System

Abstract

An object of a system according to an embodiment is to appropriately adjust a curriculum according to a learning progress and an understanding level of a user.SOLUTION: A system includes a learning progress tracking part, an understanding degree evaluation part, and a curriculum adjustment part. The learning progress tracking unit tracks the user's learning progress. The understanding level evaluation unit evaluates the user's understanding level based on the learning progress tracked by the learning progress tracking unit. The curriculum adjustment unit adjusts a curriculum based on the comprehension level evaluated by the comprehension level evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to appropriately adjust the curriculum according to the user's learning progress and level of understanding.

[0005] The system according to the embodiment aims to appropriately adjust the curriculum according to the user's learning progress and level of understanding. [Means for solving the problem]

[0006] A system according to an embodiment includes a learning progress tracking unit, a comprehension assessment unit, and a curriculum adjustment unit. The learning progress tracking unit tracks a user's learning progress. The comprehension assessment unit assesses the user's comprehension based on the learning progress tracked by the learning progress tracking unit. The curriculum adjustment unit adjusts the curriculum based on the comprehension assessed by the comprehension assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately adjust the curriculum according to the user's learning progress and level of understanding. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-assisted tool according to an embodiment of the present invention is a system that adjusts a curriculum based on a user's learning progress and level of understanding, and provides an individually optimized learning experience. This allows the AI-assisted tool to dynamically adjust the curriculum based on the user's learning progress and level of understanding, and provide an individually optimized learning experience.

[0029] An AI-assisted tool according to an embodiment includes a learning progress tracking unit, a comprehension evaluation unit, and a curriculum adjustment unit. The learning progress tracking unit tracks a user's learning progress. For example, the generation AI continuously tracks the user's learning content and progress. When the user solves a math problem, the generation AI records the answer time and accuracy rate. The generation AI also analyzes the learning progress based on prompts including the user's learning content and progress. The comprehension evaluation unit evaluates the user's comprehension based on the learning progress tracked by the learning progress tracking unit. For example, the generation AI periodically conducts tests and quizzes to evaluate the user's comprehension. The generation AI automatically generates quizzes based on the user's learning content and has the user answer them. The generation AI generates quizzes based on quiz prompts based on the user's learning content. The curriculum adjustment unit adjusts the curriculum based on the comprehension evaluated by the comprehension evaluation unit. For example, the generation AI dynamically adjusts the curriculum based on the user's learning progress and comprehension. If the user has weaknesses in a particular area, the generation AI provides a curriculum that focuses on that area. The generation AI adjusts the curriculum based on prompts that include the user's learning progress and level of understanding. This allows the AI-assisted tool according to the embodiment to dynamically adjust the curriculum based on the user's learning progress and level of understanding, providing an individually optimized learning experience.

[0030] The learning progress tracking unit can analyze a user's learning style and evaluate their progress based on that. For example, the learning progress tracking unit collects behavioral data during learning so that the generation AI can analyze the user's learning style. For example, if a user prefers visual learning materials, the frequency of their use and study time are recorded and the user is classified as a visual learner. The learning progress tracking unit also analyzes the user's learning history to evaluate their progress based on their learning style. For example, in the case of an auditory learner, progress is evaluated based on their use of audio learning materials and the results of comprehension tests. The learning progress tracking unit also analyzes the user's learning style in real time and provides appropriate feedback. For example, experiential learners are provided with many practical assignments and their progress is evaluated. This enables more personalized learning support by evaluating progress based on the user's learning style.

[0031] The learning progress tracking unit can collect the user's physiological data and evaluate progress taking into account the user's level of concentration and fatigue. For example, the learning progress tracking unit uses a generation AI to monitor the user's heart rate and evaluate the user's level of concentration and fatigue. For example, it analyzes heart rate fluctuations to identify times when the user is concentrating and times when fatigue is building up. The learning progress tracking unit also collects brain wave data and builds a system that evaluates the user's level of concentration in real time. For example, it analyzes the alpha and beta wave patterns of the brain waves to evaluate learning efficiency. The learning progress tracking unit also adjusts the user's learning progress based on the physiological data. For example, it recommends a break if the user is highly fatigued, and provides more difficult tasks if the user is highly concentrated. This allows for more accurate progress evaluation by taking the user's physiological data into consideration.

[0032] The learning progress tracking unit monitors the user's learning environment and can provide an optimal learning environment. For example, the learning progress tracking unit uses a volume sensor and a light sensor so that the generation AI can monitor the user's learning environment. For example, it records changes in volume and lighting during learning and suggests the optimal environment. The learning progress tracking unit also builds a system in which the generation AI adjusts the environment in real time based on data on the learning environment. For example, it provides a noise canceling function if the volume is too high, and adjusts the brightness if the lighting is dim. The learning progress tracking unit also uses a temperature sensor so that the generation AI can optimize the user's learning environment. For example, it recommends cooling if the room temperature is too high, and suggests heating if it is too low. This optimizes the user's learning environment, thereby improving learning efficiency.

[0033] The learning progress tracking unit can share learning progress tracking data with parents and educators and provide feedback to support learning. For example, the learning progress tracking unit builds a system in which the generation AI shares the user's learning progress data with parents and educators. For example, it periodically sends learning progress reports to share the learning situation. The learning progress tracking unit also develops a platform that allows parents and educators to provide feedback based on the learning progress data. For example, it provides a function that allows users to enter comments on learning progress and assignments. The learning progress tracking unit also builds a dashboard that allows parents and educators to check learning progress data in real time. For example, it visually displays data such as study time and correct answer rate. In this way, more effective learning support can be provided by sharing learning progress data with parents and educators.

[0034] The comprehension assessment unit can evaluate long-term changes in comprehension by referring to the user's past learning history. For example, the generation AI analyzes the user's past learning history and builds a system to evaluate long-term changes in comprehension. For example, it measures changes in comprehension based on past test results and study time. The comprehension assessment unit also visualizes trends in the user's comprehension based on learning history data. For example, it displays changes in comprehension using graphs and charts. The comprehension assessment unit also develops an algorithm for the generation AI to refer to the user's learning history and evaluate long-term changes in comprehension. For example, it predicts comprehension based on past learning content and test results. This makes it possible to evaluate long-term changes in comprehension by referring to past learning history.

[0035] The comprehension assessment unit can present real-world problems related to the user's learning content and evaluate the user's level of practical understanding. For example, the comprehension assessment unit constructs a system in which a generation AI automatically generates real-world problems related to the user's learning content and evaluates the user's level of comprehension. For example, actual problems are presented based on the learning content of mathematics. The comprehension assessment unit also develops an algorithm in which the generation AI evaluates the user's level of comprehension based on the real-world problems. For example, the generation AI measures the user's level of comprehension based on the accuracy rate and answer time of the problems answered by the user. The comprehension assessment unit also evaluates the user's level of practical understanding by presenting real-world scenarios related to the user's learning content. For example, an experiment scenario is provided based on the learning content of science. This makes it possible to evaluate the user's level of practical understanding by presenting real-world problems.

[0036] The comprehension evaluation unit can compare the user's learning content with other users and evaluate the relative level of understanding. For example, the generation AI builds a system to compare the user's learning content with other users and evaluate the relative level of understanding. For example, it compares it with the test results of other users who are studying the same learning content. In addition, to evaluate the relative level of understanding, the generation AI shares the user's learning data on the cloud and compares it with other users. For example, it compares it based on learning progress and correct answer rate. In addition, the comprehension evaluation unit develops an algorithm to compare the user's learning content with other users and evaluate the relative level of understanding. For example, it compares it based on the results of the same quiz or test. This makes it possible to evaluate the relative level of understanding by comparing it with other users.

[0037] The comprehension assessment unit can provide the results of the comprehension assessment in a format that is visually easy for users to understand. For example, the comprehension assessment unit builds a system in which the generation AI provides the results of the comprehension assessment in the form of a graph or chart. For example, changes in comprehension are displayed in a line graph. In addition, the generation AI provides a dashboard to visually display the results of the comprehension assessment. For example, the comprehension score and progress are displayed in a bar graph or pie chart. In addition, the comprehension assessment unit develops an algorithm that enables the generation AI to provide the results of the comprehension assessment in a format that is visually easy to understand. For example, the comprehension trend is displayed in a heat map. In this way, by providing the results in a format that is visually easy to understand, users can easily grasp the results of the comprehension assessment.

[0038] The curriculum adjustment unit can provide an individually customized curriculum taking into account the user's learning goals and interests. For example, the curriculum adjustment unit constructs a system in which a generation AI analyzes a user's learning goals and interests and provides an individually customized curriculum. For example, if a user is interested in science, the generation AI provides many science-related teaching materials and assignments. The curriculum adjustment unit also proposes the optimal learning path for the user based on the user's learning goals and interests. For example, if a user wants to learn programming, the generation AI provides customized steps from the basics to advanced learning. The curriculum adjustment unit also adjusts the individually customized curriculum in real time based on the user's learning history and interests. For example, if a new interest emerges, teaching materials in that field are added. This improves learning effectiveness by customizing the curriculum based on the user's learning goals and interests.

[0039] The curriculum adjustment unit can analyze the user's learning pace in real time and set an appropriate learning load. For example, the curriculum adjustment unit constructs a system in which a generation AI monitors the user's learning pace in real time and sets an appropriate learning load. For example, the learning load is adjusted based on learning speed and level of understanding. The curriculum adjustment unit also uses learning pace data to have the generation AI propose an optimal learning schedule for the user. For example, if learning is lagging behind, the schedule is relaxed, and if progress is rapid, the difficulty level is increased. The curriculum adjustment unit also develops an algorithm in which the generation AI analyzes the user's learning pace in real time and dynamically adjusts the learning load. For example, the learning load is set taking into account the user's level of concentration and fatigue. This improves learning efficiency by adjusting the learning load based on the user's learning pace.

[0040] The curriculum adjustment unit can combine different learning modules to propose new learning paths. For example, the curriculum adjustment unit builds a system in which a generative AI combines different learning modules to propose new learning paths. For example, it combines mathematics and programming modules to provide a data science learning path. The curriculum adjustment unit also develops an algorithm that uses data from the learning modules to enable a generative AI to propose the optimal learning path for the user. For example, it provides customized steps from basic to advanced. The curriculum adjustment unit also builds a system in which a generative AI combines different learning modules in real time to dynamically propose new learning paths. For example, it combines modules according to the user's interests and learning progress. This makes it possible to provide new learning paths to users by combining different learning modules.

[0041] The curriculum adjustment unit can link the results of the curriculum adjustment with calendars and task management tools so that users can manage themselves. For example, the curriculum adjustment unit builds a system in which the generation AI links the results of the curriculum adjustment with calendars and task management tools. For example, it automatically adds learning schedules to Google Calendar. The curriculum adjustment unit also links the results of the curriculum adjustment with task management tools so that users can manage themselves. For example, it automatically adds learning tasks to Trello or Asana. The curriculum adjustment unit also builds a system in which the generation AI reflects the results of the curriculum adjustment in calendars and task management tools in real time. For example, it dynamically updates the schedule according to the learning progress. This makes it easier for users to manage themselves by linking with calendars and task management tools.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The learning progress tracking unit tracks the user's learning progress. For example, the generation AI continuously tracks the user's learning content and progress. When the user solves a math problem, the generation AI records the answer time and accuracy rate. The generation AI also analyzes the learning progress based on prompts containing the user's learning content and progress. The comprehension assessment unit evaluates the user's comprehension based on the learning progress tracked by the learning progress tracking unit. For example, the generation AI periodically conducts tests and quizzes to evaluate the user's comprehension. The generation AI automatically generates quizzes based on the content the user has learned and has the user answer them. The generation AI generates quizzes based on quiz prompts based on the user's learning content. The curriculum adjustment unit adjusts the curriculum based on the comprehension assessed by the comprehension assessment unit. For example, the generation AI dynamically adjusts the curriculum based on the user's learning progress and comprehension. If the user has weaknesses in a particular area, the generation AI provides a curriculum that focuses on that area. The generation AI adjusts the curriculum based on prompts including the user's learning progress and comprehension level, thereby enabling the AI-assisted tool according to the embodiment to dynamically adjust the curriculum based on the user's learning progress and comprehension level and provide an individually optimized learning experience.

[0044] The learning progress tracking unit can analyze a user's learning style and evaluate their progress based on that. For example, the generation AI collects behavioral data during learning to analyze the user's learning style. For example, if a user prefers visual learning materials, the frequency of their use and study time are recorded and the user is classified as a visual learner. The learning progress tracking unit also analyzes the user's learning history to evaluate progress based on their learning style. For example, for an auditory learner, progress is evaluated based on their use of audio learning materials and the results of comprehension tests. The learning progress tracking unit also analyzes the user's learning style in real time and provides appropriate feedback. For example, experiential learners are provided with many practical assignments and their progress is evaluated. This enables more personalized learning support by evaluating progress based on the user's learning style.

[0045] The learning progress tracking unit can collect the user's physiological data and evaluate progress taking into account the user's level of concentration and fatigue. For example, the generation AI monitors the user's heart rate to evaluate their level of concentration and fatigue. For example, it analyzes heart rate fluctuations to identify times when they are concentrating and times when fatigue is accumulating. The learning progress tracking unit also collects brain wave data and builds a system that evaluates the user's level of concentration in real time. For example, it analyzes alpha and beta brain wave patterns to evaluate learning efficiency. The learning progress tracking unit also adjusts the user's learning progress based on the physiological data. For example, it recommends taking a break if the user is highly fatigued, and provides more difficult tasks if the user is highly concentrated. This makes it possible to more accurately evaluate progress by taking the user's physiological data into consideration.

[0046] The learning progress tracking unit can monitor the user's learning environment and provide the optimal learning environment. For example, the generation AI uses volume sensors and light sensors to monitor the user's learning environment. For example, it records changes in volume and lighting during learning and suggests the optimal environment. The learning progress tracking unit also builds a system in which the generation AI adjusts the environment in real time based on data from the learning environment. For example, it provides a noise canceling function if the volume is too high, and adjusts the brightness if the lighting is dim. The learning progress tracking unit also uses a temperature sensor to optimize the user's learning environment. For example, it recommends cooling if the room temperature is too high, and suggests heating if it is too low. This optimizes the user's learning environment, thereby improving learning efficiency.

[0047] The learning progress tracking unit can share learning progress tracking data with parents and educators and provide feedback to support learning. For example, a system can be built in which the generative AI shares a user's learning progress data with parents and educators. For example, learning progress reports can be sent periodically to share learning status. The learning progress tracking unit can also develop a platform that allows parents and educators to provide feedback based on learning progress data. For example, it can provide a function that allows users to enter comments on learning progress and assignments. The learning progress tracking unit can also build a dashboard that allows parents and educators to check learning progress data in real time. For example, it can visually display data such as study time and correct answer rate. In this way, more effective learning support can be provided by sharing learning progress data with parents and educators.

[0048] The comprehension assessment unit can refer to the user's past learning history and evaluate long-term changes in comprehension. For example, the generation AI analyzes the user's past learning history and builds a system to evaluate long-term changes in comprehension. For example, it measures changes in comprehension based on past test results and study time. The comprehension assessment unit also visualizes trends in the user's comprehension based on learning history data. For example, it displays changes in comprehension using graphs and charts. The comprehension assessment unit also develops an algorithm for the generation AI to refer to the user's learning history and evaluate long-term comprehension. For example, it predicts comprehension based on past learning content and test results. This makes it possible to evaluate long-term changes in comprehension by referring to past learning history.

[0049] The comprehension assessment unit can present real-world problems related to the user's learning content and evaluate the user's level of practical understanding. For example, a system can be constructed in which a generation AI automatically generates real-world problems related to the user's learning content and evaluates the user's level of comprehension. For example, actual problems are presented based on the mathematics learning content. The comprehension assessment unit also develops an algorithm in which the generation AI evaluates the user's level of comprehension based on the real-world problems. For example, the generation AI measures the user's level of comprehension based on the accuracy rate and answer time of the problems answered by the user. The comprehension assessment unit also evaluates the user's level of practical understanding by presenting real-world scenarios related to the user's learning content. For example, an experiment scenario is provided based on the science learning content. This makes it possible to evaluate the user's level of practical understanding by presenting real-world problems.

[0050] The comprehension assessment unit can compare the user's learning content with other users and evaluate their relative level of understanding. For example, the generation AI can compare the user's learning content with other users and build a system to evaluate their relative level of understanding. For example, it can compare it with the test results of other users who are studying the same content. In addition, to evaluate the relative level of understanding, the generation AI shares the user's learning data on the cloud and compares it with other users. For example, it can compare based on learning progress and correct answer rate. In addition, the comprehension assessment unit can develop an algorithm that compares the user's learning content with other users and evaluates their relative level of understanding. For example, it can compare based on the results of the same quiz or test. This makes it possible to evaluate the relative level of understanding by comparing with other users.

[0051] The comprehension assessment unit can provide the results of the comprehension assessment in a format that is visually easy for users to understand. For example, a system can be constructed in which the generation AI provides the results of the comprehension assessment in the form of a graph or chart. For example, changes in comprehension can be displayed in a line graph. The comprehension assessment unit can also provide a dashboard to visually display the results of the comprehension assessment. For example, comprehension scores and progress can be displayed in bar graphs or pie charts. The comprehension assessment unit can also develop an algorithm that enables the generation AI to provide the results of the comprehension assessment in a format that is visually easy to understand. For example, the comprehension trend can be displayed in a heat map. This allows the results of the comprehension assessment to be presented in a visually easy-to-understand format, making it easy for users to grasp the results of the comprehension assessment.

[0052] The curriculum adjustment unit can provide an individually customized curriculum taking into account the user's learning goals and interests. For example, a system can be constructed in which the generation AI analyzes the user's learning goals and interests and provides an individually customized curriculum. For example, if the user is interested in science, the generation AI can provide many science-related teaching materials and assignments. The curriculum adjustment unit also suggests the optimal learning path for the user based on the user's learning goals and interests. For example, if the user wants to learn programming, the generation AI can provide customized steps from the basics to advanced learning. The curriculum adjustment unit also adjusts the individually customized curriculum in real time based on the user's learning history and interests. For example, if a new interest emerges, teaching materials in that field can be added. This improves learning effectiveness by customizing the curriculum based on the user's learning goals and interests.

[0053] The curriculum adjustment unit can analyze the user's learning pace in real time and set an appropriate learning load. For example, a system can be constructed in which the generation AI monitors the user's learning pace in real time and sets an appropriate learning load. For example, the learning load can be adjusted based on learning speed and level of understanding. The curriculum adjustment unit also uses learning pace data to have the generation AI propose an optimal learning schedule for the user. For example, if learning is lagging behind, the schedule can be relaxed, and if progress is rapid, the difficulty level can be increased. The curriculum adjustment unit also develops an algorithm in which the generation AI analyzes the user's learning pace in real time and dynamically adjusts the learning load. For example, the learning load can be set taking into account the user's level of concentration and fatigue. This improves learning efficiency by adjusting the learning load based on the user's learning pace.

[0054] The curriculum adjustment unit can combine different learning modules to propose new learning paths. For example, a system can be built in which a generative AI combines different learning modules to propose new learning paths. For example, a mathematics and programming module can be combined to provide a data science learning path. The curriculum adjustment unit can also develop an algorithm that uses data from the learning modules to allow a generative AI to propose the optimal learning path for the user. For example, it can provide customized steps from basic to advanced. The curriculum adjustment unit can also build a system in which a generative AI combines different learning modules in real time to dynamically propose new learning paths. For example, modules can be combined according to the user's interests and learning progress. This makes it possible to provide new learning paths to users by combining different learning modules.

[0055] The curriculum adjustment unit can link the results of the curriculum adjustment with calendars and task management tools so that users can manage their own work. For example, the generation AI can build a system that links the results of the curriculum adjustment with calendars and task management tools. For example, it can automatically add learning schedules to Google Calendar. The curriculum adjustment unit can also link the results of the curriculum adjustment with task management tools so that users can manage their own work. For example, it can automatically add learning tasks to Trello or Asana. The curriculum adjustment unit can also build a system that allows the generation AI to reflect the results of the curriculum adjustment in calendars and task management tools in real time. For example, it can dynamically update the schedule according to learning progress. This makes it easier for users to manage their own work by linking it with calendars and task management tools.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The learning progress tracking unit tracks the user's learning progress. For example, the generation AI continuously tracks the user's learning content and progress. When the user solves a math problem, the generation AI records the answer time and accuracy rate. The generation AI also analyzes the learning progress based on prompts containing the user's learning content and progress. Step 2: The comprehension assessment unit assesses the user's level of understanding based on the learning progress tracked by the learning progress tracking unit. For example, the generation AI periodically conducts tests and quizzes to assess the user's level of understanding. The generation AI automatically generates quizzes based on what the user has learned and has the user answer them. The generation AI generates quizzes based on quiz prompts based on what the user has learned. Step 3: The curriculum adjustment unit adjusts the curriculum based on the comprehension level assessed by the comprehension assessment unit. For example, the generation AI dynamically adjusts the curriculum based on the user's learning progress and comprehension level. If the user has weaknesses in a particular area, the generation AI provides a curriculum that focuses on that area. The generation AI adjusts the curriculum based on prompts that include the user's learning progress and comprehension level.

[0058] (Example 2) An AI-assisted tool according to an embodiment of the present invention is a system that adjusts a curriculum based on a user's learning progress and level of understanding, and provides an individually optimized learning experience. This allows the AI-assisted tool to dynamically adjust the curriculum based on the user's learning progress and level of understanding, and provide an individually optimized learning experience.

[0059] An AI-assisted tool according to an embodiment includes a learning progress tracking unit, a comprehension evaluation unit, and a curriculum adjustment unit. The learning progress tracking unit tracks a user's learning progress. For example, the generation AI continuously tracks the user's learning content and progress. When the user solves a math problem, the generation AI records the answer time and accuracy rate. The generation AI also analyzes the learning progress based on prompts including the user's learning content and progress. The comprehension evaluation unit evaluates the user's comprehension based on the learning progress tracked by the learning progress tracking unit. For example, the generation AI periodically conducts tests and quizzes to evaluate the user's comprehension. The generation AI automatically generates quizzes based on the user's learning content and has the user answer them. The generation AI generates quizzes based on quiz prompts based on the user's learning content. The curriculum adjustment unit adjusts the curriculum based on the comprehension evaluated by the comprehension evaluation unit. For example, the generation AI dynamically adjusts the curriculum based on the user's learning progress and comprehension. If the user has weaknesses in a particular area, the generation AI provides a curriculum that focuses on that area. The generation AI adjusts the curriculum based on prompts that include the user's learning progress and level of understanding. This allows the AI-assisted tool according to the embodiment to dynamically adjust the curriculum based on the user's learning progress and level of understanding, providing an individually optimized learning experience.

[0060] The learning progress tracking unit can analyze a user's learning style and evaluate their progress based on that. For example, the learning progress tracking unit collects behavioral data during learning so that the generation AI can analyze the user's learning style. For example, if a user prefers visual learning materials, the frequency of their use and study time are recorded and the user is classified as a visual learner. The learning progress tracking unit also analyzes the user's learning history to evaluate their progress based on their learning style. For example, in the case of an auditory learner, progress is evaluated based on their use of audio learning materials and the results of comprehension tests. The learning progress tracking unit also analyzes the user's learning style in real time and provides appropriate feedback. For example, experiential learners are provided with many practical assignments and their progress is evaluated. This enables more personalized learning support by evaluating progress based on the user's learning style.

[0061] The learning progress tracking unit can collect the user's physiological data and evaluate progress taking into account the user's level of concentration and fatigue. For example, the learning progress tracking unit uses a generation AI to monitor the user's heart rate and evaluate the user's level of concentration and fatigue. For example, it analyzes heart rate fluctuations to identify times when the user is concentrating and times when fatigue is building up. The learning progress tracking unit also collects brain wave data and builds a system that evaluates the user's level of concentration in real time. For example, it analyzes the alpha and beta wave patterns of the brain waves to evaluate learning efficiency. The learning progress tracking unit also adjusts the user's learning progress based on the physiological data. For example, it recommends a break if the user is highly fatigued, and provides more difficult tasks if the user is highly concentrated. This allows for more accurate progress evaluation by taking the user's physiological data into consideration.

[0062] The learning progress tracking unit uses the emotion estimation function to track the stress and satisfaction a user feels while studying and can adjust the learning progress based on that. For example, the learning progress tracking unit uses a generation AI to analyze the user's facial expressions and voice to estimate the user's emotions while studying. For example, if the user is feeling stressed, it provides relaxing content. The learning progress tracking unit also builds a system that adjusts the user's learning progress based on the emotion estimation data. For example, if the user is highly satisfied, it maintains the learning pace, and if the user is highly stressed, it slows down the pace. The learning progress tracking unit also collects the user's emotion data in real time and reflects it in the evaluation of the learning progress. For example, if the user has a lot of positive emotions, it accelerates the progress, and if the user has a lot of negative emotions, it adjusts the progress. This allows the learning progress to be adjusted more appropriately by taking the user's emotions into consideration.

[0063] The learning progress tracking unit monitors the user's learning environment and can provide an optimal learning environment. For example, the learning progress tracking unit uses a volume sensor and a light sensor so that the generation AI can monitor the user's learning environment. For example, it records changes in volume and lighting during learning and suggests the optimal environment. The learning progress tracking unit also builds a system in which the generation AI adjusts the environment in real time based on data on the learning environment. For example, it provides a noise canceling function if the volume is too high, and adjusts the brightness if the lighting is dim. The learning progress tracking unit also uses a temperature sensor so that the generation AI can optimize the user's learning environment. For example, it recommends cooling if the room temperature is too high, and suggests heating if it is too low. This optimizes the user's learning environment, thereby improving learning efficiency.

[0064] The learning progress tracking unit can share learning progress tracking data with parents and educators and provide feedback to support learning. For example, the learning progress tracking unit builds a system in which the generation AI shares the user's learning progress data with parents and educators. For example, it periodically sends learning progress reports to share the learning situation. The learning progress tracking unit also develops a platform that allows parents and educators to provide feedback based on the learning progress data. For example, it provides a function that allows users to enter comments on learning progress and assignments. The learning progress tracking unit also builds a dashboard that allows parents and educators to check learning progress data in real time. For example, it visually displays data such as study time and correct answer rate. In this way, more effective learning support can be provided by sharing learning progress data with parents and educators.

[0065] The learning progress tracking unit uses the emotion estimation function to analyze the emotions expressed when a user self-assess their learning progress, thereby improving the accuracy of the self-assessment. For example, the learning progress tracking unit uses a generation AI to estimate the user's emotions when self-assessing, and improves the accuracy of the self-assessment based on that data. For example, it analyzes facial expressions and voice during self-assessment to calculate an emotion score. The learning progress tracking unit also builds a system that adjusts the user's self-assessment results based on the emotion estimation data. For example, if negative emotions are strong, it suggests revising the self-assessment. The learning progress tracking unit also collects the user's emotion data in real time and reflects it in self-assessment feedback. For example, if positive emotions are prevalent, it affirms the self-assessment, and if negative emotions are prevalent, it suggests areas for improvement. This improves the accuracy of the self-assessment by taking the user's emotions into consideration.

[0066] The comprehension assessment unit can evaluate long-term changes in comprehension by referring to the user's past learning history. For example, the generation AI analyzes the user's past learning history and builds a system to evaluate long-term changes in comprehension. For example, it measures changes in comprehension based on past test results and study time. The comprehension assessment unit also visualizes trends in the user's comprehension based on learning history data. For example, it displays changes in comprehension using graphs and charts. The comprehension assessment unit also develops an algorithm for the generation AI to refer to the user's learning history and evaluate long-term changes in comprehension. For example, it predicts comprehension based on past learning content and test results. This makes it possible to evaluate long-term changes in comprehension by referring to past learning history.

[0067] The comprehension assessment unit can present real-world problems related to the user's learning content and evaluate the user's level of practical understanding. For example, the comprehension assessment unit constructs a system in which a generation AI automatically generates real-world problems related to the user's learning content and evaluates the user's level of comprehension. For example, actual problems are presented based on the learning content of mathematics. The comprehension assessment unit also develops an algorithm in which the generation AI evaluates the user's level of comprehension based on the real-world problems. For example, the generation AI measures the user's level of comprehension based on the accuracy rate and answer time of the problems answered by the user. The comprehension assessment unit also evaluates the user's level of practical understanding by presenting real-world scenarios related to the user's learning content. For example, an experiment scenario is provided based on the learning content of science. This makes it possible to evaluate the user's level of practical understanding by presenting real-world problems.

[0068] The comprehension evaluation unit can use the emotion estimation function to analyze a user's emotional reactions to quizzes and tests and reflect this in the evaluation of their comprehension. For example, the comprehension evaluation unit constructs a system in which a generation AI analyzes a user's emotional reactions to quizzes and tests and reflects this in the evaluation of their comprehension. For example, it analyzes facial expressions and voice during the test and calculates an emotion score. The comprehension evaluation unit also develops an algorithm to evaluate a user's comprehension based on the emotion estimation data. For example, it determines that a high level of comprehension is achieved when there are many positive emotions, and determines that a low level of comprehension is achieved when there are many negative emotions. The comprehension evaluation unit also collects user emotion data in real time and reflects this in the results of the quiz or test. For example, it adjusts the evaluation of comprehension based on the emotion score. This makes the evaluation of comprehension more accurate by taking into account the user's emotional reactions.

[0069] The comprehension evaluation unit can compare the user's learning content with other users and evaluate the relative level of understanding. For example, the generation AI builds a system to compare the user's learning content with other users and evaluate the relative level of understanding. For example, it compares it with the test results of other users who are studying the same learning content. In addition, to evaluate the relative level of understanding, the generation AI shares the user's learning data on the cloud and compares it with other users. For example, it compares it based on learning progress and correct answer rate. In addition, the comprehension evaluation unit develops an algorithm to compare the user's learning content with other users and evaluate the relative level of understanding. For example, it compares it based on the results of the same quiz or test. This makes it possible to evaluate the relative level of understanding by comparing it with other users.

[0070] The comprehension assessment unit can provide the results of the comprehension assessment in a format that is visually easy for users to understand. For example, the comprehension assessment unit builds a system in which the generation AI provides the results of the comprehension assessment in the form of a graph or chart. For example, changes in comprehension are displayed in a line graph. In addition, the generation AI provides a dashboard to visually display the results of the comprehension assessment. For example, the comprehension score and progress are displayed in a bar graph or pie chart. In addition, the comprehension assessment unit develops an algorithm that enables the generation AI to provide the results of the comprehension assessment in a format that is visually easy to understand. For example, the comprehension trend is displayed in a heat map. In this way, by providing the results in a format that is visually easy to understand, users can easily grasp the results of the comprehension assessment.

[0071] The comprehension evaluation unit can use the emotion estimation function to analyze the user's emotions regarding the comprehension evaluation results and provide positive feedback. For example, the comprehension evaluation unit constructs a system in which a generation AI analyzes the user's emotions regarding the comprehension evaluation results and provides positive feedback. For example, it analyzes facial expressions and voice in response to the evaluation results and calculates an emotion score. The comprehension evaluation unit also develops an algorithm that provides positive feedback to the user based on the emotion estimation data. For example, it sends a praising message if the level of comprehension is high. The comprehension evaluation unit also collects user emotion data in real time and reflects it in feedback regarding the comprehension evaluation results. For example, it provides an encouraging message if there are a lot of positive emotions. This makes it possible to provide positive feedback by taking the user's emotions into consideration.

[0072] The curriculum adjustment unit can provide an individually customized curriculum taking into account the user's learning goals and interests. For example, the curriculum adjustment unit constructs a system in which a generation AI analyzes a user's learning goals and interests and provides an individually customized curriculum. For example, if a user is interested in science, the generation AI provides many science-related teaching materials and assignments. The curriculum adjustment unit also proposes the optimal learning path for the user based on the user's learning goals and interests. For example, if a user wants to learn programming, the generation AI provides customized steps from the basics to advanced learning. The curriculum adjustment unit also adjusts the individually customized curriculum in real time based on the user's learning history and interests. For example, if a new interest emerges, teaching materials in that field are added. This improves learning effectiveness by customizing the curriculum based on the user's learning goals and interests.

[0073] The curriculum adjustment unit can analyze the user's learning pace in real time and set an appropriate learning load. For example, the curriculum adjustment unit constructs a system in which a generation AI monitors the user's learning pace in real time and sets an appropriate learning load. For example, the learning load is adjusted based on learning speed and level of understanding. The curriculum adjustment unit also uses learning pace data to have the generation AI propose an optimal learning schedule for the user. For example, if learning is lagging behind, the schedule is relaxed, and if progress is rapid, the difficulty level is increased. The curriculum adjustment unit also develops an algorithm in which the generation AI analyzes the user's learning pace in real time and dynamically adjusts the learning load. For example, the learning load is set taking into account the user's level of concentration and fatigue. This improves learning efficiency by adjusting the learning load based on the user's learning pace.

[0074] The curriculum adjustment unit can use the emotion estimation function to analyze the user's emotional response to the curriculum and make adjustments to maintain motivation. For example, the curriculum adjustment unit builds a system in which a generation AI analyzes the user's emotional response to the curriculum and makes adjustments to maintain motivation. For example, it analyzes facial expressions and voice in response to the curriculum and calculates an emotion score. The curriculum adjustment unit also develops an algorithm to adjust the curriculum to maintain the user's motivation based on the emotion estimation data. For example, it maintains the learning load when there are many positive emotions and reduces the load when there are many negative emotions. The curriculum adjustment unit also collects the user's emotion data in real time and reflects it in curriculum adjustments. For example, if motivation is declining, it adds interesting learning materials or assignments. In this way, taking the user's emotions into consideration makes it easier to maintain motivation.

[0075] The curriculum adjustment unit can combine different learning modules to propose new learning paths. For example, the curriculum adjustment unit builds a system in which a generative AI combines different learning modules to propose new learning paths. For example, it combines mathematics and programming modules to provide a data science learning path. The curriculum adjustment unit also develops an algorithm that uses data from the learning modules to enable a generative AI to propose the optimal learning path for the user. For example, it provides customized steps from basic to advanced. The curriculum adjustment unit also builds a system in which a generative AI combines different learning modules in real time to dynamically propose new learning paths. For example, it combines modules according to the user's interests and learning progress. This makes it possible to provide new learning paths to users by combining different learning modules.

[0076] The curriculum adjustment unit can link the results of the curriculum adjustment with calendars and task management tools so that users can manage themselves. For example, the curriculum adjustment unit builds a system in which the generation AI links the results of the curriculum adjustment with calendars and task management tools. For example, it automatically adds learning schedules to Google Calendar. The curriculum adjustment unit also links the results of the curriculum adjustment with task management tools so that users can manage themselves. For example, it automatically adds learning tasks to Trello or Asana. The curriculum adjustment unit also builds a system in which the generation AI reflects the results of the curriculum adjustment in calendars and task management tools in real time. For example, it dynamically updates the schedule according to the learning progress. This makes it easier for users to manage themselves by linking with calendars and task management tools.

[0077] The curriculum adjustment unit uses the emotion estimation function to analyze the user's emotions regarding their curriculum progress and provide a positive learning experience. For example, the curriculum adjustment unit constructs a system in which a generative AI analyzes the user's emotions regarding their curriculum progress and provides a positive learning experience. For example, it analyzes facial expressions and voice regarding the progress and calculates an emotion score. The curriculum adjustment unit also develops an algorithm that provides a positive learning experience to the user based on the emotion estimation data. For example, if there are a lot of positive emotions, it sends a praising message. The curriculum adjustment unit also collects the user's emotion data in real time and reflects it in feedback regarding their curriculum progress. For example, if there are a lot of positive emotions, it affirms their progress, and if there are a lot of negative emotions, it suggests areas for improvement. In this way, a positive learning experience can be provided by taking the user's emotions into consideration.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The learning progress tracking unit tracks the user's learning progress. For example, the generation AI continuously tracks the user's learning content and progress. When the user solves a math problem, the generation AI records the answer time and accuracy rate. The generation AI also analyzes the learning progress based on prompts containing the user's learning content and progress. The comprehension assessment unit evaluates the user's comprehension based on the learning progress tracked by the learning progress tracking unit. For example, the generation AI periodically conducts tests and quizzes to evaluate the user's comprehension. The generation AI automatically generates quizzes based on the content the user has learned and has the user answer them. The generation AI generates quizzes based on quiz prompts based on the user's learning content. The curriculum adjustment unit adjusts the curriculum based on the comprehension assessed by the comprehension assessment unit. For example, the generation AI dynamically adjusts the curriculum based on the user's learning progress and comprehension. If the user has weaknesses in a particular area, the generation AI provides a curriculum that focuses on that area. The generation AI adjusts the curriculum based on prompts including the user's learning progress and comprehension level, thereby enabling the AI-assisted tool according to the embodiment to dynamically adjust the curriculum based on the user's learning progress and comprehension level and provide an individually optimized learning experience.

[0080] The learning progress tracking unit can analyze a user's learning style and evaluate their progress based on that. For example, the generation AI collects behavioral data during learning to analyze the user's learning style. For example, if a user prefers visual learning materials, the frequency of their use and study time are recorded and the user is classified as a visual learner. The learning progress tracking unit also analyzes the user's learning history to evaluate progress based on their learning style. For example, for an auditory learner, progress is evaluated based on their use of audio learning materials and the results of comprehension tests. The learning progress tracking unit also analyzes the user's learning style in real time and provides appropriate feedback. For example, experiential learners are provided with many practical assignments and their progress is evaluated. This enables more personalized learning support by evaluating progress based on the user's learning style.

[0081] The learning progress tracking unit can collect the user's physiological data and evaluate progress taking into account the user's level of concentration and fatigue. For example, the generation AI monitors the user's heart rate to evaluate their level of concentration and fatigue. For example, it analyzes heart rate fluctuations to identify times when they are concentrating and times when fatigue is accumulating. The learning progress tracking unit also collects brain wave data and builds a system that evaluates the user's level of concentration in real time. For example, it analyzes alpha and beta brain wave patterns to evaluate learning efficiency. The learning progress tracking unit also adjusts the user's learning progress based on the physiological data. For example, it recommends taking a break if the user is highly fatigued, and provides more difficult tasks if the user is highly concentrated. This makes it possible to more accurately evaluate progress by taking the user's physiological data into consideration.

[0082] The learning progress tracking unit uses the emotion estimation function to track the stress and satisfaction a user feels while studying and can adjust the learning progress based on that. For example, the generation AI analyzes the user's facial expressions and voice to estimate their emotions while studying. For example, if the user is feeling stressed, it provides relaxing content. The learning progress tracking unit also builds a system that adjusts the user's learning progress based on the emotion estimation data. For example, if satisfaction is high, it maintains the learning pace, and if stress is high, it slows down the pace. The learning progress tracking unit also collects the user's emotion data in real time and reflects it in the evaluation of the learning progress. For example, if there are a lot of positive emotions, it accelerates progress, and if there are a lot of negative emotions, it adjusts progress. This allows the learning progress to be adjusted more appropriately by taking the user's emotions into consideration.

[0083] The learning progress tracking unit can monitor the user's learning environment and provide the optimal learning environment. For example, the generation AI uses volume sensors and light sensors to monitor the user's learning environment. For example, it records changes in volume and lighting during learning and suggests the optimal environment. The learning progress tracking unit also builds a system in which the generation AI adjusts the environment in real time based on data from the learning environment. For example, it provides a noise canceling function if the volume is too high, and adjusts the brightness if the lighting is dim. The learning progress tracking unit also uses a temperature sensor to optimize the user's learning environment. For example, it recommends cooling if the room temperature is too high, and suggests heating if it is too low. This optimizes the user's learning environment, thereby improving learning efficiency.

[0084] The learning progress tracking unit can share learning progress tracking data with parents and educators and provide feedback to support learning. For example, a system can be built in which the generative AI shares a user's learning progress data with parents and educators. For example, learning progress reports can be sent periodically to share learning status. The learning progress tracking unit can also develop a platform that allows parents and educators to provide feedback based on learning progress data. For example, it can provide a function that allows users to enter comments on learning progress and assignments. The learning progress tracking unit can also build a dashboard that allows parents and educators to check learning progress data in real time. For example, it can visually display data such as study time and correct answer rate. In this way, more effective learning support can be provided by sharing learning progress data with parents and educators.

[0085] The learning progress tracking unit uses the emotion estimation function to analyze the emotions expressed when a user self-assess their learning progress, thereby improving the accuracy of the self-assessment. For example, the generation AI estimates the user's emotions when self-assessing, and uses that data to improve the accuracy of the self-assessment. For example, it analyzes facial expressions and voice during self-assessment to calculate an emotion score. The learning progress tracking unit also builds a system that adjusts the user's self-assessment results based on the emotion estimation data. For example, if negative emotions are strong, it suggests revising the self-assessment. The learning progress tracking unit also collects the user's emotion data in real time and reflects it in self-assessment feedback. For example, if positive emotions are prevalent, it will affirm the self-assessment, and if negative emotions are prevalent, it will suggest areas for improvement. In this way, the accuracy of self-assessment is improved by taking the user's emotions into consideration.

[0086] The comprehension assessment unit can refer to the user's past learning history and evaluate long-term changes in comprehension. For example, the generation AI analyzes the user's past learning history and builds a system to evaluate long-term changes in comprehension. For example, it measures changes in comprehension based on past test results and study time. The comprehension assessment unit also visualizes trends in the user's comprehension based on learning history data. For example, it displays changes in comprehension using graphs and charts. The comprehension assessment unit also develops an algorithm for the generation AI to refer to the user's learning history and evaluate long-term comprehension. For example, it predicts comprehension based on past learning content and test results. This makes it possible to evaluate long-term changes in comprehension by referring to past learning history.

[0087] The comprehension assessment unit can present real-world problems related to the user's learning content and evaluate the user's level of practical understanding. For example, a system can be constructed in which a generation AI automatically generates real-world problems related to the user's learning content and evaluates the user's level of comprehension. For example, actual problems are presented based on the mathematics learning content. The comprehension assessment unit also develops an algorithm in which the generation AI evaluates the user's level of comprehension based on the real-world problems. For example, the generation AI measures the user's level of comprehension based on the accuracy rate and answer time of the problems answered by the user. The comprehension assessment unit also evaluates the user's level of practical understanding by presenting real-world scenarios related to the user's learning content. For example, an experiment scenario is provided based on the science learning content. This makes it possible to evaluate the user's level of practical understanding by presenting real-world problems.

[0088] The comprehension evaluation unit can use the emotion estimation function to analyze a user's emotional reactions to quizzes and tests and reflect this in the evaluation of their comprehension. For example, a system can be built in which the generation AI analyzes a user's emotional reactions to quizzes and tests and reflects this in the evaluation of their comprehension. For example, it can analyze facial expressions and voice during the test and calculate an emotion score. The comprehension evaluation unit also develops an algorithm to evaluate the user's comprehension based on the emotion estimation data. For example, it can determine that a high level of comprehension is achieved when there are many positive emotions, and that a low level of comprehension is achieved when there are many negative emotions. The comprehension evaluation unit can also collect user emotion data in real time and reflect this in the results of the quiz or test. For example, it can adjust the evaluation of comprehension based on the emotion score. This makes the evaluation of comprehension more accurate by taking the user's emotional reactions into account.

[0089] The comprehension assessment unit can compare the user's learning content with other users and evaluate their relative level of understanding. For example, the generation AI can compare the user's learning content with other users and build a system to evaluate their relative level of understanding. For example, it can compare it with the test results of other users who are studying the same content. In addition, to evaluate the relative level of understanding, the generation AI shares the user's learning data on the cloud and compares it with other users. For example, it can compare it based on learning progress or correct answer rate. In addition, the comprehension assessment unit can develop an algorithm that compares the user's learning content with other users and evaluates their relative level of understanding. For example, it can compare it based on the results of the same quiz or test. This makes it possible to evaluate the relative level of understanding by comparing it with other users.

[0090] The comprehension assessment unit can provide the results of the comprehension assessment in a format that is visually easy for users to understand. For example, a system can be constructed in which the generation AI provides the results of the comprehension assessment in the form of a graph or chart. For example, changes in comprehension can be displayed in a line graph. The comprehension assessment unit can also provide a dashboard to visually display the results of the comprehension assessment. For example, comprehension scores and progress can be displayed in bar graphs or pie charts. The comprehension assessment unit can also develop an algorithm that enables the generation AI to provide the results of the comprehension assessment in a format that is visually easy to understand. For example, the comprehension trend can be displayed in a heat map. This allows the results of the comprehension assessment to be presented in a visually easy-to-understand format, making it easy for users to grasp the results of the comprehension assessment.

[0091] The comprehension assessment unit can use the emotion estimation function to analyze the user's emotions regarding the comprehension assessment results and provide positive feedback. For example, a system can be constructed in which the generation AI analyzes the user's emotions regarding the comprehension assessment results and provides positive feedback. For example, facial expressions and voice in response to the assessment results can be analyzed to calculate an emotion score. The comprehension assessment unit can also develop an algorithm to provide positive feedback to the user based on the emotion estimation data. For example, if the level of comprehension is high, a praising message can be sent. The comprehension assessment unit can also collect user emotion data in real time and reflect this in feedback regarding the comprehension assessment results. For example, if there are a lot of positive emotions, an encouraging message can be provided. This makes it possible to provide positive feedback by taking the user's emotions into consideration.

[0092] The curriculum adjustment unit can provide an individually customized curriculum taking into account the user's learning goals and interests. For example, a system can be constructed in which the generation AI analyzes the user's learning goals and interests and provides an individually customized curriculum. For example, if the user is interested in science, the generation AI can provide many science-related teaching materials and assignments. The curriculum adjustment unit also suggests the optimal learning path for the user based on the user's learning goals and interests. For example, if the user wants to learn programming, the generation AI can provide customized steps from the basics to advanced learning. The curriculum adjustment unit also adjusts the individually customized curriculum in real time based on the user's learning history and interests. For example, if a new interest emerges, teaching materials in that field can be added. This improves learning effectiveness by customizing the curriculum based on the user's learning goals and interests.

[0093] The curriculum adjustment unit can analyze the user's learning pace in real time and set an appropriate learning load. For example, a system can be constructed in which the generation AI monitors the user's learning pace in real time and sets an appropriate learning load. For example, the learning load can be adjusted based on learning speed and level of understanding. The curriculum adjustment unit also uses learning pace data to have the generation AI propose an optimal learning schedule for the user. For example, if learning is lagging behind, the schedule can be relaxed, and if progress is rapid, the difficulty level can be increased. The curriculum adjustment unit also develops an algorithm in which the generation AI analyzes the user's learning pace in real time and dynamically adjusts the learning load. For example, the learning load can be set taking into account the user's level of concentration and fatigue. This improves learning efficiency by adjusting the learning load based on the user's learning pace.

[0094] The curriculum adjustment unit can use the emotion estimation function to analyze the user's emotional response to the curriculum and make adjustments to maintain motivation. For example, a system can be constructed in which the generation AI analyzes the user's emotional response to the curriculum and makes adjustments to maintain motivation. For example, facial expressions and voices in response to the curriculum can be analyzed to calculate an emotion score. The curriculum adjustment unit can also develop an algorithm to adjust the curriculum to maintain the user's motivation based on the emotion estimation data. For example, it can maintain the learning load when there are many positive emotions and reduce the load when there are many negative emotions. The curriculum adjustment unit can also collect user emotion data in real time and reflect this in curriculum adjustments. For example, if motivation is declining, it can add interesting learning materials or assignments. This makes it easier to maintain motivation by taking the user's emotions into consideration.

[0095] The curriculum adjustment unit can combine different learning modules to propose new learning paths. For example, a system can be built in which a generative AI combines different learning modules to propose new learning paths. For example, a mathematics and programming module can be combined to provide a data science learning path. The curriculum adjustment unit can also develop an algorithm that uses data from the learning modules to allow a generative AI to propose the optimal learning path for the user. For example, it can provide customized steps from basic to advanced. The curriculum adjustment unit can also build a system in which a generative AI combines different learning modules in real time to dynamically propose new learning paths. For example, modules can be combined according to the user's interests and learning progress. This makes it possible to provide new learning paths to users by combining different learning modules.

[0096] The curriculum adjustment unit can link the results of the curriculum adjustment with calendars and task management tools so that users can manage their own work. For example, the generation AI can build a system that links the results of the curriculum adjustment with calendars and task management tools. For example, it can automatically add learning schedules to Google Calendar. The curriculum adjustment unit can also link the results of the curriculum adjustment with task management tools so that users can manage their own work. For example, it can automatically add learning tasks to Trello or Asana. The curriculum adjustment unit can also build a system that allows the generation AI to reflect the results of the curriculum adjustment in calendars and task management tools in real time. For example, it can dynamically update the schedule according to learning progress. This makes it easier for users to manage their own work by linking it with calendars and task management tools.

[0097] The curriculum adjustment unit can use the emotion estimation function to analyze the user's emotions regarding their curriculum progress and provide a positive learning experience. For example, a system can be built in which the generative AI analyzes the user's emotions regarding their curriculum progress and provides a positive learning experience. For example, facial expressions and voices regarding the progress are analyzed to calculate an emotion score. The curriculum adjustment unit also develops an algorithm based on the emotion estimation data to provide the user with a positive learning experience. For example, if there are a lot of positive emotions, it can send a praising message. The curriculum adjustment unit also collects the user's emotion data in real time and reflects it in feedback regarding their curriculum progress. For example, if there are a lot of positive emotions, it can affirm their progress, and if there are a lot of negative emotions, it can suggest areas for improvement. This makes it possible to provide a positive learning experience by taking the user's emotions into consideration.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The learning progress tracking unit tracks the user's learning progress. For example, the generation AI continuously tracks the user's learning content and progress. When the user solves a math problem, the generation AI records the answer time and accuracy rate. The generation AI also analyzes the learning progress based on prompts containing the user's learning content and progress. Step 2: The comprehension assessment unit assesses the user's level of understanding based on the learning progress tracked by the learning progress tracking unit. For example, the generation AI periodically conducts tests and quizzes to assess the user's level of understanding. The generation AI automatically generates quizzes based on what the user has learned and has the user answer them. The generation AI generates quizzes based on quiz prompts based on what the user has learned. Step 3: The curriculum adjustment unit adjusts the curriculum based on the comprehension level assessed by the comprehension assessment unit. For example, the generation AI dynamically adjusts the curriculum based on the user's learning progress and comprehension level. If the user has weaknesses in a particular area, the generation AI provides a curriculum that focuses on that area. The generation AI adjusts the curriculum based on prompts that include the user's learning progress and comprehension level.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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 specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] 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.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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 learning progress tracking unit that tracks a user's learning progress; a comprehension evaluation unit that evaluates a user's comprehension level based on the learning progress tracked by the learning progress tracking unit; a curriculum adjustment unit that adjusts a curriculum based on the level of understanding evaluated by the level of understanding evaluation unit. A system characterized by:

2. The learning progress tracking unit Analyzing the user's learning style and assessing progress accordingly 2. The system of claim 1.

3. The learning progress tracking unit Collecting physiological data of the user and evaluating progress taking into account concentration level and fatigue level 2. The system of claim 1.

4. The learning progress tracking unit Tracking the stress and satisfaction experienced by the user while studying and adjusting the study progress accordingly 2. The system of claim 1.

5. The learning progress tracking unit Monitor the user's learning environment and provide an optimal learning environment 2. The system of claim 1.

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