System

Abstract

An object of a system according to an embodiment is to provide optimal learning support according to individual learning needs of learners.SOLUTION: A system according to an embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension degree monitoring unit, and an advice providing unit. A learning history analysis part analyzes the learning history and interest of the learner. The customized content generation unit generates learning content on the basis of the result analyzed by the learning history analysis unit. The comprehension degree monitoring unit provides the learning content generated by the customized content generation unit and monitors the comprehension degree and progress of the learner. The advice providing unit provides advice based on a result of monitoring by the comprehension degree monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide optimal learning support that meets the individual learning needs of learners.

[0005] The system according to the embodiment aims to provide optimal learning support according to the individual learning needs of each learner. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension monitoring unit, and an advice providing unit. The learning history analysis unit analyzes the learning history and interests of a learner. The customized content generation unit generates learning content based on the results of the analysis by the learning history analysis unit. The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's comprehension and progress. The advice providing unit provides advice based on the results monitored by the comprehension monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal learning support according to the individual learning needs of each learner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention analyzes a learner's learning history and interests, provides learning content and questions customized by a generation AI, monitors the learner's level of understanding and progress, and provides advice on strengthening weak areas, etc. As a result, the learning support system can provide optimal individual learning support to learners, enabling them to learn efficiently and according to their level of understanding.

[0029] A learning support system according to an embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension monitoring unit, and an advice providing unit. The learning history analysis unit analyzes a learner's learning history and interests. For example, the learning history analysis unit analyzes the problems the learner has previously worked on and the content of their studies. The learning history analysis unit can also identify the learner's areas of interest. For example, if a learner is good at mathematics and interested in history, the learning history analysis unit performs analysis based on this information. The customized content generation unit generates learning content based on the results of the analysis by the learning history analysis unit. For example, the customized content generation unit generates problems of a difficulty level according to the learner's level of comprehension. The customized content generation unit can also provide learning materials related to topics of interest. For example, when a user inputs a prompt such as "I want to solve an applied math problem," the customized content generation unit generates an appropriate applied problem. The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's level of comprehension and progress. For example, the comprehension monitoring unit analyzes the correct answer rate for problems solved by the learner. The comprehension monitoring unit can also analyze the time spent studying. For example, the comprehension monitoring unit evaluates the learner's level of comprehension based on the learner's answer data and data related to the study time. The advice providing unit provides advice based on the results monitored by the comprehension monitoring unit. For example, if the learner's understanding in a specific field is lacking, the advice providing unit provides additional study content related to that field. The advice providing unit can also provide advice to strengthen the learner's weaknesses. For example, based on the evaluation result that "the learner's understanding of medieval Europe history is lacking," the advice providing unit provides additional study materials related to medieval Europe. In this way, the learning support system according to the embodiment can provide individually optimized learning support to the learner, enabling efficient learning according to the learner's level of comprehension.

[0030] The learning history analysis unit analyzes a learner's social media activity and online behavior history to identify interests with greater accuracy. For example, the learning history analysis unit analyzes a learner's social media activity to identify topics of interest and accounts they follow. For example, it analyzes Twitter and Instagram posts and follow lists to extract areas of interest to the learner. The learning history analysis unit also analyzes a learner's online behavior history to identify interests based on frequently visited websites and search history. For example, it analyzes Google search history and YouTube viewing history to extract topics of interest to the learner. The learning history analysis unit also integrates a learner's social media activity and online behavior history to identify overall interests. For example, it analyzes Facebook "likes" and comment history and Amazon purchase history to identify areas of interest to the learner. This allows for more accurate identification of a learner's interests.

[0031] The learning history analysis unit collects physiological data of the learner and can analyze the learner's concentration level and stress level during learning. The learning history analysis unit, for example, monitors the learner's heart rate in real time and analyzes the learner's concentration level and stress level. For example, based on heart rate fluctuations, it identifies times when the learner is concentrating and times when stress is high. The learning history analysis unit also collects the learner's brain wave data and analyzes the learner's concentration level and relaxation level. For example, it analyzes the alpha wave and beta wave patterns of the brain waves and suggests the optimal learning timing. The learning history analysis unit also integrates the learner's physiological data and evaluates the learner's overall concentration level and stress level. For example, it analyzes data such as heart rate, brain waves, and electrodermal activity and suggests the optimal learning timing for the learner. In this way, the learner's concentration level and stress level can be analyzed and the optimal learning timing can be suggested.

[0032] The customized content generation unit can provide learning content in an optimal format according to the learner's learning style. For example, the customized content generation unit analyzes the learner's learning style and provides video content to learners who prefer visual learning. For example, it provides animated videos that visually explain mathematical concepts. The customized content generation unit also analyzes the learner's learning style and provides audio content to learners who prefer auditory learning. For example, it provides podcasts that provide audio explanations of historical events. The customized content generation unit also analyzes the learner's learning style and provides interactive simulations to learners who prefer tactile learning. For example, it provides a simulation that allows them to virtually experience a science experiment. This makes it possible to provide learning content in an optimal format according to the learner's learning style.

[0033] The customized content generation unit can analyze a learner's past learning performance and suggest an optimal learning pace and timing for breaks. The customized content generation unit, for example, analyzes a learner's past learning performance and suggests an optimal learning pace. For example, it suggests a learning schedule based on the time periods when the learner can concentrate on studying and the length of study time. The customized content generation unit also analyzes a learner's past learning performance and suggests optimal timing for breaks. For example, it identifies when a learner feels tired and suggests that the learner take an appropriate break. The customized content generation unit also builds a system that dynamically adjusts the learning pace and timing for breaks based on the learner's learning performance data. For example, it monitors the learner's concentration and fatigue levels in real time and suggests an optimal learning pace. This makes it possible to suggest an optimal learning pace and timing for breaks based on the learner's past learning performance.

[0034] The comprehension monitoring unit can evaluate the learner's level of comprehension and concentration by analyzing facial expressions and voice tones during learning in addition to the learner's answer data. The comprehension monitoring unit, for example, analyzes facial expressions during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a camera can be used to monitor the learner's facial expressions in real time to evaluate whether the learner is concentrating. The comprehension monitoring unit can also analyze the learner's voice tones during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a microphone can be used to analyze the learner's voice tone to evaluate the learner's level of comprehension and concentration. The comprehension monitoring unit can also integrate the learner's answer data, facial expressions, and voice tones to build a system for comprehensively evaluating the learner's level of comprehension and concentration. For example, multiple data sources can be analyzed to evaluate the learner's level of comprehension and concentration in real time. This allows for more accurate evaluation of the learner's level of comprehension and concentration.

[0035] The advice providing unit can provide content for relearning related basic knowledge and prerequisites to strengthen the learner's weak points. The advice providing unit, for example, identifies the learner's weak points and provides content for relearning related basic knowledge. For example, it provides videos or problem sets for relearning basic concepts of mathematics. The advice providing unit also provides content for relearning prerequisite knowledge to strengthen the learner's weak points. For example, it provides materials for relearning basic knowledge about a specific historical period. The advice providing unit also builds a system that provides customized relearning content to strengthen the learner's weak points. For example, it automatically suggests content that needs to be re-studyed depending on the learner's level of understanding. This makes it possible to provide content for relearning basic knowledge and prerequisites to strengthen the learner's weak points.

[0036] The advice providing unit can reevaluate the learner's level of understanding of their weak points from different perspectives and approaches, and propose multifaceted reinforcement methods. For example, the advice providing unit reevaluates the learner's level of understanding of their weak points from different perspectives, and proposes multifaceted reinforcement methods. For example, it proposes a method of solving a math problem using a different method. The advice providing unit also reevaluates the learner's level of understanding of their weak points from different approaches, and proposes multifaceted reinforcement methods. For example, it proposes a method of learning about historical events from different perspectives. The advice providing unit also constructs a system that evaluates the learner's level of understanding of their weak points from multiple perspectives, and proposes the optimal reinforcement method. For example, it measures the learner's level of understanding using multiple evaluation criteria, and proposes reinforcement methods. This makes it possible to evaluate the learner's level of understanding of their weak points from multiple perspectives, and propose the optimal reinforcement method.

[0037] The advice providing unit can provide real-world problems and case studies related to the learner's weaknesses, thereby deepening practical understanding. The advice providing unit, for example, provides real-world problems related to the learner's weaknesses to deepen practical understanding. For example, by solving applied mathematical problems, the learner's actual problem-solving ability is improved. The advice providing unit also provides case studies related to the learner's weaknesses to deepen practical understanding. For example, by studying historical events as specific examples, understanding is deepened. The advice providing unit also builds a system that automatically searches for and provides real-world problems and case studies related to the learner's weaknesses. For example, the latest examples and problems related to the learner's weaknesses are provided in real time. This allows the learner to be provided with real-world problems and case studies related to their weaknesses, thereby deepening practical understanding.

[0038] The advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit provides a game in which points can be earned by solving math problems. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a competitive element. For example, the advice providing unit provides a game in which the learner can advance their studies while competing with other learners. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a reward system. For example, the advice providing unit provides a game in which the learner can earn badges or titles by achieving specific goals. This allows the learner to advance their studies while having fun.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The learning history analysis unit analyzes the learner's learning history and interests. For example, the learning history analysis unit analyzes the problems the learner has worked on in the past and the content they have learned. The learning history analysis unit can also identify the areas in which the learner is interested. For example, if a learner is good at mathematics and interested in history, the learning history analysis unit performs analysis based on this information. The customized content generation unit generates learning content based on the results of the analysis by the learning history analysis unit. For example, the customized content generation unit generates problems of a difficulty level according to the learner's level of comprehension. The customized content generation unit can also provide learning materials related to topics that interest the learner. For example, when a prompt such as "I want to solve applied mathematics problems" is input, the customized content generation unit generates appropriate applied problems. The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's level of comprehension and progress. For example, the comprehension monitoring unit analyzes the correct answer rate for problems solved by the learner. The comprehension monitoring unit can also analyze the time spent studying. For example, the comprehension monitoring unit evaluates the learner's level of comprehension based on the learner's answer data and data related to study time. The advice providing unit provides advice based on the results of monitoring by the comprehension monitoring unit. For example, if the learner's understanding in a specific field is lacking, the advice providing unit provides additional learning content related to that field. The advice providing unit can also provide advice to strengthen the learner's weaknesses. For example, based on the evaluation result that "the learner's understanding of medieval European history is lacking," the advice providing unit provides additional learning materials related to medieval Europe. In this way, the learning support system according to the embodiment can provide individually optimized learning support to the learner, enabling efficient learning according to the learner's level of comprehension.

[0041] The learning history analysis unit analyzes a learner's social media activity and online behavior history to identify interests with greater accuracy. For example, the learning history analysis unit analyzes a learner's social media activity to identify topics of interest and accounts they follow. For example, it analyzes Twitter and Instagram posts and follow lists to extract areas of interest to the learner. The learning history analysis unit also analyzes a learner's online behavior history to identify interests based on frequently visited websites and search history. For example, it analyzes Google search history and YouTube viewing history to extract topics of interest to the learner. The learning history analysis unit also integrates a learner's social media activity and online behavior history to identify overall interests. For example, it analyzes Facebook "likes" and comment history and Amazon purchase history to identify areas of interest to the learner. This allows for more accurate identification of a learner's interests.

[0042] The learning history analysis unit collects physiological data of the learner and can analyze the learner's concentration level and stress level during learning. The learning history analysis unit, for example, monitors the learner's heart rate in real time and analyzes the learner's concentration level and stress level. For example, based on heart rate fluctuations, it identifies times when the learner is concentrating and times when stress is high. The learning history analysis unit also collects the learner's brain wave data and analyzes the learner's concentration level and relaxation level. For example, it analyzes the alpha wave and beta wave patterns of the brain waves and suggests the optimal learning timing. The learning history analysis unit also integrates the learner's physiological data and evaluates the learner's overall concentration level and stress level. For example, it analyzes data such as heart rate, brain waves, and electrodermal activity and suggests the optimal learning timing for the learner. In this way, the learner's concentration level and stress level can be analyzed and the optimal learning timing can be suggested.

[0043] The customized content generation unit can provide learning content in an optimal format according to the learner's learning style. For example, the customized content generation unit analyzes the learner's learning style and provides video content to learners who prefer visual learning. For example, it provides animated videos that visually explain mathematical concepts. The customized content generation unit also analyzes the learner's learning style and provides audio content to learners who prefer auditory learning. For example, it provides podcasts that provide audio explanations of historical events. The customized content generation unit also analyzes the learner's learning style and provides interactive simulations to learners who prefer tactile learning. For example, it provides a simulation that allows them to virtually experience a science experiment. This makes it possible to provide learning content in an optimal format according to the learner's learning style.

[0044] The customized content generation unit can analyze a learner's past learning performance and suggest an optimal learning pace and timing for breaks. The customized content generation unit, for example, analyzes a learner's past learning performance and suggests an optimal learning pace. For example, it suggests a learning schedule based on the time periods when the learner can concentrate on studying and the length of study time. The customized content generation unit also analyzes a learner's past learning performance and suggests optimal timing for breaks. For example, it identifies when a learner feels tired and suggests that the learner take an appropriate break. The customized content generation unit also builds a system that dynamically adjusts the learning pace and timing for breaks based on the learner's learning performance data. For example, it monitors the learner's concentration and fatigue levels in real time and suggests an optimal learning pace. This makes it possible to suggest an optimal learning pace and timing for breaks based on the learner's past learning performance.

[0045] The comprehension monitoring unit can evaluate the learner's level of comprehension and concentration by analyzing facial expressions and voice tones during learning in addition to the learner's answer data. The comprehension monitoring unit, for example, analyzes facial expressions during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a camera can be used to monitor the learner's facial expressions in real time to evaluate whether the learner is concentrating. The comprehension monitoring unit can also analyze the learner's voice tones during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a microphone can be used to analyze the learner's voice tone to evaluate the learner's level of comprehension and concentration. The comprehension monitoring unit can also integrate the learner's answer data, facial expressions, and voice tones to build a system for comprehensively evaluating the learner's level of comprehension and concentration. For example, multiple data sources can be analyzed to evaluate the learner's level of comprehension and concentration in real time. This allows for more accurate evaluation of the learner's level of comprehension and concentration.

[0046] The advice providing unit can provide content for relearning related basic knowledge and prerequisites to strengthen the learner's weak points. The advice providing unit, for example, identifies the learner's weak points and provides content for relearning related basic knowledge. For example, it provides videos or problem sets for relearning basic concepts of mathematics. The advice providing unit also provides content for relearning prerequisite knowledge to strengthen the learner's weak points. For example, it provides materials for relearning basic knowledge about a specific historical period. The advice providing unit also builds a system that provides customized relearning content to strengthen the learner's weak points. For example, it automatically suggests content that needs to be re-studyed depending on the learner's level of understanding. This makes it possible to provide content for relearning basic knowledge and prerequisites to strengthen the learner's weak points.

[0047] The advice providing unit can reevaluate the learner's level of understanding of their weak points from different perspectives and approaches, and propose multifaceted reinforcement methods. For example, the advice providing unit reevaluates the learner's level of understanding of their weak points from different perspectives, and proposes multifaceted reinforcement methods. For example, it proposes a method of solving a math problem using a different method. The advice providing unit also reevaluates the learner's level of understanding of their weak points from different approaches, and proposes multifaceted reinforcement methods. For example, it proposes a method of learning about historical events from different perspectives. The advice providing unit also constructs a system that evaluates the learner's level of understanding of their weak points from multiple perspectives, and proposes the optimal reinforcement method. For example, it measures the learner's level of understanding using multiple evaluation criteria, and proposes reinforcement methods. This makes it possible to evaluate the learner's level of understanding of their weak points from multiple perspectives, and propose the optimal reinforcement method.

[0048] The advice providing unit can provide real-world problems and case studies related to the learner's weaknesses, thereby deepening practical understanding. The advice providing unit, for example, provides real-world problems related to the learner's weaknesses to deepen practical understanding. For example, by solving applied mathematical problems, the learner's actual problem-solving ability is improved. The advice providing unit also provides case studies related to the learner's weaknesses to deepen practical understanding. For example, by studying historical events as specific examples, understanding is deepened. The advice providing unit also builds a system that automatically searches for and provides real-world problems and case studies related to the learner's weaknesses. For example, the latest examples and problems related to the learner's weaknesses are provided in real time. This allows the learner to be provided with real-world problems and case studies related to their weaknesses, thereby deepening practical understanding.

[0049] The advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit provides a game in which points can be earned by solving math problems. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a competitive element. For example, the advice providing unit provides a game in which the learner can advance their studies while competing with other learners. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a reward system. For example, the advice providing unit provides a game in which the learner can earn badges or titles by achieving specific goals. This allows the learner to advance their studies while having fun.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The learning history analysis unit analyzes the learner's learning history and interests. For example, the learning history analysis unit can analyze the problems the learner has worked on in the past and the content they have studied, and identify the areas in which the learner is interested. For example, if a learner is good at math and interested in history, the analysis can be carried out based on this information. Step 2: The customized content generator generates learning content based on the results of the analysis by the learning history analyzer. For example, it can generate questions of varying difficulty according to the student's level of understanding and provide learning materials related to topics that interest the student. For example, if the student inputs a prompt such as "I want to solve an applied math problem," it will generate an appropriate applied problem. Step 3: The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's comprehension and progress. For example, it analyzes the correct answer rate of the questions the learner solved and the time spent studying, and evaluates the learner's comprehension based on the data on their answers and study time. Step 4: The advice provider provides advice based on the results of the comprehension monitoring. For example, if the learner lacks understanding in a particular area, they can provide additional learning content related to that area and provide advice to strengthen the learner's weaknesses. For example, based on the assessment result that "the learner lacks understanding of medieval European history," they can provide additional learning materials related to medieval Europe.

[0052] (Example 2) A learning support system according to an embodiment of the present invention analyzes a learner's learning history and interests, provides learning content and questions customized by a generation AI, monitors the learner's level of understanding and progress, and provides advice on strengthening weak areas, etc. As a result, the learning support system can provide optimal individual learning support to learners, enabling them to learn efficiently and according to their level of understanding.

[0053] A learning support system according to an embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension monitoring unit, and an advice providing unit. The learning history analysis unit analyzes a learner's learning history and interests. For example, the learning history analysis unit analyzes the problems the learner has previously worked on and the content of their studies. The learning history analysis unit can also identify the learner's areas of interest. For example, if a learner is good at mathematics and interested in history, the learning history analysis unit performs analysis based on this information. The customized content generation unit generates learning content based on the results of the analysis by the learning history analysis unit. For example, the customized content generation unit generates problems of a difficulty level according to the learner's level of comprehension. The customized content generation unit can also provide learning materials related to topics of interest. For example, when a user inputs a prompt such as "I want to solve an applied math problem," the customized content generation unit generates an appropriate applied problem. The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's level of comprehension and progress. For example, the comprehension monitoring unit analyzes the correct answer rate for problems solved by the learner. The comprehension monitoring unit can also analyze the time spent studying. For example, the comprehension monitoring unit evaluates the learner's level of comprehension based on the learner's answer data and data related to the study time. The advice providing unit provides advice based on the results monitored by the comprehension monitoring unit. For example, if the learner's understanding in a specific field is lacking, the advice providing unit provides additional study content related to that field. The advice providing unit can also provide advice to strengthen the learner's weaknesses. For example, based on the evaluation result that "the learner's understanding of medieval Europe history is lacking," the advice providing unit provides additional study materials related to medieval Europe. In this way, the learning support system according to the embodiment can provide individually optimized learning support to the learner, enabling efficient learning according to the learner's level of comprehension.

[0054] The learning history analysis unit analyzes a learner's social media activity and online behavior history to identify interests with greater accuracy. For example, the learning history analysis unit analyzes a learner's social media activity to identify topics of interest and accounts they follow. For example, it analyzes Twitter and Instagram posts and follow lists to extract areas of interest to the learner. The learning history analysis unit also analyzes a learner's online behavior history to identify interests based on frequently visited websites and search history. For example, it analyzes Google search history and YouTube viewing history to extract topics of interest to the learner. The learning history analysis unit also integrates a learner's social media activity and online behavior history to identify overall interests. For example, it analyzes Facebook "likes" and comment history and Amazon purchase history to identify areas of interest to the learner. This allows for more accurate identification of a learner's interests.

[0055] The learning history analysis unit collects physiological data of the learner and can analyze the learner's concentration level and stress level during learning. The learning history analysis unit, for example, monitors the learner's heart rate in real time and analyzes the learner's concentration level and stress level. For example, based on heart rate fluctuations, it identifies times when the learner is concentrating and times when stress is high. The learning history analysis unit also collects the learner's brain wave data and analyzes the learner's concentration level and relaxation level. For example, it analyzes the alpha wave and beta wave patterns of the brain waves and suggests the optimal learning timing. The learning history analysis unit also integrates the learner's physiological data and evaluates the learner's overall concentration level and stress level. For example, it analyzes data such as heart rate, brain waves, and electrodermal activity and suggests the optimal learning timing for the learner. In this way, the learner's concentration level and stress level can be analyzed and the optimal learning timing can be suggested.

[0056] The learning history analysis unit uses the emotion estimation function to analyze the learner's emotional responses to problems and learning content that they have worked on in the past, and can provide learning content that elicits positive emotions. For example, the learning history analysis unit analyzes the learner's emotional responses to problems that they have worked on in the past, and identifies problems that elicit positive emotions. For example, similar problems are provided based on problems that the learner enjoyed solving or topics that interested the learner. The learning history analysis unit also analyzes the learner's emotional responses to learning content, and identifies learning content that elicits positive emotions. For example, related learning content is provided based on topics that the learner has enjoyed learning about or content that the learner enjoyed learning about. The learning history analysis unit also uses the emotion estimation function to analyze the learner's emotional responses to problems and learning content that they have worked on in the past in real time, and can provide learning content that elicits positive emotions. For example, when the learner is enjoying learning, additional related content is provided. This makes it possible to provide learning content that elicits positive emotions in the learner.

[0057] The customized content generation unit can provide learning content in an optimal format according to the learner's learning style. For example, the customized content generation unit analyzes the learner's learning style and provides video content to learners who prefer visual learning. For example, it provides animated videos that visually explain mathematical concepts. The customized content generation unit also analyzes the learner's learning style and provides audio content to learners who prefer auditory learning. For example, it provides podcasts that provide audio explanations of historical events. The customized content generation unit also analyzes the learner's learning style and provides interactive simulations to learners who prefer tactile learning. For example, it provides a simulation that allows them to virtually experience a science experiment. This makes it possible to provide learning content in an optimal format according to the learner's learning style.

[0058] The customized content generation unit can analyze a learner's past learning performance and suggest an optimal learning pace and timing for breaks. The customized content generation unit, for example, analyzes a learner's past learning performance and suggests an optimal learning pace. For example, it suggests a learning schedule based on the time periods when the learner can concentrate on studying and the length of study time. The customized content generation unit also analyzes a learner's past learning performance and suggests optimal timing for breaks. For example, it identifies when a learner feels tired and suggests that the learner take an appropriate break. The customized content generation unit also builds a system that dynamically adjusts the learning pace and timing for breaks based on the learner's learning performance data. For example, it monitors the learner's concentration and fatigue levels in real time and suggests an optimal learning pace. This makes it possible to suggest an optimal learning pace and timing for breaks based on the learner's past learning performance.

[0059] The customized content generation unit can use the emotion estimation function to identify the format and topic of learning content that evokes the most positive emotions in the learner and provide customized content based on that. The customized content generation unit, for example, uses the emotion estimation function to identify the format of learning content that evokes the most positive emotions in the learner. For example, it provides the format that the learner finds most enjoyable to learn from among formats such as video, audio, and text. The customized content generation unit also uses the emotion estimation function to identify the learning topic that evokes the most positive emotions in the learner. For example, it provides related learning content based on topics that interest the learner or content that the learner enjoys learning. The customized content generation unit also analyzes the learner's emotional responses in real time and provides learning content that evokes the most positive emotions. For example, when the learner is enjoying learning, it provides additional related content. This makes it possible to provide learning content that evokes the most positive emotions in the learner.

[0060] The comprehension monitoring unit can evaluate the learner's level of comprehension and concentration by analyzing facial expressions and voice tones during learning in addition to the learner's answer data. The comprehension monitoring unit, for example, analyzes facial expressions during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a camera can be used to monitor the learner's facial expressions in real time to evaluate whether the learner is concentrating. The comprehension monitoring unit can also analyze the learner's voice tones during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a microphone can be used to analyze the learner's voice tone to evaluate the learner's level of comprehension and concentration. The comprehension monitoring unit can also integrate the learner's answer data, facial expressions, and voice tones to build a system for comprehensively evaluating the learner's level of comprehension and concentration. For example, multiple data sources can be analyzed to evaluate the learner's level of comprehension and concentration in real time. This allows for more accurate evaluation of the learner's level of comprehension and concentration.

[0061] The comprehension monitoring unit can use the emotion estimation function to analyze the emotional reactions of the learner when solving problems and provide advice to reduce stress and anxiety. For example, the comprehension monitoring unit can use the emotion estimation function to analyze the emotional reactions of the learner when solving problems and provide advice to reduce stress and anxiety. For example, if the learner is feeling stressed, the comprehension monitoring unit can provide advice to relax. The comprehension monitoring unit can also analyze the learner's emotional reactions in real time and provide specific advice to reduce stress and anxiety. For example, the comprehension monitoring unit can suggest taking deep breaths or a short break. The comprehension monitoring unit can also use the emotion estimation function to analyze the learner's emotional reactions when solving problems and provide customized advice to reduce stress and anxiety. For example, the comprehension monitoring unit can suggest an optimal relaxation method for each learner based on the learner's emotional data. This can provide advice to reduce the learner's stress and anxiety.

[0062] The advice providing unit can provide content for relearning related basic knowledge and prerequisites to strengthen the learner's weak points. The advice providing unit, for example, identifies the learner's weak points and provides content for relearning related basic knowledge. For example, it provides videos or problem sets for relearning basic concepts of mathematics. The advice providing unit also provides content for relearning prerequisite knowledge to strengthen the learner's weak points. For example, it provides materials for relearning basic knowledge about a specific historical period. The advice providing unit also builds a system that provides customized relearning content to strengthen the learner's weak points. For example, it automatically suggests content that needs to be re-studyed depending on the learner's level of understanding. This makes it possible to provide content for relearning basic knowledge and prerequisites to strengthen the learner's weak points.

[0063] The advice providing unit can reevaluate the learner's level of understanding of their weak points from different perspectives and approaches, and propose multifaceted reinforcement methods. For example, the advice providing unit reevaluates the learner's level of understanding of their weak points from different perspectives, and proposes multifaceted reinforcement methods. For example, it proposes a method of solving a math problem using a different method. The advice providing unit also reevaluates the learner's level of understanding of their weak points from different approaches, and proposes multifaceted reinforcement methods. For example, it proposes a method of learning about historical events from different perspectives. The advice providing unit also constructs a system that evaluates the learner's level of understanding of their weak points from multiple perspectives, and proposes the optimal reinforcement method. For example, it measures the learner's level of understanding using multiple evaluation criteria, and proposes reinforcement methods. This makes it possible to evaluate the learner's level of understanding of their weak points from multiple perspectives, and propose the optimal reinforcement method.

[0064] The advice providing unit can use the emotion estimation function to analyze the negative emotions that a learner has about their weaknesses and provide advice to help them transform those emotions into positive ones. For example, the advice providing unit can use the emotion estimation function to analyze the negative emotions that a learner has about their weaknesses and provide advice to help them transform those emotions into positive ones. For example, the advice providing unit can encourage the learner to have a positive perspective on areas in which they are weak. The advice providing unit can also analyze the learner's negative emotions and provide specific advice to help them transform those emotions into positive ones. For example, the advice providing unit can build confidence by having the learner reflect on successful experiences. The advice providing unit can also use the emotion estimation function to analyze the negative emotions that a learner has about their weaknesses in real time and provide customized advice to help them transform those emotions into positive ones. For example, the advice providing unit can provide individually optimal words of encouragement based on the learner's emotion data. This makes it possible to provide advice to help the learner transform their negative emotions into positive ones.

[0065] The advice providing unit can provide real-world problems and case studies related to the learner's weaknesses, thereby deepening practical understanding. The advice providing unit, for example, provides real-world problems related to the learner's weaknesses to deepen practical understanding. For example, by solving applied mathematical problems, the learner's actual problem-solving ability is improved. The advice providing unit also provides case studies related to the learner's weaknesses to deepen practical understanding. For example, by studying historical events as specific examples, understanding is deepened. The advice providing unit also builds a system that automatically searches for and provides real-world problems and case studies related to the learner's weaknesses. For example, the latest examples and problems related to the learner's weaknesses are provided in real time. This allows the learner to be provided with real-world problems and case studies related to their weaknesses, thereby deepening practical understanding.

[0066] The advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit provides a game in which points can be earned by solving math problems. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a competitive element. For example, the advice providing unit provides a game in which the learner can advance their studies while competing with other learners. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a reward system. For example, the advice providing unit provides a game in which the learner can earn badges or titles by achieving specific goals. This allows the learner to advance their studies while having fun.

[0067] The advice providing unit can use the emotion estimation function to identify the reinforcement method that will evoke the most positive emotions in the learner and provide advice incorporating that method. For example, the advice providing unit can use the emotion estimation function to identify the reinforcement method that will evoke the most positive emotions in the learner and provide advice incorporating that method. For example, the advice providing unit can provide reinforcement content based on a method that will allow the learner to enjoy learning. The advice providing unit can also analyze the emotional response of the learner and identify the reinforcement method that will evoke the most positive emotions. For example, the advice providing unit can suggest a reinforcement method based on a topic that the learner is interested in or content that the learner enjoys learning. The advice providing unit can also use the emotion estimation function to identify in real time the reinforcement method that will evoke the most positive emotions in the learner and provide advice incorporating that method. For example, the reinforcement method can be dynamically adjusted based on the emotional response of the learner. This makes it possible to provide a reinforcement method that will evoke the most positive emotions in the learner.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The learning history analysis unit analyzes the learner's learning history and interests. For example, the learning history analysis unit analyzes the problems the learner has worked on in the past and the content they have learned. The learning history analysis unit can also identify the areas in which the learner is interested. For example, if a learner is good at mathematics and interested in history, the learning history analysis unit performs analysis based on this information. The customized content generation unit generates learning content based on the results of the analysis by the learning history analysis unit. For example, the customized content generation unit generates problems of a difficulty level according to the learner's level of comprehension. The customized content generation unit can also provide learning materials related to topics that interest the learner. For example, when a prompt such as "I want to solve applied mathematics problems" is input, the customized content generation unit generates appropriate applied problems. The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's level of comprehension and progress. For example, the comprehension monitoring unit analyzes the correct answer rate for problems solved by the learner. The comprehension monitoring unit can also analyze the time spent studying. For example, the comprehension monitoring unit evaluates the learner's level of comprehension based on the learner's answer data and data related to study time. The advice providing unit provides advice based on the results of monitoring by the comprehension monitoring unit. For example, if the learner's understanding in a specific field is lacking, the advice providing unit provides additional learning content related to that field. The advice providing unit can also provide advice to strengthen the learner's weaknesses. For example, based on the evaluation result that "the learner's understanding of medieval European history is lacking," the advice providing unit provides additional learning materials related to medieval Europe. In this way, the learning support system according to the embodiment can provide individually optimized learning support to the learner, enabling efficient learning according to the learner's level of comprehension.

[0070] The learning history analysis unit analyzes a learner's social media activity and online behavior history to identify interests with greater accuracy. For example, the learning history analysis unit analyzes a learner's social media activity to identify topics of interest and accounts they follow. For example, it analyzes Twitter and Instagram posts and follow lists to extract areas of interest to the learner. The learning history analysis unit also analyzes a learner's online behavior history to identify interests based on frequently visited websites and search history. For example, it analyzes Google search history and YouTube viewing history to extract topics of interest to the learner. The learning history analysis unit also integrates a learner's social media activity and online behavior history to identify overall interests. For example, it analyzes Facebook "likes" and comment history and Amazon purchase history to identify areas of interest to the learner. This allows for more accurate identification of a learner's interests.

[0071] The learning history analysis unit collects physiological data of the learner and can analyze the learner's concentration level and stress level during learning. The learning history analysis unit, for example, monitors the learner's heart rate in real time and analyzes the learner's concentration level and stress level. For example, based on heart rate fluctuations, it identifies times when the learner is concentrating and times when stress is high. The learning history analysis unit also collects the learner's brain wave data and analyzes the learner's concentration level and relaxation level. For example, it analyzes the alpha wave and beta wave patterns of the brain waves and suggests the optimal learning timing. The learning history analysis unit also integrates the learner's physiological data and evaluates the learner's overall concentration level and stress level. For example, it analyzes data such as heart rate, brain waves, and electrodermal activity and suggests the optimal learning timing for the learner. In this way, the learner's concentration level and stress level can be analyzed and the optimal learning timing can be suggested.

[0072] The learning history analysis unit uses the emotion estimation function to analyze the learner's emotional responses to problems and learning content that they have worked on in the past, and can provide learning content that elicits positive emotions. For example, the learning history analysis unit analyzes the learner's emotional responses to problems that they have worked on in the past, and identifies problems that elicit positive emotions. For example, similar problems are provided based on problems that the learner enjoyed solving or topics that interested the learner. The learning history analysis unit also analyzes the learner's emotional responses to learning content, and identifies learning content that elicits positive emotions. For example, related learning content is provided based on topics that the learner has enjoyed learning about or content that the learner enjoyed learning about. The learning history analysis unit also uses the emotion estimation function to analyze the learner's emotional responses to problems and learning content that they have worked on in the past in real time, and can provide learning content that elicits positive emotions. For example, when the learner is enjoying learning, additional related content is provided. This makes it possible to provide learning content that elicits positive emotions in the learner.

[0073] The customized content generation unit can provide learning content in an optimal format according to the learner's learning style. For example, the customized content generation unit analyzes the learner's learning style and provides video content to learners who prefer visual learning. For example, it provides animated videos that visually explain mathematical concepts. The customized content generation unit also analyzes the learner's learning style and provides audio content to learners who prefer auditory learning. For example, it provides podcasts that provide audio explanations of historical events. The customized content generation unit also analyzes the learner's learning style and provides interactive simulations to learners who prefer tactile learning. For example, it provides a simulation that allows them to virtually experience a science experiment. This makes it possible to provide learning content in an optimal format according to the learner's learning style.

[0074] The customized content generation unit can analyze a learner's past learning performance and suggest an optimal learning pace and timing for breaks. The customized content generation unit, for example, analyzes a learner's past learning performance and suggests an optimal learning pace. For example, it suggests a learning schedule based on the time periods when the learner can concentrate on studying and the length of study time. The customized content generation unit also analyzes a learner's past learning performance and suggests optimal timing for breaks. For example, it identifies when a learner feels tired and suggests that the learner take an appropriate break. The customized content generation unit also builds a system that dynamically adjusts the learning pace and timing for breaks based on the learner's learning performance data. For example, it monitors the learner's concentration and fatigue levels in real time and suggests an optimal learning pace. This makes it possible to suggest an optimal learning pace and timing for breaks based on the learner's past learning performance.

[0075] The customized content generation unit can use the emotion estimation function to identify the format and topic of learning content that evokes the most positive emotions in the learner and provide customized content based on that. The customized content generation unit, for example, uses the emotion estimation function to identify the format of learning content that evokes the most positive emotions in the learner. For example, it provides the format that the learner finds most enjoyable to learn from among formats such as video, audio, and text. The customized content generation unit also uses the emotion estimation function to identify the learning topic that evokes the most positive emotions in the learner. For example, it provides related learning content based on topics that interest the learner or content that the learner enjoys learning. The customized content generation unit also analyzes the learner's emotional responses in real time and provides learning content that evokes the most positive emotions. For example, when the learner is enjoying learning, it provides additional related content. This makes it possible to provide learning content that evokes the most positive emotions in the learner.

[0076] The comprehension monitoring unit can evaluate the learner's level of comprehension and concentration by analyzing facial expressions and voice tones during learning in addition to the learner's answer data. The comprehension monitoring unit, for example, analyzes facial expressions during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a camera can be used to monitor the learner's facial expressions in real time to evaluate whether the learner is concentrating. The comprehension monitoring unit can also analyze the learner's voice tones during learning in addition to the learner's answer data to evaluate the learner's level of comprehension and concentration. For example, a microphone can be used to analyze the learner's voice tone to evaluate the learner's level of comprehension and concentration. The comprehension monitoring unit can also integrate the learner's answer data, facial expressions, and voice tones to build a system for comprehensively evaluating the learner's level of comprehension and concentration. For example, multiple data sources can be analyzed to evaluate the learner's level of comprehension and concentration in real time. This allows for more accurate evaluation of the learner's level of comprehension and concentration.

[0077] The comprehension monitoring unit can use the emotion estimation function to analyze the emotional reactions of the learner when solving problems and provide advice to reduce stress and anxiety. For example, the comprehension monitoring unit can use the emotion estimation function to analyze the emotional reactions of the learner when solving problems and provide advice to reduce stress and anxiety. For example, if the learner is feeling stressed, the comprehension monitoring unit can provide advice to relax. The comprehension monitoring unit can also analyze the learner's emotional reactions in real time and provide specific advice to reduce stress and anxiety. For example, the comprehension monitoring unit can suggest taking deep breaths or a short break. The comprehension monitoring unit can also use the emotion estimation function to analyze the learner's emotional reactions when solving problems and provide customized advice to reduce stress and anxiety. For example, the comprehension monitoring unit can suggest an optimal relaxation method for each learner based on the learner's emotional data. This can provide advice to reduce the learner's stress and anxiety.

[0078] The advice providing unit can provide content for relearning related basic knowledge and prerequisites to strengthen the learner's weak points. The advice providing unit, for example, identifies the learner's weak points and provides content for relearning related basic knowledge. For example, it provides videos or problem sets for relearning basic concepts of mathematics. The advice providing unit also provides content for relearning prerequisite knowledge to strengthen the learner's weak points. For example, it provides materials for relearning basic knowledge about a specific historical period. The advice providing unit also builds a system that provides customized relearning content to strengthen the learner's weak points. For example, it automatically suggests content that needs to be re-studyed depending on the learner's level of understanding. This makes it possible to provide content for relearning basic knowledge and prerequisites to strengthen the learner's weak points.

[0079] The advice providing unit can reevaluate the learner's level of understanding of their weak points from different perspectives and approaches, and propose multifaceted reinforcement methods. For example, the advice providing unit reevaluates the learner's level of understanding of their weak points from different perspectives, and proposes multifaceted reinforcement methods. For example, it proposes a method of solving a math problem using a different method. The advice providing unit also reevaluates the learner's level of understanding of their weak points from different approaches, and proposes multifaceted reinforcement methods. For example, it proposes a method of learning about historical events from different perspectives. The advice providing unit also constructs a system that evaluates the learner's level of understanding of their weak points from multiple perspectives, and proposes the optimal reinforcement method. For example, it measures the learner's level of understanding using multiple evaluation criteria, and proposes reinforcement methods. This makes it possible to evaluate the learner's level of understanding of their weak points from multiple perspectives, and propose the optimal reinforcement method.

[0080] The advice providing unit can use the emotion estimation function to analyze the negative emotions that a learner has about their weaknesses and provide advice to help them transform those emotions into positive ones. For example, the advice providing unit can use the emotion estimation function to analyze the negative emotions that a learner has about their weaknesses and provide advice to help them transform those emotions into positive ones. For example, the advice providing unit can encourage the learner to have a positive perspective on areas in which they are weak. The advice providing unit can also analyze the learner's negative emotions and provide specific advice to help them transform those emotions into positive ones. For example, the advice providing unit can build confidence by having the learner reflect on successful experiences. The advice providing unit can also use the emotion estimation function to analyze the negative emotions that a learner has about their weaknesses in real time and provide customized advice to help them transform those emotions into positive ones. For example, the advice providing unit can provide individually optimal words of encouragement based on the learner's emotion data. This makes it possible to provide advice to help the learner transform their negative emotions into positive ones.

[0081] The advice providing unit can provide real-world problems and case studies related to the learner's weaknesses, thereby deepening practical understanding. The advice providing unit, for example, provides real-world problems related to the learner's weaknesses to deepen practical understanding. For example, by solving applied mathematical problems, the learner's actual problem-solving ability is improved. The advice providing unit also provides case studies related to the learner's weaknesses to deepen practical understanding. For example, by studying historical events as specific examples, understanding is deepened. The advice providing unit also builds a system that automatically searches for and provides real-world problems and case studies related to the learner's weaknesses. For example, the latest examples and problems related to the learner's weaknesses are provided in real time. This allows the learner to be provided with real-world problems and case studies related to their weaknesses, thereby deepening practical understanding.

[0082] The advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit gamifies and provides reinforcement content for the learner's weak points, allowing the learner to advance their studies while having fun. For example, the advice providing unit provides a game in which points can be earned by solving math problems. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a competitive element. For example, the advice providing unit provides a game in which the learner can advance their studies while competing with other learners. The advice providing unit also gamifies and provides reinforcement content for the learner's weak points, incorporating a reward system. For example, the advice providing unit provides a game in which the learner can earn badges or titles by achieving specific goals. This allows the learner to advance their studies while having fun.

[0083] The advice providing unit can use the emotion estimation function to identify the reinforcement method that will evoke the most positive emotions in the learner and provide advice incorporating that method. For example, the advice providing unit can use the emotion estimation function to identify the reinforcement method that will evoke the most positive emotions in the learner and provide advice incorporating that method. For example, the advice providing unit can provide reinforcement content based on a method that will allow the learner to enjoy learning. The advice providing unit can also analyze the emotional response of the learner and identify the reinforcement method that will evoke the most positive emotions. For example, the advice providing unit can suggest a reinforcement method based on a topic that the learner is interested in or content that the learner enjoys learning. The advice providing unit can also use the emotion estimation function to identify in real time the reinforcement method that will evoke the most positive emotions in the learner and provide advice incorporating that method. For example, the reinforcement method can be dynamically adjusted based on the emotional response of the learner. This makes it possible to provide a reinforcement method that will evoke the most positive emotions in the learner.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The learning history analysis unit analyzes the learner's learning history and interests. For example, the learning history analysis unit can analyze the problems the learner has worked on in the past and the content they have studied, and identify the areas in which the learner is interested. For example, if a learner is good at math and interested in history, the analysis can be carried out based on this information. Step 2: The customized content generator generates learning content based on the results of the analysis by the learning history analyzer. For example, it can generate questions of varying difficulty according to the student's level of understanding and provide learning materials related to topics that interest the student. For example, if the student inputs a prompt such as "I want to solve an applied math problem," it will generate an appropriate applied problem. Step 3: The comprehension monitoring unit provides the learning content generated by the customized content generation unit and monitors the learner's comprehension and progress. For example, it analyzes the correct answer rate of the questions the learner solved and the time spent studying, and evaluates the learner's comprehension based on the data on their answers and study time. Step 4: The advice provider provides advice based on the results of the comprehension monitoring. For example, if the learner lacks understanding in a particular area, they can provide additional learning content related to that area and provide advice to strengthen the learner's weaknesses. For example, based on the assessment result that "the learner lacks understanding of medieval European history," they can provide additional learning materials related to medieval Europe.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 history analysis unit that analyzes the learning history and interests of a learner; a customized content generation unit that generates learning content based on the results of the analysis by the learning history analysis unit; a comprehension monitoring unit that provides the learning content generated by the customized content generating unit and monitors the learner's comprehension and progress; an advice providing unit that provides advice based on the results of monitoring by the understanding level monitoring unit; A system characterized by:

2. The learning history analysis unit Analyzing the learner's social media activity and online behavior history to more accurately identify the learner's interests 2. The system of claim 1.

3. The learning history analysis unit Collecting physiological data from the learners and analyzing their concentration levels and stress levels during learning 2. The system of claim 1.

4. The learning history analysis unit Analyzing the learner's emotional responses to problems and learning content that he or she has tackled in the past, and providing the learner with learning content that elicits positive emotions 2. The system of claim 1.

5. The customized content generation unit Providing learning content in the most suitable format according to the learner's learning style 2. The system of claim 1.

6. The customized content generation unit Analyzing the learner's past learning performance and suggesting the optimal learning pace and break timing 2. The system of claim 1.

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