it can send reminders to alleviate that stress. Furthermore, if the user is excited, it can send reminders to improve concentration. By adjusting the content of reminders according to the user's emotions, it can support more effective learning.

[0117] The monitoring unit can monitor users' learning progress in real time and send special alerts if progress is behind schedule. For example, if a user is behind on their set learning goals, it can send an alert to encourage them to continue learning. It can also send alerts suggesting supplementary learning resources if a user is behind on a particular learning topic. Furthermore, if a user is slacking off on their studies, it can send an alert to encourage them to resume learning. In this way, by providing appropriate alerts according to the user's learning progress, it can support continued learning.

[0118] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is relaxed, it will provide feedback in a relaxed manner. If the user is tense, it can provide feedback to alleviate that tension. Furthermore, if the user is excited, it can provide feedback to enhance their concentration. By adjusting the content of the feedback according to the user's emotions, it can support more effective learning.

[0119] The analysis unit can analyze a user's learning history and predict future learning plans based on past learning patterns. For example, it can identify learning methods that have been successful for the user in the past and provide a learning plan based on them. It can also identify learning content that the user has struggled with in the past and provide a complementary learning plan for that area. Furthermore, it can identify learning methods that were effective at a specific time from the user's learning history and provide a learning plan tailored to that period. In this way, by providing an optimal learning plan based on the user's past learning history, it can support more effective learning.

[0120] The reminder function can estimate the user's emotions and prioritize reminders based on those emotions. For example, if the user is relaxed, it will prioritize reminders that encourage learning in a relaxed state. If the user is stressed, it can prioritize reminders that help alleviate stress. Furthermore, if the user is excited, it can prioritize reminders that help improve concentration. By prioritizing reminders according to the user's emotions, it can support more effective learning.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives the user's MBTI assessment results. For example, the user takes the MBTI assessment through an online platform and enters the results into the reception desk. The reception desk can also use AI to analyze the user's MBTI assessment results. Step 2: The analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. For example, the analysis unit suggests optimal learning methods and materials based on the user's MBTI type. For instance, it might suggest learning methods that include group discussions for extroverted users, and methods that allow introverted users to concentrate on learning alone. Step 3: The reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit. The reminder unit, for example, monitors the user's learning progress and delivers reminders at the appropriate time. For example, if a user is slacking off on their studies, the reminder unit will send a reminder to encourage them to study. Step 4: The monitoring unit monitors learning progress based on reminders delivered by the reminder unit. For example, the monitoring unit monitors the user's learning progress in real time and records the progress status. Step 5: The feedback unit provides feedback based on the learning progress monitored by the monitoring unit. For example, the feedback unit evaluates the user's progress toward their learning goals and provides necessary advice.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, reminder unit, monitoring unit, and feedback 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, where the user inputs MBTI diagnostic results through an online platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the MBTI diagnostic results are analyzed and a personalized learning plan is provided. The reminder unit is implemented by the control unit 46A of the smart device 14, where the user's learning progress is monitored and reminders are delivered at appropriate times. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's learning progress is monitored in real time. The feedback unit is implemented by the control unit 46A of the smart device 14, where the user's progress toward their learning goals is evaluated and necessary advice is provided. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, reminder unit, monitoring unit, and feedback unit, is implemented, for example, in 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, where the user inputs MBTI diagnostic results through an online platform. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the MBTI diagnostic results are analyzed and a personalized learning plan is provided. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the user's learning progress is monitored and reminders are delivered at appropriate times. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the user's learning progress is monitored in real time. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the user's progress toward their learning goals is evaluated and necessary advice is provided. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, reminder unit, monitoring unit, and feedback unit, is implemented in 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, where the user inputs MBTI diagnostic results through an online platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the MBTI diagnostic results are analyzed and a personalized learning plan is provided. The reminder unit is implemented by the control unit 46A of the headset terminal 314, where the user's learning progress is monitored and reminders are delivered at appropriate times. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's learning progress is monitored in real time. The feedback unit is implemented by the control unit 46A of the headset terminal 314, where the user's progress toward their learning goals is evaluated and necessary advice is provided. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, reminder unit, monitoring unit, and feedback unit, is implemented in 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, where the user inputs MBTI diagnostic results through an online platform. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the MBTI diagnostic results are analyzed and a personalized learning plan is provided. The reminder unit is implemented by the control unit 46A of the robot 414, where the user's learning progress is monitored and reminders are delivered at appropriate times. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's learning progress is monitored in real time. The feedback unit is implemented by the control unit 46A of the robot 414, where the user's progress toward their learning goals is evaluated and necessary advice is provided. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The reception area for receiving MBTI diagnostic results, An analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. A reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit, A monitoring unit monitors learning progress based on reminders delivered by the reminder unit, The system includes a feedback unit that provides feedback based on the learning progress monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We suggest optimal learning methods and materials based on the user's MBTI type. The system described in Appendix 1, characterized by the features described herein. (Note 3) The reminder unit is, Monitor the user's learning progress and send reminders at appropriate times. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, Supports interaction between users and promotes the sharing of learning resources. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Evaluate the user's progress toward their learning goals and provide necessary advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and dynamically adjusts the MBTI assessment questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Referencing the user's past MBTI assessment results, we generate additional questions to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving the diagnostic results, the questions are customized based on the user's current learning status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving the diagnostic results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving diagnostic results, the system prioritizes presenting highly relevant questions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving diagnostic results, we analyze the user's social media activity and add relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the content of the personalized learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system refers to the user's past learning history to suggest the most suitable learning methods and materials. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the learning plan is customized based on the user's current learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the system proposes optimal learning resources while considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and suggests relevant learning resources. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reminder unit is, It estimates the user's emotions and adjusts the content of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reminder unit is, When sending reminders, the system refers to the user's past learning progress to send reminders at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The reminder unit is, When sending reminders, adjust the frequency of reminders based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 21) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reminder unit is, When sending reminders, the system takes the user's geographical location into consideration to send reminders at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The reminder unit is, When sending reminders, the system analyzes the user's social media activity and sends relevant reminders. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the method of monitoring learning progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, During monitoring, the system selects the optimal monitoring method by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, During monitoring, adjust the monitoring frequency based on the user's current learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, the system analyzes the user's social media activity and provides relevant monitoring results. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, we refer to the user's past learning progress to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, adjust the frequency of feedback based on the user's current learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, we will offer the most appropriate advice, taking into account the user's health condition. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 area for receiving MBTI diagnostic results, An analysis unit analyzes the MBTI diagnostic results received by the reception unit and provides a personalized learning plan. A reminder unit delivers periodic reminders based on the learning plan provided by the analysis unit, A monitoring unit monitors learning progress based on reminders delivered by the reminder unit, The system includes a feedback unit that provides feedback based on the learning progress monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned analysis unit, We suggest optimal learning methods and materials based on the user's MBTI type. The system according to feature 1.

3. The reminder unit is, Monitor user learning progress and deliver reminders at appropriate times. The system according to feature 1.

4. The aforementioned monitoring unit, Supports interaction between users and promotes the sharing of learning resources. The system according to feature 1.

5. The aforementioned feedback unit is Evaluate the user's progress toward their learning goals and provide necessary advice. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and dynamically adjusts the MBTI assessment questions based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Referencing the user's past MBTI assessment results, we generate additional questions to improve the accuracy of the assessment. The system according to feature 1.

8. The aforementioned reception unit is When receiving the diagnostic results, the questions are customized based on the user's current learning status and goals. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving the diagnostic results based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving diagnostic results, the system prioritizes presenting highly relevant questions by considering the user's geographical location. The system according to feature 1.

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