system

Abstract

The system according to this embodiment aims to generate a learning plan optimized for each student and to modify it as appropriate according to the student's learning progress. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, a lock unit, and a coaching unit. The collection unit collects student information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The modification unit modifies the learning plan generated by the generation unit. The lock unit locks the smartphone during learning time. The coaching unit uses a generating AI to coach learning through voice and pop-ups.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that it is difficult to generate a learning plan optimized for each student and appropriately modify it according to the progress of learning.

[0005] The system according to the embodiment aims to generate a learning plan optimized for each student and appropriately modify it according to the progress of learning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, a lock unit, and a coaching unit. The collection unit collects student information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The modification unit modifies the learning plan generated by the generation unit. The lock unit locks the smartphone during learning time. The coaching unit uses a generating AI to coach learning through voice and pop-ups. [Effects of the Invention]

[0007] The system according to this embodiment can generate a learning plan optimized for each student and modify it as needed according to the student's learning progress. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that utilizes generative AI to collect information such as each student's desired school, target score, study habits, and personality, and generates a personalized learning plan. The learning support system collects learning history data from wearable devices and learning apps, and the generative AI analyzes this data. Based on the analysis results, it generates a learning plan, analyzes the gap between the learning plan and the actual learning, and makes adjustments as needed. During study time, the smartphone is locked, and the generative AI maintains concentration by coaching the student through voice and pop-ups. In addition, the camera is used to record daily learning records and the results of mock exams and regular tests, and effective guidance is provided by analyzing the progress of performance and learning trends, which are saved as memories. Furthermore, it recommends music to enhance concentration in conjunction with music services. When motivation declines, the generative AI uses past learning history, performance, and music to send encouragement and improve motivation toward achieving goals. As a result, students can receive consistent learning management and efficient support, record the process in an enjoyable way to preserve it as memories, and receive encouragement to maintain their motivation. This allows the learning support system to collect and analyze student information, generate and modify learning plans, and maintain concentration during study time.

[0029] The learning support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, a locking unit, and a coaching unit. The collection unit collects student information. For example, the collection unit can collect information such as the student's desired school, target score, study habits, and personality. The collection unit can also collect learning history data from wearable devices and learning apps. For example, the collection unit can collect data such as heart rate and steps from wearable devices, and data on study time and study content from learning apps. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected learning history data and analyze the gap with the learning plan. The analysis unit analyzes the collected data in detail using techniques such as data mining and statistical analysis. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, the generation unit can generate an optimal learning plan for each student using a generation AI. The generation AI generates the learning plan using natural language processing AI, machine learning AI, etc. The modification unit modifies the learning plan generated by the generation unit. The modification unit can, for example, appropriately modify the learning plan based on analysis results. The modification unit uses methods such as feedback-based modification and data analysis-based modification. The lock unit locks the smartphone during learning time. The lock unit can, for example, lock the smartphone during learning time to maintain concentration. The lock unit can set the lock time and unlock conditions. The coaching unit uses the generation AI to coach learning through voice and pop-ups. The coaching unit can, for example, use the generation AI to provide voice coaching and pop-up messages. The generation AI uses natural language processing AI, machine learning AI, etc., to provide effective learning coaching. As a result, the learning support system according to this embodiment can collect and analyze student information, generate and modify learning plans, and maintain concentration during learning time.

[0030] The data collection department collects student information. For example, it can collect information such as students' desired schools, target scores, study habits, and personality. Specifically, it stores the desired schools and target scores entered by students in a database, and collects information on study habits and personality through questionnaires and self-assessment sheets. The data collection department can also collect learning history data from wearable devices and learning apps. For example, it can collect data such as heart rate and steps from wearable devices, and data on study time and content from learning apps. Wearable devices can provide data such as sleep patterns and stress levels in addition to heart rate and steps. This allows for an understanding of students' health status and lifestyle rhythms, which can then be reflected in learning plans. From learning apps, detailed data such as specific learning content, progress, and correct answer rates can be collected. This allows the data collection department to comprehensively understand students' learning situations and collect foundational data to provide support tailored to individual needs. Furthermore, the data collection department can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze collected learning history data and analyze the gap between that data and the learning plan. Specifically, it uses techniques such as data mining and statistical analysis to analyze the collected data in detail. For example, it analyzes learning history data in a time series to evaluate learning progress and learning effectiveness. It also analyzes data related to learning habits and personality to understand students' learning styles and motivational tendencies. Furthermore, the analysis unit uses AI to process data in real time and understand the surrounding circumstances. For example, the AI ​​analyzes learning history data to identify the level of understanding and areas of difficulty in specific subjects or topics. This allows the analysis unit to understand the learning situation of each student in detail and provide basic data to provide support tailored to individual needs. In addition, the analysis unit can utilize past data and statistical information to conduct long-term learning planning and trend analysis. For example, based on past learning data, it can predict fluctuations in learning effectiveness at specific times and conditions and formulate future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, use a generation AI to generate an optimal learning plan for each individual student. The generation AI uses natural language processing AI and machine learning AI to generate the learning plan. Specifically, the generation AI receives information such as the student's learning history data, target scores, and desired schools as input, and generates an optimal learning plan. For example, based on past learning data, the generation AI suggests learning time and methods for specific subjects or topics. Furthermore, the generation AI can use natural language processing technology to explain the learning plan to the student in easily understandable language. This allows the generation unit to provide an optimal learning plan for each student, maximizing learning effectiveness. In addition, the generation unit can continuously revise the learning plan based on real-time updated data, adapting to the latest situation. For example, it can appropriately revise the learning plan and suggest the optimal learning method according to the student's learning progress and understanding. The generation unit can also generate a more accurate learning plan by considering regional characteristics and past learning history. This allows the generation unit to always provide highly accurate learning plans based on the latest information, enabling it to provide quick and appropriate learning support.

[0033] The modification unit modifies the learning plan generated by the generation unit. The modification unit can, for example, modify the learning plan as appropriate based on analysis results. Specifically, it uses methods such as feedback-based modification and data analysis-based modification. For example, it collects feedback from students and reviews the content and progress of the learning plan. It also evaluates the effectiveness of the learning plan based on data provided by the analysis unit and modifies it as needed. In this way, the modification unit can continuously improve the accuracy and effectiveness of the learning plan and provide optimal learning support to each student. Furthermore, the modification unit can continuously modify the learning plan based on data updated in real time and respond to the latest situation. For example, it can appropriately modify the learning plan according to the student's learning progress and level of understanding and propose the optimal learning method. In addition, the modification unit can generate more accurate learning plans by considering regional characteristics and past learning history. In this way, the modification unit can always provide highly accurate learning plans based on the latest information and provide quick and appropriate learning support.

[0034] The lock function locks the smartphone during study time. For example, the lock function can help maintain concentration by locking the smartphone during study time. Specifically, it allows users to set the lock duration and unlock conditions. For instance, a set study period can be established, during which specific functions of the smartphone are locked. Unlock conditions can also be set, such as achieving a specific learning goal or after a certain break. This allows the lock function to provide an environment conducive to focused learning, maximizing learning effectiveness. Furthermore, the lock function can monitor smartphone usage during study time and adjust lock settings as needed. For example, if the smartphone is used frequently during study time, the lock duration can be extended. The lock function can also flexibly change lock settings according to learning progress and comprehension. This allows the lock function to provide an optimal learning environment for each student, maximizing learning effectiveness.

[0035] The coaching department uses generative AI to coach students through voice and pop-up messages. For example, the coaching department can use generative AI to provide voice coaching and pop-up messages. Specifically, the generative AI provides voice coaching and pop-up messages at the appropriate time according to the student's learning situation and progress. For example, if a student is falling behind in their learning progress, the generative AI will send an encouraging voice message. It can also provide explanations and hints for specific problems in pop-up messages. This allows the coaching department to help students maintain their motivation and learn effectively. Furthermore, the coaching department can collect student feedback and continuously improve the accuracy and effectiveness of the coaching content. For example, it can analyze student responses to voice coaching and pop-up messages and revise the coaching content. The coaching department can also reliably transmit information using multiple communication methods. For example, it can use not only voice coaching but also email, SMS, and in-app notifications to ensure that important information is delivered reliably. This allows the coaching department to provide students with learning support quickly and reliably, maximizing learning effectiveness.

[0036] The storage unit can use a camera to record daily learning activities, mock exam and regular test results, and analyze progress in performance and learning trends. For example, the storage unit can use a camera to photograph students' learning activities and save that data. The storage unit can save mock exam and regular test results as digital data and display progress in performance using graphs and charts. To analyze learning trends, the storage unit can analyze the collected data and identify frequently appearing questions and learning patterns. As a result, the storage unit can save and analyze learning records and progress in performance, enabling effective instruction. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input image data captured by a camera into a generating AI and have the generating AI generate learning records from the image data.

[0037] The recommendation unit can recommend music to enhance concentration in conjunction with music services. For example, the recommendation unit can analyze music data provided by music services and select music to improve students' concentration. The recommendation unit can recommend music that is considered to have the effect of improving concentration, such as classical music or ambient sounds. The recommendation unit can create music playlists and provide them to students. In this way, the recommendation unit can improve students' concentration by recommending music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input music data provided by music services into a generating AI, and the generating AI can analyze the music data and select music that enhances concentration.

[0038] The Cheer Club can send encouragement using a generating AI that utilizes past learning history, grades, and music. For example, the Cheer Club can use the generating AI to analyze a student's past learning history and grades and generate encouraging messages. The Cheer Club can use the generating AI to consider a student's musical preferences and select music that will boost their motivation, sending it as encouragement. The Cheer Club can use the generating AI to provide specific advice for achieving goals based on a student's learning history and grades. In this way, the Cheer Club can improve students' motivation by sending encouragement. Some or all of the above processes in the Cheer Club may be performed using AI, or not. For example, the Cheer Club can input a student's learning history and grade data into a generating AI, which can then generate encouraging messages and music.

[0039] The data collection unit can collect learning history data from wearable devices and learning apps. For example, the data collection unit can collect data such as heart rate and steps from wearable devices, and data on learning time and learning content from learning apps. The data collection unit can integrate this data to gain a detailed understanding of the student's learning history. This allows the data collection unit to gain a more detailed understanding of the learning history by collecting data from wearable devices and learning apps. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices and learning apps into a generating AI, which can then analyze the data to understand the learning history.

[0040] The analysis unit can analyze the collected learning history data and identify gaps with the learning plan. For example, the analysis unit can analyze the collected data using techniques such as data mining and statistical analysis to identify gaps with the learning plan. The analysis unit can clarify the specific content of the gaps and use this information to revise the learning plan. This allows the analysis unit to make appropriate corrections by analyzing the learning history data and identifying gaps with the learning plan. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and identify gaps.

[0041] The modification unit can appropriately modify the learning plan based on the analysis results. For example, the modification unit reviews the content of the learning plan based on the analysis results and makes modifications as necessary. The modification unit can use methods such as modifications based on feedback or modifications based on data analysis. As a result, the modification unit can enable more effective learning by modifying the learning plan based on the analysis results. Some or all of the above processing in the modification unit may be performed using AI, for example, or without using AI. For example, the modification unit can input the analysis results into a generating AI, and the generating AI can modify the learning plan.

[0042] The coaching department can provide learning coaching through voice and pop-ups using generative AI. For example, the coaching department can use generative AI to provide voice coaching and pop-up messages to students. The generative AI uses natural language processing AI, machine learning AI, etc., to provide effective learning coaching. The coaching department can have the generative AI analyze the student's learning situation in real time and provide coaching at the appropriate time. For example, the coaching department can have the generative AI monitor the student's learning progress and provide encouraging messages and advice as needed. This enables the coaching department to provide effective learning coaching by using generative AI. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input student learning data into the generative AI, and the generative AI can generate voice coaching and pop-up messages.

[0043] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from learning apps that students have used in the past. The data collection unit can also prioritize collecting data from wearable devices that students have used in the past. The data collection unit can select the most effective data collection method from the student's past learning history. Thus, the data collection unit can select the optimal data collection method by analyzing the past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's past learning history data into a generating AI, which can then select the optimal data collection method.

[0044] The data collection unit can filter learning history data based on the student's current learning status and areas of interest. For example, the data collection unit can prioritize the collection of data related to the subject the student is currently studying. The data collection unit can also filter and collect relevant data based on the student's areas of interest. The data collection unit can collect only the necessary data according to the student's learning status. In this way, the data collection unit can collect only the necessary data by filtering the data based on the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the student's learning status and areas of interest into a generating AI, which can then perform the filtering.

[0045] The data collection unit can prioritize the collection of highly relevant data based on the student's geographical location when collecting learning history data. For example, if the student is at school, the data collection unit can prioritize the collection of learning data at school. If the student is at home, the data collection unit can prioritize the collection of learning data at home. If the student is at the library, the data collection unit can prioritize the collection of learning data at the library. This allows the data collection unit to collect more appropriate data by collecting highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into a generating AI, which can then select highly relevant data.

[0046] The data collection unit can analyze students' social media activity and collect relevant data when collecting learning history data. For example, the data collection unit can collect learning content that students have shared on social media. The data collection unit can also collect information on educational accounts that students follow on social media. The data collection unit can collect information on learning groups that students participate in on social media. This allows the data collection unit to collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input students' social media activity data into a generating AI, which can then select relevant data.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning history data during the analysis. For example, the analysis unit can perform a detailed analysis on important learning history data. The analysis unit can also perform a simplified analysis on less important learning history data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the learning history data. This allows the analysis unit to perform more effective analysis by adjusting the level of detail of the analysis based on the importance of the learning history data. 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 learning history data into a generating AI, which can evaluate the importance of the data and adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of the learning history data during analysis. For example, the analysis unit can apply a mathematics-specific analysis algorithm to mathematics learning history data. The analysis unit can also apply an English-specific analysis algorithm to English learning history data. The analysis unit can apply a science-specific analysis algorithm to science learning history data. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the learning history data. 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 learning history data into a generating AI, which can then select an appropriate analysis algorithm according to the data category.

[0049] The analysis unit can determine the priority of analysis based on the submission date of the learning history data during analysis. For example, the analysis unit can prioritize the analysis of recently submitted learning history data. The analysis unit can also postpone the analysis of older learning history data. The analysis unit can adjust the priority of analysis in stages based on the submission date. This allows the analysis unit to perform more effective analysis by determining the priority of analysis based on the submission date of the learning history data. 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 submission date of the learning history data into a generating AI, and the generating AI can determine the priority.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the learning history data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant learning history data. The analysis unit can also postpone the analysis of less relevant learning history data. The analysis unit can adjust the order of analysis step by step based on the relevance of the learning history data. This allows the analysis unit to perform more effective analysis by adjusting the order of analysis based on the relevance of the learning history data. 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 relevance of the learning history data into a generating AI, and the generating AI can determine the order of analysis.

[0051] The generation unit can adjust the level of detail of the learning plan based on the importance of the analysis results when generating the learning plan. For example, the generation unit can generate a detailed learning plan based on important analysis results. The generation unit can also generate a simplified learning plan based on less important analysis results. The generation unit can adjust the level of detail of the learning plan in stages according to the importance of the analysis results. This allows the generation unit to create more effective learning plans by adjusting the level of detail of the plan based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI, which can evaluate the importance of the data and adjust the level of detail of the plan.

[0052] The generation unit can apply different generation algorithms depending on the student's learning style when generating learning plans. For example, the generation unit can generate a visual learning plan for students with a visual learning style. The generation unit can also generate an auditory learning plan for students with an auditory learning style. The generation unit can generate an experiential learning plan for students with an experiential learning style. In this way, the generation unit can provide a more appropriate learning plan by applying different generation algorithms depending on the student's learning style. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning style data into a generation AI, which can then select an appropriate generation algorithm.

[0053] The generation unit can determine the priority of learning plans based on the submission timing of analysis results when generating learning plans. For example, the generation unit can prioritize the generation of learning plans based on recently submitted analysis results. The generation unit can also postpone the generation of older analysis results. The generation unit can adjust the priority of learning plans in stages based on the submission timing. This allows the generation unit to create more effective learning plans by determining the priority of plans based on the submission timing of analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing of analysis results into a generation AI, which can then determine the priority.

[0054] The generation unit can adjust the order of learning plans based on the relevance of the analysis results when generating learning plans. For example, the generation unit can prioritize generating learning plans based on highly relevant analysis results. The generation unit can also postpone less relevant analysis results. The generation unit can adjust the order of learning plans step by step based on the relevance of the analysis results. This allows the generation unit to create more effective learning plans by adjusting the order of plans based on the relevance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI, which can then determine the order of the plans.

[0055] The modification unit can adjust the level of detail of the modifications based on the importance of the analysis results when modifying the learning plan. For example, the modification unit can make detailed modifications based on important analysis results. The modification unit can also make simplified modifications based on less important analysis results. The modification unit can adjust the level of detail of the modifications in stages according to the importance of the analysis results. This allows the modification unit to make more effective modifications to the learning plan by adjusting the level of detail of the modifications based on the importance of the analysis results. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the importance of the analysis results into a generating AI, and the generating AI can adjust the level of detail of the modifications.

[0056] The revision unit can apply different revision algorithms depending on the student's learning style when revising the learning plan. For example, the revision unit can perform visual revisions for students with a visual learning style. The revision unit can also perform auditory revisions for students with an auditory learning style. The revision unit can perform experiential revisions for students with an experiential learning style. This allows the revision unit to apply different revision algorithms according to the student's learning style, enabling more appropriate revisions to the learning plan. Some or all of the above processing in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input student learning style data into a generating AI, which can then select an appropriate revision algorithm.

[0057] The revision unit can determine the priority of revisions based on the submission timing of analysis results when revising the learning plan. For example, the revision unit can prioritize revisions based on recently submitted analysis results. The revision unit can also postpone revisions based on older submission dates. The revision unit can adjust the priority of revisions in stages based on the submission dates. This allows the revision unit to revise the learning plan more effectively by determining the priority of revisions based on the submission dates of analysis results. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the submission dates of analysis results into a generating AI, which can then determine the priority.

[0058] The lock function can adjust the lock strength according to the student's learning situation when the smartphone is locked. For example, the lock function can apply a strong lock when the student is concentrating. It can also apply a moderate lock when the student is relaxed. It can also apply a gentle lock when the student is tired. In this way, the lock function can provide a more appropriate learning environment by adjusting the lock strength according to the learning situation. Some or all of the above processing in the lock function may be performed using AI, for example, or without AI. For example, the lock function can input student learning situation data into a generating AI, which can then adjust the lock strength.

[0059] The locking mechanism can apply different locking methods to students depending on their learning style when the smartphone is locked. For example, the locking mechanism can apply a visual locking method to students with a visual learning style, an auditory locking method to students with an auditory learning style, and an experiential locking method to students with an experiential learning style. By applying different locking methods according to the learning style, the locking mechanism can provide a more appropriate learning environment. Some or all of the above processing in the locking mechanism may be performed using AI, for example, or without AI. For example, the locking mechanism can input student learning style data into a generating AI, which can then select an appropriate locking method.

[0060] The locking unit can select the optimal locking method by referring to the student's device usage history when locking the smartphone. For example, the locking unit can prioritize locking methods that the student has used in the past. The locking unit can also select the most effective locking method from the student's device usage history. The locking unit can adjust the locking method in stages based on the student's device usage history. In this way, the locking unit can provide a more appropriate learning environment by selecting the optimal locking method based on the device usage history. Some or all of the above processing in the locking unit may be performed using AI, for example, or not using AI. For example, the locking unit can input the student's device usage history data into a generating AI, and the generating AI can select the optimal locking method.

[0061] The coaching unit can adjust the level of detail in coaching based on the importance of the learning plan. For example, the coaching unit can provide detailed coaching for important learning plans, and simplified coaching for less important learning plans. The coaching unit can adjust the level of detail in coaching in stages according to the importance of the learning plan. This allows the coaching unit to provide more effective learning coaching by adjusting the level of detail based on the importance of the learning plan. Some or all of the above processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input learning plan importance data into a generating AI, which can then adjust the level of detail in coaching.

[0062] The coaching unit can apply different coaching algorithms to students according to their learning style during coaching sessions. For example, the coaching unit can provide visual coaching to students with a visual learning style, auditory coaching to students with an auditory learning style, and experiential coaching to students with an experiential learning style. This allows the coaching unit to provide more effective learning coaching by applying different coaching algorithms according to students' learning styles. Some or all of the above-described processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input student learning style data into a generating AI, which can then select an appropriate coaching algorithm.

[0063] The coaching department can prioritize coaching sessions based on the submission date of the learning plan. For example, the coaching department can prioritize coaching based on the most recently submitted learning plan. The coaching department can also postpone coaching sessions with older submission dates. The coaching department can adjust the coaching priority in stages based on the submission date. This allows the coaching department to provide more effective learning coaching by prioritizing coaching based on the submission date of the learning plan. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input learning plan submission date data into a generating AI, which can then determine the coaching priority.

[0064] The coaching unit can adjust the order of coaching sessions based on the relevance of the learning plans. For example, the coaching unit can prioritize coaching based on highly relevant learning plans. The coaching unit can also postpone less relevant learning plans. The coaching unit can adjust the order of coaching sessions in stages based on the relevance of the learning plans. This allows the coaching unit to provide more effective learning coaching by adjusting the order of coaching sessions based on the relevance of the learning plans. Some or all of the above processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input data on the relevance of learning plans into a generating AI, which can then determine the order of coaching sessions.

[0065] The storage unit can adjust the level of detail in saving learning records based on their importance. For example, it can perform detailed saving for important learning records. For less important learning records, it can perform simplified saving. The storage unit can adjust the level of detail in stages according to the importance of the learning records. This allows the storage unit to save learning records more appropriately by adjusting the level of detail based on the importance of the records. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input learning record importance data into a generating AI, which can then adjust the level of detail in saving.

[0066] The storage unit can apply different storage algorithms depending on the student's learning style when saving learning records. For example, the storage unit can apply a visual storage method to students with a visual learning style. For students with an auditory learning style, it can apply an auditory storage method. For students with an experiential learning style, it can apply an experiential storage method. This allows the storage unit to save learning records more appropriately by applying different storage algorithms depending on the student's learning style. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input student learning style data into a generating AI, which can then select an appropriate storage algorithm.

[0067] The storage unit can select the optimal storage method by referring to the student's device usage history when saving learning records. For example, the storage unit can prioritize applying storage methods previously used by the student. The storage unit can also select the most effective storage method from the student's device usage history. The storage unit can adjust the storage method in stages based on the student's device usage history. This allows the storage unit to select the optimal storage method based on the device usage history, enabling more appropriate storage of learning records. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the student's device usage history data into a generating AI, which can then select the optimal storage method.

[0068] The recommendation unit can adjust the level of detail in its music recommendations according to the student's learning situation. For example, if a student is concentrating, the recommendation unit can recommend music that enhances concentration in detail. If a student is relaxed, the recommendation unit can also recommend music that promotes relaxation in a concise manner. If a student is tired, the recommendation unit can recommend music that promotes refreshment in detail. In this way, the recommendation unit can provide more appropriate music by adjusting the level of detail in its recommendations according to the student's learning situation. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input student learning situation data into a generating AI, which can then adjust the level of detail in its recommendations.

[0069] The recommendation unit can apply different recommendation algorithms to students based on their musical preferences when recommending music. For example, if a student prefers classical music, the recommendation unit can recommend classical music. If a student prefers pop music, the recommendation unit can recommend pop music. If a student prefers jazz music, the recommendation unit can recommend jazz music. In this way, the recommendation unit can provide more appropriate music by applying different recommendation algorithms according to the student's musical preferences. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input student musical preference data into a generating AI, which can then select an appropriate recommendation algorithm.

[0070] The recommendation unit can recommend the most suitable music by referring to the student's music history when recommending music. For example, the recommendation unit can recommend the most suitable music based on music the student has listened to in the past. The recommendation unit can also recommend music that enhances concentration based on the student's music history. The recommendation unit can analyze the student's music history and recommend music that promotes relaxation. In this way, the recommendation unit can provide more appropriate music by recommending the most suitable music based on the music history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the student's music history data into a generating AI, which can then select the most suitable music.

[0071] The cheering unit can adjust the level of detail of the cheers it sends based on the importance of past learning history. For example, the cheering unit can send detailed cheers based on important learning history. It can also send simple cheers based on less important learning history. The cheering unit can adjust the level of detail of the cheers in stages according to the importance of the learning history. This allows the cheering unit to send more effective cheers by adjusting the level of detail of the cheers based on the importance of past learning history. Some or all of the above processing in the cheering unit may be performed using AI, for example, or without AI. For example, the cheering unit can input past learning history data into a generating AI, which can evaluate the importance of the data and adjust the level of detail of the cheers.

[0072] The cheering unit can apply different cheering algorithms depending on the student's learning style when sending cheers. For example, the cheering unit can send visual cheers to students with a visual learning style. The cheering unit can also send auditory cheers to students with an auditory learning style. The cheering unit can send experiential cheers to students with an experiential learning style. In this way, the cheering unit can send more effective cheers by applying different cheering algorithms according to the student's learning style. Some or all of the above processing in the cheering unit may be performed using AI, for example, or without AI. For example, the cheering unit can input student learning style data into a generating AI, which can then select an appropriate cheering algorithm.

[0073] The Cheer Department can send the most appropriate cheer by referring to past learning history when sending a cheer. For example, the Cheer Department can send the most appropriate cheer based on a student's past learning history. The Cheer Department can also send cheer to improve concentration based on a student's learning history. The Cheer Department can analyze a student's learning history and send cheer to help them relax. In this way, the Cheer Department can send more effective cheers by referring to past learning history and sending the most appropriate cheer. Some or all of the above processes in the Cheer Department may be performed using AI, for example, or not using AI. For example, the Cheer Department can input past learning history data into a generating AI, which can then select the most appropriate cheer.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The learning support system may also include an environmental monitoring unit that monitors the student's learning environment. This unit can collect environmental data, such as room temperature, humidity, and lighting brightness, and provide advice to maintain an optimal learning environment. For example, if the temperature is too high, it can display a message recommending the use of air conditioning. If the lighting is too dim, it can prompt the student to increase the brightness. In this way, the environmental monitoring unit can support students in continuing their studies in a comfortable learning environment.

[0076] The learning support system can also include a progress visualization unit that visualizes students' learning progress. This unit can, for example, display the degree of achievement against the learning plan using graphs or charts, allowing students to grasp their progress at a glance. For instance, it can display daily study time and the number of completed tasks using bar graphs. It can also display the remaining number of tasks and time to reach the goal using pie charts. This allows the progress visualization unit to visually confirm students' learning progress and maintain their motivation.

[0077] The learning support system can also include a content provider that offers learning content tailored to each student's learning style. For example, the content provider can provide learning content that heavily utilizes videos and diagrams for students with a visual learning style. For students with an auditory learning style, it can provide audio explanations and podcast-style learning content. For students with an experiential learning style, it can provide interactive learning content that includes experiments and simulations. This allows the content provider to offer optimal learning content tailored to each student's learning style.

[0078] The learning support system can also include an evaluation unit to assess students' learning outcomes. For example, the evaluation unit can conduct regular mock exams and evaluate students' academic abilities based on the results. The evaluation unit can analyze the mock exam results in detail to identify strengths and weaknesses. Furthermore, the evaluation unit can assess students' progress toward their learning plans and provide feedback. This allows the evaluation unit to help students objectively understand their own learning outcomes and incorporate them into their future learning plans.

[0079] Learning support systems can also incorporate gamification features to further enhance students' motivation to learn. For example, gamification features could provide a system where students earn points for completing learning tasks. By accumulating points, they can earn badges and titles. Furthermore, a system that levels up students based on their learning progress can be implemented. In this way, gamification features allow students to learn while having fun, thereby increasing their motivation to learn.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The data collection unit collects student information. For example, the data collection unit can collect information such as the student's desired school, target score, study habits, and personality. The data collection unit can also collect learning history data from wearable devices and learning apps. For example, the data collection unit can collect data such as heart rate and steps from wearable devices, and data on study time and learning content from learning apps. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected learning history data and analyze the gap with the learning plan. The analysis unit uses techniques such as data mining and statistical analysis to analyze the collected data in detail. Step 3: The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, use a generation AI to generate an optimal learning plan for each student. The generation AI generates the learning plan using natural language processing AI, machine learning AI, etc. Step 4: The modification unit modifies the learning plan generated by the generation unit. The modification unit can, for example, modify the learning plan as appropriate based on the analysis results. The modification unit uses methods such as feedback-based modification or data analysis-based modification. Step 5: The lock function locks the smartphone during study time. For example, the lock function can lock the smartphone during study time to maintain concentration. The lock function allows you to set the lock time, unlock conditions, etc. Step 6: The coaching department uses generative AI to coach learning through voice and pop-ups. The coaching department can, for example, use generative AI to provide voice coaching and pop-up messages. The generative AI uses natural language processing AI, machine learning AI, etc., to provide effective learning coaching.

[0082] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that utilizes generative AI to collect information such as each student's desired school, target score, study habits, and personality, and generates a personalized learning plan. The learning support system collects learning history data from wearable devices and learning apps, and the generative AI analyzes this data. Based on the analysis results, it generates a learning plan, analyzes the gap between the learning plan and the actual learning, and makes adjustments as needed. During study time, the smartphone is locked, and the generative AI maintains concentration by coaching the student through voice and pop-ups. In addition, the camera is used to record daily learning records and the results of mock exams and regular tests, and effective guidance is provided by analyzing the progress of performance and learning trends, which are saved as memories. Furthermore, it recommends music to enhance concentration in conjunction with music services. When motivation declines, the generative AI uses past learning history, performance, and music to send encouragement and improve motivation toward achieving goals. As a result, students can receive consistent learning management and efficient support, record the process in an enjoyable way to preserve it as memories, and receive encouragement to maintain their motivation. This allows the learning support system to collect and analyze student information, generate and modify learning plans, and maintain concentration during study time.

[0083] The learning support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, a locking unit, and a coaching unit. The collection unit collects student information. For example, the collection unit can collect information such as the student's desired school, target score, study habits, and personality. The collection unit can also collect learning history data from wearable devices and learning apps. For example, the collection unit can collect data such as heart rate and steps from wearable devices, and data on study time and study content from learning apps. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected learning history data and analyze the gap with the learning plan. The analysis unit analyzes the collected data in detail using techniques such as data mining and statistical analysis. The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. For example, the generation unit can generate an optimal learning plan for each student using a generation AI. The generation AI generates the learning plan using natural language processing AI, machine learning AI, etc. The modification unit modifies the learning plan generated by the generation unit. The modification unit can, for example, appropriately modify the learning plan based on analysis results. The modification unit uses methods such as feedback-based modification and data analysis-based modification. The lock unit locks the smartphone during learning time. The lock unit can, for example, lock the smartphone during learning time to maintain concentration. The lock unit can set the lock time and unlock conditions. The coaching unit uses the generation AI to coach learning through voice and pop-ups. The coaching unit can, for example, use the generation AI to provide voice coaching and pop-up messages. The generation AI uses natural language processing AI, machine learning AI, etc., to provide effective learning coaching. As a result, the learning support system according to this embodiment can collect and analyze student information, generate and modify learning plans, and maintain concentration during learning time.

[0084] The data collection department collects student information. For example, it can collect information such as students' desired schools, target scores, study habits, and personality. Specifically, it stores the desired schools and target scores entered by students in a database, and collects information on study habits and personality through questionnaires and self-assessment sheets. The data collection department can also collect learning history data from wearable devices and learning apps. For example, it can collect data such as heart rate and steps from wearable devices, and data on study time and content from learning apps. Wearable devices can provide data such as sleep patterns and stress levels in addition to heart rate and steps. This allows for an understanding of students' health status and lifestyle rhythms, which can then be reflected in learning plans. From learning apps, detailed data such as specific learning content, progress, and correct answer rates can be collected. This allows the data collection department to comprehensively understand students' learning situations and collect foundational data to provide support tailored to individual needs. Furthermore, the data collection department can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0085] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze collected learning history data and analyze the gap between that data and the learning plan. Specifically, it uses techniques such as data mining and statistical analysis to analyze the collected data in detail. For example, it analyzes learning history data in a time series to evaluate learning progress and learning effectiveness. It also analyzes data related to learning habits and personality to understand students' learning styles and motivational tendencies. Furthermore, the analysis unit uses AI to process data in real time and understand the surrounding circumstances. For example, the AI ​​analyzes learning history data to identify the level of understanding and areas of difficulty in specific subjects or topics. This allows the analysis unit to understand the learning situation of each student in detail and provide basic data to provide support tailored to individual needs. In addition, the analysis unit can utilize past data and statistical information to conduct long-term learning planning and trend analysis. For example, based on past learning data, it can predict fluctuations in learning effectiveness at specific times and conditions and formulate future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0086] The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, use a generation AI to generate an optimal learning plan for each individual student. The generation AI uses natural language processing AI and machine learning AI to generate the learning plan. Specifically, the generation AI receives information such as the student's learning history data, target scores, and desired schools as input, and generates an optimal learning plan. For example, based on past learning data, the generation AI suggests learning time and methods for specific subjects or topics. Furthermore, the generation AI can use natural language processing technology to explain the learning plan to the student in easily understandable language. This allows the generation unit to provide an optimal learning plan for each student, maximizing learning effectiveness. In addition, the generation unit can continuously revise the learning plan based on real-time updated data, adapting to the latest situation. For example, it can appropriately revise the learning plan and suggest the optimal learning method according to the student's learning progress and understanding. The generation unit can also generate a more accurate learning plan by considering regional characteristics and past learning history. This allows the generation unit to always provide highly accurate learning plans based on the latest information, enabling it to provide quick and appropriate learning support.

[0087] The modification unit modifies the learning plan generated by the generation unit. The modification unit can, for example, modify the learning plan as appropriate based on analysis results. Specifically, it uses methods such as feedback-based modification and data analysis-based modification. For example, it collects feedback from students and reviews the content and progress of the learning plan. It also evaluates the effectiveness of the learning plan based on data provided by the analysis unit and modifies it as needed. In this way, the modification unit can continuously improve the accuracy and effectiveness of the learning plan and provide optimal learning support to each student. Furthermore, the modification unit can continuously modify the learning plan based on data updated in real time and respond to the latest situation. For example, it can appropriately modify the learning plan according to the student's learning progress and level of understanding and propose the optimal learning method. In addition, the modification unit can generate more accurate learning plans by considering regional characteristics and past learning history. In this way, the modification unit can always provide highly accurate learning plans based on the latest information and provide quick and appropriate learning support.

[0088] The lock function locks the smartphone during study time. For example, the lock function can help maintain concentration by locking the smartphone during study time. Specifically, it allows users to set the lock duration and unlock conditions. For instance, a set study period can be established, during which specific functions of the smartphone are locked. Unlock conditions can also be set, such as achieving a specific learning goal or after a certain break. This allows the lock function to provide an environment conducive to focused learning, maximizing learning effectiveness. Furthermore, the lock function can monitor smartphone usage during study time and adjust lock settings as needed. For example, if the smartphone is used frequently during study time, the lock duration can be extended. The lock function can also flexibly change lock settings according to learning progress and comprehension. This allows the lock function to provide an optimal learning environment for each student, maximizing learning effectiveness.

[0089] The coaching department uses generative AI to coach students through voice and pop-up messages. For example, the coaching department can use generative AI to provide voice coaching and pop-up messages. Specifically, the generative AI provides voice coaching and pop-up messages at the appropriate time according to the student's learning situation and progress. For example, if a student is falling behind in their learning progress, the generative AI will send an encouraging voice message. It can also provide explanations and hints for specific problems in pop-up messages. This allows the coaching department to help students maintain their motivation and learn effectively. Furthermore, the coaching department can collect student feedback and continuously improve the accuracy and effectiveness of the coaching content. For example, it can analyze student responses to voice coaching and pop-up messages and revise the coaching content. The coaching department can also reliably transmit information using multiple communication methods. For example, it can use not only voice coaching but also email, SMS, and in-app notifications to ensure that important information is delivered reliably. This allows the coaching department to provide students with learning support quickly and reliably, maximizing learning effectiveness.

[0090] The storage unit can use a camera to record daily learning activities, mock exam and regular test results, and analyze progress in performance and learning trends. For example, the storage unit can use a camera to photograph students' learning activities and save that data. The storage unit can save mock exam and regular test results as digital data and display progress in performance using graphs and charts. To analyze learning trends, the storage unit can analyze the collected data and identify frequently appearing questions and learning patterns. As a result, the storage unit can save and analyze learning records and progress in performance, enabling effective instruction. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input image data captured by a camera into a generating AI and have the generating AI generate learning records from the image data.

[0091] The recommendation unit can recommend music to enhance concentration in conjunction with music services. For example, the recommendation unit can analyze music data provided by music services and select music to improve students' concentration. The recommendation unit can recommend music that is considered to have the effect of improving concentration, such as classical music or ambient sounds. The recommendation unit can create music playlists and provide them to students. In this way, the recommendation unit can improve students' concentration by recommending music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input music data provided by music services into a generating AI, and the generating AI can analyze the music data and select music that enhances concentration.

[0092] The Cheer Club can send encouragement using a generating AI that utilizes past learning history, grades, and music. For example, the Cheer Club can use the generating AI to analyze a student's past learning history and grades and generate encouraging messages. The Cheer Club can use the generating AI to consider a student's musical preferences and select music that will boost their motivation, sending it as encouragement. The Cheer Club can use the generating AI to provide specific advice for achieving goals based on a student's learning history and grades. In this way, the Cheer Club can improve students' motivation by sending encouragement. Some or all of the above processes in the Cheer Club may be performed using AI, or not. For example, the Cheer Club can input a student's learning history and grade data into a generating AI, which can then generate encouraging messages and music.

[0093] The data collection unit can collect learning history data from wearable devices and learning apps. For example, the data collection unit can collect data such as heart rate and steps from wearable devices, and data on learning time and learning content from learning apps. The data collection unit can integrate this data to gain a detailed understanding of the student's learning history. This allows the data collection unit to gain a more detailed understanding of the learning history by collecting data from wearable devices and learning apps. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices and learning apps into a generating AI, which can then analyze the data to understand the learning history.

[0094] The analysis unit can analyze the collected learning history data and identify gaps with the learning plan. For example, the analysis unit can analyze the collected data using techniques such as data mining and statistical analysis to identify gaps with the learning plan. The analysis unit can clarify the specific content of the gaps and use this information to revise the learning plan. This allows the analysis unit to make appropriate corrections by analyzing the learning history data and identifying gaps with the learning plan. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and identify gaps.

[0095] The modification unit can appropriately modify the learning plan based on the analysis results. For example, the modification unit reviews the content of the learning plan based on the analysis results and makes modifications as necessary. The modification unit can use methods such as modifications based on feedback or modifications based on data analysis. As a result, the modification unit can enable more effective learning by modifying the learning plan based on the analysis results. Some or all of the above processing in the modification unit may be performed using AI, for example, or without using AI. For example, the modification unit can input the analysis results into a generating AI, and the generating AI can modify the learning plan.

[0096] The coaching department can provide learning coaching through voice and pop-ups using generative AI. For example, the coaching department can use generative AI to provide voice coaching and pop-up messages to students. The generative AI uses natural language processing AI, machine learning AI, etc., to provide effective learning coaching. The coaching department can have the generative AI analyze the student's learning situation in real time and provide coaching at the appropriate time. For example, the coaching department can have the generative AI monitor the student's learning progress and provide encouraging messages and advice as needed. This enables the coaching department to provide effective learning coaching by using generative AI. Some or all of the above processes in the coaching department may be performed using AI, for example, or not using AI. For example, the coaching department can input student learning data into the generative AI, and the generative AI can generate voice coaching and pop-up messages.

[0097] The data collection unit can estimate a student's emotions and adjust the timing of data collection based on the estimated emotions. For example, if a student is stressed, the data collection unit can collect data during a relaxed period. If a student is focused, the data collection unit can collect data at that time. If a student is tired, the data collection unit can collect data after a break. This allows the data collection unit to collect more appropriate data by adjusting the collection timing according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input student emotion data into a generative AI, which can then estimate the emotions and adjust the collection timing.

[0098] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from learning apps that students have used in the past. The data collection unit can also prioritize collecting data from wearable devices that students have used in the past. The data collection unit can select the most effective data collection method from the student's past learning history. Thus, the data collection unit can select the optimal data collection method by analyzing the past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's past learning history data into a generating AI, which can then select the optimal data collection method.

[0099] The data collection unit can filter learning history data based on the student's current learning status and areas of interest. For example, the data collection unit can prioritize the collection of data related to the subject the student is currently studying. The data collection unit can also filter and collect relevant data based on the student's areas of interest. The data collection unit can collect only the necessary data according to the student's learning status. In this way, the data collection unit can collect only the necessary data by filtering the data based on the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the student's learning status and areas of interest into a generating AI, which can then perform the filtering.

[0100] The data collection unit can estimate students' emotions and prioritize the data to be collected based on the estimated emotions. For example, if a student is stressed, the data collection unit can prioritize the collection of data related to relaxation. If a student is focused, the data collection unit can prioritize the collection of data related to learning. If a student is tired, the data collection unit can prioritize the collection of data related to rest. This allows the data collection unit to collect data more effectively by prioritizing data according to students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input student emotion data into a generative AI, which can estimate emotions and determine the data priority.

[0101] The data collection unit can prioritize the collection of highly relevant data based on the student's geographical location when collecting learning history data. For example, if the student is at school, the data collection unit can prioritize the collection of learning data at school. If the student is at home, the data collection unit can prioritize the collection of learning data at home. If the student is at the library, the data collection unit can prioritize the collection of learning data at the library. This allows the data collection unit to collect more appropriate data by collecting highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into a generating AI, which can then select highly relevant data.

[0102] The data collection unit can analyze students' social media activity and collect relevant data when collecting learning history data. For example, the data collection unit can collect learning content that students have shared on social media. The data collection unit can also collect information on educational accounts that students follow on social media. The data collection unit can collect information on learning groups that students participate in on social media. This allows the data collection unit to collect relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input students' social media activity data into a generating AI, which can then select relevant data.

[0103] The analysis unit can estimate the student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the student is relaxed, the analysis unit can provide detailed analysis results. If the student is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. If the student is excited, the analysis unit can provide visually stimulating analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the student'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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input student emotion data into a generative AI, which can then estimate the emotions and adjust the presentation of the analysis.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning history data during the analysis. For example, the analysis unit can perform a detailed analysis on important learning history data. The analysis unit can also perform a simplified analysis on less important learning history data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the learning history data. This allows the analysis unit to perform more effective analysis by adjusting the level of detail of the analysis based on the importance of the learning history data. 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 learning history data into a generating AI, which can evaluate the importance of the data and adjust the level of detail of the analysis.

[0105] The analysis unit can apply different analysis algorithms depending on the category of the learning history data during analysis. For example, the analysis unit can apply a mathematics-specific analysis algorithm to mathematics learning history data. The analysis unit can also apply an English-specific analysis algorithm to English learning history data. The analysis unit can apply a science-specific analysis algorithm to science learning history data. This allows the analysis unit to perform more appropriate analysis by applying different analysis algorithms depending on the category of the learning history data. 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 learning history data into a generating AI, which can then select an appropriate analysis algorithm according to the data category.

[0106] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the student is relaxed, the analysis unit can perform a detailed analysis. If the student is in a hurry, the analysis unit can perform a concise analysis. If the student is excited, the analysis unit can perform a visually stimulating analysis. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the student's emotions. 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 not using AI. For example, the analysis unit can input student emotion data into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0107] The analysis unit can determine the priority of analysis based on the submission date of the learning history data during analysis. For example, the analysis unit can prioritize the analysis of recently submitted learning history data. The analysis unit can also postpone the analysis of older learning history data. The analysis unit can adjust the priority of analysis in stages based on the submission date. This allows the analysis unit to perform more effective analysis by determining the priority of analysis based on the submission date of the learning history data. 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 submission date of the learning history data into a generating AI, and the generating AI can determine the priority.

[0108] The analysis unit can adjust the order of analysis based on the relevance of the learning history data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant learning history data. The analysis unit can also postpone the analysis of less relevant learning history data. The analysis unit can adjust the order of analysis step by step based on the relevance of the learning history data. This allows the analysis unit to perform more effective analysis by adjusting the order of analysis based on the relevance of the learning history data. 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 relevance of the learning history data into a generating AI, and the generating AI can determine the order of analysis.

[0109] The generation unit can estimate the student's emotions and adjust the method of generating the learning plan based on the estimated emotions. For example, if the student is relaxed, the generation unit can generate a detailed learning plan. If the student is in a hurry, the generation unit can generate a concise learning plan. If the student is excited, the generation unit can generate a visually stimulating learning plan. In this way, the generation unit can provide a more appropriate learning plan by adjusting the method of generating the learning plan according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input student emotion data into a generation AI, which can estimate emotions and adjust the method of generating the learning plan.

[0110] The generation unit can adjust the level of detail of the learning plan based on the importance of the analysis results when generating the learning plan. For example, the generation unit can generate a detailed learning plan based on important analysis results. The generation unit can also generate a simplified learning plan based on less important analysis results. The generation unit can adjust the level of detail of the learning plan in stages according to the importance of the analysis results. This allows the generation unit to create more effective learning plans by adjusting the level of detail of the plan based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analysis results into a generation AI, which can evaluate the importance of the data and adjust the level of detail of the plan.

[0111] The generation unit can apply different generation algorithms depending on the student's learning style when generating learning plans. For example, the generation unit can generate a visual learning plan for students with a visual learning style. The generation unit can also generate an auditory learning plan for students with an auditory learning style. The generation unit can generate an experiential learning plan for students with an experiential learning style. In this way, the generation unit can provide a more appropriate learning plan by applying different generation algorithms depending on the student's learning style. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning style data into a generation AI, which can then select an appropriate generation algorithm.

[0112] The generation unit can estimate a student's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if a student is relaxed, the generation unit can generate a detailed learning plan. If a student is in a hurry, the generation unit can generate a concise learning plan. If a student is excited, the generation unit can generate a visually stimulating learning plan. This allows the generation unit to provide a more appropriate learning plan by adjusting its length according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input student emotion data into a generation AI, which can estimate the emotions and adjust the length of the learning plan.

[0113] The generation unit can determine the priority of learning plans based on the submission timing of analysis results when generating learning plans. For example, the generation unit can prioritize the generation of learning plans based on recently submitted analysis results. The generation unit can also postpone the generation of older analysis results. The generation unit can adjust the priority of learning plans in stages based on the submission timing. This allows the generation unit to create more effective learning plans by determining the priority of plans based on the submission timing of analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing of analysis results into a generation AI, which can then determine the priority.

[0114] The generation unit can adjust the order of learning plans based on the relevance of the analysis results when generating learning plans. For example, the generation unit can prioritize generating learning plans based on highly relevant analysis results. The generation unit can also postpone less relevant analysis results. The generation unit can adjust the order of learning plans step by step based on the relevance of the analysis results. This allows the generation unit to create more effective learning plans by adjusting the order of plans based on the relevance of the analysis results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the analysis results into a generation AI, which can then determine the order of the plans.

[0115] The editing unit can estimate the student's emotions and adjust how the learning plan is modified based on the estimated emotions. For example, if the student is relaxed, the editing unit can make detailed modifications. If the student is in a hurry, the editing unit can make concise modifications. If the student is excited, the editing unit can make visually stimulating modifications. This allows the editing unit to make more appropriate modifications to the learning plan by adjusting the modification method according to the student's emotions. 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 editing unit may be performed using AI or not using AI. For example, the editing unit can input student emotion data into the generative AI, which can estimate the emotions and adjust the modification method.

[0116] The modification unit can adjust the level of detail of the modifications based on the importance of the analysis results when modifying the learning plan. For example, the modification unit can make detailed modifications based on important analysis results. The modification unit can also make simplified modifications based on less important analysis results. The modification unit can adjust the level of detail of the modifications in stages according to the importance of the analysis results. This allows the modification unit to make more effective modifications to the learning plan by adjusting the level of detail of the modifications based on the importance of the analysis results. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the importance of the analysis results into a generating AI, and the generating AI can adjust the level of detail of the modifications.

[0117] The revision unit can apply different revision algorithms depending on the student's learning style when revising the learning plan. For example, the revision unit can perform visual revisions for students with a visual learning style. The revision unit can also perform auditory revisions for students with an auditory learning style. The revision unit can perform experiential revisions for students with an experiential learning style. This allows the revision unit to apply different revision algorithms according to the student's learning style, enabling more appropriate revisions to the learning plan. Some or all of the above processing in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input student learning style data into a generating AI, which can then select an appropriate revision algorithm.

[0118] The editing unit can estimate the student's emotions and adjust the frequency of revisions to the learning plan based on the estimated emotions. For example, if the student is relaxed, the editing unit can make revisions more frequently. If the student is in a hurry, the editing unit can reduce the frequency of revisions. If the student is excited, the editing unit can make revisions at a moderate frequency. This allows the editing unit to make more appropriate revisions to the learning plan by adjusting the frequency of revisions according to the student's emotions. 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 editing unit may be performed using AI, or not using AI. For example, the editing unit can input student emotion data into the generative AI, which can estimate the emotions and adjust the frequency of revisions.

[0119] The revision unit can determine the priority of revisions based on the submission timing of analysis results when revising the learning plan. For example, the revision unit can prioritize revisions based on recently submitted analysis results. The revision unit can also postpone revisions based on older submission dates. The revision unit can adjust the priority of revisions in stages based on the submission dates. This allows the revision unit to revise the learning plan more effectively by determining the priority of revisions based on the submission dates of analysis results. Some or all of the above processes in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input the submission dates of analysis results into a generating AI, which can then determine the priority.

[0120] The lock unit can estimate the student's emotions and adjust the smartphone lock timing based on the estimated emotions. For example, if the student is concentrating, the lock unit can lock the smartphone at the start of learning. If the student is relaxed, the lock unit can lock the smartphone during learning. If the student is tired, the lock unit can lock the smartphone after a break. In this way, the lock unit can provide a more appropriate learning environment by adjusting the lock timing according to the student's emotions. 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 lock unit may be performed using AI, for example, or not using AI. For example, the lock unit can input student emotion data into the generative AI, which can estimate the emotions and adjust the lock timing.

[0121] The lock function can adjust the lock strength according to the student's learning situation when the smartphone is locked. For example, the lock function can apply a strong lock when the student is concentrating. It can also apply a moderate lock when the student is relaxed. It can also apply a gentle lock when the student is tired. In this way, the lock function can provide a more appropriate learning environment by adjusting the lock strength according to the learning situation. Some or all of the above processing in the lock function may be performed using AI, for example, or without AI. For example, the lock function can input student learning situation data into a generating AI, which can then adjust the lock strength.

[0122] The locking mechanism can apply different locking methods to students depending on their learning style when the smartphone is locked. For example, the locking mechanism can apply a visual locking method to students with a visual learning style, an auditory locking method to students with an auditory learning style, and an experiential locking method to students with an experiential learning style. By applying different locking methods according to the learning style, the locking mechanism can provide a more appropriate learning environment. Some or all of the above processing in the locking mechanism may be performed using AI, for example, or without AI. For example, the locking mechanism can input student learning style data into a generating AI, which can then select an appropriate locking method.

[0123] The locking unit can estimate the student's emotions and adjust the timing of unlocking based on the estimated emotions. For example, if the student is concentrating, the locking unit can unlock at the end of the learning session. If the student is relaxed, the locking unit can unlock at an appropriate time during the learning session. If the student is tired, the locking unit can unlock after a break. In this way, the locking unit can provide a more appropriate learning environment by adjusting the timing of unlocking according to the student's emotions. 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 locking unit may be performed using AI, for example, or not using AI. For example, the locking unit can input student emotion data into the generative AI, which can estimate the emotions and adjust the timing of unlocking.

[0124] The locking unit can select the optimal locking method by referring to the student's device usage history when locking the smartphone. For example, the locking unit can prioritize locking methods that the student has used in the past. The locking unit can also select the most effective locking method from the student's device usage history. The locking unit can adjust the locking method in stages based on the student's device usage history. In this way, the locking unit can provide a more appropriate learning environment by selecting the optimal locking method based on the device usage history. Some or all of the above processing in the locking unit may be performed using AI, for example, or not using AI. For example, the locking unit can input the student's device usage history data into a generating AI, and the generating AI can select the optimal locking method.

[0125] The coaching unit can estimate a student's emotions and adjust the coaching approach based on the estimated emotions. For example, if a student is relaxed, the coaching unit can provide detailed coaching. If a student is in a hurry, the coaching unit can provide concise coaching. If a student is excited, the coaching unit can provide visually stimulating coaching. This allows the coaching unit to provide more effective learning coaching by adjusting the coaching approach according to the student'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 processing in the coaching unit may be performed using AI or not. For example, the coaching unit can input student emotion data into a generative AI, which can estimate the emotions and adjust the coaching approach.

[0126] The coaching unit can adjust the level of detail in coaching based on the importance of the learning plan. For example, the coaching unit can provide detailed coaching for important learning plans, and simplified coaching for less important learning plans. The coaching unit can adjust the level of detail in coaching in stages according to the importance of the learning plan. This allows the coaching unit to provide more effective learning coaching by adjusting the level of detail based on the importance of the learning plan. Some or all of the above processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input learning plan importance data into a generating AI, which can then adjust the level of detail in coaching.

[0127] The coaching unit can apply different coaching algorithms to students according to their learning style during coaching sessions. For example, the coaching unit can provide visual coaching to students with a visual learning style, auditory coaching to students with an auditory learning style, and experiential coaching to students with an experiential learning style. This allows the coaching unit to provide more effective learning coaching by applying different coaching algorithms according to students' learning styles. Some or all of the above-described processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input student learning style data into a generating AI, which can then select an appropriate coaching algorithm.

[0128] The coaching unit can estimate a student's emotions and adjust the length of the coaching session based on the estimated emotions. For example, if a student is relaxed, the coaching unit can provide detailed coaching. If a student is in a hurry, the coaching unit can provide concise coaching. If a student is excited, the coaching unit can provide visually stimulating coaching. This allows the coaching unit to provide more effective learning coaching by adjusting the length of the coaching session according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coaching unit may be performed using AI or not using AI. For example, the coaching unit can input student emotion data into a generative AI, which can estimate the emotions and adjust the length of the coaching session.

[0129] The coaching department can prioritize coaching sessions based on the submission date of the learning plan. For example, the coaching department can prioritize coaching based on the most recently submitted learning plan. The coaching department can also postpone coaching sessions with older submission dates. The coaching department can adjust the coaching priority in stages based on the submission date. This allows the coaching department to provide more effective learning coaching by prioritizing coaching based on the submission date of the learning plan. Some or all of the above processes in the coaching department may be performed using AI, for example, or not. For example, the coaching department can input learning plan submission date data into a generating AI, which can then determine the coaching priority.

[0130] The coaching unit can adjust the order of coaching sessions based on the relevance of the learning plans. For example, the coaching unit can prioritize coaching based on highly relevant learning plans. The coaching unit can also postpone less relevant learning plans. The coaching unit can adjust the order of coaching sessions in stages based on the relevance of the learning plans. This allows the coaching unit to provide more effective learning coaching by adjusting the order of coaching sessions based on the relevance of the learning plans. Some or all of the above processes in the coaching unit may be performed using AI, for example, or without AI. For example, the coaching unit can input data on the relevance of learning plans into a generating AI, which can then determine the order of coaching sessions.

[0131] The storage unit can estimate a student's emotions and adjust how learning records are saved based on the estimated emotions. For example, if a student is relaxed, the storage unit can save a detailed learning record. If a student is in a hurry, the storage unit can save a concise learning record. If a student is excited, the storage unit can save a visually stimulating learning record. This allows the storage unit to save learning records more appropriately by adjusting the saving method according to the student's emotions. 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 storage unit may be performed using AI or not using AI. For example, the storage unit can input student emotion data into a generative AI, which can estimate the emotions and adjust the saving method.

[0132] The storage unit can adjust the level of detail in saving learning records based on their importance. For example, it can perform detailed saving for important learning records. For less important learning records, it can perform simplified saving. The storage unit can adjust the level of detail in stages according to the importance of the learning records. This allows the storage unit to save learning records more appropriately by adjusting the level of detail based on the importance of the records. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input learning record importance data into a generating AI, which can then adjust the level of detail in saving.

[0133] The storage unit can apply different storage algorithms depending on the student's learning style when saving learning records. For example, the storage unit can apply a visual storage method to students with a visual learning style. For students with an auditory learning style, it can apply an auditory storage method. For students with an experiential learning style, it can apply an experiential storage method. This allows the storage unit to save learning records more appropriately by applying different storage algorithms depending on the student's learning style. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input student learning style data into a generating AI, which can then select an appropriate storage algorithm.

[0134] The storage unit can estimate a student's emotions and determine the priority of records to save based on the estimated emotions. For example, if a student is relaxed, the storage unit can prioritize saving detailed learning records. If a student is in a hurry, the storage unit can prioritize saving concise learning records. If a student is excited, the storage unit can prioritize saving visually stimulating learning records. This allows the storage unit to save more appropriate learning records by prioritizing records according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not using AI. For example, the storage unit can input student emotion data into a generative AI, which can estimate emotions and determine the priority of records.

[0135] The storage unit can select the optimal storage method by referring to the student's device usage history when saving learning records. For example, the storage unit can prioritize applying storage methods previously used by the student. The storage unit can also select the most effective storage method from the student's device usage history. The storage unit can adjust the storage method in stages based on the student's device usage history. This allows the storage unit to select the optimal storage method based on the device usage history, enabling more appropriate storage of learning records. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the student's device usage history data into a generating AI, which can then select the optimal storage method.

[0136] The recommendation unit can estimate a student's emotions and adjust its music recommendation method based on the estimated emotions. For example, if a student is relaxed, the recommendation unit can recommend relaxing music. If a student is concentrating, the recommendation unit can recommend music that enhances concentration. If a student is tired, the recommendation unit can recommend refreshing music. In this way, the recommendation unit can provide more appropriate music by adjusting its music recommendation method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input student emotion data into a generative AI, which can estimate emotions and adjust the music recommendation method.

[0137] The recommendation unit can adjust the level of detail in its music recommendations according to the student's learning situation. For example, if a student is concentrating, the recommendation unit can recommend music that enhances concentration in detail. If a student is relaxed, the recommendation unit can also recommend music that promotes relaxation in a concise manner. If a student is tired, the recommendation unit can recommend music that promotes refreshment in detail. In this way, the recommendation unit can provide more appropriate music by adjusting the level of detail in its recommendations according to the student's learning situation. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input student learning situation data into a generating AI, which can then adjust the level of detail in its recommendations.

[0138] The recommendation unit can apply different recommendation algorithms to students based on their musical preferences when recommending music. For example, if a student prefers classical music, the recommendation unit can recommend classical music. If a student prefers pop music, the recommendation unit can recommend pop music. If a student prefers jazz music, the recommendation unit can recommend jazz music. In this way, the recommendation unit can provide more appropriate music by applying different recommendation algorithms according to the student's musical preferences. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input student musical preference data into a generating AI, which can then select an appropriate recommendation algorithm.

[0139] The recommendation unit can estimate a student's emotions and determine the priority of music recommendations based on those emotions. For example, if a student is relaxed, the recommendation unit can prioritize recommending relaxing music. If a student is concentrating, the recommendation unit can prioritize recommending music that enhances concentration. If a student is tired, the recommendation unit can prioritize recommending music that helps them refresh. In this way, the recommendation unit can provide more appropriate music by prioritizing music according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input student emotion data into a generative AI, which can estimate the emotions and determine the priority of music.

[0140] The recommendation unit can recommend the most suitable music by referring to the student's music history when recommending music. For example, the recommendation unit can recommend the most suitable music based on music the student has listened to in the past. The recommendation unit can also recommend music that enhances concentration based on the student's music history. The recommendation unit can analyze the student's music history and recommend music that promotes relaxation. In this way, the recommendation unit can provide more appropriate music by recommending the most suitable music based on the music history. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the student's music history data into a generating AI, which can then select the most suitable music.

[0141] The cheering unit can estimate a student's emotions and adjust the way it expresses encouragement based on the estimated emotions. For example, if a student is relaxed, the cheering unit can send a detailed cheer. If a student is in a hurry, the cheering unit can send a concise cheer. If a student is excited, the cheering unit can send a visually stimulating cheer. In this way, the cheering unit can send more effective cheers by adjusting the way it expresses encouragement according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cheering unit may be performed using AI, for example, or not using AI. For example, the cheering unit can input student emotion data into a generative AI, which can estimate the emotion and adjust the way it expresses encouragement.

[0142] The cheering unit can adjust the level of detail of the cheers it sends based on the importance of past learning history. For example, the cheering unit can send detailed cheers based on important learning history. It can also send simple cheers based on less important learning history. The cheering unit can adjust the level of detail of the cheers in stages according to the importance of the learning history. This allows the cheering unit to send more effective cheers by adjusting the level of detail of the cheers based on the importance of past learning history. Some or all of the above processing in the cheering unit may be performed using AI, for example, or without AI. For example, the cheering unit can input past learning history data into a generating AI, which can evaluate the importance of the data and adjust the level of detail of the cheers.

[0143] The cheering unit can apply different cheering algorithms depending on the student's learning style when sending cheers. For example, the cheering unit can send visual cheers to students with a visual learning style. The cheering unit can also send auditory cheers to students with an auditory learning style. The cheering unit can send experiential cheers to students with an experiential learning style. In this way, the cheering unit can send more effective cheers by applying different cheering algorithms according to the student's learning style. Some or all of the above processing in the cheering unit may be performed using AI, for example, or without AI. For example, the cheering unit can input student learning style data into a generating AI, which can then select an appropriate cheering algorithm.

[0144] The Cheer Department can estimate a student's emotions and adjust the timing of sending cheers based on the estimated emotions. For example, if a student is relaxed, the Cheer Department can send a cheer at the end of a learning session. If a student is in a hurry, the Cheer Department can send a cheer at an appropriate time during the learning session. If a student is tired, the Cheer Department can send a cheer after a break. In this way, the Cheer Department can send more effective cheers by adjusting the timing of sending cheers according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Cheer Department may be performed using AI, for example, or not using AI. For example, the Cheer Department can input student emotion data into a generative AI, which can estimate the emotion and adjust the timing of sending cheers.

[0145] The Cheer Department can send the most appropriate cheer by referring to past learning history when sending a cheer. For example, the Cheer Department can send the most appropriate cheer based on a student's past learning history. The Cheer Department can also send cheer to improve concentration based on a student's learning history. The Cheer Department can analyze a student's learning history and send cheer to help them relax. In this way, the Cheer Department can send more effective cheers by referring to past learning history and sending the most appropriate cheer. Some or all of the above processes in the Cheer Department may be performed using AI, for example, or not using AI. For example, the Cheer Department can input past learning history data into a generating AI, which can then select the most appropriate cheer.

[0146] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0147] The learning support system may also include an environmental monitoring unit that monitors the student's learning environment. This unit can collect environmental data, such as room temperature, humidity, and lighting brightness, and provide advice to maintain an optimal learning environment. For example, if the temperature is too high, it can display a message recommending the use of air conditioning. If the lighting is too dim, it can prompt the student to increase the brightness. In this way, the environmental monitoring unit can support students in continuing their studies in a comfortable learning environment.

[0148] The learning support system can also include a progress visualization unit that visualizes students' learning progress. This unit can, for example, display the degree of achievement against the learning plan using graphs or charts, allowing students to grasp their progress at a glance. For instance, it can display daily study time and the number of completed tasks using bar graphs. It can also display the remaining number of tasks and time to reach the goal using pie charts. This allows the progress visualization unit to visually confirm students' learning progress and maintain their motivation.

[0149] The learning support system can also include a content provider that offers learning content tailored to each student's learning style. For example, the content provider can provide learning content that heavily utilizes videos and diagrams for students with a visual learning style. For students with an auditory learning style, it can provide audio explanations and podcast-style learning content. For students with an experiential learning style, it can provide interactive learning content that includes experiments and simulations. This allows the content provider to offer optimal learning content tailored to each student's learning style.

[0150] The learning support system can also include an evaluation unit to assess students' learning outcomes. For example, the evaluation unit can conduct regular mock exams and evaluate students' academic abilities based on the results. The evaluation unit can analyze the mock exam results in detail to identify strengths and weaknesses. Furthermore, the evaluation unit can assess students' progress toward their learning plans and provide feedback. This allows the evaluation unit to help students objectively understand their own learning outcomes and incorporate them into their future learning plans.

[0151] Learning support systems can also incorporate gamification features to further enhance students' motivation to learn. For example, gamification features could provide a system where students earn points for completing learning tasks. By accumulating points, they can earn badges and titles. Furthermore, a system that levels up students based on their learning progress can be implemented. In this way, gamification features allow students to learn while having fun, thereby increasing their motivation to learn.

[0152] The learning support system may also include an emotion adjustment unit that estimates the student's emotions and adjusts the learning plan based on those emotions. For example, if a student is feeling stressed, the emotion adjustment unit can ease the learning plan and increase the amount of time for relaxation. If the student is focused, it can also strengthen the learning plan and set more tasks. If the student is tired, it can change the learning plan to prioritize rest. In this way, the emotion adjustment unit can flexibly adjust the learning plan according to the student's emotions.

[0153] The learning support system may further include an environment adjustment unit that estimates the student's emotions and adjusts the learning environment based on those emotions. For example, if the student is relaxed, the environment adjustment unit can play relaxing music. If the student is concentrating, it can play ambient sounds to enhance concentration. If the student is tired, it can play refreshing music. In this way, the environment adjustment unit can optimize the learning environment according to the student's emotions.

[0154] The learning support system may also include a content adjustment unit that estimates the student's emotions and adjusts the learning content based on those emotions. For example, if the student is relaxed, the content adjustment unit can provide more challenging tasks. If the student is focused, it can provide more tasks. If the student is tired, it can change the learning content to easier tasks or focus on review. In this way, the content adjustment unit can flexibly adjust the learning content according to the student's emotions.

[0155] The learning support system may further include a timing adjustment unit that estimates the student's emotions and adjusts the timing of learning based on those emotions. For example, the timing adjustment unit can adjust the timing of starting learning if the student is relaxed. It can also adjust the timing of continuing learning if the student is focused. If the student is tired, it can adjust the timing of taking a break. In this way, the timing adjustment unit can optimize the timing of learning according to the student's emotions.

[0156] The learning support system may also include a feedback adjustment unit that estimates the student's emotions and adjusts the learning feedback based on those emotions. For example, the feedback adjustment unit can provide detailed feedback when the student is relaxed, offer specific advice when the student is focused, or provide encouraging messages when the student is tired. This allows the feedback adjustment unit to flexibly adjust feedback according to the student's emotions.

[0157] The following briefly describes the processing flow for example form 2.

[0158] Step 1: The data collection unit collects student information. For example, the data collection unit can collect information such as the student's desired school, target score, study habits, and personality. The data collection unit can also collect learning history data from wearable devices and learning apps. For example, the data collection unit can collect data such as heart rate and steps from wearable devices, and data on study time and learning content from learning apps. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected learning history data and analyze the gap with the learning plan. The analysis unit uses techniques such as data mining and statistical analysis to analyze the collected data in detail. Step 3: The generation unit generates a learning plan based on the analysis results obtained by the analysis unit. The generation unit can, for example, use a generation AI to generate an optimal learning plan for each student. The generation AI generates the learning plan using natural language processing AI, machine learning AI, etc. Step 4: The modification unit modifies the learning plan generated by the generation unit. The modification unit can, for example, modify the learning plan as appropriate based on the analysis results. The modification unit uses methods such as feedback-based modification or data analysis-based modification. Step 5: The lock function locks the smartphone during study time. For example, the lock function can lock the smartphone during study time to maintain concentration. The lock function allows you to set the lock time, unlock conditions, etc. Step 6: The coaching department uses generative AI to coach learning through voice and pop-ups. The coaching department can, for example, use generative AI to provide voice coaching and pop-up messages. The generative AI uses natural language processing AI, machine learning AI, etc., to provide effective learning coaching.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, lock unit, coaching unit, storage unit, recommendation unit, encouragement unit, and emotion estimation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects student information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and analyzes the gap with the learning plan. The generation unit generates a learning plan using the generation AI using the specific processing unit 290 of the data processing unit 12, for example. The modification unit modifies the learning plan as appropriate using the specific processing unit 290 of the data processing unit 12, for example. The lock unit locks the smartphone during learning time using the control unit 46A of the smart device 14, for example. The coaching unit coaches learning using voice and pop-ups using the generation AI using the control unit 46A of the smart device 14, for example. The storage unit, for example, uses the camera 42 of the smart device 14 to capture records of learning, and the specific processing unit 290 of the data processing device 12 analyzes the progress of grades and learning trends. The recommendation unit, for example, uses the specific processing unit 290 of the data processing device 12 to link with music services and recommend music that enhances concentration. The encouragement unit, for example, uses the specific processing unit 290 of the data processing device 12 to send encouragement using generated AI. The emotion estimation unit, for example, uses the emotion engine of the specific processing unit 290 of the data processing device 12 to estimate the student's emotions and adjust the collection timing. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0163] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, lock unit, coaching unit, storage unit, recommendation unit, encouragement unit, and emotion estimation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects student information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and analyzes the gap with the learning plan. The generation unit generates a learning plan using the generation AI using the specific processing unit 290 of the data processing unit 12. The modification unit modifies the learning plan as appropriate using the specific processing unit 290 of the data processing unit 12. The lock unit locks the smartphone during learning time using the control unit 46A of the smart glasses 214. The coaching unit coaches learning using voice and pop-ups with the generation AI using the control unit 46A of the smart glasses 214. The storage unit, for example, uses the camera 42 of the smart glasses 214 to capture records of learning, and the specific processing unit 290 of the data processing device 12 analyzes the progress of performance and learning trends. The recommendation unit, for example, uses the specific processing unit 290 of the data processing device 12 to link with music services and recommend music that enhances concentration. The encouragement unit, for example, uses the specific processing unit 290 of the data processing device 12 to send encouragement using generated AI. The emotion estimation unit, for example, uses the emotion engine of the specific processing unit 290 of the data processing device 12 to estimate the student's emotions and adjust the collection timing. The correspondence between each unit and the device and control unit is not limited to the examples described above, and various changes are possible.

[0179] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, lock unit, coaching unit, storage unit, recommendation unit, encouragement unit, and emotion estimation unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects student information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and analyzes the gap with the learning plan. The generation unit generates a learning plan using the generation AI using the specific processing unit 290 of the data processing unit 12. The modification unit modifies the learning plan as appropriate using the specific processing unit 290 of the data processing unit 12. The lock unit locks the smartphone during learning time using the control unit 46A of the headset terminal 314. The coaching unit coaches learning using voice and pop-ups using the generation AI using the control unit 46A of the headset terminal 314. The storage unit, for example, uses the camera 42 of the headset terminal 314 to capture records of learning, and the specific processing unit 290 of the data processing device 12 analyzes the progress of grades and learning trends. The recommendation unit, for example, uses the specific processing unit 290 of the data processing device 12 to link with music services and recommend music that enhances concentration. The encouragement unit, for example, uses the specific processing unit 290 of the data processing device 12 to send encouragement using generated AI. The emotion estimation unit, for example, uses the emotion engine of the specific processing unit 290 of the data processing device 12 to estimate the student's emotions and adjust the collection timing. The correspondence between each unit and the device and control unit is not limited to the examples described above, and various changes are possible.

[0195] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, lock unit, coaching unit, storage unit, recommendation unit, encouragement unit, and emotion estimation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects student information using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and analyzes the gap with the learning plan. The generation unit generates a learning plan using the generation AI using the specific processing unit 290 of the data processing unit 12. The modification unit modifies the learning plan as appropriate using the specific processing unit 290 of the data processing unit 12. The lock unit locks the smartphone during learning time using the control unit 46A of the robot 414. The coaching unit coaches learning using voice and pop-ups using the generation AI using the control unit 46A of the robot 414. The storage unit, for example, uses the camera 42 of the robot 414 to capture records of learning, and the specific processing unit 290 of the data processing device 12 analyzes the progress of performance and learning trends. The recommendation unit, for example, uses the specific processing unit 290 of the data processing device 12 to link with a music service and recommend music that enhances concentration. The encouragement unit, for example, uses the specific processing unit 290 of the data processing device 12 to send encouragement using generated AI. The emotion estimation unit, for example, uses the emotion engine of the specific processing unit 290 of the data processing device 12 to estimate the student's emotions and adjust the collection timing. The correspondence between each unit and the device and control unit is not limited to the examples described above, and various changes are possible.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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."

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] (Note 1) The student information collection department, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates a learning plan based on the analysis results obtained by the analysis unit, A modification unit that modifies the learning plan generated by the generation unit, A lock function that locks the smartphone during study time, It includes a coaching unit in which a generating AI coaches learning through voice and pop-ups. A system characterized by the following features. (Note 2) It features a storage unit that uses a camera to record daily learning activities, mock exam and regular test results, and analyzes academic progress and learning trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a recommendation section that works in conjunction with music services to recommend music that enhances concentration. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system includes a cheering section where a generating AI uses past learning history, grades, and music to send encouraging messages. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect learning history data from wearable devices and learning apps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze the collected learning history data and identify the gap between it and the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned modification section is, The learning plan will be modified as needed based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned coaching department, Use generative AI to coach learning through voice and pop-ups. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of learning history data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting learning history data, filtering is performed based on the student's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting learning history data, the system prioritizes collecting data that is highly relevant based on the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting learning history data, analyze students' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the students' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the training history data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the learning history data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the training history data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the learning history data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates students' emotions and adjusts the method of generating learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a learning plan, adjust the level of detail in the plan based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating learning plans, different generation algorithms are applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is The system estimates students' emotions and adjusts the length of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating a learning plan, the plan's priority is determined based on the timing of the submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating a learning plan, adjust the order of the plan based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned modification section is, The system estimates students' emotions and adjusts how learning plans are modified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned modification section is, When revising the learning plan, adjust the level of detail of the revision based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned modification section is, When revising learning plans, different revision algorithms are applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned modification section is, The system estimates students' emotions and adjusts the frequency of revisions to the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned modification section is, When revising the study plan, prioritize revisions based on the submission deadline for analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned locking mechanism is The system estimates students' emotions and adjusts the smartphone lock timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned locking mechanism is When the smartphone is locked, adjust the lock strength according to the student's learning status The system according to appended note 1, characterized in that (Appended note 34) The lock part When the smartphone is locked, apply different locking methods according to the student's learning style The system according to appended note 1, characterized in that (Appended note 35) The lock part Estimate the student's emotion and adjust the timing of unlocking based on the estimated student's emotion The system according to appended note 1, characterized in that (Appended note 36) The lock part When the smartphone is locked, select the optimal locking method by referring to the student's device usage history The system according to appended note 1, characterized in that (Appended note 37) The coaching part Estimate the student's emotion and adjust the expression method of coaching based on the estimated student's emotion The system according to appended note 1, characterized in that (Appended note 38) The coaching part When coaching, adjust the detail level of coaching based on the importance of the learning plan The system according to appended note 1, characterized in that (Appended note 39) The coaching part When coaching, apply different coaching algorithms according to the student's learning style The system according to appended note 1, characterized in that (Appended note 40) The coaching part Estimate the student's emotion and adjust the length of coaching based on the estimated student's emotion The system according to appended note 1, characterized in that (Appended note 41) The aforementioned coaching department, During coaching sessions, the priority of coaching is determined based on when the learning plan is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned coaching department, During coaching sessions, adjust the order of coaching based on the relevance of the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned storage unit is The system estimates students' emotions and adjusts how learning records are saved based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned storage unit is When saving learning records, adjust the level of detail saved based on the importance of the record. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned storage unit is When saving learning records, different saving algorithms are applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned storage unit is Estimate students' emotions and determine the priority of records to save based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned storage unit is When saving learning records, the system selects the optimal saving method by referring to the student's device usage history. The system described in Appendix 1, characterized by the features described herein. (Note 48) The recommendation unit is, The system estimates students' emotions and adjusts music recommendation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The recommendation unit is, When making music recommendations, adjust the level of detail of the recommendations according to the learning situation of the students. The system according to Appendix 1, characterized in that. (Appendix 50) The recommendation unit When making music recommendations, apply different recommendation algorithms according to the music preferences of the students. The system according to Appendix 1, characterized in that. (Appendix 51) The recommendation unit Estimate the emotions of the students and determine the priority of the recommended music based on the estimated emotions of the students. The system according to Appendix 1, characterized in that. (Appendix 52) The recommendation unit When making music recommendations, refer to the music history of the students and recommend the most suitable music. The system according to Appendix 1, characterized in that. (Appendix 53) The email unit Estimate the emotions of the students and adjust the expression method of the email based on the estimated emotions of the students. The system according to Appendix 1, characterized in that. (Appendix 54) The email unit When sending an email, adjust the level of detail of the email based on the importance of the past learning history. The system according to Appendix 1, characterized in that. (Appendix 55) The email unit When sending an email, apply different email algorithms according to the learning style of the students. The system according to Appendix 1, characterized in that. (Appendix 56) The email unit Estimate the emotions of the students and adjust the sending timing of the email based on the estimated emotions of the students. The system according to Appendix 1, characterized in that. (Appendix 57) The email unit When sending encouragement, the system will refer to past learning history to send the most appropriate encouragement. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0231] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The student information collection department, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates a learning plan based on the analysis results obtained by the analysis unit, A modification unit that modifies the learning plan generated by the generation unit, A lock function that locks the smartphone during study time, It includes a coaching unit in which a generating AI coaches learning through voice and pop-ups. A system characterized by the following features.

2. It features a storage unit that uses a camera to record daily learning activities, mock exam and regular test results, and analyzes academic progress and learning trends. The system according to feature 1.

3. It features a recommendation section that works in conjunction with music services to recommend music that enhances concentration. The system according to feature 1.

4. The system includes a cheering section where a generating AI uses past learning history, grades, and music to send encouraging messages. The system according to feature 1.

5. The aforementioned collection unit is Collect learning history data from wearable devices and learning apps. The system according to feature 1.

6. The aforementioned analysis unit, We analyze the collected learning history data and identify the gap between it and the learning plan. The system according to feature 1.

7. The aforementioned modification section is, The learning plan will be modified as needed based on the analysis results. The system according to feature 1.

8. The aforementioned coaching department, Use generative AI to coach learning through voice and pop-ups. The system according to feature 1.

9. The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of learning history data collection based on the estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system according to feature 1.

11. The aforementioned collection unit is When collecting learning history data, filtering is performed based on the student's current learning status and areas of interest. The system according to feature 1.

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