system

Abstract

The system according to this embodiment aims to enhance learning effectiveness by providing real-time advice and feedback tailored to the user's learning style. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a planning unit, a monitoring unit, an advice unit, and a reminder unit. The analysis unit analyzes the user's learning style. The provision unit provides advice and feedback in real time based on the results analyzed by the analysis unit. The planning unit assists in the planning of learning plans. The monitoring unit monitors learning progress. The advice unit provides personalized advice. The reminder unit provides periodic motivation reminders.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 conventional technology, it is difficult to provide real-time advice and feedback according to the user's learning style, and there is room for improvement in maximizing the learning effect.

[0005] The system according to the embodiment aims to provide real-time advice and feedback according to the user's learning style and enhance the learning effect.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a planning unit, a monitoring unit, an advice unit, and a reminder unit. The analysis unit analyzes the user's learning style. The provision unit provides real-time advice and feedback based on the results analyzed by the analysis unit. The planning unit assists in the planning of learning plans. The monitoring unit monitors learning progress. The advice unit provides personalized advice. The reminder unit provides periodic motivation reminders. [Effects of the Invention]

[0007] The system according to this embodiment can enhance learning effectiveness by providing real-time advice and feedback tailored to the user's learning style. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 reception 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 reception 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 uses an AI agent to analyze a user's learning style and provides advice and motivation in real time. This learning support system enhances learning effectiveness, reduces stress during studying, and promotes efficient learning by analyzing the user's learning style and providing advice and feedback in real time. For example, the learning support system analyzes the user's learning style. In this process, data such as the user's learning history, learning methods, and learning time are collected and analyzed by the AI ​​agent. For example, it can identify when the user is most likely to concentrate and which learning methods are effective. This makes it possible to create an optimal learning plan for the user. Next, based on the analysis results, it provides advice and feedback in real time. For example, if the user is losing concentration, the AI ​​agent may provide advice such as, "It would be good to take a short break." Also, if the user does not understand the learning content, the AI ​​agent may provide feedback such as, "Let's review this part again." This allows the user to proceed with learning efficiently. Furthermore, it provides support for planning learning plans, monitoring learning progress, personalized advice, and regular motivation reminders. For example, the AI ​​agent monitors the user's learning progress and provides advice to help them achieve their goals. Furthermore, by regularly providing motivational reminders such as "Let's work hard towards our goals," the system can maintain user motivation. This mechanism allows users to maintain concentration and learn efficiently. In addition, the AI ​​agent further enhances learning effectiveness by providing advice in natural language and customizing it according to individual user profiles. For example, if a user is learning English, the AI ​​agent can provide specific advice such as "Let's memorize the meaning of this word." This allows the user to learn effectively. In this way, the learning support system can enhance the user's learning effectiveness and promote efficient learning.

[0029] The learning support system according to this embodiment comprises an analysis unit, a provision unit, a planning unit, a monitoring unit, an advice unit, and a reminder unit. The analysis unit analyzes the user's learning style. The analysis unit collects and analyzes data such as the user's learning history, learning methods, and learning time. For example, the analysis unit can identify when the user is most likely to concentrate and which learning methods are effective. The provision unit provides real-time advice and feedback based on the results analyzed by the analysis unit. For example, if the user is losing concentration, the provision unit will provide advice such as, "It would be good to take a short break." Also, if the user does not understand the learning content, the provision unit will provide feedback such as, "Let's review this part again." The planning unit provides support for planning learning plans. For example, the planning unit plans an optimal learning plan for the user. The planning unit proposes an effective learning plan based on data such as the user's learning history, learning methods, and learning time. The monitoring unit monitors learning progress. The monitoring unit, for example, monitors the user's learning progress in real time and provides advice to help them achieve their goals. The monitoring unit periodically checks the user's learning progress and provides feedback as needed. The advice unit provides personalized advice. The advice unit provides individualized advice according to the user's learning style and progress. The advice unit provides specific advice based on the user's characteristics. The reminder unit provides regular motivational reminders. The reminder unit provides regular motivational reminders such as "Let's work hard towards our goals." The reminder unit provides encouraging messages and notifications of goal achievement to maintain the user's motivation. As a result, the learning support system according to this embodiment can improve learning effectiveness and promote efficient learning by analyzing the user's learning style and providing advice and feedback in real time.

[0030] The analysis unit analyzes the user's learning style. For example, it collects and analyzes data such as the user's learning history, learning methods, and learning time. Specifically, it collects detailed data such as what learning materials the user has used in the past, what learning methods they have tried, and what times of day they studied. This data is important for revealing the user's learning patterns and tendencies. Based on this data, the analysis unit uses algorithms to identify when the user is most likely to concentrate and which learning methods are most effective. For example, machine learning algorithms can be used to extract patterns from the user's learning history and recommend the optimal learning time and method. Furthermore, the analysis unit continuously collects data on the user's learning style and updates the analysis results in real time. This allows for quick responses and the provision of optimal learning methods even if the user's learning style changes. In addition, the analysis unit can anonymize user learning data, protecting privacy while utilizing the data. This allows users to use the system with peace of mind. By analyzing the user's learning style in detail, the analysis unit can provide personalized learning support and maximize learning effectiveness.

[0031] The service provider provides real-time advice and feedback based on the results analyzed by the analysis unit. Specifically, if a user is losing focus, it will provide advice such as, "It would be good to take a short break." If a user does not understand the learning material, it will provide feedback such as, "Let's review this part again." The service provider has a system in place to monitor the user's learning progress in real time and provide advice at the appropriate time. For example, if a user is studying for a long time, the service provider will automatically generate advice prompting a break and notify the user. Also, if a user repeatedly makes mistakes on a particular problem, the service provider will provide supplementary materials and practice problems related to that problem to support deeper understanding. The service provider uses algorithms to provide personalized advice based on the user's learning data. This allows users to know the optimal learning methods and timing for themselves, enabling them to learn efficiently. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the service provider to provide optimal learning support to users and enhance learning effectiveness.

[0032] The Planning Department provides support for developing learning plans. Specifically, it develops optimal learning plans for users. Based on data such as the user's learning history, learning methods, and learning time, the Planning Department proposes effective learning plans. For example, it collects and analyzes detailed data such as what materials the user has used in the past, what learning methods they have tried, and what times of day they studied. This allows the Planning Department to clarify the user's learning patterns and tendencies and develop an optimal learning plan. The Planning Department provides individualized learning plans according to the user's goals and learning style. For example, if a user is studying for a specific exam, it proposes an effective learning plan that takes into account the schedule until the exam date. Also, if a user has difficulty in a particular area, it can develop a learning plan that focuses on that area. The Planning Department uses algorithms to propose optimal learning plans based on the user's learning data. This allows users to know the learning plan that is best suited to them and to proceed with their learning efficiently. Furthermore, the Planning Department can collect user feedback and continuously improve the accuracy and effectiveness of the learning plans. In this way, the Planning Department can provide optimal learning support to users and enhance learning effectiveness.

[0033] The monitoring unit monitors learning progress. Specifically, it monitors the user's learning progress in real time and provides advice to help them achieve their goals. The monitoring unit periodically checks the user's learning progress and provides feedback as needed. For example, it grasps in real time how far the user has progressed towards their set learning goals and provides advice to help them achieve those goals. The monitoring unit uses an algorithm to evaluate progress based on the user's learning data. This allows the user to accurately understand their learning progress and proceed with effective learning toward achieving their goals. Furthermore, the monitoring unit displays the user's learning progress in visual formats such as graphs and charts, allowing the user to grasp their progress at a glance. This allows the user to visually confirm their learning progress and maintain their motivation. By closely monitoring the user's learning progress, the monitoring unit can provide personalized learning support and maximize learning effectiveness.

[0034] The advice department provides personalized advice. Specifically, it offers individualized advice based on the user's learning style and progress. The advice department provides specific advice based on the user's characteristics. For example, if a user has difficulty in a particular area, it will suggest learning methods and materials that focus on that area. Also, if a user finds it easier to concentrate at a certain time, it will advise them to study during that time. The advice department uses an algorithm to provide optimal advice based on the user's learning data. This allows users to know the best learning methods and timing for themselves, enabling them to learn efficiently. Furthermore, the advice department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. In this way, the advice department can provide optimal learning support to users and enhance learning effectiveness.

[0035] The reminder function provides regular motivational reminders. Specifically, it regularly sends motivational reminders such as "Let's work hard towards our goals." The reminder function also sends encouraging messages and notifications of goal achievement to maintain user motivation. For example, it regularly notifies users of their progress toward their set learning goals, boosting their motivation to achieve those goals. It also regularly sends encouraging messages to help users continue learning and maintain their motivation. The reminder function uses an algorithm that provides reminders at the optimal time based on the user's learning data. This allows users to accurately understand their learning progress and proceed with effective learning toward achieving their goals. Furthermore, the reminder function can collect user feedback and continuously improve the accuracy and effectiveness of its reminders. This allows the reminder function to provide users with optimal learning support and enhance learning effectiveness.

[0036] The analysis unit can collect and analyze data on the user's learning history, learning methods, and learning time. For example, the analysis unit can collect the user's past learning records and analyze the learning history. The analysis unit can also collect the user's learning methods and analyze which learning methods are effective. The analysis unit can also collect the user's learning time and analyze which time periods the user tends to concentrate on. In this way, by collecting and analyzing data such as the user's learning history, learning methods, and learning time, the system can provide the user with the optimal learning style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning history data into a generating AI and have the generating AI perform the analysis of the learning history.

[0037] The service provider can provide appropriate advice and feedback according to the user's learning progress. For example, if the user is losing focus, the service provider may advise, "It would be good to take a short break." If the user does not understand the learning material, the service provider may also provide feedback such as, "Let's review this part again." The service provider can also monitor the user's learning progress in real time and provide appropriate advice. This enhances learning effectiveness by providing appropriate advice and feedback according to the user's learning progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning progress data into a generating AI and have the generating AI perform the provision of advice and feedback.

[0038] The planning unit can create a learning plan suitable for the user. For example, the planning unit proposes an optimal learning plan based on data such as the user's learning history, learning methods, and learning time. The planning unit can also create a specific learning plan that matches the user's learning goals. The planning unit can also provide an effective learning plan that is tailored to the user's learning style. By creating an optimal learning plan for the user, efficient learning can be promoted. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's learning data into a generating AI and have the generating AI create a learning plan.

[0039] The monitoring unit can monitor the user's learning progress. For example, the monitoring unit can monitor the user's learning progress in real time and provide advice to help them achieve their goals. The monitoring unit can also periodically check the user's learning progress and provide feedback as needed. The monitoring unit can also analyze the user's learning progress in detail and understand their progress. This allows the monitoring unit to understand the user's learning progress and provide appropriate advice. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user learning progress data into a generating AI and have the generating AI perform progress monitoring.

[0040] The advice unit can provide users with personalized advice. For example, the advice unit can provide personalized advice according to the user's learning style and progress. The advice unit can also provide specific advice based on the user's characteristics. The advice unit can also provide personalized advice based on the user's learning history and learning methods. By providing users with personalized advice, the system can propose the most suitable learning method for each individual user. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's learning data into a generating AI and have the generating AI perform the task of providing personalized advice.

[0041] The reminder unit can provide regular motivational reminders. For example, it can periodically provide motivational reminders such as "Let's work hard towards our goals." The reminder unit can also provide encouraging messages and notifications of goal achievement to maintain the user's motivation. The reminder unit can also provide motivational reminders at appropriate times according to the user's learning progress. By providing regular motivational reminders, it is possible to maintain the user's motivation and improve the learning effect. Some or all of the above processes in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can input the user's learning data into a generating AI and have the generating AI perform the task of providing motivational reminders.

[0042] The analysis unit can incorporate the user's lifestyle and health status into its analysis, in addition to the user's learning history. For example, the analysis unit can analyze the user's sleep patterns and suggest the optimal learning time. It can also analyze the user's eating habits and suggest a learning style that corresponds to their energy level. It can also analyze the user's exercise habits and suggest a learning style that enhances concentration. By incorporating the user's lifestyle and health status into the analysis, a more personalized learning style can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform analysis based on the lifestyle.

[0043] The analysis unit can base its analysis of learning styles on the user's learning environment. For example, the analysis unit can analyze the noise level of the user's learning environment and suggest a learning style for a quiet environment. The analysis unit can also analyze the lighting conditions of the user's learning environment and suggest a learning style for optimal lighting conditions. The analysis unit can also analyze the temperature of the user's learning environment and suggest a learning style for a comfortable temperature. This allows for the provision of a more appropriate learning style by considering the user's learning environment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning environment data into a generating AI and have the generating AI perform an analysis based on the learning environment.

[0044] The analysis unit can analyze the user's social media activity and incorporate relevant data when analyzing learning styles. For example, the analysis unit can analyze the user's social media activity time and suggest the optimal learning time. The analysis unit can also analyze the user's interests on social media and suggest a relevant learning style. The analysis unit can also analyze the user's social media interaction patterns and suggest a style suitable for collaborative learning. In this way, by analyzing the user's social media activity and incorporating relevant data, a more personalized learning style can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform analysis based on social media activity.

[0045] The analysis unit can perform analysis based on the user's geographical location information when analyzing learning styles. For example, the analysis unit can analyze the user's geographical location information and suggest the optimal learning location. The analysis unit can also analyze the user's geographical location information and suggest a learning style that utilizes local learning resources. The analysis unit can also analyze the user's geographical location information and suggest a learning style that takes travel time into consideration. In this way, by performing analysis while considering the user's geographical location information, a more appropriate learning style can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform analysis based on geographical location information.

[0046] The service provider can reflect the user's level of achievement of learning goals in the advice and feedback it provides. For example, the service provider can analyze the user's level of achievement of learning goals and provide advice according to that level. The service provider can also analyze the user's level of achievement of learning goals and provide feedback according to that level. The service provider can also analyze the user's level of achievement of learning goals and suggest the next steps according to that level. This allows for the provision of more effective advice and feedback by reflecting the user's level of achievement of learning goals. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's level of achievement of learning goals into a generating AI and have the generating AI provide advice and feedback based on the level of achievement of goals.

[0047] The service provider can include specific action suggestions tailored to the user's learning progress in the advice and feedback it provides. For example, the service provider can analyze the user's learning progress and suggest specific tasks to tackle next. The service provider can also analyze the user's learning progress and suggest specific learning methods tailored to that progress. The service provider can also analyze the user's learning progress and suggest specific goal setting tailored to that progress. By including specific action suggestions tailored to the user's learning progress, more effective learning can be promoted. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning progress data into a generating AI and have the generating AI provide action suggestions tailored to that progress.

[0048] The service provider can incorporate feedback from the user's peers and community into the advice and feedback it provides. For example, the service provider can analyze feedback from the user's peers and reflect it in the advice. The service provider can also analyze feedback from the user's community and reflect it in the feedback. The service provider can also analyze feedback from the user's peers and community and suggest the next steps. This allows for the provision of more effective advice and feedback by incorporating feedback from the user's peers and community. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input feedback data from the user's peers and community into a generating AI and have the generating AI perform the analysis of the feedback.

[0049] The service provider may include suggestions for improving the user's learning environment in the advice and feedback it provides. For example, the service provider may analyze the user's learning environment and propose an optimal learning environment. The service provider may also analyze the user's learning environment and make specific suggestions for improving the environment. The service provider may also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, a more effective learning environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0050] The planning unit can base its learning plan development on the user's short-term and long-term learning goals. For example, the planning unit can analyze the user's short-term goals and develop a specific learning plan to achieve them. The planning unit can also analyze the user's long-term goals and develop a specific learning plan to achieve them. The planning unit can also analyze the user's short-term and long-term goals and develop a balanced learning plan. This allows for the provision of a more effective learning plan by considering the user's short-term and long-term learning goals. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's learning goal data into a generating AI and have the generating AI develop a learning plan based on those goals.

[0051] The planning unit can include break times and refreshment methods tailored to the user's learning style when formulating a learning plan. For example, the planning unit can analyze the user's learning style and suggest optimal break times. The planning unit can also analyze the user's learning style and suggest refreshment methods. The planning unit can analyze the user's learning style and formulate a learning plan that combines break times and refreshment methods. This allows for the provision of more effective learning plans by including break times and refreshment methods tailored to the user's learning style. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input user learning style data into a generating AI and have the generating AI formulate a learning plan that includes break times and refreshment methods.

[0052] The planning unit can include collaborative learning plans with the user's learning partners and community when developing a learning plan. For example, the planning unit can develop a collaborative learning plan with the user's learning partners. The planning unit can also develop a collaborative learning plan with the user's community. The planning unit can develop collaborative learning plans with the user's learning partners and community and share progress. This allows for the provision of a more effective learning plan by including collaborative learning plans with the user's learning partners and community. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input data on the user's learning partners and community into a generating AI and have the generating AI develop the collaborative learning plan.

[0053] The planning unit can include suggestions for improving the user's learning environment when formulating a learning plan. For example, the planning unit can analyze the user's learning environment and propose an optimal learning environment. The planning unit can also analyze the user's learning environment and make specific suggestions for improving the environment. The planning unit can also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, a more effective learning plan can be provided. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input user learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0054] The monitoring unit can display the user's progress toward learning goals in real time when monitoring learning progress. For example, the monitoring unit can analyze the user's progress toward learning goals in real time and display the progress. The monitoring unit can also analyze the user's progress toward learning goals in real time and provide feedback according to the progress. The monitoring unit can also analyze the user's progress toward learning goals in real time and suggest the next step. This makes it easier to grasp the user's learning progress by displaying the user's progress toward learning goals in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's progress toward learning goals into a generating AI and have the generating AI perform the real-time display.

[0055] The monitoring unit can detect changes in the user's learning environment and provide appropriate advice when monitoring learning progress. For example, the monitoring unit can detect changes in the noise level of the user's learning environment and suggest learning in a quieter environment. The monitoring unit can also detect changes in the lighting conditions of the user's learning environment and suggest learning under optimal lighting conditions. The monitoring unit can also detect changes in the temperature of the user's learning environment and suggest learning at a comfortable temperature. By detecting changes in the user's learning environment and providing appropriate advice, a more effective learning environment can be maintained. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user learning environment data into a generating AI and have the generating AI provide advice based on changes in the environment.

[0056] The monitoring unit can include a function to share the user's learning progress with their learning partners and community when monitoring learning progress. For example, the monitoring unit can provide a function to share the user's learning progress with their learning partners, thereby promoting collaborative learning. The monitoring unit can also provide a function to share the user's learning progress with their community, thereby increasing learning motivation. The monitoring unit can also provide a function to share the user's learning progress with their learning partners and community, and receive feedback. In this way, by including a function to share the user's learning progress with their learning partners and community, collaborative learning can be promoted and learning motivation can be increased. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input data on the user's learning partners and community into a generating AI and have the generating AI perform the provision of the progress sharing function.

[0057] The monitoring unit can incorporate data about the user's learning environment when monitoring learning progress. For example, the monitoring unit can analyze data about the user's learning environment and propose an optimal learning environment. The monitoring unit can also analyze data about the user's learning environment and make specific suggestions for improving the environment. The monitoring unit can also analyze data about the user's learning environment and make specific action suggestions for improving the environment. In this way, by incorporating data about the user's learning environment, a more effective learning environment can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user learning environment data into a generating AI and have the generating AI execute suggestions based on the environment data.

[0058] The advice unit can reflect the user's level of achievement of learning goals in personalized advice. For example, the advice unit can analyze the user's level of achievement of learning goals and provide advice according to that level. The advice unit can also analyze the user's level of achievement of learning goals and provide feedback according to that level. The advice unit can also analyze the user's level of achievement of learning goals and suggest the next steps according to that level. This allows for the provision of more effective advice by reflecting the user's level of achievement of learning goals. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's level of achievement of learning goals into a generating AI and have the generating AI provide advice based on the level of achievement.

[0059] The advice unit can include specific action suggestions tailored to the user's learning progress in its personalized advice. For example, the advice unit can analyze the user's learning progress and suggest specific tasks to tackle next. The advice unit can also analyze the user's learning progress and suggest specific learning methods tailored to that progress. The advice unit can also analyze the user's learning progress and suggest specific goal setting tailored to that progress. By including specific action suggestions tailored to the user's learning progress, more effective learning can be promoted. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's learning progress data into a generating AI and have the generating AI provide action suggestions tailored to that progress.

[0060] The advice unit can incorporate feedback from the user's peers and community into personalized advice. For example, the advice unit can analyze feedback from the user's peers and reflect it in the advice. The advice unit can also analyze feedback from the user's community and reflect it in the advice. The advice unit can also analyze feedback from the user's peers and community and suggest the next steps. This allows for more effective advice by incorporating feedback from the user's peers and community. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input feedback data from the user's peers and community into a generating AI and have the generating AI perform the analysis of the feedback.

[0061] The advice unit can include suggestions for improving the user's learning environment in its personalized advice. For example, the advice unit can analyze the user's learning environment and propose an optimal learning environment. The advice unit can also analyze the user's learning environment and make specific suggestions for improving the environment. The advice unit can also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, a more effective learning environment can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0062] The reminder unit can reflect the user's learning goal achievement level in motivational reminders. For example, the reminder unit can analyze the user's learning goal achievement level and provide reminders according to that level. The reminder unit can also analyze the user's learning goal achievement level and provide feedback according to that level. The reminder unit can also analyze the user's learning goal achievement level and suggest the next steps according to that level. This allows for more effective reminders by reflecting the user's learning goal achievement level. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's learning goal achievement level data into a generating AI and have the generating AI provide reminders based on the goal achievement level.

[0063] The reminder function can include specific action suggestions tailored to the user's learning progress in its motivational reminders. For example, the reminder function can analyze the user's learning progress and suggest specific tasks to tackle next. It can also analyze the user's learning progress and suggest specific learning methods tailored to that progress. It can also analyze the user's learning progress and suggest specific goal setting tailored to that progress. By including specific action suggestions tailored to the user's learning progress, the reminder function can provide more effective reminders. Some or all of the above-described processes in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input the user's learning progress data into a generating AI and have the generating AI provide action suggestions tailored to that progress.

[0064] The reminder function can incorporate feedback from the user's learning partners and community into motivational reminders. For example, the reminder function can analyze feedback from the user's learning partners and reflect it in the reminders. It can also analyze feedback from the user's community and reflect it in the reminders. The reminder function can analyze feedback from the user's learning partners and community and suggest the next steps. This allows for more effective reminders by incorporating feedback from the user's learning partners and community. Some or all of the above processing in the reminder function may be performed using AI, for example, or not. For example, the reminder function can input feedback data from the user's learning partners and community into a generating AI and have the generating AI perform the analysis of the feedback.

[0065] The reminder unit can include suggestions for improving the user's learning environment in its motivational reminders. For example, the reminder unit can analyze the user's learning environment and suggest an optimal learning environment. The reminder unit can also analyze the user's learning environment and make specific suggestions for improving the environment. The reminder unit can also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, more effective reminders can be provided. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The analysis unit can consider the user's hobbies and interests when analyzing their learning style. For example, if a user is interested in music, it can suggest learning methods that incorporate music. If a user is interested in sports, it can provide example problems and questions related to sports. Furthermore, if a user is interested in art, it can suggest learning methods that incorporate art. This allows for the provision of learning styles that take into account the user's hobbies and interests, thereby increasing their motivation to learn.

[0068] The system can visually display the user's learning progress according to their learning status. For example, by displaying the user's learning progress in graphs or charts, users can visually grasp their progress. Furthermore, by color-coding the user's learning progress, it's possible to see at a glance which parts are progressing. Additionally, by displaying the user's learning progress with animations, users can check their progress in an enjoyable way. In this way, visually displaying learning progress can increase motivation to learn.

[0069] The planning department can implement a reward system tailored to the user's learning goals when developing user learning plans. For example, badges or points can be awarded when a user achieves a specific learning goal. Rewards can also be provided when a user achieves a certain amount of study time. Furthermore, rewards can be given when a user progresses through their learning plan. By implementing such a reward system, the department can increase user motivation to learn.

[0070] The monitoring unit can incorporate a predictive function based on the user's learning history when monitoring the user's learning progress. For example, it can predict future learning progress based on the user's past learning history. It can also predict points where learning may stagnate based on the user's learning history and take countermeasures in advance. Furthermore, it can predict the most effective timing for learning based on the user's learning history and propose an optimal learning plan. In this way, by introducing a predictive function, more effective learning support can be provided.

[0071] The advice section can provide features that allow users to share their learning progress according to their learning style. For example, users can share their learning progress with fellow learners, encouraging each other as they progress. Users can also receive feedback by sharing their learning progress with the learning community. Furthermore, users can boost their motivation by sharing their learning progress with family and friends. In this way, providing a feature to share learning progress can increase motivation.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The analysis unit analyzes the user's learning style. The analysis unit collects and analyzes data such as the user's learning history, learning methods, and learning time. This makes it possible to identify when the user is most likely to concentrate and which learning methods are most effective. Step 2: The service provider provides real-time advice and feedback based on the results analyzed by the analysis unit. For example, if a user is losing focus, it will provide advice such as, "It would be good to take a short break." Also, if a user does not understand the learning material, it will provide feedback such as, "Let's review this part again." Step 3: The Planning Department provides support for developing learning plans. The Planning Department develops the optimal learning plan for the user and proposes an effective learning plan based on data such as the user's learning history, learning methods, and learning time. Step 4: The monitoring unit monitors learning progress. The monitoring unit monitors the user's learning progress in real time and provides advice to help them achieve their goals. It regularly checks the user's learning progress and provides feedback as needed. Step 5: The advice unit provides personalized advice. The advice unit provides individualized advice based on the user's learning style and progress. It provides specific advice based on the user's characteristics. Step 6: The reminder section provides regular motivational reminders. The reminder section regularly provides motivational reminders such as "Let's work hard towards our goals." To maintain user motivation, it provides encouraging messages and notifications when goals are achieved.

[0074] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that uses an AI agent to analyze a user's learning style and provides advice and motivation in real time. This learning support system enhances learning effectiveness, reduces stress during studying, and promotes efficient learning by analyzing the user's learning style and providing advice and feedback in real time. For example, the learning support system analyzes the user's learning style. In this process, data such as the user's learning history, learning methods, and learning time are collected and analyzed by the AI ​​agent. For example, it can identify when the user is most likely to concentrate and which learning methods are effective. This makes it possible to create an optimal learning plan for the user. Next, based on the analysis results, it provides advice and feedback in real time. For example, if the user is losing concentration, the AI ​​agent may provide advice such as, "It would be good to take a short break." Also, if the user does not understand the learning content, the AI ​​agent may provide feedback such as, "Let's review this part again." This allows the user to proceed with learning efficiently. Furthermore, it provides support for planning learning plans, monitoring learning progress, personalized advice, and regular motivation reminders. For example, the AI ​​agent monitors the user's learning progress and provides advice to help them achieve their goals. Furthermore, by regularly providing motivational reminders such as "Let's work hard towards our goals," the system can maintain user motivation. This mechanism allows users to maintain concentration and learn efficiently. In addition, the AI ​​agent further enhances learning effectiveness by providing advice in natural language and customizing it according to individual user profiles. For example, if a user is learning English, the AI ​​agent can provide specific advice such as "Let's memorize the meaning of this word." This allows the user to learn effectively. In this way, the learning support system can enhance the user's learning effectiveness and promote efficient learning.

[0075] The learning support system according to this embodiment comprises an analysis unit, a provision unit, a planning unit, a monitoring unit, an advice unit, and a reminder unit. The analysis unit analyzes the user's learning style. The analysis unit collects and analyzes data such as the user's learning history, learning methods, and learning time. For example, the analysis unit can identify when the user is most likely to concentrate and which learning methods are effective. The provision unit provides real-time advice and feedback based on the results analyzed by the analysis unit. For example, if the user is losing concentration, the provision unit will provide advice such as, "It would be good to take a short break." Also, if the user does not understand the learning content, the provision unit will provide feedback such as, "Let's review this part again." The planning unit provides support for planning learning plans. For example, the planning unit plans an optimal learning plan for the user. The planning unit proposes an effective learning plan based on data such as the user's learning history, learning methods, and learning time. The monitoring unit monitors learning progress. The monitoring unit, for example, monitors the user's learning progress in real time and provides advice to help them achieve their goals. The monitoring unit periodically checks the user's learning progress and provides feedback as needed. The advice unit provides personalized advice. The advice unit provides individualized advice according to the user's learning style and progress. The advice unit provides specific advice based on the user's characteristics. The reminder unit provides regular motivational reminders. The reminder unit provides regular motivational reminders such as "Let's work hard towards our goals." The reminder unit provides encouraging messages and notifications of goal achievement to maintain the user's motivation. As a result, the learning support system according to this embodiment can improve learning effectiveness and promote efficient learning by analyzing the user's learning style and providing advice and feedback in real time.

[0076] The analysis unit analyzes the user's learning style. For example, it collects and analyzes data such as the user's learning history, learning methods, and learning time. Specifically, it collects detailed data such as what learning materials the user has used in the past, what learning methods they have tried, and what times of day they studied. This data is important for revealing the user's learning patterns and tendencies. Based on this data, the analysis unit uses algorithms to identify when the user is most likely to concentrate and which learning methods are most effective. For example, machine learning algorithms can be used to extract patterns from the user's learning history and recommend the optimal learning time and method. Furthermore, the analysis unit continuously collects data on the user's learning style and updates the analysis results in real time. This allows for quick responses and the provision of optimal learning methods even if the user's learning style changes. In addition, the analysis unit can anonymize user learning data, protecting privacy while utilizing the data. This allows users to use the system with peace of mind. By analyzing the user's learning style in detail, the analysis unit can provide personalized learning support and maximize learning effectiveness.

[0077] The service provider provides real-time advice and feedback based on the results analyzed by the analysis unit. Specifically, if a user is losing focus, it will provide advice such as, "It would be good to take a short break." If a user does not understand the learning material, it will provide feedback such as, "Let's review this part again." The service provider has a system in place to monitor the user's learning progress in real time and provide advice at the appropriate time. For example, if a user is studying for a long time, the service provider will automatically generate advice prompting a break and notify the user. Also, if a user repeatedly makes mistakes on a particular problem, the service provider will provide supplementary materials and practice problems related to that problem to support deeper understanding. The service provider uses algorithms to provide personalized advice based on the user's learning data. This allows users to know the optimal learning methods and timing for themselves, enabling them to learn efficiently. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its advice. This allows the service provider to provide optimal learning support to users and enhance learning effectiveness.

[0078] The Planning Department provides support for developing learning plans. Specifically, it develops optimal learning plans for users. Based on data such as the user's learning history, learning methods, and learning time, the Planning Department proposes effective learning plans. For example, it collects and analyzes detailed data such as what materials the user has used in the past, what learning methods they have tried, and what times of day they studied. This allows the Planning Department to clarify the user's learning patterns and tendencies and develop an optimal learning plan. The Planning Department provides individualized learning plans according to the user's goals and learning style. For example, if a user is studying for a specific exam, it proposes an effective learning plan that takes into account the schedule until the exam date. Also, if a user has difficulty in a particular area, it can develop a learning plan that focuses on that area. The Planning Department uses algorithms to propose optimal learning plans based on the user's learning data. This allows users to know the learning plan that is best suited to them and to proceed with their learning efficiently. Furthermore, the Planning Department can collect user feedback and continuously improve the accuracy and effectiveness of the learning plans. In this way, the Planning Department can provide optimal learning support to users and enhance learning effectiveness.

[0079] The monitoring unit monitors learning progress. Specifically, it monitors the user's learning progress in real time and provides advice to help them achieve their goals. The monitoring unit periodically checks the user's learning progress and provides feedback as needed. For example, it grasps in real time how far the user has progressed towards their set learning goals and provides advice to help them achieve those goals. The monitoring unit uses an algorithm to evaluate progress based on the user's learning data. This allows the user to accurately understand their learning progress and proceed with effective learning toward achieving their goals. Furthermore, the monitoring unit displays the user's learning progress in visual formats such as graphs and charts, allowing the user to grasp their progress at a glance. This allows the user to visually confirm their learning progress and maintain their motivation. By closely monitoring the user's learning progress, the monitoring unit can provide personalized learning support and maximize learning effectiveness.

[0080] The advice department provides personalized advice. Specifically, it offers individualized advice based on the user's learning style and progress. The advice department provides specific advice based on the user's characteristics. For example, if a user has difficulty in a particular area, it will suggest learning methods and materials that focus on that area. Also, if a user finds it easier to concentrate at a certain time, it will advise them to study during that time. The advice department uses an algorithm to provide optimal advice based on the user's learning data. This allows users to know the best learning methods and timing for themselves, enabling them to learn efficiently. Furthermore, the advice department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. In this way, the advice department can provide optimal learning support to users and enhance learning effectiveness.

[0081] The reminder function provides regular motivational reminders. Specifically, it regularly sends motivational reminders such as "Let's work hard towards our goals." The reminder function also sends encouraging messages and notifications of goal achievement to maintain user motivation. For example, it regularly notifies users of their progress toward their set learning goals, boosting their motivation to achieve those goals. It also regularly sends encouraging messages to help users continue learning and maintain their motivation. The reminder function uses an algorithm that provides reminders at the optimal time based on the user's learning data. This allows users to accurately understand their learning progress and proceed with effective learning toward achieving their goals. Furthermore, the reminder function can collect user feedback and continuously improve the accuracy and effectiveness of its reminders. This allows the reminder function to provide users with optimal learning support and enhance learning effectiveness.

[0082] The analysis unit can collect and analyze data on the user's learning history, learning methods, and learning time. For example, the analysis unit can collect the user's past learning records and analyze the learning history. The analysis unit can also collect the user's learning methods and analyze which learning methods are effective. The analysis unit can also collect the user's learning time and analyze which time periods the user tends to concentrate on. In this way, by collecting and analyzing data such as the user's learning history, learning methods, and learning time, the system can provide the user with the optimal learning style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning history data into a generating AI and have the generating AI perform the analysis of the learning history.

[0083] The service provider can provide appropriate advice and feedback according to the user's learning progress. For example, if the user is losing focus, the service provider may advise, "It would be good to take a short break." If the user does not understand the learning material, the service provider may also provide feedback such as, "Let's review this part again." The service provider can also monitor the user's learning progress in real time and provide appropriate advice. This enhances learning effectiveness by providing appropriate advice and feedback according to the user's learning progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning progress data into a generating AI and have the generating AI perform the provision of advice and feedback.

[0084] The planning unit can create a learning plan suitable for the user. For example, the planning unit proposes an optimal learning plan based on data such as the user's learning history, learning methods, and learning time. The planning unit can also create a specific learning plan that matches the user's learning goals. The planning unit can also provide an effective learning plan that is tailored to the user's learning style. By creating an optimal learning plan for the user, efficient learning can be promoted. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's learning data into a generating AI and have the generating AI create a learning plan.

[0085] The monitoring unit can monitor the user's learning progress. For example, the monitoring unit can monitor the user's learning progress in real time and provide advice to help them achieve their goals. The monitoring unit can also periodically check the user's learning progress and provide feedback as needed. The monitoring unit can also analyze the user's learning progress in detail and understand their progress. This allows the monitoring unit to understand the user's learning progress and provide appropriate advice. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user learning progress data into a generating AI and have the generating AI perform progress monitoring.

[0086] The advice unit can provide users with personalized advice. For example, the advice unit can provide personalized advice according to the user's learning style and progress. The advice unit can also provide specific advice based on the user's characteristics. The advice unit can also provide personalized advice based on the user's learning history and learning methods. By providing users with personalized advice, the system can propose the most suitable learning method for each individual user. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's learning data into a generating AI and have the generating AI perform the task of providing personalized advice.

[0087] The reminder unit can provide regular motivational reminders. For example, it can periodically provide motivational reminders such as "Let's work hard towards our goals." The reminder unit can also provide encouraging messages and notifications of goal achievement to maintain the user's motivation. The reminder unit can also provide motivational reminders at appropriate times according to the user's learning progress. By providing regular motivational reminders, it is possible to maintain the user's motivation and improve the learning effect. Some or all of the above processes in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can input the user's learning data into a generating AI and have the generating AI perform the task of providing motivational reminders.

[0088] The analysis unit can estimate the user's emotions and adjust the learning style analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing a learning style that promotes relaxation. If the user is focused, the analysis unit can also analyze a learning style that helps maintain focus. If the user is tired, the analysis unit can also analyze a learning style that is effective in a short amount of time. In this way, by adjusting the learning style analysis method based on the user's emotions, a more effective learning style can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method based on emotions.

[0089] The analysis unit can incorporate the user's lifestyle and health status into its analysis, in addition to the user's learning history. For example, the analysis unit can analyze the user's sleep patterns and suggest the optimal learning time. It can also analyze the user's eating habits and suggest a learning style that corresponds to their energy level. It can also analyze the user's exercise habits and suggest a learning style that enhances concentration. By incorporating the user's lifestyle and health status into the analysis, a more personalized learning style can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform analysis based on the lifestyle.

[0090] The analysis unit can base its analysis of learning styles on the user's learning environment. For example, the analysis unit can analyze the noise level of the user's learning environment and suggest a learning style for a quiet environment. The analysis unit can also analyze the lighting conditions of the user's learning environment and suggest a learning style for optimal lighting conditions. The analysis unit can also analyze the temperature of the user's learning environment and suggest a learning style for a comfortable temperature. This allows for the provision of a more appropriate learning style by considering the user's learning environment. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning environment data into a generating AI and have the generating AI perform an analysis based on the learning environment.

[0091] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize learning styles that are effective in reducing stress and reflect them in the analysis results. If the user is focused, the analysis unit can also prioritize learning styles that help maintain focus and reflect them in the analysis results. If the user is tired, the analysis unit can also prioritize learning styles that are effective in a short amount of time and reflect them in the analysis results. In this way, by determining the priority of the analysis results based on the user's emotions, a more effective learning style can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the determination of priority of analysis results based on emotions.

[0092] The analysis unit can analyze the user's social media activity and incorporate relevant data when analyzing learning styles. For example, the analysis unit can analyze the user's social media activity time and suggest the optimal learning time. The analysis unit can also analyze the user's interests on social media and suggest a relevant learning style. The analysis unit can also analyze the user's social media interaction patterns and suggest a style suitable for collaborative learning. In this way, by analyzing the user's social media activity and incorporating relevant data, a more personalized learning style can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform analysis based on social media activity.

[0093] The analysis unit can perform analysis based on the user's geographical location information when analyzing learning styles. For example, the analysis unit can analyze the user's geographical location information and suggest the optimal learning location. The analysis unit can also analyze the user's geographical location information and suggest a learning style that utilizes local learning resources. The analysis unit can also analyze the user's geographical location information and suggest a learning style that takes travel time into consideration. In this way, by performing analysis while considering the user's geographical location information, a more appropriate learning style can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform analysis based on geographical location information.

[0094] The service provider can estimate the user's emotions and adjust the way advice and feedback are expressed based on the estimated emotions. For example, if the user is stressed, the service provider will offer advice in gentle language. If the user is focused, the service provider can also offer specific and detailed feedback. If the user is tired, the service provider can offer concise and easy-to-understand advice. By adjusting the way advice and feedback are expressed based on the user's emotions, more effective advice and feedback can be provided. 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. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the expression based on the emotion.

[0095] The service provider can reflect the user's level of achievement of learning goals in the advice and feedback it provides. For example, the service provider can analyze the user's level of achievement of learning goals and provide advice according to that level. The service provider can also analyze the user's level of achievement of learning goals and provide feedback according to that level. The service provider can also analyze the user's level of achievement of learning goals and suggest the next steps according to that level. This allows for the provision of more effective advice and feedback by reflecting the user's level of achievement of learning goals. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's level of achievement of learning goals into a generating AI and have the generating AI provide advice and feedback based on the level of achievement of goals.

[0096] The service provider can include specific action suggestions tailored to the user's learning progress in the advice and feedback it provides. For example, the service provider can analyze the user's learning progress and suggest specific tasks to tackle next. The service provider can also analyze the user's learning progress and suggest specific learning methods tailored to that progress. The service provider can also analyze the user's learning progress and suggest specific goal setting tailored to that progress. By including specific action suggestions tailored to the user's learning progress, more effective learning can be promoted. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's learning progress data into a generating AI and have the generating AI provide action suggestions tailored to that progress.

[0097] The service provider can estimate the user's emotions and adjust the timing of advice and feedback based on the estimated emotions. For example, if the user is stressed, the service provider can provide advice at a time when the user can relax. If the user is focused, the service provider can also provide feedback at a time that does not interrupt their concentration. If the user is tired, the service provider can provide advice after a break. By adjusting the timing of advice and feedback based on the user's emotions, more effective advice and feedback can be provided. 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. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the emotion-based timing adjustment.

[0098] The service provider can incorporate feedback from the user's peers and community into the advice and feedback it provides. For example, the service provider can analyze feedback from the user's peers and reflect it in the advice. The service provider can also analyze feedback from the user's community and reflect it in the feedback. The service provider can also analyze feedback from the user's peers and community and suggest the next steps. This allows for the provision of more effective advice and feedback by incorporating feedback from the user's peers and community. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input feedback data from the user's peers and community into a generating AI and have the generating AI perform the analysis of the feedback.

[0099] The service provider may include suggestions for improving the user's learning environment in the advice and feedback it provides. For example, the service provider may analyze the user's learning environment and propose an optimal learning environment. The service provider may also analyze the user's learning environment and make specific suggestions for improving the environment. The service provider may also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, a more effective learning environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0100] The planning unit can estimate the user's emotions and adjust the learning plan development method based on the estimated user emotions. For example, if the user is feeling stressed, the planning unit can develop a relaxing learning plan. If the user is concentrating, the planning unit can also develop a learning plan to maintain concentration. If the user is tired, the planning unit can also develop a short and effective learning plan. In this way, by adjusting the learning plan development method based on the user's emotions, a more effective learning plan can be provided. 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. Some or all of the above processing in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the planning method based on emotions.

[0101] The planning unit can base its learning plan development on the user's short-term and long-term learning goals. For example, the planning unit can analyze the user's short-term goals and develop a specific learning plan to achieve them. The planning unit can also analyze the user's long-term goals and develop a specific learning plan to achieve them. The planning unit can also analyze the user's short-term and long-term goals and develop a balanced learning plan. This allows for the provision of a more effective learning plan by considering the user's short-term and long-term learning goals. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input the user's learning goal data into a generating AI and have the generating AI develop a learning plan based on those goals.

[0102] The planning unit can include break times and refreshment methods tailored to the user's learning style when formulating a learning plan. For example, the planning unit can analyze the user's learning style and suggest optimal break times. The planning unit can also analyze the user's learning style and suggest refreshment methods. The planning unit can analyze the user's learning style and formulate a learning plan that combines break times and refreshment methods. This allows for the provision of more effective learning plans by including break times and refreshment methods tailored to the user's learning style. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input user learning style data into a generating AI and have the generating AI formulate a learning plan that includes break times and refreshment methods.

[0103] The planning unit can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is stressed, the planning unit will prioritize learning plans that are effective in reducing stress. If the user is focused, the planning unit can also prioritize learning plans that help maintain focus. If the user is tired, the planning unit can also prioritize short, effective learning plans. This allows for the provision of more effective learning plans by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0104] The planning unit can include collaborative learning plans with the user's learning partners and community when developing a learning plan. For example, the planning unit can develop a collaborative learning plan with the user's learning partners. The planning unit can also develop a collaborative learning plan with the user's community. The planning unit can develop collaborative learning plans with the user's learning partners and community and share progress. This allows for the provision of a more effective learning plan by including collaborative learning plans with the user's learning partners and community. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input data on the user's learning partners and community into a generating AI and have the generating AI develop the collaborative learning plan.

[0105] The planning unit can include suggestions for improving the user's learning environment when formulating a learning plan. For example, the planning unit can analyze the user's learning environment and propose an optimal learning environment. The planning unit can also analyze the user's learning environment and make specific suggestions for improving the environment. The planning unit can also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, a more effective learning plan can be provided. Some or all of the above processes in the planning unit may be performed using AI, for example, or without AI. For example, the planning unit can input user learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0106] The monitoring unit can estimate the user's emotions and adjust the learning progress monitoring method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a monitoring method that is effective in reducing stress. If the user is concentrating, the monitoring unit can also provide a monitoring method to maintain concentration. If the user is tired, the monitoring unit can also provide a short and effective monitoring method. This makes it possible to monitor learning progress more effectively by adjusting the learning progress monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the monitoring method based on emotions.

[0107] The monitoring unit can display the user's progress toward learning goals in real time when monitoring learning progress. For example, the monitoring unit can analyze the user's progress toward learning goals in real time and display the progress. The monitoring unit can also analyze the user's progress toward learning goals in real time and provide feedback according to the progress. The monitoring unit can also analyze the user's progress toward learning goals in real time and suggest the next step. This makes it easier to grasp the user's learning progress by displaying the user's progress toward learning goals in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's progress toward learning goals into a generating AI and have the generating AI perform the real-time display.

[0108] The monitoring unit can detect changes in the user's learning environment and provide appropriate advice when monitoring learning progress. For example, the monitoring unit can detect changes in the noise level of the user's learning environment and suggest learning in a quieter environment. The monitoring unit can also detect changes in the lighting conditions of the user's learning environment and suggest learning under optimal lighting conditions. The monitoring unit can also detect changes in the temperature of the user's learning environment and suggest learning at a comfortable temperature. By detecting changes in the user's learning environment and providing appropriate advice, a more effective learning environment can be maintained. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user learning environment data into a generating AI and have the generating AI provide advice based on changes in the environment.

[0109] The monitoring unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a visually relaxing display method. If the user is focused, the monitoring unit can also provide a display method that includes detailed information. If the user is tired, the monitoring unit can also provide a concise and easy-to-understand display method. By adjusting the display method of learning progress based on the user's emotions, a more effective display of learning progress becomes possible. 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. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on emotions.

[0110] The monitoring unit can include a function to share the user's learning progress with their learning partners and community when monitoring learning progress. For example, the monitoring unit can provide a function to share the user's learning progress with their learning partners, thereby promoting collaborative learning. The monitoring unit can also provide a function to share the user's learning progress with their community, thereby increasing learning motivation. The monitoring unit can also provide a function to share the user's learning progress with their learning partners and community, and receive feedback. In this way, by including a function to share the user's learning progress with their learning partners and community, collaborative learning can be promoted and learning motivation can be increased. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input data on the user's learning partners and community into a generating AI and have the generating AI perform the provision of the progress sharing function.

[0111] The monitoring unit can incorporate data about the user's learning environment when monitoring learning progress. For example, the monitoring unit can analyze data about the user's learning environment and propose an optimal learning environment. The monitoring unit can also analyze data about the user's learning environment and make specific suggestions for improving the environment. The monitoring unit can also analyze data about the user's learning environment and make specific action suggestions for improving the environment. In this way, by incorporating data about the user's learning environment, a more effective learning environment can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user learning environment data into a generating AI and have the generating AI execute suggestions based on the environment data.

[0112] The advice unit can estimate the user's emotions and adjust the content of personalized advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can provide advice to help them relax. If the user is concentrating, the advice unit can also provide advice to help them maintain their concentration. If the user is tired, the advice unit can also provide quick and effective advice. By adjusting the content of personalized advice based on the user's emotions, more effective advice can be provided. 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. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's emotion data into the generative AI and have the generative AI adjust the content of the advice based on the emotion.

[0113] The advice unit can reflect the user's level of achievement of learning goals in personalized advice. For example, the advice unit can analyze the user's level of achievement of learning goals and provide advice according to that level. The advice unit can also analyze the user's level of achievement of learning goals and provide feedback according to that level. The advice unit can also analyze the user's level of achievement of learning goals and suggest the next steps according to that level. This allows for the provision of more effective advice by reflecting the user's level of achievement of learning goals. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's level of achievement of learning goals into a generating AI and have the generating AI provide advice based on the level of achievement.

[0114] The advice unit can include specific action suggestions tailored to the user's learning progress in its personalized advice. For example, the advice unit can analyze the user's learning progress and suggest specific tasks to tackle next. The advice unit can also analyze the user's learning progress and suggest specific learning methods tailored to that progress. The advice unit can also analyze the user's learning progress and suggest specific goal setting tailored to that progress. By including specific action suggestions tailored to the user's learning progress, more effective learning can be promoted. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's learning progress data into a generating AI and have the generating AI provide action suggestions tailored to that progress.

[0115] The advice unit can estimate the user's emotions and adjust the timing of personalized advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can provide advice at a time when the user can relax. If the user is concentrating, the advice unit can also provide advice at a time that does not interrupt their concentration. If the user is tired, the advice unit can provide advice after a break. By adjusting the timing of personalized advice based on the user's emotions, more effective advice can be provided. 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. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based timing adjustment.

[0116] The advice unit can incorporate feedback from the user's peers and community into personalized advice. For example, the advice unit can analyze feedback from the user's peers and reflect it in the advice. The advice unit can also analyze feedback from the user's community and reflect it in the advice. The advice unit can also analyze feedback from the user's peers and community and suggest the next steps. This allows for more effective advice by incorporating feedback from the user's peers and community. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input feedback data from the user's peers and community into a generating AI and have the generating AI perform the analysis of the feedback.

[0117] The advice unit can include suggestions for improving the user's learning environment in its personalized advice. For example, the advice unit can analyze the user's learning environment and propose an optimal learning environment. The advice unit can also analyze the user's learning environment and make specific suggestions for improving the environment. The advice unit can also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, a more effective learning environment can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0118] The reminder unit can estimate the user's emotions and adjust the content of motivational reminders based on those emotions. For example, if the user is feeling stressed, the reminder unit can provide a relaxing reminder. If the user is concentrating, the reminder unit can also provide a reminder to help maintain concentration. If the user is tired, the reminder unit can provide a short and effective reminder. By adjusting the content of motivational reminders based on the user's emotions, more effective reminders can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI, or not using AI. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI adjust the content of the reminder based on the emotion.

[0119] The reminder unit can reflect the user's learning goal achievement level in motivational reminders. For example, the reminder unit can analyze the user's learning goal achievement level and provide reminders according to that level. The reminder unit can also analyze the user's learning goal achievement level and provide feedback according to that level. The reminder unit can also analyze the user's learning goal achievement level and suggest the next steps according to that level. This allows for more effective reminders by reflecting the user's learning goal achievement level. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's learning goal achievement level data into a generating AI and have the generating AI provide reminders based on the goal achievement level.

[0120] The reminder function can include specific action suggestions tailored to the user's learning progress in its motivational reminders. For example, the reminder function can analyze the user's learning progress and suggest specific tasks to tackle next. It can also analyze the user's learning progress and suggest specific learning methods tailored to that progress. It can also analyze the user's learning progress and suggest specific goal setting tailored to that progress. By including specific action suggestions tailored to the user's learning progress, the reminder function can provide more effective reminders. Some or all of the above-described processes in the reminder function may be performed using AI, for example, or without AI. For example, the reminder function can input the user's learning progress data into a generating AI and have the generating AI provide action suggestions tailored to that progress.

[0121] The reminder unit can estimate the user's emotions and adjust the timing of motivational reminders based on the estimated emotions. The reminder unit can, for example, provide a system that alerts the user to stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into a generative AI and have the generative AI perform emotion-based timing adjustments.

[0122] The reminder function can incorporate feedback from the user's learning partners and community into motivational reminders. For example, the reminder function can analyze feedback from the user's learning partners and reflect it in the reminders. It can also analyze feedback from the user's community and reflect it in the reminders. The reminder function can analyze feedback from the user's learning partners and community and suggest the next steps. This allows for more effective reminders by incorporating feedback from the user's learning partners and community. Some or all of the above processing in the reminder function may be performed using AI, for example, or not. For example, the reminder function can input feedback data from the user's learning partners and community into a generating AI and have the generating AI perform the analysis of the feedback.

[0123] The reminder unit can include suggestions for improving the user's learning environment in its motivational reminders. For example, the reminder unit can analyze the user's learning environment and suggest an optimal learning environment. The reminder unit can also analyze the user's learning environment and make specific suggestions for improving the environment. The reminder unit can also analyze the user's learning environment and make specific action suggestions for improving the environment. By including suggestions for improving the user's learning environment, more effective reminders can be provided. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's learning environment data into a generating AI and have the generating AI execute suggestions for improving the environment.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The analysis unit can consider the user's hobbies and interests when analyzing their learning style. For example, if a user is interested in music, it can suggest learning methods that incorporate music. If a user is interested in sports, it can provide example problems and questions related to sports. Furthermore, if a user is interested in art, it can suggest learning methods that incorporate art. This allows for the provision of learning styles that take into account the user's hobbies and interests, thereby increasing their motivation to learn.

[0126] The system can visually display the user's learning progress according to their learning status. For example, by displaying the user's learning progress in graphs or charts, users can visually grasp their progress. Furthermore, by color-coding the user's learning progress, it's possible to see at a glance which parts are progressing. Additionally, by displaying the user's learning progress with animations, users can check their progress in an enjoyable way. In this way, visually displaying learning progress can increase motivation to learn.

[0127] The planning department can implement a reward system tailored to the user's learning goals when developing user learning plans. For example, badges or points can be awarded when a user achieves a specific learning goal. Rewards can also be provided when a user achieves a certain amount of study time. Furthermore, rewards can be given when a user progresses through their learning plan. By implementing such a reward system, the department can increase user motivation to learn.

[0128] The monitoring unit can incorporate a predictive function based on the user's learning history when monitoring the user's learning progress. For example, it can predict future learning progress based on the user's past learning history. It can also predict points where learning may stagnate based on the user's learning history and take countermeasures in advance. Furthermore, it can predict the most effective timing for learning based on the user's learning history and propose an optimal learning plan. In this way, by introducing a predictive function, more effective learning support can be provided.

[0129] The advice section can provide features that allow users to share their learning progress according to their learning style. For example, users can share their learning progress with fellow learners, encouraging each other as they progress. Users can also receive feedback by sharing their learning progress with the learning community. Furthermore, users can boost their motivation by sharing their learning progress with family and friends. In this way, providing a feature to share learning progress can increase motivation.

[0130] The analysis unit can estimate the user's emotions and adjust the learning style analysis method based on the estimated emotions. For example, if the user is stressed, it will prioritize analyzing learning styles that promote relaxation. If the user is focused, it can analyze learning styles that help maintain focus. If the user is tired, it can analyze learning styles that are effective in a short amount of time. In this way, by adjusting the learning style analysis method based on the user's emotions, a more effective learning style can be provided.

[0131] The service provider can estimate the user's emotions and adjust the way advice and feedback are presented based on those emotions. For example, if the user is stressed, it can provide advice in gentle language. If the user is focused, it can provide specific and detailed feedback. If the user is tired, it can provide concise and easy-to-understand advice. By adjusting the way advice and feedback are presented based on the user's emotions, more effective advice and feedback can be provided.

[0132] The planning department can estimate the user's emotions and adjust the learning plan development method based on those emotions. For example, if the user is stressed, it can develop a relaxing learning plan. If the user is focused, it can develop a learning plan to maintain their concentration. If the user is tired, it can develop a short, effective learning plan. By adjusting the learning plan development method based on the user's emotions, it is possible to provide a more effective learning plan.

[0133] The monitoring unit can estimate the user's emotions and adjust the learning progress monitoring method based on the estimated emotions. For example, if the user is feeling stressed, it can provide a monitoring method that is effective in reducing stress. If the user is concentrating, it can also provide a monitoring method that helps maintain concentration. If the user is tired, it can provide a short and effective monitoring method. By adjusting the learning progress monitoring method based on the user's emotions, more effective learning progress monitoring becomes possible.

[0134] The advice section can estimate the user's emotions and adjust the content of personalized advice based on those emotions. For example, if the user is feeling stressed, it can provide advice to help them relax. If the user is concentrating, it can provide advice to help them maintain their concentration. If the user is tired, it can provide quick and effective advice. In this way, by adjusting the content of personalized advice based on the user's emotions, more effective advice can be provided.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The analysis unit analyzes the user's learning style. The analysis unit collects and analyzes data such as the user's learning history, learning methods, and learning time. This makes it possible to identify when the user is most likely to concentrate and which learning methods are most effective. Step 2: The service provider provides real-time advice and feedback based on the results analyzed by the analysis unit. For example, if a user is losing focus, it will provide advice such as, "It would be good to take a short break." Also, if a user does not understand the learning material, it will provide feedback such as, "Let's review this part again." Step 3: The Planning Department provides support for developing learning plans. The Planning Department develops the optimal learning plan for the user and proposes an effective learning plan based on data such as the user's learning history, learning methods, and learning time. Step 4: The monitoring unit monitors learning progress. The monitoring unit monitors the user's learning progress in real time and provides advice to help them achieve their goals. It regularly checks the user's learning progress and provides feedback as needed. Step 5: The advice unit provides personalized advice. The advice unit provides individualized advice based on the user's learning style and progress. It provides specific advice based on the user's characteristics. Step 6: The reminder section provides regular motivational reminders. The reminder section regularly provides motivational reminders such as "Let's work hard towards our goals." To maintain user motivation, it provides encouraging messages and notifications when goals are achieved.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the analysis unit, provision unit, planning unit, monitoring unit, advice unit, and reminder unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the analysis unit, provision unit, planning unit, monitoring unit, advice unit, and reminder unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the analysis unit, provision unit, planning unit, monitoring unit, advice unit, and reminder unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the analysis unit, provision unit, planning unit, monitoring unit, advice unit, and reminder unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reminder unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) An analysis unit that analyzes the user's learning style, A provisioning unit that provides advice and feedback in real time based on the results analyzed by the aforementioned analysis unit, The Planning Department provides support for developing learning plans, The monitoring department monitors learning progress, The advice department provides personalized advice, It includes a reminder section that provides regular motivational reminders. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Collect and analyze data on users' learning history, learning methods, and learning time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide appropriate advice and feedback according to the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Develop a learning plan tailored to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Monitor the user's learning progress The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Providing personalized advice to users The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reminder unit, Regularly remind yourself of your motivation The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the learning style analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, In addition to the user's learning history, the analysis incorporates the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing learning styles, the user's learning environment is used as the basis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing learning styles, we analyze users' social media activity and incorporate relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing learning styles, the analysis is performed based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice and feedback are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, The advice and feedback provided will reflect the user's progress toward achieving their learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The advice and feedback provided should include specific action suggestions tailored to the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of advice and feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We incorporate feedback from users' fellow learners and the community into the advice and feedback we provide. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The advice and feedback we provide should include suggestions for improving the user's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, It estimates the user's emotions and adjusts the learning plan development method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When developing a learning plan, base it on the user's short-term and long-term learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, When creating a learning plan, include break times and refreshment methods tailored to the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, 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 24) The aforementioned planning department, When developing a learning plan, include collaborative learning plans with your learning partners and community. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning department, When developing a learning plan, include suggestions for improving the user's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The 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 27) The monitoring unit, When monitoring learning progress, the system displays the user's progress toward achieving learning goals in real time. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, When monitoring learning progress, it detects changes in the user's learning environment and provides appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, When monitoring learning progress, include a feature to share progress with the user's learning partners and community. The system described in Appendix 1, characterized by the features described herein. (Note 31) The monitoring unit, When monitoring learning progress, incorporate data about the user's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advice section, It estimates the user's emotions and adjusts the personalized advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, Personalized advice reflects the user's progress toward achieving their learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, Personalized advice includes specific action suggestions tailored to the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, It estimates the user's emotions and adjusts the timing of personalized advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advice section, Personalized advice incorporates feedback from users' learning partners and the community. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advice section, Personalized advice should include suggestions for improving the user's learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned reminder unit, The system estimates the user's emotions and adjusts the content of motivational reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned reminder unit, Motivation reminders should reflect the user's progress toward achieving their learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned reminder unit, Motivation reminders should include specific action suggestions tailored to the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of motivational reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned reminder unit, Incorporate feedback from users' learning partners and the community into motivational reminders. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned reminder unit, Include suggestions for improving the user's learning environment in motivational reminders. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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. An analysis unit that analyzes the user's learning style, A provisioning unit that provides advice and feedback in real time based on the results analyzed by the aforementioned analysis unit, The Planning Department provides support for developing learning plans, The monitoring department monitors learning progress, The advice department provides personalized advice, It includes a reminder section that provides regular motivational reminders. A system characterized by the following features.

2. The aforementioned analysis unit, Collect and analyze data on users' learning history, learning methods, and learning time. The system according to feature 1.

3. The aforementioned supply unit is, Provide appropriate advice and feedback according to the user's learning progress. The system according to feature 1.

4. The aforementioned planning department, Develop a learning plan tailored to the user. The system according to feature 1.

5. The monitoring unit, Monitor the user's learning progress The system according to feature 1.

6. The aforementioned advice section, Providing personalized advice to users The system according to feature 1.

7. The aforementioned reminder unit, Regularly remind yourself of your motivation The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the learning style analysis method based on the estimated user emotions. The system according to feature 1.

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