System

Abstract

An object of a system according to an embodiment is to provide an optimal learning plan according to learning needs and to adjust learning contents by monitoring progress.SOLUTION: A system includes an analysis unit, a proposal unit, a provision unit, a monitoring unit, and an adjustment unit. The analysis unit analyzes the learning needs. The proposing section proposes a learning plan based on the data analyzed by the analyzing section. The providing unit provides an online class or a teaching material based on the learning plan proposed by the proposal unit. The monitoring unit monitors the progress of the class or teaching material provided by the providing unit. The adjustment unit adjusts the learning content based on the progress monitored by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide optimal learning plans tailored to learning needs or monitor progress, leaving room for improvement.

[0005] The system according to the embodiment aims to provide an optimal learning plan according to learning needs, monitor progress, and adjust learning content. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, a provision unit, a monitoring unit, and an adjustment unit. The analysis unit analyzes learning needs. The proposal unit proposes a study plan based on data analyzed by the analysis unit. The provision unit provides online lessons or learning materials based on the study plan proposed by the proposal unit. The monitoring unit monitors progress in the lessons or learning materials provided by the provision unit. The adjustment unit adjusts the study content based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal learning plan according to learning needs, monitor progress, and adjust learning content. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention uses AI to create an environment that makes it easy for middle-aged and elderly people to take courses. The learning support system analyzes the learning needs of middle-aged and elderly people, proposes optimal learning plans, provides online classes and learning materials, monitors learning progress, and adjusts the learning content as needed. This mechanism allows middle-aged and elderly people to study at their own pace and supports their future. For example, the learning support system analyzes the learning needs of middle-aged and elderly people. It collects data such as past learning history, interests, and lifestyle, and analyzes it using AI to propose a learning plan tailored to each individual's needs. The learning support system then provides online classes and learning materials based on the proposed learning plan. This allows middle-aged and elderly people to study from the comfort of their own homes. For example, they can access video classes and digital learning materials using electronic devices. Furthermore, the learning support system monitors learning progress using AI and adjusts the learning content as needed. For example, if students are behind in their studies, it can provide supplementary materials or adjust the learning pace to allow them to continue studying comfortably. This allows middle-aged and elderly people to study at their own pace and supports their future. This allows middle-aged and older people to study at their own pace, and supports them in their future lives. For example, acquiring new skills after retirement can help them find new employment or broaden their hobbies. It is also possible to build new relationships through learning.

[0029] A learning assistance system according to an embodiment includes an analysis unit, a proposal unit, a provision unit, a monitoring unit, and an adjustment unit. The analysis unit analyzes learning needs. The analysis unit analyzes data such as a user's past learning history, interests, and lifestyle using, for example, data mining technology. The analysis unit can also predict the user's learning needs using a machine learning algorithm. The analysis unit can also analyze the user's learning patterns using statistical analysis technology. The proposal unit proposes a study plan based on the data analyzed by the analysis unit. The proposal unit generates a study plan optimal for the user's learning needs using, for example, AI. The proposal unit can also propose a customized study plan based on the user's learning goals and schedule. The proposal unit can also adjust the study plan based on user feedback. The provision unit provides online classes and learning materials based on the study plan proposed by the proposal unit. The provision unit provides, for example, video lectures and e-books. The provision unit can also provide interactive quizzes and simulations. The provision unit can also provide additional learning materials according to the user's learning progress. The monitoring unit monitors the progress of the lessons and learning materials provided by the providing unit. The monitoring unit, for example, records the user's study time and the number of completed assignments. The monitoring unit can also track the user's test scores. Furthermore, the monitoring unit can monitor the user's learning pace in real time. The adjustment unit adjusts the learning content based on the progress monitored by the monitoring unit. The adjustment unit, for example, adjusts the difficulty level of the learning content. The adjustment unit can also adjust the learning pace. Furthermore, the adjustment unit can provide additional supplementary learning materials according to the user's learning needs. As a result, the learning support system according to the embodiment can enable middle-aged and elderly people to study at their own pace and support them in their future lives.

[0030] The learning assistance system includes a collection unit that collects data. The collection unit collects data. For example, the collection unit collects the user's past learning history. The collection unit can also collect data related to the user's interests. The collection unit can also collect data related to the user's lifestyle. For example, the collection unit obtains the user's learning history from a database. The collection unit can also collect data related to the user's interests from questionnaires or feedback. The collection unit can also collect data related to the user's lifestyle from sensors or wearable devices. This allows the learning assistance system to collect data necessary for analyzing learning needs.

[0031] The learning assistance system includes a supplementary learning unit that provides supplementary learning materials. The supplementary learning unit provides supplementary learning materials. The supplementary learning unit provides, for example, additional practice questions. The supplementary learning unit can also provide supplementary explanatory videos. The supplementary learning unit can also provide retakes. For example, the supplementary learning unit generates additional practice questions according to the user's learning progress. The supplementary learning unit can also provide supplementary explanatory videos according to the user's level of understanding. The supplementary learning unit can also provide retakes to evaluate the user's learning outcomes. This allows the learning assistance system to provide supplementary learning materials when the user is behind in their learning.

[0032] The learning support system includes a pace adjustment unit that adjusts the learning pace. The pace adjustment unit adjusts the learning pace. For example, the pace adjustment unit increases or decreases the learning time. The pace adjustment unit can also adjust the difficulty of the tasks. Furthermore, the pace adjustment unit can also insert breaks. For example, the pace adjustment unit adjusts the learning time according to the user's learning progress. The pace adjustment unit can also adjust the difficulty of the tasks according to the user's level of understanding. Furthermore, the pace adjustment unit can also insert breaks at appropriate times according to the user's level of fatigue. In this way, the learning support system can adjust the learning pace, allowing the user to continue learning without strain.

[0033] During the analysis, the analysis unit analyzes the user's past learning history in detail and can predict changes in learning needs. The analysis unit, for example, uses data mining technology to analyze the user's past learning history in detail. For example, the analysis unit retrieves the user's learning history data from a database and analyzes their learning patterns. The analysis unit can also predict changes in the user's learning needs using a machine learning algorithm. For example, the analysis unit predicts what content the user should learn next based on the user's past learning history. Furthermore, the analysis unit can analyze the user's learning progress using statistical analysis technology. For example, the analysis unit analyzes the user's learning progress from the user's learning history and proposes an appropriate learning plan. As a result, the analysis unit can propose a more appropriate learning plan by predicting changes in learning needs based on the user's past learning history.

[0034] During analysis, the analysis unit can apply different analysis algorithms based on the user's lifestyle and interests. The analysis unit, for example, collects data related to the user's lifestyle and applies the analysis algorithm. For example, the analysis unit applies the analysis algorithm to propose a study plan that matches the user's lifestyle rhythm. The analysis unit can also apply an analysis algorithm to customize study content based on the user's interests. For example, the analysis unit adjusts the analysis algorithm to propose a study plan related to the user's hobby. Furthermore, the analysis unit can propose a more appropriate study plan by applying different analysis algorithms based on the user's lifestyle and interests. As a result, the analysis unit can propose a more appropriate study plan by applying the analysis algorithm based on the user's lifestyle and interests.

[0035] The analysis unit can improve the accuracy of the analysis by reflecting user feedback during analysis. The analysis unit, for example, improves the analysis algorithm based on feedback provided by the user. For example, the analysis unit collects user feedback data and reflects it in the analysis algorithm. The analysis unit can also improve the accuracy of the study plan by reflecting user feedback. For example, the analysis unit adjusts the content of the study plan based on user feedback. Furthermore, the analysis unit can incorporate user feedback into the analysis to propose a more appropriate study plan. For example, the analysis unit updates the analysis algorithm based on user feedback and improves the analysis accuracy. In this way, the analysis unit can improve the analysis accuracy by reflecting user feedback, and propose a more appropriate study plan.

[0036] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the study plan when making a suggestion. For example, the suggestion unit evaluates the importance of the study plan and adjusts the level of detail of the suggestion. For example, the suggestion unit includes a detailed explanation for a study plan with a high level of importance. The suggestion unit can also provide a concise explanation for a study plan with a low level of importance. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the study plan. For example, the suggestion unit evaluates the importance of the study plan based on the degree of achievement of the study goal or the user's priority. In this way, the suggestion unit can suggest a more appropriate study plan by adjusting the level of detail of the suggestion based on the importance of the study plan.

[0037] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the study plan. For example, the suggestion unit applies a suggestion algorithm depending on the category of the study plan. For example, the suggestion unit can apply a specialized suggestion algorithm to a study plan aimed at improving skills. The suggestion unit can also apply a relaxation suggestion algorithm to a study plan aimed at a hobby. Furthermore, the suggestion unit can apply a health-related suggestion algorithm to a study plan aimed at health. For example, the suggestion unit selects an appropriate suggestion algorithm based on the category of the study plan. In this way, the suggestion unit can suggest a more appropriate study plan by applying a suggestion algorithm depending on the category of the study plan.

[0038] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, analyzes the user's past suggestion results and improves the accuracy of the suggestion. For example, the suggestion unit improves the suggestion algorithm based on proposals previously accepted by the user. The suggestion unit can also make more appropriate suggestions by referring to the user's past suggestion results. Furthermore, the suggestion unit can update the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit retrieves the user's past suggestion results from a database and improves the accuracy of the suggestion. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results, and propose a more appropriate study plan.

[0039] The providing unit can adjust the level of detail of the provided learning plan based on the importance of the learning plan when providing it. The providing unit, for example, evaluates the importance of the learning plan and adjusts the level of detail of the provided learning plan. For example, the providing unit includes detailed explanations for learning plans with high importance. The providing unit can also provide brief explanations for learning plans with low importance. Furthermore, the providing unit can adjust the level of detail of the provided learning plan based on the importance of the learning plan. For example, the providing unit evaluates the importance of the learning plan based on the degree of achievement of learning goals and the user's priorities. In this way, the providing unit can provide a more appropriate learning environment by adjusting the level of detail of the provided learning plan based on the importance of the learning plan.

[0040] The providing unit can apply different providing algorithms depending on the category of the learning plan when providing the learning plan. For example, the providing unit applies a providing algorithm depending on the category of the learning plan. For example, the providing unit applies a specialized providing algorithm to a learning plan aimed at improving skills. The providing unit can also apply a relaxing providing algorithm to a learning plan aimed at a hobby. Furthermore, the providing unit can apply a health-related providing algorithm to a learning plan aimed at health. For example, the providing unit selects an appropriate providing algorithm based on the category of the learning plan. In this way, the providing unit can provide a more appropriate learning environment by applying a providing algorithm depending on the category of the learning plan.

[0041] The providing unit can improve the accuracy of the provision when providing information by referring to the user's past provision results. The providing unit, for example, analyzes the user's past provision results and improves the accuracy of the provision. For example, the providing unit improves the provision algorithm based on the provision content accepted by the user in the past. The providing unit can also provide more appropriate information by referring to the user's past provision results. Furthermore, the providing unit can update the provision algorithm based on the user's past provision results. For example, the providing unit acquires the user's past provision results from a database and improves the accuracy of the provision. As a result, the providing unit can improve the accuracy of the provision by referring to the user's past provision results, and provide a more appropriate learning environment.

[0042] During monitoring, the monitoring unit can adjust the level of detail of the monitoring based on the importance of the learning plan. The monitoring unit, for example, evaluates the importance of the learning plan and adjusts the level of detail of the monitoring. For example, the monitoring unit performs detailed monitoring for learning plans with high importance. The monitoring unit can also perform brief monitoring for learning plans with low importance. Furthermore, the monitoring unit can adjust the level of detail of the monitoring according to the importance of the learning plan. For example, the monitoring unit evaluates the importance of the learning plan based on the degree of achievement of learning goals and the user's priorities. In this way, the monitoring unit can adjust the level of detail of the monitoring according to the importance of the learning plan, thereby enabling more appropriate learning support.

[0043] During monitoring, the monitoring unit can apply different monitoring algorithms depending on the category of the learning plan. The monitoring unit applies the monitoring algorithm depending on, for example, the category of the learning plan. For example, the monitoring unit can apply a specialized monitoring algorithm to a learning plan aimed at improving skills. The monitoring unit can also apply a relaxation monitoring algorithm to a learning plan aimed at a hobby. Furthermore, the monitoring unit can apply a health-related monitoring algorithm to a learning plan aimed at health. For example, the monitoring unit selects an appropriate monitoring algorithm based on the category of the learning plan. This allows the monitoring unit to provide more appropriate learning support by applying the monitoring algorithm depending on the category of the learning plan.

[0044] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results. The monitoring unit, for example, analyzes the user's past monitoring results and improves the accuracy of monitoring. For example, the monitoring unit improves the monitoring algorithm based on the monitoring results the user has accepted in the past. The monitoring unit can also perform more appropriate monitoring by referring to the user's past monitoring results. Furthermore, the monitoring unit can update the monitoring algorithm based on the user's past monitoring results. For example, the monitoring unit acquires the user's past monitoring results from a database and improves the accuracy of monitoring. As a result, the monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results, thereby enabling more appropriate learning support.

[0045] During adjustment, the adjustment unit can adjust the level of detail of the adjustment based on the importance of the study plan. The adjustment unit, for example, evaluates the importance of the study plan and adjusts the level of detail of the adjustment. For example, the adjustment unit performs detailed adjustments on study plans with high importance. The adjustment unit can also perform simple adjustments on study plans with low importance. Furthermore, the adjustment unit can adjust the level of detail of the adjustments according to the importance of the study plan. For example, the adjustment unit evaluates the importance of the study plan based on the degree of achievement of the study goal or the user's priority. In this way, the adjustment unit can adjust the level of detail of the adjustments according to the importance of the study plan, thereby enabling more appropriate study support.

[0046] During adjustment, the adjustment unit can apply different adjustment algorithms depending on the category of the learning plan. The adjustment unit applies the adjustment algorithm depending on, for example, the category of the learning plan. For example, the adjustment unit applies a specialized adjustment algorithm to a learning plan aimed at improving skills. The adjustment unit can also apply a relaxation adjustment algorithm to a learning plan aimed at a hobby. Furthermore, the adjustment unit can apply a health-related adjustment algorithm to a learning plan aimed at health. For example, the adjustment unit selects an appropriate adjustment algorithm based on the category of the learning plan. This allows the adjustment unit to apply the adjustment algorithm depending on the category of the learning plan, thereby enabling more appropriate learning support.

[0047] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment results. The adjustment unit, for example, analyzes the user's past adjustment results and improves the accuracy of the adjustment. For example, the adjustment unit improves the adjustment algorithm based on the adjustment results previously accepted by the user. The adjustment unit can also perform more appropriate adjustment by referring to the user's past adjustment results. Furthermore, the adjustment unit can update the adjustment algorithm based on the user's past adjustment results. For example, the adjustment unit acquires the user's past adjustment results from a database and improves the accuracy of the adjustment. As a result, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment results, thereby enabling more appropriate learning support.

[0048] The collection unit can improve the accuracy of the data by collecting the user's past learning history in detail during collection. The collection unit, for example, collects the user's past learning history in detail. For example, the collection unit obtains the user's learning history data from a database and collects it in detail. The collection unit can also collect detailed information about the user's learning progress from the user's learning history. Furthermore, the collection unit can improve the accuracy of the data based on the user's past learning history. For example, the collection unit collects the user's past learning history in detail to improve the accuracy of the data. In this way, the collection unit can improve the accuracy of the data by collecting the user's past learning history in detail, thereby enabling more appropriate learning support.

[0049] The collection unit can apply different collection algorithms based on the user's lifestyle and interests during collection. The collection unit applies the collection algorithm based on, for example, the user's lifestyle and interests. For example, the collection unit applies a collection algorithm to collect data that matches the user's lifestyle rhythm. The collection unit can also apply a collection algorithm to customize data based on the user's interests. Furthermore, by applying different collection algorithms based on the user's lifestyle and interests, more appropriate data collection is possible. For example, the collection unit adjusts the collection algorithm to collect data related to the user's hobby field. As a result, the collection unit applies the collection algorithm based on the user's lifestyle and interests, more appropriate data collection is possible.

[0050] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data by taking into account the user's geographical location information. For example, the collection unit collects appropriate data based on the characteristics of the area where the user lives. The collection unit can also collect data that utilizes local educational resources based on the user's geographical location information. Furthermore, the collection unit can collect data tailored to local culture and customs by taking into account the user's geographical location information. For example, the collection unit acquires the user's geographical location information using GPS data or address information. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information, thereby enabling more appropriate data collection.

[0051] At the time of collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities and collects related data. For example, the collection unit collects data based on topics in which the user is interested on social media. The collection unit can also analyze the user's social media activities and collect data that may be of interest to the user. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the content of social media posts, the number of likes, the number of followers, etc. This allows the collection unit to analyze the user's social media activities and collect related data, thereby enabling more appropriate data collection.

[0052] During tutoring, the tutoring unit can adjust the level of detail of the tutoring based on the importance of the study plan. The tutoring unit, for example, evaluates the importance of the study plan and adjusts the level of detail of the tutoring. For example, the tutoring unit provides detailed tutoring for a study plan with a high level of importance. The tutoring unit can also provide brief tutoring for a study plan with a low level of importance. Furthermore, the tutoring unit can adjust the level of detail of the tutoring based on the importance of the study plan. For example, the tutoring unit evaluates the importance of the study plan based on the degree of achievement of the study goal or the user's priority. In this way, the tutoring unit can adjust the level of detail of the tutoring based on the importance of the study plan, thereby enabling more appropriate tutoring.

[0053] The tutoring unit can apply different tutoring algorithms depending on the category of the study plan during tutoring. The tutoring unit applies tutoring algorithms depending on, for example, the category of the study plan. For example, the tutoring unit can apply a specialized tutoring algorithm to a study plan aimed at improving skills. The tutoring unit can also apply a relaxation tutoring algorithm to a study plan aimed at a hobby. Furthermore, the tutoring unit can apply a health-related tutoring algorithm to a study plan aimed at health. For example, the tutoring unit selects an appropriate tutoring algorithm based on the category of the study plan. This allows the tutoring unit to apply a tutoring algorithm depending on the category of the study plan, enabling more appropriate tutoring.

[0054] During supplementary lessons, the supplementary lesson department can provide supplementary learning materials with priority given to the user's geographical location information. The supplementary lesson department, for example, provides supplementary learning materials with priority given to the user's geographical location information. For example, the supplementary lesson department provides appropriate supplementary learning materials based on the characteristics of the area where the user lives. The supplementary lesson department can also provide supplementary learning materials that utilize local educational resources based on the user's geographical location information. Furthermore, the supplementary lesson department can provide supplementary learning materials tailored to the local culture and customs with priority given to the user's geographical location information. For example, the supplementary lesson department acquires the user's geographical location information using GPS data or address information. This allows the supplementary lesson department to provide supplementary learning materials with priority given to the user's geographical location information, thereby enabling more appropriate supplementary learning.

[0055] The tutoring unit can analyze the user's social media activity and provide relevant tutoring materials during tutoring. The tutoring unit, for example, analyzes the user's social media activity and provides relevant tutoring materials. For example, the tutoring unit provides tutoring materials based on topics the user is interested in on social media. The tutoring unit can also analyze the user's social media activity and provide tutoring materials that may be of interest to the user. Furthermore, the tutoring unit can provide relevant tutoring materials based on the activity of the user's friends on social media. For example, the tutoring unit analyzes the content of social media posts, the number of likes, the number of followers, etc. This allows the tutoring unit to analyze the user's social media activity and provide relevant tutoring materials, enabling more appropriate tutoring.

[0056] When adjusting the pace, the pace adjustment unit can adjust the level of detail of the pace based on the importance of the learning plan. For example, the pace adjustment unit evaluates the importance of the learning plan and adjusts the level of detail of the pace. For example, the pace adjustment unit performs detailed pace adjustment for a learning plan with high importance. The pace adjustment unit can also perform simple pace adjustment for a learning plan with low importance. Furthermore, the pace adjustment unit can adjust the level of detail of the pace according to the importance of the learning plan. For example, the pace adjustment unit evaluates the importance of the learning plan based on the degree of achievement of the learning goal or the user's priority. In this way, the pace adjustment unit can adjust the level of detail of the pace according to the importance of the learning plan, thereby enabling more appropriate learning support.

[0057] When adjusting the pace, the pace adjustment unit can apply different pace adjustment algorithms depending on the category of the learning plan. The pace adjustment unit applies, for example, a pace adjustment algorithm depending on the category of the learning plan. For example, the pace adjustment unit applies a specialized pace adjustment algorithm to a learning plan aimed at improving skills. The pace adjustment unit can also apply a relaxation pace adjustment algorithm to a learning plan aimed at a hobby. Furthermore, the pace adjustment unit can apply a health-related pace adjustment algorithm to a learning plan aimed at health. For example, the pace adjustment unit selects an appropriate pace adjustment algorithm based on the category of the learning plan. In this way, the pace adjustment unit can provide more appropriate learning support by applying a pace adjustment algorithm depending on the category of the learning plan.

[0058] The pace adjustment unit can improve the accuracy of the pace when adjusting the pace by referring to the user's past pace adjustment results. The pace adjustment unit, for example, analyzes the user's past pace adjustment results and improves the accuracy of the pace. For example, the pace adjustment unit improves the pace adjustment algorithm based on the pace adjustment results accepted by the user in the past. The pace adjustment unit can also perform more appropriate pace adjustment by referring to the user's past pace adjustment results. Furthermore, the pace adjustment unit can update the pace adjustment algorithm based on the user's past pace adjustment results. For example, the pace adjustment unit acquires the user's past pace adjustment results from a database and improves the accuracy of the pace. In this way, the pace adjustment unit can improve the accuracy of the pace by referring to the user's past pace adjustment results, thereby enabling more appropriate learning support.

[0059] When adjusting the pace, the pace adjustment unit can adjust the learning pace taking into account the user's geographical location information. The pace adjustment unit adjusts the learning pace, for example, taking into account the user's geographical location information. For example, the pace adjustment unit adjusts the learning pace to proceed at an appropriate pace based on the characteristics of the area where the user lives. The pace adjustment unit can also adjust the pace by utilizing local educational resources based on the user's geographical location information. Furthermore, the pace adjustment unit can adjust the learning pace to proceed at a pace that suits the local culture and customs, taking into account the user's geographical location information. For example, the pace adjustment unit acquires the user's geographical location information using GPS data or address information. As a result, the pace adjustment unit can adjust the learning pace taking into account the user's geographical location information, thereby enabling more appropriate learning support.

[0060] When adjusting the pace, the pace adjustment unit can analyze the user's social media activity and adjust the related learning pace. The pace adjustment unit, for example, analyzes the user's social media activity and adjusts the related learning pace. For example, the pace adjustment unit adjusts the learning pace based on topics in which the user is interested on social media. The pace adjustment unit can also analyze the user's social media activity and adjust the learning pace based on topics that the user is likely to be interested in. Furthermore, the pace adjustment unit can adjust the related learning pace based on the activity of the user's friends on social media. For example, the pace adjustment unit analyzes the content of social media posts, the number of likes, the number of followers, etc. As a result, the pace adjustment unit can analyze the user's social media activity and adjust the related learning pace, thereby enabling more appropriate learning support.

[0061] The pace adjustment unit can customize the pace adjustment method by reflecting the user's past feedback when adjusting the pace. The pace adjustment unit improves the pace adjustment method, for example, based on the user's past feedback. For example, the pace adjustment unit collects feedback provided by the user in the past and reflects it in the pace adjustment algorithm. The pace adjustment unit can also improve the accuracy of adjusting the learning pace by reflecting the user's past feedback. Furthermore, the pace adjustment unit can incorporate the user's past feedback into the analysis to adjust a more appropriate learning pace. For example, the pace adjustment unit updates the pace adjustment algorithm and customizes the pace adjustment method based on the user's past feedback. In this way, the pace adjustment unit can customize the pace adjustment method by reflecting the user's past feedback, enabling more appropriate learning support.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The analysis unit can also monitor the user's health condition and adjust the method of analyzing learning needs based on the health data. For example, the analysis unit can obtain the user's heart rate and sleep patterns from the wearable device and evaluate the user's health condition. The analysis unit can also collect data on the user's diet and exercise habits to comprehensively analyze the user's health condition. Furthermore, the analysis unit can suggest a learning plan to reduce stress based on the user's health condition. This allows the system to suggest a more appropriate learning plan by adjusting the method of analyzing learning needs based on the user's health condition.

[0064] The collection unit can also analyze the user's social media activities and collect data based on their interests. For example, the collection unit can analyze the accounts the user follows on social media and the content of their posts to identify their interests. The collection unit can also analyze the time and frequency of the user's social media activities to optimize the timing of data collection. Furthermore, the collection unit can take into account the user's friendships on social media to collect related data. This allows for more appropriate learning support by collecting data based on the user's social media activities.

[0065] The tutoring unit can also adjust the format of the tutoring materials according to the user's learning style. For example, the tutoring unit can provide visual learning materials to a user who prefers visual learning. It can also provide audio learning materials to a user who prefers auditory learning. It can also provide interactive simulation learning materials to a user who prefers practical learning. This allows for more effective learning support by adjusting the format of the tutoring materials according to the user's learning style.

[0066] The pace adjustment unit can also monitor the user's learning environment and adjust the learning pace based on the environmental data. For example, the pace adjustment unit can use sensors to acquire the noise level and lighting conditions in the user's learning environment and evaluate the environment as being suitable for learning. The pace adjustment unit can also monitor the temperature and humidity of the user's learning environment and make adjustments to provide a comfortable learning environment. Furthermore, the pace adjustment unit can adjust the learning pace in real time in response to changes in the user's learning environment. This allows for more effective learning support by adjusting the learning pace based on the user's learning environment.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The analysis unit analyzes learning needs. The analysis unit uses data mining technology to analyze data such as the user's past learning history, interests, and lifestyle. It can also predict the user's learning needs using machine learning algorithms and analyze the user's learning patterns using statistical analysis technology. Step 2: The suggestion unit proposes a study plan based on the data analyzed by the analysis unit. The suggestion unit uses AI to generate a study plan that best suits the user's learning needs and proposes a customized study plan based on the user's learning goals and schedule. The suggestion unit can also adjust the study plan based on user feedback. Step 3: The provider provides online lessons and learning materials based on the learning plan proposed by the suggester. The provider provides video lectures, e-books, interactive quizzes and simulations, and can also provide additional learning materials according to the user's learning progress. Step 4: The monitoring unit monitors the progress of the lessons and learning materials provided by the provider. The monitoring unit can record the user's study time and the number of assignments completed, track the user's test scores, and monitor the user's learning pace in real time. Step 5: The adjustment unit adjusts the learning content based on the progress monitored by the monitoring unit. The adjustment unit can adjust the difficulty level and learning pace of the learning content and can also provide additional supplementary materials according to the user's learning needs.

[0069] (Example 2) A learning support system according to an embodiment of the present invention uses AI to create an environment that makes it easy for middle-aged and elderly people to take courses. The learning support system analyzes the learning needs of middle-aged and elderly people, proposes optimal learning plans, provides online classes and learning materials, monitors learning progress, and adjusts the learning content as needed. This mechanism allows middle-aged and elderly people to study at their own pace and supports their future. For example, the learning support system analyzes the learning needs of middle-aged and elderly people. It collects data such as past learning history, interests, and lifestyle, and analyzes it using AI to propose a learning plan tailored to each individual's needs. The learning support system then provides online classes and learning materials based on the proposed learning plan. This allows middle-aged and elderly people to study from the comfort of their own homes. For example, they can access video classes and digital learning materials using electronic devices. Furthermore, the learning support system monitors learning progress using AI and adjusts the learning content as needed. For example, if students are behind in their studies, it can provide supplementary materials or adjust the learning pace to allow them to continue studying comfortably. This allows middle-aged and elderly people to study at their own pace and supports their future. This allows middle-aged and older people to study at their own pace, and supports them in their future lives. For example, acquiring new skills after retirement can help them find new employment or broaden their hobbies. It is also possible to build new relationships through learning.

[0070] A learning assistance system according to an embodiment includes an analysis unit, a proposal unit, a provision unit, a monitoring unit, and an adjustment unit. The analysis unit analyzes learning needs. The analysis unit analyzes data such as a user's past learning history, interests, and lifestyle using, for example, data mining technology. The analysis unit can also predict the user's learning needs using a machine learning algorithm. The analysis unit can also analyze the user's learning patterns using statistical analysis technology. The proposal unit proposes a study plan based on the data analyzed by the analysis unit. The proposal unit generates a study plan optimal for the user's learning needs using, for example, AI. The proposal unit can also propose a customized study plan based on the user's learning goals and schedule. The proposal unit can also adjust the study plan based on user feedback. The provision unit provides online classes and learning materials based on the study plan proposed by the proposal unit. The provision unit provides, for example, video lectures and e-books. The provision unit can also provide interactive quizzes and simulations. The provision unit can also provide additional learning materials according to the user's learning progress. The monitoring unit monitors the progress of the lessons and learning materials provided by the providing unit. The monitoring unit, for example, records the user's study time and the number of completed assignments. The monitoring unit can also track the user's test scores. Furthermore, the monitoring unit can monitor the user's learning pace in real time. The adjustment unit adjusts the learning content based on the progress monitored by the monitoring unit. The adjustment unit, for example, adjusts the difficulty level of the learning content. The adjustment unit can also adjust the learning pace. Furthermore, the adjustment unit can provide additional supplementary learning materials according to the user's learning needs. As a result, the learning support system according to the embodiment can enable middle-aged and elderly people to study at their own pace and support them in their future lives.

[0071] The learning assistance system includes a collection unit that collects data. The collection unit collects data. For example, the collection unit collects the user's past learning history. The collection unit can also collect data related to the user's interests. The collection unit can also collect data related to the user's lifestyle. For example, the collection unit obtains the user's learning history from a database. The collection unit can also collect data related to the user's interests from questionnaires or feedback. The collection unit can also collect data related to the user's lifestyle from sensors or wearable devices. This allows the learning assistance system to collect data necessary for analyzing learning needs.

[0072] The learning assistance system includes a supplementary learning unit that provides supplementary learning materials. The supplementary learning unit provides supplementary learning materials. The supplementary learning unit provides, for example, additional practice questions. The supplementary learning unit can also provide supplementary explanatory videos. The supplementary learning unit can also provide retakes. For example, the supplementary learning unit generates additional practice questions according to the user's learning progress. The supplementary learning unit can also provide supplementary explanatory videos according to the user's level of understanding. The supplementary learning unit can also provide retakes to evaluate the user's learning outcomes. This allows the learning assistance system to provide supplementary learning materials when the user is behind in their learning.

[0073] The learning support system includes a pace adjustment unit that adjusts the learning pace. The pace adjustment unit adjusts the learning pace. For example, the pace adjustment unit increases or decreases the learning time. The pace adjustment unit can also adjust the difficulty of the tasks. Furthermore, the pace adjustment unit can also insert breaks. For example, the pace adjustment unit adjusts the learning time according to the user's learning progress. The pace adjustment unit can also adjust the difficulty of the tasks according to the user's level of understanding. Furthermore, the pace adjustment unit can also insert breaks at appropriate times according to the user's level of fatigue. In this way, the learning support system can adjust the learning pace, allowing the user to continue learning without strain.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis method for learning needs based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The analysis unit can also estimate the user's emotions using survey results. For example, the analysis unit analyzes survey data answered by the user and estimates the emotion. This allows the analysis unit to adjust the analysis method for learning needs according to the user's emotions. For example, if the user is feeling stressed, the analysis method can be adjusted to suggest studying in a relaxing environment. Also, if the user is excited, the analysis unit can apply an analysis method to increase concentration and analyze the learning needs. Furthermore, if the user is tired, the analysis method can be adjusted to suggest a less stressful learning plan. By adjusting the analysis method for learning needs according to the user's emotions, a more appropriate learning plan can be suggested.

[0075] During the analysis, the analysis unit analyzes the user's past learning history in detail and can predict changes in learning needs. The analysis unit, for example, uses data mining technology to analyze the user's past learning history in detail. For example, the analysis unit retrieves the user's learning history data from a database and analyzes their learning patterns. The analysis unit can also predict changes in the user's learning needs using a machine learning algorithm. For example, the analysis unit predicts what content the user should learn next based on the user's past learning history. Furthermore, the analysis unit can analyze the user's learning progress using statistical analysis technology. For example, the analysis unit analyzes the user's learning progress from the user's learning history and proposes an appropriate learning plan. As a result, the analysis unit can propose a more appropriate learning plan by predicting changes in learning needs based on the user's past learning history.

[0076] During analysis, the analysis unit can apply different analysis algorithms based on the user's lifestyle and interests. The analysis unit, for example, collects data related to the user's lifestyle and applies the analysis algorithm. For example, the analysis unit applies the analysis algorithm to propose a study plan that matches the user's lifestyle rhythm. The analysis unit can also apply an analysis algorithm to customize study content based on the user's interests. For example, the analysis unit adjusts the analysis algorithm to propose a study plan related to the user's hobby. Furthermore, the analysis unit can propose a more appropriate study plan by applying different analysis algorithms based on the user's lifestyle and interests. As a result, the analysis unit can propose a more appropriate study plan by applying the analysis algorithm based on the user's lifestyle and interests.

[0077] The analysis unit can improve the accuracy of the analysis by reflecting user feedback during analysis. The analysis unit, for example, improves the analysis algorithm based on feedback provided by the user. For example, the analysis unit collects user feedback data and reflects it in the analysis algorithm. The analysis unit can also improve the accuracy of the study plan by reflecting user feedback. For example, the analysis unit adjusts the content of the study plan based on user feedback. Furthermore, the analysis unit can incorporate user feedback into the analysis to propose a more appropriate study plan. For example, the analysis unit updates the analysis algorithm based on user feedback and improves the analysis accuracy. In this way, the analysis unit can improve the analysis accuracy by reflecting user feedback, and propose a more appropriate study plan.

[0078] The suggestion unit can estimate the user's emotions and adjust the method for proposing a study plan based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the suggestion unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the suggestion unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The suggestion unit can also estimate the user's emotions using survey results. For example, the suggestion unit analyzes survey data answered by the user and estimates the emotion. This allows the suggestion unit to adjust the method for proposing a study plan based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a study plan that helps the user relax. Furthermore, if the user is excited, the suggestion unit can suggest a study plan that helps the user concentrate. Furthermore, if the user is tired, the suggestion unit can suggest a study plan that is less stressful. This allows the suggestion unit to suggest a more appropriate study plan by adjusting the method for proposing a study plan based on the user's emotions.

[0079] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the study plan when making a suggestion. For example, the suggestion unit evaluates the importance of the study plan and adjusts the level of detail of the suggestion. For example, the suggestion unit includes a detailed explanation for a study plan with a high level of importance. The suggestion unit can also provide a concise explanation for a study plan with a low level of importance. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the study plan. For example, the suggestion unit evaluates the importance of the study plan based on the degree of achievement of the study goal or the user's priority. In this way, the suggestion unit can suggest a more appropriate study plan by adjusting the level of detail of the suggestion based on the importance of the study plan.

[0080] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the study plan. For example, the suggestion unit applies a suggestion algorithm depending on the category of the study plan. For example, the suggestion unit can apply a specialized suggestion algorithm to a study plan aimed at improving skills. The suggestion unit can also apply a relaxation suggestion algorithm to a study plan aimed at a hobby. Furthermore, the suggestion unit can apply a health-related suggestion algorithm to a study plan aimed at health. For example, the suggestion unit selects an appropriate suggestion algorithm based on the category of the study plan. In this way, the suggestion unit can suggest a more appropriate study plan by applying a suggestion algorithm depending on the category of the study plan.

[0081] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, analyzes the user's past suggestion results and improves the accuracy of the suggestion. For example, the suggestion unit improves the suggestion algorithm based on proposals previously accepted by the user. The suggestion unit can also make more appropriate suggestions by referring to the user's past suggestion results. Furthermore, the suggestion unit can update the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit retrieves the user's past suggestion results from a database and improves the accuracy of the suggestion. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results, and propose a more appropriate study plan.

[0082] The providing unit can estimate the user's emotions and adjust the method of providing online classes and teaching materials based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit acquires the user's voice data using a microphone and estimates the emotion using a voice analysis algorithm. The providing unit can also estimate the user's emotions using survey results. For example, the providing unit analyzes survey data answered by the user and estimates the emotion. This allows the providing unit to adjust the method of providing online classes and teaching materials according to the user's emotions. For example, if the user is feeling stressed, the providing unit can provide online classes in a relaxing environment. Also, if the user is excited, the providing unit can provide teaching materials to improve concentration. Furthermore, if the user is tired, the providing unit can provide teaching materials that are less stressful. This allows the providing unit to provide a more appropriate learning environment by adjusting the method of providing online classes and teaching materials according to the user's emotions.

[0083] The providing unit can adjust the level of detail of the provided learning plan based on the importance of the learning plan when providing it. The providing unit, for example, evaluates the importance of the learning plan and adjusts the level of detail of the provided learning plan. For example, the providing unit includes detailed explanations for learning plans with high importance. The providing unit can also provide brief explanations for learning plans with low importance. Furthermore, the providing unit can adjust the level of detail of the provided learning plan based on the importance of the learning plan. For example, the providing unit evaluates the importance of the learning plan based on the degree of achievement of learning goals and the user's priorities. In this way, the providing unit can provide a more appropriate learning environment by adjusting the level of detail of the provided learning plan based on the importance of the learning plan.

[0084] The providing unit can apply different providing algorithms depending on the category of the learning plan when providing the learning plan. For example, the providing unit applies a providing algorithm depending on the category of the learning plan. For example, the providing unit applies a specialized providing algorithm to a learning plan aimed at improving skills. The providing unit can also apply a relaxing providing algorithm to a learning plan aimed at a hobby. Furthermore, the providing unit can apply a health-related providing algorithm to a learning plan aimed at health. For example, the providing unit selects an appropriate providing algorithm based on the category of the learning plan. In this way, the providing unit can provide a more appropriate learning environment by applying a providing algorithm depending on the category of the learning plan.

[0085] The providing unit can improve the accuracy of the provision when providing information by referring to the user's past provision results. The providing unit, for example, analyzes the user's past provision results and improves the accuracy of the provision. For example, the providing unit improves the provision algorithm based on the provision content accepted by the user in the past. The providing unit can also provide more appropriate information by referring to the user's past provision results. Furthermore, the providing unit can update the provision algorithm based on the user's past provision results. For example, the providing unit acquires the user's past provision results from a database and improves the accuracy of the provision. As a result, the providing unit can improve the accuracy of the provision by referring to the user's past provision results, and provide a more appropriate learning environment.

[0086] The monitoring unit can estimate the user's emotions and adjust the learning progress monitoring method based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the monitoring unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The monitoring unit can also estimate the user's emotions using voice analysis technology. For example, the monitoring unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The monitoring unit can also estimate the user's emotions using survey results. For example, the monitoring unit analyzes survey data answered by the user and estimates the emotion. This allows the monitoring unit to adjust the learning progress monitoring method according to the user's emotions. For example, if the user is feeling stressed, monitoring can be performed in a relaxing environment. Furthermore, if the user is excited, a monitoring method to increase concentration can be applied. Furthermore, if the user is tired, a less stressful monitoring method can be applied. This allows more appropriate learning support to be provided by adjusting the learning progress monitoring method according to the user's emotions.

[0087] During monitoring, the monitoring unit can adjust the level of detail of the monitoring based on the importance of the learning plan. The monitoring unit, for example, evaluates the importance of the learning plan and adjusts the level of detail of the monitoring. For example, the monitoring unit performs detailed monitoring for learning plans with high importance. The monitoring unit can also perform brief monitoring for learning plans with low importance. Furthermore, the monitoring unit can adjust the level of detail of the monitoring according to the importance of the learning plan. For example, the monitoring unit evaluates the importance of the learning plan based on the degree of achievement of learning goals and the user's priorities. In this way, the monitoring unit can adjust the level of detail of the monitoring according to the importance of the learning plan, thereby enabling more appropriate learning support.

[0088] During monitoring, the monitoring unit can apply different monitoring algorithms depending on the category of the learning plan. The monitoring unit applies the monitoring algorithm depending on, for example, the category of the learning plan. For example, the monitoring unit can apply a specialized monitoring algorithm to a learning plan aimed at improving skills. The monitoring unit can also apply a relaxation monitoring algorithm to a learning plan aimed at a hobby. Furthermore, the monitoring unit can apply a health-related monitoring algorithm to a learning plan aimed at health. For example, the monitoring unit selects an appropriate monitoring algorithm based on the category of the learning plan. This allows the monitoring unit to provide more appropriate learning support by applying the monitoring algorithm depending on the category of the learning plan.

[0089] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results. The monitoring unit, for example, analyzes the user's past monitoring results and improves the accuracy of monitoring. For example, the monitoring unit improves the monitoring algorithm based on the monitoring results the user has accepted in the past. The monitoring unit can also perform more appropriate monitoring by referring to the user's past monitoring results. Furthermore, the monitoring unit can update the monitoring algorithm based on the user's past monitoring results. For example, the monitoring unit acquires the user's past monitoring results from a database and improves the accuracy of monitoring. As a result, the monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results, thereby enabling more appropriate learning support.

[0090] The adjustment unit can estimate the user's emotions and adjust the adjustment method of the learning content based on the estimated user's emotions. The adjustment unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the adjustment unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The adjustment unit can also estimate the user's emotions using voice analysis technology. For example, the adjustment unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The adjustment unit can also estimate the user's emotions using survey results. For example, the adjustment unit analyzes survey data answered by the user and estimates the emotion. This allows the adjustment unit to adjust the adjustment method of the learning content according to the user's emotions. For example, if the user is feeling stressed, the adjustment can be made to learning content that is relaxing. Also, if the user is excited, the adjustment can be made to learning content that increases concentration. Furthermore, if the user is tired, the adjustment can be made to learning content that is less stressful. As a result, by adjusting the adjustment method of the learning content according to the user's emotions, more appropriate learning support is possible.

[0091] During adjustment, the adjustment unit can adjust the level of detail of the adjustment based on the importance of the study plan. The adjustment unit, for example, evaluates the importance of the study plan and adjusts the level of detail of the adjustment. For example, the adjustment unit performs detailed adjustments on study plans with high importance. The adjustment unit can also perform simple adjustments on study plans with low importance. Furthermore, the adjustment unit can adjust the level of detail of the adjustments according to the importance of the study plan. For example, the adjustment unit evaluates the importance of the study plan based on the degree of achievement of the study goal or the user's priority. In this way, the adjustment unit can adjust the level of detail of the adjustments according to the importance of the study plan, thereby enabling more appropriate study support.

[0092] During adjustment, the adjustment unit can apply different adjustment algorithms depending on the category of the learning plan. The adjustment unit applies the adjustment algorithm depending on, for example, the category of the learning plan. For example, the adjustment unit applies a specialized adjustment algorithm to a learning plan aimed at improving skills. The adjustment unit can also apply a relaxation adjustment algorithm to a learning plan aimed at a hobby. Furthermore, the adjustment unit can apply a health-related adjustment algorithm to a learning plan aimed at health. For example, the adjustment unit selects an appropriate adjustment algorithm based on the category of the learning plan. This allows the adjustment unit to apply the adjustment algorithm depending on the category of the learning plan, thereby enabling more appropriate learning support.

[0093] During adjustment, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment results. The adjustment unit, for example, analyzes the user's past adjustment results and improves the accuracy of the adjustment. For example, the adjustment unit improves the adjustment algorithm based on the adjustment results previously accepted by the user. The adjustment unit can also perform more appropriate adjustment by referring to the user's past adjustment results. Furthermore, the adjustment unit can update the adjustment algorithm based on the user's past adjustment results. For example, the adjustment unit acquires the user's past adjustment results from a database and improves the accuracy of the adjustment. As a result, the adjustment unit can improve the accuracy of the adjustment by referring to the user's past adjustment results, thereby enabling more appropriate learning support.

[0094] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit acquires the user's facial expression data using a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit acquires the user's voice data using a microphone and estimates the emotions using an emotion analysis algorithm. The collection unit can also estimate the user's emotions using survey results. For example, the collection unit analyzes survey data answered by the user and estimates the emotions. This allows the collection unit to adjust the timing of data collection according to the user's emotions. For example, if the user is relaxed, the timing of data collection can be adjusted to avoid stress. Furthermore, if the user is excited, the timing of data collection can be adjusted to increase the user's concentration. Furthermore, if the user is tired, data collection can be performed at a time that puts less strain on the user. This allows more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0095] The collection unit can improve the accuracy of the data by collecting the user's past learning history in detail during collection. The collection unit, for example, collects the user's past learning history in detail. For example, the collection unit obtains the user's learning history data from a database and collects it in detail. The collection unit can also collect detailed information about the user's learning progress from the user's learning history. Furthermore, the collection unit can improve the accuracy of the data based on the user's past learning history. For example, the collection unit collects the user's past learning history in detail to improve the accuracy of the data. In this way, the collection unit can improve the accuracy of the data by collecting the user's past learning history in detail, thereby enabling more appropriate learning support.

[0096] The collection unit can apply different collection algorithms based on the user's lifestyle and interests during collection. The collection unit applies the collection algorithm based on, for example, the user's lifestyle and interests. For example, the collection unit applies a collection algorithm to collect data that matches the user's lifestyle rhythm. The collection unit can also apply a collection algorithm to customize data based on the user's interests. Furthermore, by applying different collection algorithms based on the user's lifestyle and interests, more appropriate data collection is possible. For example, the collection unit adjusts the collection algorithm to collect data related to the user's hobby field. As a result, the collection unit applies the collection algorithm based on the user's lifestyle and interests, more appropriate data collection is possible.

[0097] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The collection unit can also estimate the user's emotions using survey results. For example, the collection unit analyzes survey data answered by the user and estimates the emotion. This allows the collection unit to determine the priority of data to be collected based on the user's emotions. For example, if the user is stressed, data that helps relaxation can be preferentially collected. Also, if the user is excited, data that improves concentration can be preferentially collected. Furthermore, if the user is tired, data that is less stressful can be preferentially collected. This allows more appropriate data collection by determining the priority of data to be collected based on the user's emotions.

[0098] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, collects data by taking into account the user's geographical location information. For example, the collection unit collects appropriate data based on the characteristics of the area where the user lives. The collection unit can also collect data that utilizes local educational resources based on the user's geographical location information. Furthermore, the collection unit can collect data tailored to local culture and customs by taking into account the user's geographical location information. For example, the collection unit acquires the user's geographical location information using GPS data or address information. This allows the collection unit to prioritize collecting highly relevant data by taking into account the user's geographical location information, thereby enabling more appropriate data collection.

[0099] At the time of collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities and collects related data. For example, the collection unit collects data based on topics in which the user is interested on social media. The collection unit can also analyze the user's social media activities and collect data that may be of interest to the user. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the content of social media posts, the number of likes, the number of followers, etc. This allows the collection unit to analyze the user's social media activities and collect related data, thereby enabling more appropriate data collection.

[0100] The supplementary learning unit can estimate the user's emotions and adjust the method of providing supplementary learning materials based on the estimated user's emotions. The supplementary learning unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the supplementary learning unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The supplementary learning unit can also estimate the user's emotions using voice analysis technology. For example, the supplementary learning unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The supplementary learning unit can also estimate the user's emotions using survey results. For example, the supplementary learning unit analyzes survey data answered by the user and estimates the emotion. This allows the supplementary learning unit to adjust the method of providing supplementary learning materials according to the user's emotions. For example, if the user is feeling stressed, the supplementary learning unit can provide relaxing supplementary learning materials. Also, if the user is excited, the supplementary learning unit can provide supplementary learning materials that improve concentration. Furthermore, if the user is tired, the supplementary learning unit can provide less burdensome supplementary learning materials. This allows more appropriate supplementary learning by adjusting the method of providing supplementary learning materials according to the user's emotions.

[0101] During tutoring, the tutoring unit can adjust the level of detail of the tutoring based on the importance of the study plan. The tutoring unit, for example, evaluates the importance of the study plan and adjusts the level of detail of the tutoring. For example, the tutoring unit provides detailed tutoring for a study plan with a high level of importance. The tutoring unit can also provide brief tutoring for a study plan with a low level of importance. Furthermore, the tutoring unit can adjust the level of detail of the tutoring based on the importance of the study plan. For example, the tutoring unit evaluates the importance of the study plan based on the degree of achievement of the study goal or the user's priority. In this way, the tutoring unit can adjust the level of detail of the tutoring based on the importance of the study plan, thereby enabling more appropriate tutoring.

[0102] The tutoring unit can apply different tutoring algorithms depending on the category of the study plan during tutoring. The tutoring unit applies tutoring algorithms depending on, for example, the category of the study plan. For example, the tutoring unit can apply a specialized tutoring algorithm to a study plan aimed at improving skills. The tutoring unit can also apply a relaxation tutoring algorithm to a study plan aimed at a hobby. Furthermore, the tutoring unit can apply a health-related tutoring algorithm to a study plan aimed at health. For example, the tutoring unit selects an appropriate tutoring algorithm based on the category of the study plan. This allows the tutoring unit to apply a tutoring algorithm depending on the category of the study plan, enabling more appropriate tutoring.

[0103] The supplementary learning unit can estimate the user's emotions and determine the priority of supplementary learning materials to be provided based on the estimated user's emotions. The supplementary learning unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the supplementary learning unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The supplementary learning unit can also estimate the user's emotions using voice analysis technology. For example, the supplementary learning unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. Furthermore, the supplementary learning unit can estimate the user's emotions using survey results. For example, the supplementary learning unit analyzes survey data answered by the user and estimates the emotion. This allows the supplementary learning unit to determine the priority of supplementary learning materials to be provided based on the user's emotions. For example, if the user is feeling stressed, supplementary learning materials that help the user relax can be provided preferentially. Also, if the user is excited, supplementary learning materials that help the user improve their concentration can be provided preferentially. Furthermore, if the user is tired, supplementary learning materials that are less stressful can be provided preferentially. This allows for more appropriate supplementary learning by determining the priority of supplementary learning materials to be provided according to the user's emotions.

[0104] During supplementary lessons, the supplementary lesson department can provide supplementary learning materials with priority given to the user's geographical location information. The supplementary lesson department, for example, provides supplementary learning materials with priority given to the user's geographical location information. For example, the supplementary lesson department provides appropriate supplementary learning materials based on the characteristics of the area where the user lives. The supplementary lesson department can also provide supplementary learning materials that utilize local educational resources based on the user's geographical location information. Furthermore, the supplementary lesson department can provide supplementary learning materials tailored to the local culture and customs with priority given to the user's geographical location information. For example, the supplementary lesson department acquires the user's geographical location information using GPS data or address information. This allows the supplementary lesson department to provide supplementary learning materials with priority given to the user's geographical location information, thereby enabling more appropriate supplementary learning.

[0105] The tutoring unit can analyze the user's social media activity and provide relevant tutoring materials during tutoring. The tutoring unit, for example, analyzes the user's social media activity and provides relevant tutoring materials. For example, the tutoring unit provides tutoring materials based on topics the user is interested in on social media. The tutoring unit can also analyze the user's social media activity and provide tutoring materials that may be of interest to the user. Furthermore, the tutoring unit can provide relevant tutoring materials based on the activity of the user's friends on social media. For example, the tutoring unit analyzes the content of social media posts, the number of likes, the number of followers, etc. This allows the tutoring unit to analyze the user's social media activity and provide relevant tutoring materials, enabling more appropriate tutoring.

[0106] The pace adjustment unit can estimate the user's emotions and adjust the learning pace adjustment method based on the estimated user's emotions. The pace adjustment unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the pace adjustment unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The pace adjustment unit can also estimate the user's emotions using voice analysis technology. For example, the pace adjustment unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The pace adjustment unit can also estimate the user's emotions using survey results. For example, the pace adjustment unit analyzes survey data answered by the user and estimates the emotion. This allows the pace adjustment unit to adjust the learning pace adjustment method according to the user's emotions. For example, if the user is feeling stressed, the pace adjustment unit can adjust the learning pace to progress at a pace that allows the user to relax. Also, if the user is excited, the pace adjustment unit can adjust the learning pace to progress at a pace that increases concentration. Furthermore, if the user is tired, the pace adjustment unit can adjust the learning pace to progress at a pace that puts less strain on the user. This allows more appropriate learning support by adjusting the learning pace adjustment method according to the user's emotions.

[0107] When adjusting the pace, the pace adjustment unit can adjust the level of detail of the pace based on the importance of the learning plan. For example, the pace adjustment unit evaluates the importance of the learning plan and adjusts the level of detail of the pace. For example, the pace adjustment unit performs detailed pace adjustment for a learning plan with high importance. The pace adjustment unit can also perform simple pace adjustment for a learning plan with low importance. Furthermore, the pace adjustment unit can adjust the level of detail of the pace according to the importance of the learning plan. For example, the pace adjustment unit evaluates the importance of the learning plan based on the degree of achievement of the learning goal or the user's priority. In this way, the pace adjustment unit can adjust the level of detail of the pace according to the importance of the learning plan, thereby enabling more appropriate learning support.

[0108] When adjusting the pace, the pace adjustment unit can apply different pace adjustment algorithms depending on the category of the learning plan. The pace adjustment unit applies, for example, a pace adjustment algorithm depending on the category of the learning plan. For example, the pace adjustment unit applies a specialized pace adjustment algorithm to a learning plan aimed at improving skills. The pace adjustment unit can also apply a relaxation pace adjustment algorithm to a learning plan aimed at a hobby. Furthermore, the pace adjustment unit can apply a health-related pace adjustment algorithm to a learning plan aimed at health. For example, the pace adjustment unit selects an appropriate pace adjustment algorithm based on the category of the learning plan. In this way, the pace adjustment unit can provide more appropriate learning support by applying a pace adjustment algorithm depending on the category of the learning plan.

[0109] The pace adjustment unit can improve the accuracy of the pace when adjusting the pace by referring to the user's past pace adjustment results. The pace adjustment unit, for example, analyzes the user's past pace adjustment results and improves the accuracy of the pace. For example, the pace adjustment unit improves the pace adjustment algorithm based on the pace adjustment results accepted by the user in the past. The pace adjustment unit can also perform more appropriate pace adjustment by referring to the user's past pace adjustment results. Furthermore, the pace adjustment unit can update the pace adjustment algorithm based on the user's past pace adjustment results. For example, the pace adjustment unit acquires the user's past pace adjustment results from a database and improves the accuracy of the pace. In this way, the pace adjustment unit can improve the accuracy of the pace by referring to the user's past pace adjustment results, thereby enabling more appropriate learning support.

[0110] The pace adjustment unit can estimate the user's emotions and determine the priority of the learning pace to be adjusted based on the estimated user's emotions. The pace adjustment unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the pace adjustment unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The pace adjustment unit can also estimate the user's emotions using voice analysis technology. For example, the pace adjustment unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The pace adjustment unit can also estimate the user's emotions using survey results. For example, the pace adjustment unit analyzes survey data answered by the user and estimates the emotion. This allows the pace adjustment unit to determine the priority of the learning pace to be adjusted based on the user's emotions. For example, if the user is stressed, the pace adjustment unit can prioritize a pace that allows relaxation. Also, if the user is excited, the pace adjustment unit can prioritize a pace that increases concentration. Furthermore, if the user is tired, the pace adjustment unit can prioritize a pace that puts less strain on the user. This allows more appropriate learning support to be provided by determining the priority of the learning pace to be adjusted based on the user's emotions.

[0111] When adjusting the pace, the pace adjustment unit can adjust the learning pace taking into account the user's geographical location information. The pace adjustment unit adjusts the learning pace, for example, taking into account the user's geographical location information. For example, the pace adjustment unit adjusts the learning pace to proceed at an appropriate pace based on the characteristics of the area where the user lives. The pace adjustment unit can also adjust the pace by utilizing local educational resources based on the user's geographical location information. Furthermore, the pace adjustment unit can adjust the learning pace to proceed at a pace that suits the local culture and customs, taking into account the user's geographical location information. For example, the pace adjustment unit acquires the user's geographical location information using GPS data or address information. As a result, the pace adjustment unit can adjust the learning pace taking into account the user's geographical location information, thereby enabling more appropriate learning support.

[0112] When adjusting the pace, the pace adjustment unit can analyze the user's social media activity and adjust the related learning pace. The pace adjustment unit, for example, analyzes the user's social media activity and adjusts the related learning pace. For example, the pace adjustment unit adjusts the learning pace based on topics in which the user is interested on social media. The pace adjustment unit can also analyze the user's social media activity and adjust the learning pace based on topics that the user is likely to be interested in. Furthermore, the pace adjustment unit can adjust the related learning pace based on the activity of the user's friends on social media. For example, the pace adjustment unit analyzes the content of social media posts, the number of likes, the number of followers, etc. As a result, the pace adjustment unit can analyze the user's social media activity and adjust the related learning pace, thereby enabling more appropriate learning support.

[0113] The pace adjustment unit can customize the pace adjustment method by reflecting the user's past feedback when adjusting the pace. The pace adjustment unit improves the pace adjustment method, for example, based on the user's past feedback. For example, the pace adjustment unit collects feedback provided by the user in the past and reflects it in the pace adjustment algorithm. The pace adjustment unit can also improve the accuracy of adjusting the learning pace by reflecting the user's past feedback. Furthermore, the pace adjustment unit can incorporate the user's past feedback into the analysis to adjust a more appropriate learning pace. For example, the pace adjustment unit updates the pace adjustment algorithm and customizes the pace adjustment method based on the user's past feedback. In this way, the pace adjustment unit can customize the pace adjustment method by reflecting the user's past feedback, enabling more appropriate learning support. === Hard Collateral 1-1 === For example, each of a plurality of elements including an analysis unit, a suggestion unit, a provision unit, a monitoring unit, an adjustment unit, a collection unit, a supplementary learning unit, and a pace adjustment unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data such as the user's past learning history, interests, and lifestyle. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a learning plan based on the analyzed data. The provision unit is realized by the control unit 46A of the smart device 14 and provides online lessons and learning materials. The monitoring unit is realized by the control unit 46A of the smart device 14 and monitors learning progress. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts learning content. The collection unit is realized by the control unit 46A of the smart device 14 and collects data. The supplementary learning unit is realized by the control unit 46A of the smart device 14 and provides supplementary learning materials. The pace adjusting section is realized by the specific processing section 290 of the data processing device 12, and adjusts the learning pace. === Hard Collateral 1-2 === For example, each of multiple elements including an analysis unit, a suggestion unit, a provision unit, a monitoring unit, an adjustment unit, a collection unit, a supplementary learning unit, and a pace adjustment unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data such as the user's past learning history, interests, and lifestyle. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a learning plan based on the analyzed data. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides online lessons and learning materials. The monitoring unit is realized by the control unit 46A of the smart glasses 214 and monitors learning progress. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts learning content. The collection unit is realized by the control unit 46A of the smart glasses 214 and collects data. The supplementary learning unit is realized by the control unit 46A of the smart glasses 214 and provides supplementary learning materials. The pace adjusting section is realized by the specific processing section 290 of the data processing device 12, and adjusts the learning pace. === Hard Collateral 1-3 === For example, each of multiple elements including an analysis unit, a suggestion unit, a provision unit, a monitoring unit, an adjustment unit, a collection unit, a supplementary learning unit, and a pace adjustment unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data such as the user's past learning history, interests, and lifestyle. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a learning plan based on the analyzed data. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides online lessons and learning materials. The monitoring unit is realized by the control unit 46A of the headset type terminal 314 and monitors learning progress. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts learning content. The collection unit is realized by the control unit 46A of the headset type terminal 314 and collects data. The supplementary learning unit is realized by the control unit 46A of the headset type terminal 314 and provides supplementary learning materials. The pace adjusting section is realized by the specific processing section 290 of the data processing device 12, and adjusts the learning pace. === Hard Collateral 1-4 === For example, each of a plurality of elements including an analysis unit, a suggestion unit, a provision unit, a monitoring unit, an adjustment unit, a collection unit, a supplementary learning unit, and a pace adjustment unit is realized by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data such as the user's past learning history, interests, and lifestyle. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a learning plan based on the analyzed data. The provision unit is realized by the control unit 46A of the robot 414 and provides online lessons and learning materials. The monitoring unit is realized by the control unit 46A of the robot 414 and monitors learning progress. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the learning content. The collection unit is realized by the control unit 46A of the robot 414 and collects data. The supplementary learning unit is realized by the control unit 46A of the robot 414 and provides supplementary learning materials. The pace adjusting section is realized by the specific processing section 290 of the data processing device 12, and adjusts the learning pace.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The analysis unit can also monitor the user's health condition and adjust the method of analyzing learning needs based on the health data. For example, the analysis unit can obtain the user's heart rate and sleep patterns from the wearable device and evaluate the user's health condition. The analysis unit can also collect data on the user's diet and exercise habits to comprehensively analyze the user's health condition. Furthermore, the analysis unit can suggest a learning plan to reduce stress based on the user's health condition. This allows the system to suggest a more appropriate learning plan by adjusting the method of analyzing learning needs based on the user's health condition.

[0116] The collection unit can also analyze the user's social media activities and collect data based on their interests. For example, the collection unit can analyze the accounts the user follows on social media and the content of their posts to identify their interests. The collection unit can also analyze the time and frequency of the user's social media activities to optimize the timing of data collection. Furthermore, the collection unit can take into account the user's friendships on social media to collect related data. This allows for more appropriate learning support by collecting data based on the user's social media activities.

[0117] The tutoring unit can also adjust the format of the tutoring materials according to the user's learning style. For example, the tutoring unit can provide visual learning materials to a user who prefers visual learning. It can also provide audio learning materials to a user who prefers auditory learning. It can also provide interactive simulation learning materials to a user who prefers practical learning. This allows for more effective learning support by adjusting the format of the tutoring materials according to the user's learning style.

[0118] The pace adjustment unit can also monitor the user's learning environment and adjust the learning pace based on the environmental data. For example, the pace adjustment unit can use sensors to acquire the noise level and lighting conditions in the user's learning environment and evaluate the environment as being suitable for learning. The pace adjustment unit can also monitor the temperature and humidity of the user's learning environment and make adjustments to provide a comfortable learning environment. Furthermore, the pace adjustment unit can adjust the learning pace in real time in response to changes in the user's learning environment. This allows for more effective learning support by adjusting the learning pace based on the user's learning environment.

[0119] The analysis unit can estimate the user's emotions and adjust the analysis method for learning needs based on the estimated user's emotions. For example, the analysis unit estimates the user's emotions using facial expression recognition technology. For example, the analysis unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit acquires the user's voice data using a microphone and estimates the emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotions using survey results. For example, the analysis unit analyzes survey data answered by the user and estimates the emotion. This allows the analysis unit to adjust the analysis method for learning needs according to the user's emotions. For example, if the user is feeling stressed, the analysis method can be adjusted to suggest studying in a relaxing environment. Also, if the user is excited, the analysis unit can apply an analysis method to increase concentration and analyze the learning needs. Furthermore, if the user is tired, the analysis method can be adjusted to suggest a less stressful learning plan. By adjusting the analysis method for learning needs according to the user's emotions, a more appropriate learning plan can be suggested.

[0120] The suggestion unit can estimate the user's emotions and adjust the method for proposing a study plan based on the estimated user's emotions. For example, the suggestion unit estimates the user's emotions using facial expression recognition technology. For example, the suggestion unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the user's emotions using voice analysis technology. For example, the suggestion unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The suggestion unit can also estimate the user's emotions using survey results. For example, the suggestion unit analyzes survey data answered by the user and estimates the emotion. This allows the suggestion unit to adjust the method for proposing a study plan based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a study plan that helps the user relax. Furthermore, if the user is excited, the suggestion unit can suggest a study plan that helps the user concentrate. Furthermore, if the user is tired, the suggestion unit can suggest a study plan that is less stressful. This allows the suggestion unit to suggest a more appropriate study plan by adjusting the method for proposing a study plan based on the user's emotions.

[0121] The providing unit can estimate the user's emotions and adjust the method of providing online classes and teaching materials based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the providing unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit acquires the user's voice data using a microphone and estimates the emotion using a voice analysis algorithm. The providing unit can also estimate the user's emotions using survey results. For example, the providing unit analyzes survey data answered by the user and estimates the emotion. This allows the providing unit to adjust the method of providing online classes and teaching materials according to the user's emotions. For example, if the user is feeling stressed, the providing unit can provide online classes in a relaxing environment. Also, if the user is excited, the providing unit can provide teaching materials to improve concentration. Furthermore, if the user is tired, the providing unit can provide teaching materials that are less stressful. This allows the providing unit to provide a more appropriate learning environment by adjusting the method of providing online classes and teaching materials according to the user's emotions.

[0122] The monitoring unit can estimate the user's emotions and adjust the learning progress monitoring method based on the estimated user's emotions. The monitoring unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the monitoring unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The monitoring unit can also estimate the user's emotions using voice analysis technology. For example, the monitoring unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The monitoring unit can also estimate the user's emotions using survey results. For example, the monitoring unit analyzes survey data answered by the user and estimates the emotion. This allows the monitoring unit to adjust the learning progress monitoring method according to the user's emotions. For example, if the user is feeling stressed, monitoring can be performed in a relaxing environment. Furthermore, if the user is excited, a monitoring method to increase concentration can be applied. Furthermore, if the user is tired, a less stressful monitoring method can be applied. This allows more appropriate learning support to be provided by adjusting the learning progress monitoring method according to the user's emotions.

[0123] The adjustment unit can estimate the user's emotions and adjust the adjustment method of the learning content based on the estimated user's emotions. The adjustment unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the adjustment unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The adjustment unit can also estimate the user's emotions using voice analysis technology. For example, the adjustment unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The adjustment unit can also estimate the user's emotions using survey results. For example, the adjustment unit analyzes survey data answered by the user and estimates the emotion. This allows the adjustment unit to adjust the adjustment method of the learning content according to the user's emotions. For example, if the user is feeling stressed, the adjustment can be made to learning content that is relaxing. Also, if the user is excited, the adjustment can be made to learning content that increases concentration. Furthermore, if the user is tired, the adjustment can be made to learning content that is less stressful. As a result, by adjusting the adjustment method of the learning content according to the user's emotions, more appropriate learning support is possible.

[0124] The pace adjustment unit can estimate the user's emotions and adjust the learning pace adjustment method based on the estimated user's emotions. The pace adjustment unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the pace adjustment unit acquires the user's facial expression data using a camera and estimates the emotion using an emotion estimation algorithm. The pace adjustment unit can also estimate the user's emotions using voice analysis technology. For example, the pace adjustment unit acquires the user's voice data using a microphone and estimates the emotion using an emotion analysis algorithm. The pace adjustment unit can also estimate the user's emotions using survey results. For example, the pace adjustment unit analyzes survey data answered by the user and estimates the emotion. This allows the pace adjustment unit to adjust the learning pace adjustment method according to the user's emotions. For example, if the user is feeling stressed, the pace adjustment unit can adjust the learning pace to progress at a pace that allows the user to relax. Also, if the user is excited, the pace adjustment unit can adjust the learning pace to progress at a pace that increases concentration. Furthermore, if the user is tired, the pace adjustment unit can adjust the learning pace to progress at a pace that puts less strain on the user. This allows more appropriate learning support by adjusting the learning pace adjustment method according to the user's emotions.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The analysis unit analyzes learning needs. The analysis unit uses data mining technology to analyze data such as the user's past learning history, interests, and lifestyle. It can also predict the user's learning needs using machine learning algorithms and analyze the user's learning patterns using statistical analysis technology. Step 2: The suggestion unit proposes a study plan based on the data analyzed by the analysis unit. The suggestion unit uses AI to generate a study plan that best suits the user's learning needs and proposes a customized study plan based on the user's learning goals and schedule. The suggestion unit can also adjust the study plan based on user feedback. Step 3: The provider provides online lessons and learning materials based on the learning plan proposed by the suggester. The provider provides video lectures, e-books, interactive quizzes and simulations, and can also provide additional learning materials according to the user's learning progress. Step 4: The monitoring unit monitors the progress of the lessons and learning materials provided by the provider. The monitoring unit can record the user's study time and the number of assignments completed, track the user's test scores, and monitor the user's learning pace in real time. Step 5: The adjustment unit adjusts the learning content based on the progress monitored by the monitoring unit. The adjustment unit can adjust the difficulty level and learning pace of the learning content and can also provide additional supplementary materials according to the user's learning needs.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes learning needs; a suggestion unit that proposes a study plan based on the data analyzed by the analysis unit; a providing unit that provides online lessons or teaching materials based on the learning plan proposed by the proposing unit; a monitoring unit that monitors the progress of the lesson or teaching material provided by the providing unit; an adjustment unit that adjusts the learning content based on the progress monitored by the monitoring unit; Equipped with A system characterized by:

2. Equipped with a collection unit for collecting data 2. The system of claim 1.

3. Have a supplementary learning department that provides supplementary learning materials 2. The system of claim 1.

4. Equipped with a pace adjustment function that adjusts the learning pace 2. The system of claim 1.

5. The analysis unit Inferring user emotions and adjusting the analysis method of learning needs based on the estimated user emotions 2. The system of claim 1.

6. The analysis unit During analysis, the system analyzes the user's past learning history in detail and predicts changes in learning needs.

2. The system of claim 1.

7. The analysis unit During analysis, different analysis algorithms are applied based on the user's lifestyle and interests.

2. The system of claim 1.

8. The analysis unit During analysis, improve analysis accuracy by incorporating user feedback 2. The system of claim 1.

9. The proposal unit Estimate the user's emotions and adjust the way the learning plan is suggested based on the estimated user emotions.

2. The system of claim 1.

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