System

Abstract

To provide an education program personalized according to individual understanding.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects learning data of a user. The analysis unit analyzes the data collected by the collection unit and evaluates understanding level of the user. The generation unit generates a personalized education program based on degree of understanding evaluated by the analysis unit. The providing unit provides the education program generated by the generating unit to the user. The monitoring unit monitors learning progress of the user based on the education program provided by the providing unit, and adjusts the program as necessary.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to provide personalized educational programs tailored to each individual's level of understanding, posing challenges to fairness in education.

[0005] The system according to the embodiment aims to provide a personalized educational program tailored to each individual's level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects learning data of a user. The analysis unit analyzes the data collected by the collection unit and evaluates the user's level of understanding. The generation unit generates a personalized educational program based on the level of understanding evaluated by the analysis unit. The provision unit provides the user with the educational program generated by the generation unit. The monitoring unit monitors the user's learning progress based on the educational program provided by the provision unit, and adjusts the program as necessary. Effect of the Invention

[0007] The system according to the embodiment can provide a personalized educational program that is tailored to the individual's level of understanding. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention is a system that provides a personalized educational program tailored to each individual's level of understanding. This system aims to provide educational equity to children around the world and eliminate children who cannot receive an education due to poverty or other circumstances. For example, the system collects data to evaluate the user's learning situation and level of understanding. For example, data such as the user's past learning history, test results, and learning speed are collected. This data is input into the AI. Next, the AI ​​analyzes the collected data and evaluates the user's level of understanding. The AI ​​identifies the user's strengths and weaknesses and determines which areas require additional learning. For example, if the user lacks understanding in a specific area of ​​mathematics, a learning program focusing on that area is provided. Furthermore, the AI ​​generates a personalized educational program based on the user's level of understanding. For example, the AI ​​provides teaching materials in a format that is easy for the user to understand and asks questions of appropriate difficulty. In this way, the user's learning effect can be maximized. The generated educational program is provided to the user. The user can proceed with learning through the teaching materials and problems provided by the AI. The AI ​​also monitors the user's learning progress in real time and adjusts the program as necessary. For example, if the user stumbles on a particular problem, the AI ​​provides additional explanations and practice problems. This system will allow children around the world to receive an equal education. With a thorough education, the wisdom and experience of 6 billion people will accumulate, and with the use of AI, it will be possible to permeate the world and evolve into a more efficient and productive world. This will allow the AI ​​system to provide personalized educational programs tailored to each individual's level of understanding.

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects learning data of a user. The learning data of a user includes, for example, a past learning history, a test result, a learning speed, and the like, but is not limited to such examples. The collection unit, for example, acquires the user's past learning history from a database. The collection unit can also acquire the user's test results from an online test system. Furthermore, the collection unit can also acquire the user's learning speed from a learning management system. For example, the collection unit acquires the user's past learning history from a database, acquires the test results from an online test system, and acquires the learning speed from a learning management system. The analysis unit analyzes the data collected by the collection unit and evaluates the user's understanding. For example, the analysis unit analyzes the user's test results and calculates the correct answer rate. Furthermore, the analysis unit can analyze the user's learning history and evaluate the progress of learning. Furthermore, the analysis unit can analyze the user's learning speed and evaluate the efficiency of learning. For example, the analysis unit analyzes the user's test results, calculates the correct answer rate, analyzes the learning history, evaluates the progress of the learning, analyzes the learning speed, and evaluates the efficiency of the learning. The generation unit generates a personalized educational program based on the level of understanding evaluated by the analysis unit. The generation unit, for example, selects teaching materials with an appropriate level of difficulty based on the user's level of understanding. The generation unit can also select the format of the teaching materials according to the user's learning style. Furthermore, the generation unit can customize the content of the learning program based on the user's learning goals. For example, the generation unit selects teaching materials with an appropriate level of difficulty based on the user's level of understanding, selects the format of the teaching materials according to the learning style, and customizes the content of the learning program based on the learning goals. The provision unit provides the user with the educational program generated by the generation unit. The provision unit provides the educational program, for example, through an online platform. The provision unit can also provide the educational program through a mobile app. Furthermore, the provision unit can provide the educational program through email.For example, the providing unit provides an educational program through an online platform, provides an educational program through a mobile app, and provides an educational program through email. The monitoring unit monitors the user's learning progress based on the educational program provided by the providing unit, and adjusts the program as necessary. The monitoring unit, for example, monitors the user's learning progress in real time. The monitoring unit can also provide additional explanations and practice questions based on the user's learning progress. Furthermore, the monitoring unit can adjust the content of the learning program based on the user's learning progress. For example, the monitoring unit monitors the user's learning progress in real time, provides additional explanations and practice questions, and adjusts the content of the learning program. As a result, the AI ​​system according to the embodiment can provide a personalized educational program tailored to each individual's level of understanding.

[0030] The collection unit collects the learning data of the user. The learning data of the user includes, for example, past learning history, test results, learning speed, etc., but is not limited to such examples. For example, the collection unit acquires the user's past learning history from a database. The collection unit can also acquire the user's test results from an online test system. Furthermore, the collection unit can acquire the user's learning speed from a learning management system. For example, the collection unit acquires the user's past learning history from a database, acquires the test results from an online test system, and acquires the learning speed from a learning management system. The collection unit can centrally manage these data and update them in real time. For example, when a user takes a new test, the results are immediately sent to the collection unit and added to the database. Furthermore, the collection unit can take measures to regularly back up the user's learning data and prevent data loss. The collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, the collection frequency can be increased to grasp in detail the fluctuations in learning speed during a specific period. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system. Furthermore, in order to protect the privacy of the user, the collection unit encrypts the data and controls access to the data to ensure the security of the data, thereby enabling the collection unit to gain the trust of the user.

[0031] The analysis unit analyzes the data collected by the collection unit and evaluates the user's level of understanding. For example, the analysis unit analyzes the user's test results and calculates the correct answer rate. The analysis unit can also analyze the user's learning history and evaluate the progress of the learning. Furthermore, the analysis unit can analyze the user's learning speed and evaluate the efficiency of the learning. For example, the analysis unit analyzes the user's test results, calculates the correct answer rate, analyzes the learning history, evaluates the progress of the learning, analyzes the learning speed, and evaluates the efficiency of the learning. The analysis unit analyzes these data using AI to grasp the user's learning patterns and tendencies. For example, a machine learning algorithm can be used to extract a learning progress pattern from the user's past learning data and predict the future progress of the learning. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual learning patterns and abnormal data and issue an early warning. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system. The analysis unit continuously monitors the user's learning data and periodically updates the analysis results to respond to the latest situation. This allows the analysis unit to accurately evaluate the user's level of understanding and support the provision of appropriate educational programs.

[0032] The generation unit generates a personalized educational program based on the level of understanding evaluated by the analysis unit. The generation unit, for example, selects teaching materials of an appropriate level of difficulty based on the user's level of understanding. The generation unit can also select the format of the teaching materials according to the user's learning style. Furthermore, the generation unit can customize the content of the learning program based on the user's learning goals. For example, the generation unit selects teaching materials of an appropriate level of difficulty based on the user's level of understanding, selects the format of the teaching materials according to the learning style, and customizes the content of the learning program based on the learning goals. The generation unit automates these processes using AI to efficiently generate a personalized educational program. For example, natural language processing technology can be used to extract important keywords and topics from the user's learning history and test results, and appropriate teaching materials can be selected based on the extracted keywords and topics. Furthermore, the generation unit can collect user feedback and continuously improve the content of the educational program. For example, if a user gives a high rating to a specific teaching material, the user can recommend the teaching material to other users. This allows the generation unit to provide a high-quality educational program that meets the needs of the user. The generation unit can centrally manage the generated educational programs and cooperate with other systems and departments as necessary. This enables the generation unit to generate training programs efficiently and effectively, thereby improving the performance of the entire system.

[0033] The providing unit provides the user with the educational program generated by the generating unit. The providing unit provides the educational program through, for example, an online platform. The providing unit can also provide the educational program through a mobile app. Furthermore, the providing unit can also provide the educational program through e-mail. For example, the providing unit provides the educational program through an online platform, provides the educational program through a mobile app, and provides the educational program through e-mail. The providing unit provides the educational program using multiple providing means in consideration of the convenience of the user. For example, the educational program can be provided through a mobile app so that the user can continue learning even when on the go. The providing unit can also monitor the user's learning progress in real time and provide additional teaching materials and explanations as necessary. This allows the providing unit to maximize the learning effect of the user. Furthermore, the providing unit can collect user feedback and continuously improve the content and format of the educational program to be provided. For example, if a user gives a high rating to a specific teaching material, the teaching material can be recommended to other users. This allows the providing unit to provide a high-quality educational program according to the needs of the user. The providing unit can centrally manage the usage status of the provided educational program and cooperate with other systems and departments as necessary. This enables the providing unit to provide educational programs efficiently and effectively, thereby improving the performance of the entire system.

[0034] The monitoring unit monitors the user's learning progress based on the educational program provided by the provision unit, and adjusts the program as necessary. The monitoring unit, for example, monitors the user's learning progress in real time. The monitoring unit can also provide additional explanations and practice questions based on the user's learning progress. Furthermore, the monitoring unit can adjust the content of the learning program based on the user's learning progress. For example, the monitoring unit monitors the user's learning progress in real time, provides additional explanations and practice questions, and adjusts the content of the learning program. The monitoring unit automates these processes using AI to efficiently monitor the user's learning progress. For example, a machine learning algorithm can be used to extract learning progress patterns from the user's learning data and detect abnormal progress or stagnation early. Furthermore, the monitoring unit can collect user feedback and continuously improve the content and format of the learning program. For example, if a user finds it difficult to understand a particular teaching material, the teaching material can be reviewed and more easily understood explanations and additional practice questions can be provided. This allows the monitoring unit to maximize the user's learning effect. The monitoring unit can centrally manage the monitored learning progress data and cooperate with other systems and departments as necessary. This enables the monitoring unit to efficiently and effectively monitor the user's learning progress, thereby improving the performance of the entire system.

[0035] The collection unit can collect at least one of the data of the user's past learning history or test results, and learning speed. The collection unit, for example, acquires the user's past learning history from a database. For example, the collection unit acquires the user's past learning history from a database and records the learning content and learning time. The collection unit can also acquire the user's test results from an online test system. For example, the collection unit acquires the user's test results from an online test system and records the type of test and the correct answer rate. Furthermore, the collection unit can also acquire the user's learning speed from a learning management system. For example, the collection unit acquires the user's learning speed from a learning management system and records the amount of learning per unit time and the learning progress speed. In this way, the collection unit can provide more accurate learning data by collecting data such as the user's past learning history, test results, and learning speed. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can have the AI ​​execute processes to retrieve the user's past learning history from a database, retrieve test results from an online testing system, and retrieve learning speed from a learning management system.

[0036] The analysis unit can analyze the collected data and identify the strengths and weaknesses of the user. The analysis unit, for example, analyzes the test results of the user and calculates the correct answer rate. For example, the analysis unit analyzes the test results of the user, calculates the correct answer rate, and identifies the strong subjects and weak areas. The analysis unit can also analyze the learning history of the user and evaluate the progress of the learning. For example, the analysis unit analyzes the learning history of the user, evaluates the progress of the learning, and grasps the progress of the learning. Furthermore, the analysis unit can analyze the learning speed of the user and evaluate the efficiency of the learning. For example, the analysis unit analyzes the learning speed of the user, evaluates the efficiency of the learning, and adjusts the pace of the learning. In this way, the analysis unit can provide a more effective educational program by identifying the strengths and weaknesses of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can have the AI ​​perform the following processes: analyzing a user's test results, calculating the accuracy rate, analyzing the learning history, evaluating the learning progress, analyzing the learning speed, and evaluating the efficiency of learning.

[0037] The generation unit can provide the teaching materials in a format that is easy for the user to understand based on the user's level of understanding. The generation unit, for example, selects teaching materials of an appropriate level of difficulty based on the user's level of understanding. For example, the generation unit selects teaching materials of an appropriate level of difficulty based on the user's level of understanding and provides them in a format that is easy for the user to understand. The generation unit can also select the format of the teaching materials according to the user's learning style. For example, the generation unit selects a format such as visual teaching materials, audio teaching materials, and text teaching materials according to the user's learning style. Furthermore, the generation unit can also customize the contents of the learning program based on the user's learning goals. For example, the generation unit customizes the contents of the learning program based on the user's learning goals and provides them in a format that is easy for the user to understand. As a result, the generation unit can maximize the learning effect by providing the teaching materials in a format that is easy for the user to understand. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have the AI ​​perform processes to select learning materials of an appropriate level of difficulty based on the user's level of understanding, select the format of the learning materials according to the learning style, and customize the content of the learning program based on the learning goals.

[0038] The providing unit can provide the generated educational program to the user. The providing unit provides the educational program, for example, through an online platform. For example, the providing unit provides the educational program through an online platform so that the user can access it. The providing unit can also provide the educational program through a mobile app. For example, the providing unit provides the educational program through a mobile app so that the user can learn on a smartphone or tablet. The providing unit can also provide the educational program through e-mail. For example, the providing unit provides the educational program through e-mail so that the user can receive it by e-mail. Thereby, the providing unit can advance learning by providing the generated educational program to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute processing of providing the educational program through an online platform, providing the educational program through a mobile app, and providing the educational program through e-mail.

[0039] The monitoring unit can monitor the user's learning progress in real time and provide additional explanations or practice questions as necessary. The monitoring unit, for example, monitors the user's learning progress in real time. For example, the monitoring unit monitors the user's learning progress in real time and grasps the learning progress. The monitoring unit can also provide additional explanations and practice questions based on the user's learning progress. For example, the monitoring unit provides additional explanations when the user stumbles on a specific problem. The monitoring unit can also provide practice questions when the user lacks understanding in a specific area. For example, the monitoring unit provides additional explanations when the user stumbles on a specific problem, and provides practice questions when the user lacks understanding in a specific area. Thereby, the monitoring unit can monitor the user's learning progress in real time and provide additional explanations and practice questions as necessary, thereby maximizing the learning effect. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can monitor the user's learning progress in real time and cause AI to execute a process of providing additional explanations and practice questions.

[0040] The collection unit can analyze the user's past learning history and select an appropriate data collection method. The collection unit, for example, analyzes the user's past learning history and selects an appropriate data collection method. For example, the collection unit preferentially collects data on subjects in which the user achieved high grades in the past. The collection unit can also collect data on subjects in which the user was weak in the past in detail. Furthermore, the collection unit can analyze the user's learning pattern and collect data at the optimal timing. For example, the collection unit preferentially collects data on subjects in which the user achieved high grades in the past, collects data on subjects in which the user was weak in detail, analyzes the learning pattern, and collects data at the optimal timing. Thereby, the collection unit can select the optimal data collection method by analyzing the user's past learning history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the user's past learning history and cause AI to execute a process of selecting an appropriate data collection method.

[0041] The collection unit can perform filtering based on the user's current learning environment and field of interest when collecting the learning data. For example, when collecting the learning data, the collection unit performs filtering based on the user's current learning environment and field of interest. For example, when the user is studying in a quiet environment, the collection unit collects data suitable for that environment. In addition, when the user is interested in a specific field, the collection unit can also preferentially collect data related to that field. Furthermore, when the user is studying in a group, the collection unit can also collect data of the entire group. For example, when the user is studying in a quiet environment, the collection unit collects data suitable for that environment, when the user is interested in a specific field, the collection unit preferentially collects data related to that field, and when studying in a group, the collection unit collects data of the entire group. Thereby, the collection unit can collect more relevant data by filtering data based on the user's current learning environment and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, when collecting the learning data, the collection unit can cause AI to execute a process of filtering based on the user's current learning environment and field of interest.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting the learning data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting the learning data. For example, when the user lives in a specific area, the collection unit preferentially collects data related to the area. In addition, when the user is traveling, the collection unit can also collect data related to the travel destination. Furthermore, when the user attends a specific school, the collection unit can also collect data related to the curriculum of the school. For example, when the user lives in a specific area, the collection unit preferentially collects data related to the area, when the user is traveling, the collection unit collects data related to the travel destination, and when the user attends a specific school, the collection unit collects data related to the curriculum of the school. In this way, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to execute a process of preferentially collecting highly relevant data in consideration of the geographical location information of the user when collecting the learning data.

[0043] The collection unit can analyze the social media activity of the user and collect related data when collecting the learning data. For example, the collection unit analyzes the social media activity of the user and collects related data when collecting the learning data. For example, the collection unit collects data based on the learning content shared by the user on social media. The collection unit can also collect data from education-related accounts followed by the user. Furthermore, the collection unit can collect data from an online learning community in which the user participates. For example, the collection unit collects data based on the learning content shared by the user on social media, collects data from education-related accounts followed by the user, and collects data from the online learning community. In this way, the collection unit can collect related data by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to execute a process of analyzing the social media activity of the user and collecting related data when collecting the learning data.

[0044] The analysis unit can adjust the analysis algorithm by referring to the user's past learning data during the analysis. The analysis unit adjusts the analysis algorithm by referring to the user's past learning data, for example, during the analysis. For example, the analysis unit optimizes the analysis algorithm based on data on subjects in which the user has achieved high grades in the past. The analysis unit can also adjust the analysis algorithm based on data on subjects in which the user has had difficulty in the past. Furthermore, the analysis unit can analyze the user's learning pattern and select the optimal analysis algorithm. For example, the analysis unit optimizes the analysis algorithm based on data on subjects in which the user has had high grades in the past, adjusts the analysis algorithm based on data on subjects in which the user has had difficulty, and analyzes the learning pattern to select the optimal analysis algorithm. Thereby, the analysis unit can optimize the analysis algorithm by referring to the user's past learning data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute a process of adjusting the analysis algorithm by referring to the user's past learning data during the analysis.

[0045] The analysis unit can apply different analysis methods according to the learning style of the user during analysis. For example, the analysis unit applies different analysis methods according to the learning style of the user during analysis. For example, when the user is a visual learner, the analysis unit applies a visual data analysis method. In addition, when the user is an auditory learner, the analysis unit can also apply an analysis method using audio data. Furthermore, when the user is an experiential learner, the analysis unit can also apply a practical data analysis method. For example, when the user is a visual learner, the analysis unit applies a visual data analysis method, when the user is an auditory learner, the analysis method using audio data, and when the user is an experiential learner, the analysis unit applies a practical data analysis method. Thereby, the analysis unit can perform more effective analysis by applying different analysis methods according to the learning style of the user. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute a process of applying different analysis methods according to the learning style of the user during analysis.

[0046] The analysis unit can determine the analysis priority based on the user's learning progress at the time of analysis. The analysis unit, for example, determines the analysis priority based on the user's learning progress at the time of analysis. For example, the analysis unit prioritizes analysis of data of subjects in which the user is slow to progress. The analysis unit can also postpone data of subjects in which the user is fast to progress. Furthermore, the analysis unit can also determine the analysis priority based on the user's learning goal. For example, the analysis unit prioritizes analysis of data of subjects in which the user is slow to progress, postpones data of subjects in which the user is fast to progress, and determines the analysis priority based on the learning goal. As a result, the analysis unit determines the analysis priority based on the user's learning progress, thereby enabling more effective analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute a process of determining the analysis priority based on the user's learning progress at the time of analysis.

[0047] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. For example, the analysis unit performs the analysis based on the literature the user has previously referred to. The analysis unit can also perform the analysis by referring to the literature the user is currently studying. Furthermore, the analysis unit can perform the analysis by referring to the latest literature related to the user's field of study. For example, the analysis unit performs the analysis based on the literature the user has previously referred to, performs the analysis by referring to the literature the user is currently studying, and performs the analysis by referring to the latest literature related to the field of study. Thereby, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to perform a process of improving the accuracy of the analysis by referring to the user's related literature during the analysis.

[0048] The generation unit can adjust the level of detail of the teaching material based on the user's level of understanding at the time of generation. The generation unit adjusts the level of detail of the teaching material based on the user's level of understanding at the time of generation, for example. For example, the generation unit provides detailed teaching material when the user finds it easy to understand. The generation unit can also provide simplified teaching material when the user finds it difficult to understand. Furthermore, the generation unit can provide teaching material of an appropriate level of difficulty according to the user's level of understanding. For example, the generation unit provides detailed teaching material when the user finds it easy to understand, and provides simplified teaching material when the user finds it difficult to understand, providing teaching material of an appropriate level of difficulty according to the user's level of understanding. As a result, the generation unit adjusts the level of detail of the teaching material based on the user's level of understanding, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of adjusting the level of detail of the teaching material based on the user's level of understanding at the time of generation.

[0049] The generation unit can apply different teaching material generation algorithms according to the user's learning style at the time of generation. The generation unit applies different teaching material generation algorithms according to the user's learning style at the time of generation, for example. For example, the generation unit generates visual teaching materials when the user is a visual learner. The generation unit can also generate audio teaching materials when the user is an auditory learner. Furthermore, the generation unit can generate practical teaching materials when the user is an experiential learner. For example, the generation unit generates visual teaching materials when the user is a visual learner, generates audio teaching materials when the user is an auditory learner, and generates practical teaching materials when the user is an experiential learner. Thereby, the generation unit applies different teaching material generation algorithms according to the user's learning style, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of applying different teaching material generation algorithms according to the user's learning style at the time of generation.

[0050] The generating unit can determine the priority of the teaching materials based on the user's learning progress at the time of generation. The generating unit, for example, determines the priority of the teaching materials based on the user's learning progress at the time of generation. For example, the generating unit generates teaching materials preferentially for subjects in which the user is slow to progress. The generating unit can also postpone teaching materials for subjects in which the user is fast to progress. Furthermore, the generating unit can determine the priority of the teaching materials based on the user's learning goal. For example, the generating unit generates teaching materials preferentially for subjects in which the user is slow to progress, postpones teaching materials for subjects in which the user is fast to progress, and determines the priority of the teaching materials based on the learning goal. As a result, the generating unit determines the priority of the teaching materials based on the user's learning progress, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can cause AI to execute a process of determining the priority of the teaching materials based on the user's learning progress at the time of generation.

[0051] The generation unit can customize the contents of the teaching materials by referring to data in the user's related field at the time of generation. The generation unit customizes the contents of the teaching materials by referring to data in the user's related field at the time of generation, for example. For example, the generation unit generates teaching materials based on data related to the field in which the user is studying. The generation unit can also customize the teaching materials by referring to data in the field in which the user is interested. Furthermore, the generation unit can generate teaching materials based on data related to the user's learning goal. For example, the generation unit generates teaching materials based on data related to the field in which the user is studying, customizes the teaching materials by referring to data in the field in which the user is interested, and generates teaching materials based on data related to the learning goal. As a result, the generation unit customizes the contents of the teaching materials by referring to data in the user's related field, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of customizing the contents of the teaching materials by referring to data in the user's related field at the time of generation.

[0052] The providing unit can select the optimal providing method by referring to the user's past learning history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past learning history at the time of providing. For example, the providing unit preferentially selects a providing method that was effective for the user in the past. The providing unit can also avoid a providing method that the user had difficulty with in the past. Furthermore, the providing unit can analyze the user's learning history and select the optimal providing method. For example, the providing unit preferentially selects a providing method that was effective for the user in the past, avoids a providing method that the user had difficulty with, and analyzes the learning history to select the optimal providing method. As a result, the providing unit selects the optimal providing method by referring to the user's past learning history, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of selecting the optimal providing method by referring to the user's past learning history at the time of providing.

[0053] The providing unit can customize the providing means based on the user's learning environment at the time of providing. The providing unit customizes the providing means based on the user's learning environment at the time of providing, for example. For example, the providing unit provides audio teaching materials when the user is studying in a quiet environment. In addition, the providing unit can also provide teaching materials optimized for a mobile device when the user is on the move. Furthermore, the providing unit can provide teaching materials suitable for collaborative learning when the user is studying in a group. For example, the providing unit provides audio teaching materials when the user is studying in a quiet environment, provides teaching materials optimized for a mobile device when the user is on the move, and provides teaching materials suitable for collaborative learning when studying in a group. As a result, the providing unit customizes the providing means based on the user's learning environment, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of customizing the providing means based on the user's learning environment at the time of providing.

[0054] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user lives in a specific area, the providing unit provides teaching materials related to the area. In addition, when the user is traveling, the providing unit can also provide teaching materials related to the travel destination. Furthermore, when the user attends a specific school, the providing unit can also provide teaching materials related to the curriculum of the school. For example, when the user lives in a specific area, the providing unit provides teaching materials related to the area, when the user is traveling, provides teaching materials related to the travel destination, and when the user attends a specific school, provides teaching materials related to the curriculum of the school. As a result, the providing unit selects the optimal providing method in consideration of the geographical location information of the user, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of selecting the optimal providing method in consideration of the geographical location information of the user at the time of providing.

[0055] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. For example, the providing unit analyzes the user's social media activity and suggests a means of provision at the time of provision. For example, the providing unit provides educational materials based on learning contents shared by the user on social media. The providing unit can also provide educational materials from education-related accounts followed by the user. Furthermore, the providing unit can provide educational materials from an online learning community in which the user participates. For example, the providing unit provides educational materials based on learning contents shared by the user on social media, provides educational materials from education-related accounts followed by the user, and provides educational materials from an online learning community. As a result, the providing unit analyzes the user's social media activity and suggests a means of provision, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of analyzing the user's social media activity and suggesting a means of provision at the time of provision.

[0056] The monitoring unit can optimize the monitoring algorithm by referring to the user's past learning data during monitoring. The monitoring unit optimizes the monitoring algorithm by referring to the user's past learning data during monitoring, for example. For example, the monitoring unit optimizes the monitoring algorithm based on data on subjects in which the user has achieved high grades in the past. The monitoring unit can also adjust the monitoring algorithm based on data on subjects in which the user has had difficulty in the past. Furthermore, the monitoring unit can analyze the user's learning pattern and select an optimal monitoring algorithm. For example, the monitoring unit optimizes the monitoring algorithm based on data on subjects in which the user has had high grades in the past, adjusts the monitoring algorithm based on data on subjects in which the user has had difficulty, and analyzes the learning pattern to select an optimal monitoring algorithm. As a result, the monitoring unit optimizes the monitoring algorithm by referring to the user's past learning data, thereby enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of optimizing the monitoring algorithm by referring to the user's past learning data during monitoring.

[0057] The monitoring unit can apply different monitoring methods according to the learning style of the user during monitoring. The monitoring unit, for example, applies different monitoring methods according to the learning style of the user during monitoring. For example, when the user is a visual learner, the monitoring unit applies a visual progress monitoring method. Also, when the user is an auditory learner, the monitoring unit can apply a progress monitoring method using audio data. Furthermore, when the user is an experiential learner, the monitoring unit can apply a practical progress monitoring method. For example, when the user is a visual learner, the monitoring unit applies a visual progress monitoring method, when the user is an auditory learner, the monitoring unit applies a progress monitoring method using audio data, and when the user is an experiential learner, the monitoring unit applies a practical progress monitoring method. Thereby, the monitoring unit applies different monitoring methods according to the learning style of the user, thereby enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of applying different monitoring methods according to the learning style of the user during monitoring.

[0058] The monitoring unit can determine the monitoring priority based on the user's learning progress during monitoring. The monitoring unit, for example, determines the monitoring priority based on the user's learning progress during monitoring. For example, the monitoring unit preferentially monitors the progress of subjects in which the user is lagging behind. The monitoring unit can also postpone the progress of subjects in which the user is making fast progress. Furthermore, the monitoring unit can determine the monitoring priority based on the user's learning goals. For example, the monitoring unit preferentially monitors the progress of subjects in which the user is lagging behind, postpones the progress of subjects in which the user is making fast progress, and determines the monitoring priority based on the learning goals. As a result, the monitoring unit determines the monitoring priority based on the user's learning progress, enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of determining the monitoring priority based on the user's learning progress during monitoring.

[0059] The monitoring unit can improve the accuracy of monitoring by referring to data in a field related to the user during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to data in a field related to the user during monitoring. For example, the monitoring unit monitors progress based on data related to the field in which the user is studying. The monitoring unit can also monitor progress by referring to data in a field in which the user is interested. Furthermore, the monitoring unit can monitor progress based on data related to the user's learning goal. For example, the monitoring unit monitors progress based on data related to the field in which the user is studying, monitors progress by referring to data in the field in which the user is interested, and monitors progress based on data related to the learning goal. As a result, the monitoring unit improves the accuracy of monitoring by referring to data in a field related to the user, thereby enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of improving the accuracy of monitoring by referring to data in a field related to the user during monitoring.

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

[0061] When analyzing the user's learning data, the analysis unit can take into account not only the user's learning history but also the user's lifestyle data. For example, by collecting data such as the user's sleep pattern, meal times, and exercise frequency, and incorporating these data into the analysis, the user's learning efficiency can be evaluated more accurately. The analysis unit can also suggest the optimal timing for learning based on the user's lifestyle data. For example, it can identify the time period when the user can concentrate the most and recommend that the user study during that time period. Furthermore, the analysis unit can adjust the content of the learning program using the user's lifestyle data. For example, if the user is relaxing after exercising, it can set a more difficult question at that time. As a result, the analysis unit can provide a more effective learning program by taking into account the user's lifestyle data.

[0062] When collecting the user's learning data, the collection unit can analyze the user's social media activities. For example, data such as learning contents and comments shared by the user on social media and education-related accounts followed by the user can be collected and incorporated into the learning data. The collection unit can also collect data from online learning communities in which the user participates. For example, questions asked by the user on online forums and interactions with other users can be collected and used as learning data. Furthermore, the collection unit can identify learning interests and concerns based on the user's social media activities. For example, education-related content frequently viewed by the user can be analyzed and a learning program related to that field can be provided. This allows the collection unit to collect more personalized learning data by utilizing the user's social media activities.

[0063] The generating unit can generate learning materials according to the learning style of the user based on the learning data of the user. For example, if the user is a visual learner, visual learning materials can be generated, and if the user is an auditory learner, audio learning materials can be generated. The generating unit can also adjust the difficulty and content of the learning materials according to the learning pace of the user. For example, if the user is learning at a fast pace, difficult questions can be provided, and if the user is learning slowly, basic content can be provided. Furthermore, the generating unit can customize the content of the learning program based on the learning goals of the user. For example, if the user is studying for a specific exam, questions related to that exam can be provided with a focus. In this way, the generating unit can maximize the learning effect by providing learning materials according to the learning style, pace, and goals of the user.

[0064] The monitoring unit can take into account the user's learning environment when monitoring the user's learning progress. For example, if the user is learning in a quiet environment, a monitoring method suitable for that environment can be applied. Also, if the user is learning while moving, a monitoring method optimized for mobile devices can be applied. Furthermore, if the user is learning in a group, the learning progress of the entire group can be monitored. For example, the progress of each member in the group can be grasped and individual feedback can be provided as necessary. In this way, the monitoring unit can provide more effective learning support by applying a monitoring method according to the user's learning environment.

[0065] When analyzing the user's learning data, the analysis unit can take into account not only the user's learning history but also the user's lifestyle data. For example, by collecting data such as the user's sleep pattern, meal times, and exercise frequency, and incorporating these data into the analysis, the user's learning efficiency can be evaluated more accurately. The analysis unit can also suggest the optimal timing for learning based on the user's lifestyle data. For example, it can identify the time period when the user can concentrate the most and recommend that the user study during that time period. Furthermore, the analysis unit can adjust the content of the learning program using the user's lifestyle data. For example, if the user is relaxing after exercising, it can set a more difficult question at that time. As a result, the analysis unit can provide a more effective learning program by taking into account the user's lifestyle data.

[0066] The monitoring unit can take into account the user's learning environment when monitoring the user's learning progress. For example, if the user is learning in a quiet environment, a monitoring method suitable for that environment can be applied. Also, if the user is learning while moving, a monitoring method optimized for mobile devices can be applied. Furthermore, if the user is learning in a group, the learning progress of the entire group can be monitored. For example, the progress of each member in the group can be grasped and individual feedback can be provided as necessary. In this way, the monitoring unit can provide more effective learning support by applying a monitoring method according to the user's learning environment.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The collection unit collects the user's learning data. The user's learning data includes past learning history, test results, learning speed, etc. The collection unit, for example, obtains the user's past learning history from a database, obtains the test results from an online testing system, and obtains the learning speed from a learning management system. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's level of understanding. For example, the analysis unit analyzes the user's test results, calculates the percentage of correct answers, analyzes the learning history, evaluates the progress of learning, analyzes the learning speed, and evaluates the efficiency of learning. Step 3: The generator generates a personalized educational program based on the level of understanding evaluated by the analyzer. For example, the generator selects learning materials with an appropriate level of difficulty based on the user's level of understanding, selects the format of the learning materials according to the learning style, and customizes the content of the learning program based on the learning goals. Step 4: The providing unit provides the educational program generated by the generating unit to the user. The providing unit provides the educational program through, for example, an online platform, a mobile app, or email. Step 5: The monitoring unit monitors the user's learning progress based on the educational program provided by the provision unit, and adjusts the program as necessary. For example, the monitoring unit monitors the user's learning progress in real time, provides additional explanations and practice questions, and adjusts the content of the learning program.

[0069] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that provides a personalized educational program tailored to each individual's level of understanding. This system aims to provide educational equity to children around the world and eliminate children who cannot receive an education due to poverty or other circumstances. For example, the system collects data to evaluate the user's learning situation and level of understanding. For example, data such as the user's past learning history, test results, and learning speed are collected. This data is input into the AI. Next, the AI ​​analyzes the collected data and evaluates the user's level of understanding. The AI ​​identifies the user's strengths and weaknesses and determines which areas require additional learning. For example, if the user lacks understanding in a specific area of ​​mathematics, a learning program focusing on that area is provided. Furthermore, the AI ​​generates a personalized educational program based on the user's level of understanding. For example, the AI ​​provides teaching materials in a format that is easy for the user to understand and asks questions of appropriate difficulty. In this way, the user's learning effect can be maximized. The generated educational program is provided to the user. The user can proceed with learning through the teaching materials and problems provided by the AI. The AI ​​also monitors the user's learning progress in real time and adjusts the program as necessary. For example, if the user stumbles on a particular problem, the AI ​​provides additional explanations and practice problems. This system will allow children around the world to receive an equal education. With a thorough education, the wisdom and experience of 6 billion people will accumulate, and with the use of AI, it will be possible to permeate the world and evolve into a more efficient and productive world. This will allow the AI ​​system to provide personalized educational programs tailored to each individual's level of understanding.

[0070] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a monitoring unit. The collection unit collects learning data of a user. The learning data of a user includes, for example, a past learning history, a test result, a learning speed, and the like, but is not limited to such examples. The collection unit, for example, acquires the user's past learning history from a database. The collection unit can also acquire the user's test results from an online test system. Furthermore, the collection unit can also acquire the user's learning speed from a learning management system. For example, the collection unit acquires the user's past learning history from a database, acquires the test results from an online test system, and acquires the learning speed from a learning management system. The analysis unit analyzes the data collected by the collection unit and evaluates the user's understanding. For example, the analysis unit analyzes the user's test results and calculates the correct answer rate. Furthermore, the analysis unit can analyze the user's learning history and evaluate the progress of learning. Furthermore, the analysis unit can analyze the user's learning speed and evaluate the efficiency of learning. For example, the analysis unit analyzes the user's test results, calculates the correct answer rate, analyzes the learning history, evaluates the progress of the learning, analyzes the learning speed, and evaluates the efficiency of the learning. The generation unit generates a personalized educational program based on the level of understanding evaluated by the analysis unit. The generation unit, for example, selects teaching materials with an appropriate level of difficulty based on the user's level of understanding. The generation unit can also select the format of the teaching materials according to the user's learning style. Furthermore, the generation unit can customize the content of the learning program based on the user's learning goals. For example, the generation unit selects teaching materials with an appropriate level of difficulty based on the user's level of understanding, selects the format of the teaching materials according to the learning style, and customizes the content of the learning program based on the learning goals. The provision unit provides the user with the educational program generated by the generation unit. The provision unit provides the educational program, for example, through an online platform. The provision unit can also provide the educational program through a mobile app. Furthermore, the provision unit can provide the educational program through email.For example, the providing unit provides an educational program through an online platform, provides an educational program through a mobile app, and provides an educational program through email. The monitoring unit monitors the user's learning progress based on the educational program provided by the providing unit, and adjusts the program as necessary. The monitoring unit, for example, monitors the user's learning progress in real time. The monitoring unit can also provide additional explanations and practice questions based on the user's learning progress. Furthermore, the monitoring unit can adjust the content of the learning program based on the user's learning progress. For example, the monitoring unit monitors the user's learning progress in real time, provides additional explanations and practice questions, and adjusts the content of the learning program. As a result, the AI ​​system according to the embodiment can provide a personalized educational program tailored to each individual's level of understanding.

[0071] The collection unit collects the learning data of the user. The learning data of the user includes, for example, past learning history, test results, learning speed, etc., but is not limited to such examples. For example, the collection unit acquires the user's past learning history from a database. The collection unit can also acquire the user's test results from an online test system. Furthermore, the collection unit can acquire the user's learning speed from a learning management system. For example, the collection unit acquires the user's past learning history from a database, acquires the test results from an online test system, and acquires the learning speed from a learning management system. The collection unit can centrally manage these data and update them in real time. For example, when a user takes a new test, the results are immediately sent to the collection unit and added to the database. Furthermore, the collection unit can take measures to regularly back up the user's learning data and prevent data loss. The collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, the collection frequency can be increased to grasp in detail the fluctuations in learning speed during a specific period. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system. Furthermore, in order to protect the privacy of the user, the collection unit encrypts the data and controls access to the data to ensure the security of the data, thereby enabling the collection unit to gain the trust of the user.

[0072] The analysis unit analyzes the data collected by the collection unit and evaluates the user's level of understanding. For example, the analysis unit analyzes the user's test results and calculates the correct answer rate. The analysis unit can also analyze the user's learning history and evaluate the progress of the learning. Furthermore, the analysis unit can analyze the user's learning speed and evaluate the efficiency of the learning. For example, the analysis unit analyzes the user's test results, calculates the correct answer rate, analyzes the learning history, evaluates the progress of the learning, analyzes the learning speed, and evaluates the efficiency of the learning. The analysis unit analyzes these data using AI to grasp the user's learning patterns and tendencies. For example, a machine learning algorithm can be used to extract a learning progress pattern from the user's past learning data and predict the future progress of the learning. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual learning patterns and abnormal data and issue an early warning. As a result, the analysis unit can not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system. The analysis unit continuously monitors the user's learning data and periodically updates the analysis results to respond to the latest situation. This allows the analysis unit to accurately evaluate the user's level of understanding and support the provision of appropriate educational programs.

[0073] The generation unit generates a personalized educational program based on the level of understanding evaluated by the analysis unit. The generation unit, for example, selects teaching materials of an appropriate level of difficulty based on the user's level of understanding. The generation unit can also select the format of the teaching materials according to the user's learning style. Furthermore, the generation unit can customize the content of the learning program based on the user's learning goals. For example, the generation unit selects teaching materials of an appropriate level of difficulty based on the user's level of understanding, selects the format of the teaching materials according to the learning style, and customizes the content of the learning program based on the learning goals. The generation unit automates these processes using AI to efficiently generate a personalized educational program. For example, natural language processing technology can be used to extract important keywords and topics from the user's learning history and test results, and appropriate teaching materials can be selected based on the extracted keywords and topics. Furthermore, the generation unit can collect user feedback and continuously improve the content of the educational program. For example, if a user gives a high rating to a specific teaching material, the user can recommend the teaching material to other users. This allows the generation unit to provide a high-quality educational program that meets the needs of the user. The generation unit can centrally manage the generated educational programs and cooperate with other systems and departments as necessary. This enables the generation unit to generate training programs efficiently and effectively, thereby improving the performance of the entire system.

[0074] The providing unit provides the user with the educational program generated by the generating unit. The providing unit provides the educational program through, for example, an online platform. The providing unit can also provide the educational program through a mobile app. Furthermore, the providing unit can also provide the educational program through e-mail. For example, the providing unit provides the educational program through an online platform, provides the educational program through a mobile app, and provides the educational program through e-mail. The providing unit provides the educational program using multiple providing means in consideration of the convenience of the user. For example, the educational program can be provided through a mobile app so that the user can continue learning even when on the go. The providing unit can also monitor the user's learning progress in real time and provide additional teaching materials and explanations as necessary. This allows the providing unit to maximize the learning effect of the user. Furthermore, the providing unit can collect user feedback and continuously improve the content and format of the educational program to be provided. For example, if a user gives a high rating to a specific teaching material, the teaching material can be recommended to other users. This allows the providing unit to provide a high-quality educational program according to the needs of the user. The providing unit can centrally manage the usage status of the provided educational program and cooperate with other systems and departments as necessary. This enables the providing unit to provide educational programs efficiently and effectively, thereby improving the performance of the entire system.

[0075] The monitoring unit monitors the user's learning progress based on the educational program provided by the provision unit, and adjusts the program as necessary. The monitoring unit, for example, monitors the user's learning progress in real time. The monitoring unit can also provide additional explanations and practice questions based on the user's learning progress. Furthermore, the monitoring unit can adjust the content of the learning program based on the user's learning progress. For example, the monitoring unit monitors the user's learning progress in real time, provides additional explanations and practice questions, and adjusts the content of the learning program. The monitoring unit automates these processes using AI to efficiently monitor the user's learning progress. For example, a machine learning algorithm can be used to extract learning progress patterns from the user's learning data and detect abnormal progress or stagnation early. Furthermore, the monitoring unit can collect user feedback and continuously improve the content and format of the learning program. For example, if a user finds it difficult to understand a particular teaching material, the teaching material can be reviewed and more easily understood explanations and additional practice questions can be provided. This allows the monitoring unit to maximize the user's learning effect. The monitoring unit can centrally manage the monitored learning progress data and cooperate with other systems and departments as necessary. This enables the monitoring unit to efficiently and effectively monitor the user's learning progress, thereby improving the performance of the entire system.

[0076] The collection unit can collect at least one of the data of the user's past learning history or test results, and learning speed. The collection unit, for example, acquires the user's past learning history from a database. For example, the collection unit acquires the user's past learning history from a database and records the learning content and learning time. The collection unit can also acquire the user's test results from an online test system. For example, the collection unit acquires the user's test results from an online test system and records the type of test and the correct answer rate. Furthermore, the collection unit can also acquire the user's learning speed from a learning management system. For example, the collection unit acquires the user's learning speed from a learning management system and records the amount of learning per unit time and the learning progress speed. In this way, the collection unit can provide more accurate learning data by collecting data such as the user's past learning history, test results, and learning speed. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can have the AI ​​execute processes to retrieve the user's past learning history from a database, retrieve test results from an online testing system, and retrieve learning speed from a learning management system.

[0077] The analysis unit can analyze the collected data and identify the strengths and weaknesses of the user. The analysis unit, for example, analyzes the test results of the user and calculates the correct answer rate. For example, the analysis unit analyzes the test results of the user, calculates the correct answer rate, and identifies the strong subjects and weak areas. The analysis unit can also analyze the learning history of the user and evaluate the progress of the learning. For example, the analysis unit analyzes the learning history of the user, evaluates the progress of the learning, and grasps the progress of the learning. Furthermore, the analysis unit can analyze the learning speed of the user and evaluate the efficiency of the learning. For example, the analysis unit analyzes the learning speed of the user, evaluates the efficiency of the learning, and adjusts the pace of the learning. In this way, the analysis unit can provide a more effective educational program by identifying the strengths and weaknesses of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can have the AI ​​perform the following processes: analyzing a user's test results, calculating the accuracy rate, analyzing the learning history, evaluating the learning progress, analyzing the learning speed, and evaluating the efficiency of learning.

[0078] The generation unit can provide the teaching materials in a format that is easy for the user to understand based on the user's level of understanding. The generation unit, for example, selects teaching materials of an appropriate level of difficulty based on the user's level of understanding. For example, the generation unit selects teaching materials of an appropriate level of difficulty based on the user's level of understanding and provides them in a format that is easy for the user to understand. The generation unit can also select the format of the teaching materials according to the user's learning style. For example, the generation unit selects a format such as visual teaching materials, audio teaching materials, and text teaching materials according to the user's learning style. Furthermore, the generation unit can also customize the contents of the learning program based on the user's learning goals. For example, the generation unit customizes the contents of the learning program based on the user's learning goals and provides them in a format that is easy for the user to understand. As a result, the generation unit can maximize the learning effect by providing the teaching materials in a format that is easy for the user to understand. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can have the AI ​​perform processes to select learning materials of an appropriate level of difficulty based on the user's level of understanding, select the format of the learning materials according to the learning style, and customize the content of the learning program based on the learning goals.

[0079] The providing unit can provide the generated educational program to the user. The providing unit provides the educational program, for example, through an online platform. For example, the providing unit provides the educational program through an online platform so that the user can access it. The providing unit can also provide the educational program through a mobile app. For example, the providing unit provides the educational program through a mobile app so that the user can learn on a smartphone or tablet. The providing unit can also provide the educational program through e-mail. For example, the providing unit provides the educational program through e-mail so that the user can receive it by e-mail. Thereby, the providing unit can advance learning by providing the generated educational program to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute processing of providing the educational program through an online platform, providing the educational program through a mobile app, and providing the educational program through e-mail.

[0080] The monitoring unit can monitor the user's learning progress in real time and provide additional explanations or practice questions as necessary. The monitoring unit, for example, monitors the user's learning progress in real time. For example, the monitoring unit monitors the user's learning progress in real time and grasps the learning progress. The monitoring unit can also provide additional explanations and practice questions based on the user's learning progress. For example, the monitoring unit provides additional explanations when the user stumbles on a specific problem. The monitoring unit can also provide practice questions when the user lacks understanding in a specific area. For example, the monitoring unit provides additional explanations when the user stumbles on a specific problem, and provides practice questions when the user lacks understanding in a specific area. Thereby, the monitoring unit can monitor the user's learning progress in real time and provide additional explanations and practice questions as necessary, thereby maximizing the learning effect. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can monitor the user's learning progress in real time and cause AI to execute a process of providing additional explanations and practice questions.

[0081] The collection unit can estimate the user's emotion and adjust the timing of collecting the learning data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting the learning data based on the estimated user's emotion. For example, when the user is stressed, the collection unit delays data collection until the user is in a relaxed state. In addition, when the user is concentrating, the collection unit can also collect the learning data at that timing. Furthermore, when the user is tired, the collection unit can collect data after a break. For example, when the user is stressed, the collection unit delays data collection until the user is in a relaxed state, when the user is concentrating, the collection unit collects the learning data at that timing, and when the user is tired, the collection unit collects data after a break. Thereby, the collection unit can collect data at a more appropriate timing by adjusting the timing of collecting the learning data based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may estimate the user's emotion and cause an AI to execute a process of adjusting the timing of collecting learning data based on the estimated emotion.

[0082] The collection unit can analyze the user's past learning history and select an appropriate data collection method. The collection unit, for example, analyzes the user's past learning history and selects an appropriate data collection method. For example, the collection unit preferentially collects data on subjects in which the user achieved high grades in the past. The collection unit can also collect data on subjects in which the user was weak in the past in detail. Furthermore, the collection unit can analyze the user's learning pattern and collect data at the optimal timing. For example, the collection unit preferentially collects data on subjects in which the user achieved high grades in the past, collects data on subjects in which the user was weak in detail, analyzes the learning pattern, and collects data at the optimal timing. Thereby, the collection unit can select the optimal data collection method by analyzing the user's past learning history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the user's past learning history and cause AI to execute a process of selecting an appropriate data collection method.

[0083] The collection unit can perform filtering based on the user's current learning environment and field of interest when collecting the learning data. For example, when collecting the learning data, the collection unit performs filtering based on the user's current learning environment and field of interest. For example, when the user is studying in a quiet environment, the collection unit collects data suitable for that environment. In addition, when the user is interested in a specific field, the collection unit can also preferentially collect data related to that field. Furthermore, when the user is studying in a group, the collection unit can also collect data of the entire group. For example, when the user is studying in a quiet environment, the collection unit collects data suitable for that environment, when the user is interested in a specific field, the collection unit preferentially collects data related to that field, and when studying in a group, the collection unit collects data of the entire group. Thereby, the collection unit can collect more relevant data by filtering data based on the user's current learning environment and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, when collecting the learning data, the collection unit can cause AI to execute a process of filtering based on the user's current learning environment and field of interest.

[0084] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, when the user is excited, the collection unit preferentially collects interesting data. In addition, when the user is relaxed, the collection unit can also preferentially collect basic data. Furthermore, when the user is tired, the collection unit can also preferentially collect simple data. For example, when the user is excited, the collection unit preferentially collects interesting data, when the user is relaxed, the collection unit preferentially collects basic data, and when the user is tired, the collection unit preferentially collects simple data. This allows the collection unit to determine the priority of data to be collected based on the user's emotion, thereby enabling more effective data collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can cause the AI ​​to execute a process of inferring a user's emotions and determining the priority of data to be collected based on the inferred emotions.

[0085] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting the learning data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting the learning data. For example, when the user lives in a specific area, the collection unit preferentially collects data related to the area. In addition, when the user is traveling, the collection unit can also collect data related to the travel destination. Furthermore, when the user attends a specific school, the collection unit can also collect data related to the curriculum of the school. For example, when the user lives in a specific area, the collection unit preferentially collects data related to the area, when the user is traveling, the collection unit collects data related to the travel destination, and when the user attends a specific school, the collection unit collects data related to the curriculum of the school. In this way, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to execute a process of preferentially collecting highly relevant data in consideration of the geographical location information of the user when collecting the learning data.

[0086] The collection unit can analyze the social media activity of the user and collect related data when collecting the learning data. For example, the collection unit analyzes the social media activity of the user and collects related data when collecting the learning data. For example, the collection unit collects data based on the learning content shared by the user on social media. The collection unit can also collect data from education-related accounts followed by the user. Furthermore, the collection unit can collect data from an online learning community in which the user participates. For example, the collection unit collects data based on the learning content shared by the user on social media, collects data from education-related accounts followed by the user, and collects data from the online learning community. In this way, the collection unit can collect related data by analyzing the social media activity of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause AI to execute a process of analyzing the social media activity of the user and collecting related data when collecting the learning data.

[0087] The analysis unit can estimate the user's emotion and adjust the method of data analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the method of data analysis based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. Also, the analysis unit can perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is excited. For example, the analysis unit performs a detailed analysis when the user is relaxed, performs a simplified analysis when the user is in a hurry, and provides a visually easy-to-understand analysis result when the user is excited. Thereby, the analysis unit adjusts the method of data analysis based on the user's emotion, thereby enabling more appropriate analysis. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can cause the AI ​​to execute a process of inferring a user's emotions and adjusting the method of data analysis based on the inferred emotions.

[0088] The analysis unit can adjust the analysis algorithm by referring to the user's past learning data during the analysis. The analysis unit adjusts the analysis algorithm by referring to the user's past learning data, for example, during the analysis. For example, the analysis unit optimizes the analysis algorithm based on data on subjects in which the user has achieved high grades in the past. The analysis unit can also adjust the analysis algorithm based on data on subjects in which the user has had difficulty in the past. Furthermore, the analysis unit can analyze the user's learning pattern and select the optimal analysis algorithm. For example, the analysis unit optimizes the analysis algorithm based on data on subjects in which the user has had high grades in the past, adjusts the analysis algorithm based on data on subjects in which the user has had difficulty, and analyzes the learning pattern to select the optimal analysis algorithm. Thereby, the analysis unit can optimize the analysis algorithm by referring to the user's past learning data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute a process of adjusting the analysis algorithm by referring to the user's past learning data during the analysis.

[0089] The analysis unit can apply different analysis methods according to the learning style of the user during analysis. For example, the analysis unit applies different analysis methods according to the learning style of the user during analysis. For example, when the user is a visual learner, the analysis unit applies a visual data analysis method. In addition, when the user is an auditory learner, the analysis unit can also apply an analysis method using audio data. Furthermore, when the user is an experiential learner, the analysis unit can also apply a practical data analysis method. For example, when the user is a visual learner, the analysis unit applies a visual data analysis method, when the user is an auditory learner, the analysis method using audio data, and when the user is an experiential learner, the analysis unit applies a practical data analysis method. Thereby, the analysis unit can perform more effective analysis by applying different analysis methods according to the learning style of the user. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute a process of applying different analysis methods according to the learning style of the user during analysis.

[0090] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. The analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. For example, the analysis unit provides a simple and highly visible display method when the user is nervous, provides a display method including detailed information when the user is relaxed, and provides a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis result based on the user's emotion, thereby enabling more appropriate display. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may estimate the user's emotion and cause the AI ​​to execute a process of adjusting the display method of the analysis result based on the estimated emotion.

[0091] The analysis unit can determine the analysis priority based on the user's learning progress at the time of analysis. The analysis unit, for example, determines the analysis priority based on the user's learning progress at the time of analysis. For example, the analysis unit prioritizes analysis of data of subjects in which the user is slow to progress. The analysis unit can also postpone data of subjects in which the user is fast to progress. Furthermore, the analysis unit can also determine the analysis priority based on the user's learning goal. For example, the analysis unit prioritizes analysis of data of subjects in which the user is slow to progress, postpones data of subjects in which the user is fast to progress, and determines the analysis priority based on the learning goal. As a result, the analysis unit determines the analysis priority based on the user's learning progress, thereby enabling more effective analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can cause AI to execute a process of determining the analysis priority based on the user's learning progress at the time of analysis.

[0092] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. For example, the analysis unit performs the analysis based on the literature the user has previously referred to. The analysis unit can also perform the analysis by referring to the literature the user is currently studying. Furthermore, the analysis unit can perform the analysis by referring to the latest literature related to the user's field of study. For example, the analysis unit performs the analysis based on the literature the user has previously referred to, performs the analysis by referring to the literature the user is currently studying, and performs the analysis by referring to the latest literature related to the field of study. Thereby, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause AI to perform a process of improving the accuracy of the analysis by referring to the user's related literature during the analysis.

[0093] The generation unit can estimate the user's emotion and adjust the generation method of the educational program based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the generation method of the educational program based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates an educational program that proceeds at a leisurely pace. Also, when the user is in a hurry, the generation unit can generate an effective educational program in a short time. Furthermore, when the user is excited, the generation unit can generate a visually stimulating educational program. For example, when the user is relaxed, the generation unit generates an educational program that proceeds at a leisurely pace, when the user is in a hurry, generates an effective educational program in a short time, and when the user is excited, generates a visually stimulating educational program. Thereby, the generation unit can provide a more effective educational program by adjusting the generation method of the educational program based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may estimate the user's emotion and cause the AI ​​to execute a process of adjusting the generation method of the educational program based on the estimated emotion.

[0094] The generation unit can adjust the level of detail of the teaching material based on the user's level of understanding at the time of generation. The generation unit adjusts the level of detail of the teaching material based on the user's level of understanding at the time of generation, for example. For example, the generation unit provides detailed teaching material when the user finds it easy to understand. The generation unit can also provide simplified teaching material when the user finds it difficult to understand. Furthermore, the generation unit can provide teaching material of an appropriate level of difficulty according to the user's level of understanding. For example, the generation unit provides detailed teaching material when the user finds it easy to understand, and provides simplified teaching material when the user finds it difficult to understand, providing teaching material of an appropriate level of difficulty according to the user's level of understanding. As a result, the generation unit adjusts the level of detail of the teaching material based on the user's level of understanding, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of adjusting the level of detail of the teaching material based on the user's level of understanding at the time of generation.

[0095] The generation unit can apply different teaching material generation algorithms according to the user's learning style at the time of generation. The generation unit applies different teaching material generation algorithms according to the user's learning style at the time of generation, for example. For example, the generation unit generates visual teaching materials when the user is a visual learner. The generation unit can also generate audio teaching materials when the user is an auditory learner. Furthermore, the generation unit can generate practical teaching materials when the user is an experiential learner. For example, the generation unit generates visual teaching materials when the user is a visual learner, generates audio teaching materials when the user is an auditory learner, and generates practical teaching materials when the user is an experiential learner. Thereby, the generation unit applies different teaching material generation algorithms according to the user's learning style, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of applying different teaching material generation algorithms according to the user's learning style at the time of generation.

[0096] The generation unit can estimate the user's emotion and adjust the length of the teaching material based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the teaching material based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates teaching material that is short and to the point. Also, when the user is relaxed, the generation unit can generate longer teaching material including detailed explanations. Furthermore, when the user is excited, the generation unit can generate teaching material with added visually stimulating effects. For example, when the user is in a hurry, the generation unit generates teaching material that is short and to the point, when the user is relaxed, generates longer teaching material including detailed explanations, and when the user is excited, generates teaching material with added visually stimulating effects. Thereby, the generation unit adjusts the length of the teaching material based on the user's emotion, thereby enabling more effective learning. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of estimating the user's emotion and adjusting the length of the learning material based on the estimated emotion.

[0097] The generating unit can determine the priority of the teaching materials based on the user's learning progress at the time of generation. The generating unit, for example, determines the priority of the teaching materials based on the user's learning progress at the time of generation. For example, the generating unit generates teaching materials preferentially for subjects in which the user is slow to progress. The generating unit can also postpone teaching materials for subjects in which the user is fast to progress. Furthermore, the generating unit can determine the priority of the teaching materials based on the user's learning goal. For example, the generating unit generates teaching materials preferentially for subjects in which the user is slow to progress, postpones teaching materials for subjects in which the user is fast to progress, and determines the priority of the teaching materials based on the learning goal. As a result, the generating unit determines the priority of the teaching materials based on the user's learning progress, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can cause AI to execute a process of determining the priority of the teaching materials based on the user's learning progress at the time of generation.

[0098] The generation unit can customize the contents of the teaching materials by referring to data in the user's related field at the time of generation. The generation unit customizes the contents of the teaching materials by referring to data in the user's related field at the time of generation, for example. For example, the generation unit generates teaching materials based on data related to the field in which the user is studying. The generation unit can also customize the teaching materials by referring to data in the field in which the user is interested. Furthermore, the generation unit can generate teaching materials based on data related to the user's learning goal. For example, the generation unit generates teaching materials based on data related to the field in which the user is studying, customizes the teaching materials by referring to data in the field in which the user is interested, and generates teaching materials based on data related to the learning goal. As a result, the generation unit customizes the contents of the teaching materials by referring to data in the user's related field, thereby enabling more effective learning. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause AI to execute a process of customizing the contents of the teaching materials by referring to data in the user's related field at the time of generation.

[0099] The providing unit can estimate the user's emotion and adjust the method of providing the educational program based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the educational program based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides the educational program at a leisurely pace. In addition, when the user is in a hurry, the providing unit can also provide an effective educational program in a short time. Furthermore, when the user is excited, the providing unit can also provide a visually stimulating educational program. For example, when the user is relaxed, the providing unit provides an educational program at a leisurely pace, when the user is in a hurry, the providing unit provides an effective educational program in a short time, and when the user is excited, the providing unit provides a visually stimulating educational program. Thereby, the providing unit adjusts the method of providing the educational program based on the user's emotion, thereby enabling more effective learning. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the provision unit can cause the AI ​​to execute a process of estimating a user's emotions and adjusting the way in which an educational program is provided based on the estimated emotions.

[0100] The providing unit can select the optimal providing method by referring to the user's past learning history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past learning history at the time of providing. For example, the providing unit preferentially selects a providing method that was effective for the user in the past. The providing unit can also avoid a providing method that the user had difficulty with in the past. Furthermore, the providing unit can analyze the user's learning history and select the optimal providing method. For example, the providing unit preferentially selects a providing method that was effective for the user in the past, avoids a providing method that the user had difficulty with, and analyzes the learning history to select the optimal providing method. As a result, the providing unit selects the optimal providing method by referring to the user's past learning history, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of selecting the optimal providing method by referring to the user's past learning history at the time of providing.

[0101] The providing unit can customize the providing means based on the user's learning environment at the time of providing. The providing unit customizes the providing means based on the user's learning environment at the time of providing, for example. For example, the providing unit provides audio teaching materials when the user is studying in a quiet environment. In addition, the providing unit can also provide teaching materials optimized for a mobile device when the user is on the move. Furthermore, the providing unit can provide teaching materials suitable for collaborative learning when the user is studying in a group. For example, the providing unit provides audio teaching materials when the user is studying in a quiet environment, provides teaching materials optimized for a mobile device when the user is on the move, and provides teaching materials suitable for collaborative learning when studying in a group. As a result, the providing unit customizes the providing means based on the user's learning environment, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of customizing the providing means based on the user's learning environment at the time of providing.

[0102] The providing unit can estimate the user's emotion and adjust the order of providing the educational programs based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the order of providing the educational programs based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides educational materials with a high level of difficulty first. Also, when the user is tired, the providing unit can provide easy educational materials first. Furthermore, when the user is excited, the providing unit can provide educational materials that attract the user's interest first. For example, when the user is relaxed, the providing unit provides educational materials with a high level of difficulty first, when the user is tired, the providing unit provides easy educational materials first, and when the user is excited, the providing unit provides educational materials that attract the user's interest first. As a result, the providing unit adjusts the order of providing the educational programs based on the user's emotion, thereby enabling more effective learning. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the provision unit can cause the AI ​​to execute a process of estimating a user's emotions and adjusting the order in which educational programs are presented based on the estimated emotions.

[0103] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user lives in a specific area, the providing unit provides teaching materials related to the area. In addition, when the user is traveling, the providing unit can also provide teaching materials related to the travel destination. Furthermore, when the user attends a specific school, the providing unit can also provide teaching materials related to the curriculum of the school. For example, when the user lives in a specific area, the providing unit provides teaching materials related to the area, when the user is traveling, provides teaching materials related to the travel destination, and when the user attends a specific school, provides teaching materials related to the curriculum of the school. As a result, the providing unit selects the optimal providing method in consideration of the geographical location information of the user, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of selecting the optimal providing method in consideration of the geographical location information of the user at the time of providing.

[0104] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. For example, the providing unit analyzes the user's social media activity and suggests a means of provision at the time of provision. For example, the providing unit provides educational materials based on learning contents shared by the user on social media. The providing unit can also provide educational materials from education-related accounts followed by the user. Furthermore, the providing unit can provide educational materials from an online learning community in which the user participates. For example, the providing unit provides educational materials based on learning contents shared by the user on social media, provides educational materials from education-related accounts followed by the user, and provides educational materials from an online learning community. As a result, the providing unit analyzes the user's social media activity and suggests a means of provision, thereby enabling more effective learning. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can cause AI to execute a process of analyzing the user's social media activity and suggesting a means of provision at the time of provision.

[0105] The monitoring unit can estimate the user's emotion and adjust the monitoring method of the learning progress based on the estimated user's emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the monitoring method of the learning progress based on the estimated user's emotion. For example, the monitoring unit performs detailed progress monitoring when the user is relaxed. Also, the monitoring unit can perform simplified progress monitoring when the user is in a hurry. Furthermore, the monitoring unit can perform visually easy-to-understand progress monitoring when the user is excited. For example, the monitoring unit performs detailed progress monitoring when the user is relaxed, performs simplified progress monitoring when the user is in a hurry, and performs visually easy-to-understand progress monitoring when the user is excited. Thereby, the monitoring unit adjusts the monitoring method of the learning progress based on the user's emotion, thereby enabling more effective learning. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can cause the AI ​​to execute a process of estimating the user's emotions and adjusting the method of monitoring the learning progress based on the estimated emotions.

[0106] The monitoring unit can optimize the monitoring algorithm by referring to the user's past learning data during monitoring. The monitoring unit optimizes the monitoring algorithm by referring to the user's past learning data during monitoring, for example. For example, the monitoring unit optimizes the monitoring algorithm based on data on subjects in which the user has achieved high grades in the past. The monitoring unit can also adjust the monitoring algorithm based on data on subjects in which the user has had difficulty in the past. Furthermore, the monitoring unit can analyze the user's learning pattern and select an optimal monitoring algorithm. For example, the monitoring unit optimizes the monitoring algorithm based on data on subjects in which the user has had high grades in the past, adjusts the monitoring algorithm based on data on subjects in which the user has had difficulty, and analyzes the learning pattern to select an optimal monitoring algorithm. As a result, the monitoring unit optimizes the monitoring algorithm by referring to the user's past learning data, thereby enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of optimizing the monitoring algorithm by referring to the user's past learning data during monitoring.

[0107] The monitoring unit can apply different monitoring methods according to the learning style of the user during monitoring. The monitoring unit, for example, applies different monitoring methods according to the learning style of the user during monitoring. For example, when the user is a visual learner, the monitoring unit applies a visual progress monitoring method. Also, when the user is an auditory learner, the monitoring unit can apply a progress monitoring method using audio data. Furthermore, when the user is an experiential learner, the monitoring unit can apply a practical progress monitoring method. For example, when the user is a visual learner, the monitoring unit applies a visual progress monitoring method, when the user is an auditory learner, the monitoring unit applies a progress monitoring method using audio data, and when the user is an experiential learner, the monitoring unit applies a practical progress monitoring method. Thereby, the monitoring unit applies different monitoring methods according to the learning style of the user, thereby enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of applying different monitoring methods according to the learning style of the user during monitoring.

[0108] The monitoring unit can estimate the user's emotion and adjust the display method of the learning progress based on the estimated user's emotion. The monitoring unit, for example, estimates the user's emotion and adjusts the display method of the learning progress based on the estimated user's emotion. For example, when the user is nervous, the monitoring unit provides a simple and highly visible display method. Also, when the user is relaxed, the monitoring unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. For example, when the user is nervous, the monitoring unit provides a simple and highly visible display method, when the user is relaxed, provides a display method including detailed information, and when the user is in a hurry, provides a display method that focuses on the main points. Thereby, the monitoring unit adjusts the display method of the learning progress based on the user's emotion, thereby enabling more effective learning. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may cause AI to execute a process of estimating the user's emotion and adjusting the display method of the learning progress based on the estimated emotion.

[0109] The monitoring unit can determine the monitoring priority based on the user's learning progress during monitoring. The monitoring unit, for example, determines the monitoring priority based on the user's learning progress during monitoring. For example, the monitoring unit preferentially monitors the progress of subjects in which the user is lagging behind. The monitoring unit can also postpone the progress of subjects in which the user is making fast progress. Furthermore, the monitoring unit can determine the monitoring priority based on the user's learning goals. For example, the monitoring unit preferentially monitors the progress of subjects in which the user is lagging behind, postpones the progress of subjects in which the user is making fast progress, and determines the monitoring priority based on the learning goals. As a result, the monitoring unit determines the monitoring priority based on the user's learning progress, enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of determining the monitoring priority based on the user's learning progress during monitoring.

[0110] The monitoring unit can improve the accuracy of monitoring by referring to data in a field related to the user during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to data in a field related to the user during monitoring. For example, the monitoring unit monitors progress based on data related to the field in which the user is studying. The monitoring unit can also monitor progress by referring to data in a field in which the user is interested. Furthermore, the monitoring unit can monitor progress based on data related to the user's learning goal. For example, the monitoring unit monitors progress based on data related to the field in which the user is studying, monitors progress by referring to data in the field in which the user is interested, and monitors progress based on data related to the learning goal. As a result, the monitoring unit improves the accuracy of monitoring by referring to data in a field related to the user, thereby enabling more effective learning. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can cause AI to execute a process of improving the accuracy of monitoring by referring to data in a field related to the user during monitoring.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] When analyzing the user's learning data, the analysis unit can take into account not only the user's learning history but also the user's lifestyle data. For example, by collecting data such as the user's sleep pattern, meal times, and exercise frequency, and incorporating these data into the analysis, the user's learning efficiency can be evaluated more accurately. The analysis unit can also suggest the optimal timing for learning based on the user's lifestyle data. For example, it can identify the time period when the user can concentrate the most and recommend that the user study during that time period. Furthermore, the analysis unit can adjust the content of the learning program using the user's lifestyle data. For example, if the user is relaxing after exercising, it can set a more difficult question at that time. As a result, the analysis unit can provide a more effective learning program by taking into account the user's lifestyle data.

[0113] When collecting the user's learning data, the collection unit can analyze the user's social media activities. For example, data such as learning contents and comments shared by the user on social media and education-related accounts followed by the user can be collected and incorporated into the learning data. The collection unit can also collect data from online learning communities in which the user participates. For example, questions asked by the user on online forums and interactions with other users can be collected and used as learning data. Furthermore, the collection unit can identify learning interests and concerns based on the user's social media activities. For example, education-related content frequently viewed by the user can be analyzed and a learning program related to that field can be provided. This allows the collection unit to collect more personalized learning data by utilizing the user's social media activities.

[0114] When analyzing the user's learning data, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, when the user is stressed, the analysis unit can perform a simplified analysis to avoid placing a burden on the user. Also, when the user is relaxed, a detailed analysis can be performed to promote deeper understanding. Furthermore, when the user is excited, a visually easy-to-understand analysis result can be provided. For example, the user's learning progress can be visually shown using graphs and charts. This allows the analysis unit to provide more effective learning support by adjusting the analysis method based on the user's emotions.

[0115] The generating unit can generate learning materials according to the learning style of the user based on the learning data of the user. For example, if the user is a visual learner, visual learning materials can be generated, and if the user is an auditory learner, audio learning materials can be generated. The generating unit can also adjust the difficulty and content of the learning materials according to the learning pace of the user. For example, if the user is learning at a fast pace, difficult questions can be provided, and if the user is learning slowly, basic content can be provided. Furthermore, the generating unit can customize the content of the learning program based on the learning goals of the user. For example, if the user is studying for a specific exam, questions related to that exam can be provided with a focus. In this way, the generating unit can maximize the learning effect by providing learning materials according to the learning style, pace, and goals of the user.

[0116] When providing the generated educational program to the user, the providing unit can estimate the user's emotions and adjust the providing method based on the estimated emotions. For example, when the user is relaxed, the educational program can be provided at a leisurely pace, and when the user is in a hurry, an effective educational program can be provided in a short time. In addition, when the user is excited, a visually stimulating educational program can be provided. For example, teaching materials incorporating animation and interactive elements can be provided. Furthermore, the providing unit can adjust the content of the educational program based on the user's emotions. For example, when the user is stressed, relaxing content can be provided, and when the user is concentrating, difficult questions can be provided. In this way, the providing unit can provide more effective learning support by adjusting the providing method based on the user's emotions.

[0117] The monitoring unit can take into account the user's learning environment when monitoring the user's learning progress. For example, if the user is learning in a quiet environment, a monitoring method suitable for that environment can be applied. Also, if the user is learning while moving, a monitoring method optimized for mobile devices can be applied. Furthermore, if the user is learning in a group, the learning progress of the entire group can be monitored. For example, the progress of each member in the group can be grasped and individual feedback can be provided as necessary. In this way, the monitoring unit can provide more effective learning support by applying a monitoring method according to the user's learning environment.

[0118] The collection unit can estimate the user's emotion and adjust the timing of collecting the learning data based on the estimated emotion. For example, if the user is stressed, data collection can be delayed until the user is in a relaxed state. Also, if the user is concentrating, the learning data can be collected at that timing. Furthermore, if the user is tired, data collection can be performed after a break. In this way, the collection unit can collect data at a more appropriate timing by adjusting the timing of collecting the learning data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can estimate the user's emotion and cause an AI to execute a process of adjusting the timing of collecting the learning data based on the estimated emotion.

[0119] When analyzing the user's learning data, the analysis unit can take into account not only the user's learning history but also the user's lifestyle data. For example, by collecting data such as the user's sleep pattern, meal times, and exercise frequency, and incorporating these data into the analysis, the user's learning efficiency can be evaluated more accurately. The analysis unit can also suggest the optimal timing for learning based on the user's lifestyle data. For example, it can identify the time period when the user can concentrate the most and recommend that the user study during that time period. Furthermore, the analysis unit can adjust the content of the learning program using the user's lifestyle data. For example, if the user is relaxing after exercising, it can set a more difficult question at that time. As a result, the analysis unit can provide a more effective learning program by taking into account the user's lifestyle data.

[0120] When providing the generated educational program to the user, the providing unit can estimate the user's emotions and adjust the providing method based on the estimated emotions. For example, when the user is relaxed, the educational program can be provided at a leisurely pace, and when the user is in a hurry, an effective educational program can be provided in a short time. In addition, when the user is excited, a visually stimulating educational program can be provided. For example, teaching materials incorporating animation and interactive elements can be provided. Furthermore, the providing unit can adjust the content of the educational program based on the user's emotions. For example, when the user is stressed, relaxing content can be provided, and when the user is concentrating, difficult questions can be provided. In this way, the providing unit can provide more effective learning support by adjusting the providing method based on the user's emotions.

[0121] The monitoring unit can take into account the user's learning environment when monitoring the user's learning progress. For example, if the user is learning in a quiet environment, a monitoring method suitable for that environment can be applied. Also, if the user is learning while moving, a monitoring method optimized for mobile devices can be applied. Furthermore, if the user is learning in a group, the learning progress of the entire group can be monitored. For example, the progress of each member in the group can be grasped and individual feedback can be provided as necessary. In this way, the monitoring unit can provide more effective learning support by applying a monitoring method according to the user's learning environment.

[0122] The process flow of the second embodiment will be briefly described below.

[0123] Step 1: The collection unit collects the user's learning data. The user's learning data includes past learning history, test results, learning speed, etc. The collection unit, for example, obtains the user's past learning history from a database, obtains the test results from an online testing system, and obtains the learning speed from a learning management system. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's level of understanding. For example, the analysis unit analyzes the user's test results, calculates the percentage of correct answers, analyzes the learning history, evaluates the progress of learning, analyzes the learning speed, and evaluates the efficiency of learning. Step 3: The generator generates a personalized educational program based on the level of understanding evaluated by the analyzer. For example, the generator selects learning materials with an appropriate level of difficulty based on the user's level of understanding, selects the format of the learning materials according to the learning style, and customizes the content of the learning program based on the learning goals. Step 4: The providing unit provides the educational program generated by the generating unit to the user. The providing unit provides the educational program through, for example, an online platform, a mobile app, or email. Step 5: The monitoring unit monitors the user's learning progress based on the educational program provided by the provision unit, and adjusts the program as necessary. For example, the monitoring unit monitors the user's learning progress in real time, provides additional explanations and practice questions, and adjusts the content of the learning program.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0125] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0126] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects learning data of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and evaluates the user's understanding. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized educational program based on the evaluated understanding. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated educational program to the user. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14 and monitors the user's learning progress and adjusts the program as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0131] 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.

[0132] 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 the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] 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 performed in a secure state.

[0135] Fig. 4 shows an example of 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.

[0136] 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.

[0137] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0141] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects learning data of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and evaluates the user's understanding. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized educational program based on the evaluated understanding. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated educational program to the user. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and monitors the user's learning progress and adjusts the program as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0147] 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.

[0148] 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 the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0150] 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 performed in a secure state.

[0151] 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.

[0152] 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.

[0153] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects learning data of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and evaluates the user's understanding. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized education program based on the evaluated understanding. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated education program to the user. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314 and monitors the user's learning progress and adjusts the program as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0163] 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.

[0164] 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 the voice according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0166] 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 performed in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0168] 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.

[0169] 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.

[0170] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0172] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] 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 a voice indicating a user input for 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.

[0174] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0176] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects learning data of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data and evaluates the user's understanding. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personalized educational program based on the evaluated understanding. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated educational program to the user. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the user's learning progress and adjusts the program as necessary. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0178] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0180] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0182] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0184] 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.

[0185] 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 Universal Serial Bus (USB) 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.

[0186] In addition, 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 upon request from the data processing device 12.

[0187] 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.

[0188] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0189] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0190] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0191] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0193] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0195] (Appendix 1) A collection unit that collects learning data of users; an analysis unit that analyzes the data collected by the collection unit and evaluates a user's level of understanding; a generation unit that generates a personalized educational program based on the level of understanding evaluated by the analysis unit; a providing unit that provides a user with the education program generated by the generating unit; A monitoring unit that monitors the user's learning progress based on the educational program provided by the providing unit and adjusts the program as necessary. A system characterized by: (Appendix 2) The collecting unit includes: Collect at least one of the following data: the user's past learning history, test results, and learning speed 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to identify users' strengths and weaknesses 2. The system of claim 1, (Appendix 4) The generation unit is Provide educational materials in a format that is easy for users to understand, based on their level of understanding 2. The system of claim 1, (Appendix 5) The providing unit is Providing the generated educational program to the user 2. The system of claim 1, (Appendix 6) The monitoring unit is Monitor users' learning progress in real time and provide additional explanations or practice questions as needed 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate user emotions and adjust the timing of learning data collection based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze users' past learning history and select the appropriate data collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting learning data, it is filtered based on the user's current learning environment and areas of interest. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting learning data, the system prioritizes collecting data that is highly relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting learning data, analyze users' social media activities and collect relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user sentiment and adjust data analysis methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the analysis algorithm is adjusted by referring to the user's past learning data. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis methods are applied depending on the user's learning style. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on the user's learning progress 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 19) The generation unit is To estimate a user's emotion and adjust a method for generating an educational program based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The generation unit is During generation, adjust the level of detail of the learning material based on the user's level of understanding 2. The system of claim 1, (Appendix 21) The generation unit is During generation, different learning material generation algorithms are applied depending on the user's learning style. 2. The system of claim 1, (Appendix 22) The generation unit is Estimate the user's emotions and adjust the length of the learning material based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The generation unit is During generation, learning materials are prioritized based on the user's learning progress. 2. The system of claim 1, (Appendix 24) The generation unit is When generating the material, the content of the material is customized by referencing data on the user's related fields. 2. The system of claim 1, (Appendix 25) The providing unit is To estimate a user's emotion and adjust a method of providing an educational program based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The providing unit is When providing the information, the optimal method of providing the information is selected by referring to the user's past learning history. 2. The system of claim 1, (Appendix 27) The providing unit is At the time of delivery, customize the delivery method based on the user's learning environment 2. The system of claim 1, (Appendix 28) The providing unit is To estimate a user's emotion and adjust the order of providing educational programs based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is At the time of delivery, the service analyzes the user's social media activity to suggest delivery methods. 2. The system of claim 1, (Appendix 31) The monitoring unit is Inferring a user's emotions and adjusting a learning progress monitoring method based on the estimated user's emotions 2. The system of claim 1, (Appendix 32) The monitoring unit is During monitoring, the monitoring algorithm is optimized by referencing the user's past learning data. 2. The system of claim 1, (Appendix 33) The monitoring unit is When monitoring, apply different monitoring techniques depending on the user's learning style. 2. The system of claim 1, (Appendix 34) The monitoring unit is Inferring a user's emotions and adjusting the way in which learning progress is displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 35) The monitoring unit is During monitoring, prioritize monitoring based on the user's learning progress 2. The system of claim 1, (Appendix 36) The monitoring unit is During monitoring, refer to data from the user's related fields to improve the accuracy of monitoring 2. The system of claim 1, [Explanation of symbols]

[0196] 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. A collection unit that collects learning data of users; an analysis unit that analyzes the data collected by the collection unit and evaluates a user's level of understanding; a generation unit that generates a personalized educational program based on the level of understanding evaluated by the analysis unit; a providing unit that provides a user with the education program generated by the generating unit; A monitoring unit that monitors the user's learning progress based on the educational program provided by the providing unit and adjusts the program as necessary. A system characterized by:

2. The collecting unit includes: Collect at least one of the following data: the user's past learning history, test results, and learning speed 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data to identify users' strengths and weaknesses 2. The system of claim 1 .

4. The generation unit is Provide educational materials in a format that is easy for users to understand, based on their level of understanding 2. The system of claim 1 .

5. The monitoring unit is Monitor users' learning progress in real time and provide additional explanations or practice questions as needed 2. The system of claim 1 .

6. The collecting unit includes: Estimate user emotions and adjust the timing of learning data collection based on the estimated user emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyze users' past learning history and select the appropriate data collection method 2. The system of claim 1 .

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