System

Abstract

An object of the system according to the embodiment is to provide an optimal learning plan based on a learning history and a goal of a user, and to continuously check a progress status and an understanding level of learning.SOLUTION: A system according to an embodiment comprises a learning history analyzer, a learning plan generator, a progress checker, and a feedback provider. The learning history analysis unit analyzes the learning history and goal of the user. The learning plan generation section generates an optimum learning plan based on the data analyzed by the learning history analysis section. The progress checking section checks the progress and understanding of learning based on the learning plan generated by the learning plan generating section. The feedback providing unit provides feedback based on a result checked by the progress checking unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal learning plans based on a user's learning history and goals, and do not continuously check learning progress and level of understanding, so there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal study plan based on the user's study history and goals, and to continuously check the user's study progress and level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning history analysis unit, a learning plan generation unit, a progress check unit, and a feedback provision unit. The learning history analysis unit analyzes the user's learning history and goals. The learning plan generation unit generates an optimal learning plan based on the data analyzed by the learning history analysis unit. The progress check unit checks the learning progress and level of understanding based on the learning plan generated by the learning plan generation unit. The feedback provision unit provides feedback based on the results of the check by the progress check unit. [Effects of the Invention]

[0007] The system according to the embodiment provides an optimal learning plan based on the user's learning history and goals, and can continuously check the user's learning progress and level of understanding. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 utilizes a generative AI to analyze a user's learning history and goals, and provides an optimal learning plan. Furthermore, the system continuously checks the user's learning progress and level of understanding, and provides feedback to improve the efficiency and effectiveness of learning. This allows the learning support system to analyze a user's learning history and goals, and provide an optimal learning plan, thereby improving the efficiency and effectiveness of learning.

[0029] A learning assistance system according to an embodiment includes a learning history analysis unit, a learning plan generation unit, a progress check unit, and a feedback provision unit. The learning history analysis unit analyzes a user's learning history and goals. For example, the learning history analysis unit collects data such as the user's previous learning content, test results, and study time, and identifies the user's strengths and weaknesses based on the collected data. The learning history analysis unit also generates an optimal learning plan taking into account the user's set learning goals. The learning plan generation unit generates an optimal learning plan based on the data analyzed by the learning history analysis unit. For example, if a user is studying for a mathematics exam, the learning plan generation unit suggests specific learning content and a schedule to address the user's weaknesses. The progress check unit checks the user's learning progress and level of understanding based on the learning plan generated by the learning plan generation unit. For example, the progress check unit periodically tests the user's learning content and analyzes the results to evaluate the user's level of understanding. The feedback provision unit provides feedback based on the results of the check by the progress check unit. For example, if the user's understanding is lacking in a particular area, the feedback provision unit suggests additional learning materials or practice questions related to that area. Furthermore, if the user is making steady progress toward the goal, the feedback providing unit sends an encouraging message. As a result, the learning assistance system according to the embodiment can analyze the user's learning history and goals, and provide an optimal learning plan, thereby improving the efficiency and effectiveness of learning.

[0030] The learning history analysis unit can identify the user's learning style and generate a learning plan accordingly. For example, the learning history analysis unit analyzes the user's past learning data and identifies which learning style is most effective: visual, auditory, or experiential. For example, the learning history analysis unit can suggest learning materials that make extensive use of diagrams and graphs to visual users. The learning history analysis unit can also suggest audio learning materials to auditory users. The learning history analysis unit can also suggest practical exercises to experiential users. This can improve the effectiveness of learning by providing an optimal learning plan according to the user's learning style.

[0031] The learning history analysis unit can provide a more personalized learning plan by taking into account the user's lifestyle habits and interests. For example, the learning history analysis unit collects lifestyle habit data (e.g., sleep time and exercise habits) in addition to the user's learning history and reflects this in the learning plan. For example, it can suggest nighttime study times for a night-owl user. The learning history analysis unit also takes into account the user's interests and reflects this in the learning plan. For example, if the user is interested in a particular field, it can prioritize suggesting learning content related to that field. This can improve the effectiveness of learning by providing a learning plan based on the user's lifestyle habits and interests.

[0032] The learning history analysis unit can be applied to different educational levels and fields of expertise, and can accommodate a wide range of users. The learning history analysis unit adjusts the analysis algorithm for learning history and goals to accommodate different educational levels, for example, from elementary school to university. For example, elementary school students are provided with a learning plan that focuses on basic content. The learning history analysis unit also provides learning plans according to specialized fields. For example, a science student can be provided with a learning plan specialized in science subjects. A humanities student can also be provided with a learning plan specialized in humanities subjects. This makes it possible to provide learning plans that are suitable for a wide range of users, thereby improving the effectiveness of learning.

[0033] The learning history analysis unit can share the results of the analysis of learning history and goals with parents and teachers, thereby strengthening support at home and at school. The learning history analysis unit, for example, builds a system for sharing the results of the analysis of learning history and goals with parents and teachers. For example, it can automatically generate regular reports and send them by email. The learning history analysis unit can also provide a dashboard that allows parents and teachers to check the results of the analysis of learning history and goals in real time. This can strengthen support at home and at school, thereby improving the effectiveness of learning.

[0034] The study plan generation unit can propose optimal study session lengths and break timings, taking into account the user's study pace and concentration duration. The study plan generation unit, for example, analyzes the user's study pace and concentration duration and proposes optimal study session lengths. For example, it can set sessions in 30-minute increments, which allows for easier concentration maintenance. The study plan generation unit can also propose appropriate break timings when the user's concentration begins to wane. The study plan generation unit can also adjust the progress rate of the study content according to the user's study pace. This can improve the effectiveness of learning by providing optimal study sessions according to the user's study pace and concentration.

[0035] The study plan generation unit can incorporate study methods and approaches that have been successful for the user in the past to recreate the user's successful experience. The study plan generation unit, for example, analyzes the user's past study history to identify successful study methods and approaches. For example, it re-suggests a study method that has earned the user a high score in the past. The study plan generation unit can also incorporate approaches that have been successful for the user in the past into the study plan. The study plan generation unit can also suggest specific study content and schedules for recreating the user's successful experience. In this way, the effectiveness of learning can be improved by recreating the user's successful experience.

[0036] The learning plan generation unit can work in conjunction with an online learning platform and an educational app to create an environment in which a user can study anytime, anywhere. The learning plan generation unit, for example, works in conjunction with an online learning platform to provide an environment in which a user can study anytime, anywhere. For example, it provides a learning plan that can be accessed via a smartphone or tablet. The learning plan generation unit can also work in conjunction with an educational app to create an environment in which a user can easily execute the learning plan. The learning plan generation unit can also provide the resources necessary for a user to execute the learning plan. This can improve the effectiveness of learning by providing an environment in which a user can study anytime, anywhere.

[0037] The learning plan generation unit can incorporate elements of group learning and paired learning to promote cooperation and competition with other users. The learning plan generation unit, for example, incorporates elements of group learning into the learning plan to promote cooperation with other users. For example, it can propose online discussions and joint projects. The learning plan generation unit can also incorporate elements of paired learning to promote competition with other users. The learning plan generation unit can also suggest specific methods for a user to cooperate with other users to advance their learning. This can enhance the effectiveness of learning by promoting cooperation and competition with other users.

[0038] The progress check unit can take into consideration the user's learning environment and recommend studying in the optimal environment. The progress check unit, for example, collects learning environment data for the user and suggests the optimal learning environment. For example, for a user who finds it effective to study in a quiet place, the progress check unit can recommend a library or a study room. The progress check unit can also suggest appropriate music for a user who finds it effective to study while listening to music. The progress check unit can also adjust the learning plan based on the user's learning environment. This can improve the effectiveness of learning by providing the optimal learning environment based on the user's learning environment.

[0039] The progress check unit can analyze patterns of questions that the user got wrong and identify common misunderstandings and weaknesses. For example, the progress check unit can analyze patterns of questions that the user got wrong and identify common misunderstandings and weaknesses. For example, if there are many misunderstandings about a particular concept, the progress check unit can propose a plan to focus on studying that concept. The progress check unit can also identify the user's misunderstandings and weaknesses and adjust the study plan based on that. The progress check unit can also analyze patterns of questions that the user got wrong and propose specific methods for identifying common misunderstandings and weaknesses. In this way, the effectiveness of learning can be improved by identifying the user's misunderstandings and weaknesses.

[0040] The progress check unit can compare the checks of the user's learning progress and understanding with those of other users to grasp the relative progress. The progress check unit, for example, compares the user's learning progress and understanding with those of other users to build a system for grasping the relative progress. For example, the progress check unit makes a comparison with classmates or users who have the same goal. The progress check unit can also suggest a specific method for grasping the user's relative progress. The progress check unit can also adjust the study plan based on the comparison with other users. This makes it possible to improve the effectiveness of learning by grasping the relative progress through comparison with other users.

[0041] The progress check unit can visualize the learning progress and provide it to the user, allowing for intuitive understanding. The progress check unit, for example, builds a system that visualizes the learning progress and provides it to the user. For example, the progress is displayed in a graph or chart. The progress check unit can also suggest a specific method for visualizing the progress so that the user can intuitively understand it. The progress check unit can also provide tools and resources for visualizing the learning progress. In this way, the learning progress can be visualized to allow the user to intuitively understand it.

[0042] The feedback providing unit can customize the feedback to suit the user's learning style and preferences, and provide more effective advice. The feedback providing unit customizes the content of the feedback to suit the user's learning style and preferences, for example. For example, feedback that makes extensive use of diagrams and graphs is provided to a visual user. The feedback providing unit can also provide audio feedback to an auditory user. The feedback providing unit can also provide practical advice to an experiential user. In this way, the effectiveness of learning can be improved by providing feedback customized based on the user's learning style and preferences.

[0043] The feedback providing unit can provide consistent advice based on a history of feedback the user has received in the past. The feedback providing unit, for example, considers the history of feedback the user has received in the past and builds a system that provides consistent advice. For example, the feedback providing unit provides new advice by referring to the content of past feedback. The feedback providing unit can also suggest a specific method for providing consistent advice based on the user's feedback history. The feedback providing unit can also consider the history of feedback the user has received in the past and provide tools and resources for providing consistent advice. This can improve the effectiveness of learning by providing consistent advice based on the user's feedback history.

[0044] The feedback providing unit can provide the feedback in an audio or video format to make it easier to understand visually or aurally. For example, the feedback providing unit can provide the feedback in an audio format to make it easier to understand aurally. For example, the feedback providing unit can provide the feedback as an audio message. The feedback providing unit can also provide the feedback in a video format to make it easier to understand visually. The feedback providing unit can also suggest a specific method for providing the feedback in a format that is easier for the user to understand visually or aurally. Thus, providing the feedback in an audio or video format can make it easier for the user to understand.

[0045] The feedback providing unit can also share the feedback with the user's parents and teachers, thereby strengthening support at home and at school. The feedback providing unit, for example, builds a system for sharing feedback with the user's parents and teachers. For example, it can automatically generate regular reports and send them by email. The feedback providing unit can also provide a dashboard that allows parents and teachers to check the feedback in real time. The feedback providing unit can also suggest specific methods for strengthening support at home and at school. In this way, strengthening support at home and at school can improve the effectiveness of learning.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The learning history analysis unit can not only analyze the user's learning history and goals, but also take into account the user's health data. For example, it can analyze the user's sleep patterns and exercise habits and reflect them in the learning plan. For users who are sleep-deprived, it can suggest a plan that adjusts study time and prioritizes rest. For users who exercise regularly, it can also suggest a learning plan that makes use of the refreshment time after exercise. This can improve the effectiveness of learning by providing an optimal learning plan based on the user's health condition.

[0048] The learning history analysis unit can not only identify the user's learning style, but also take into account the user's learning environment. For example, it can compare the effectiveness of a user studying at home with that of studying at a cafe and suggest the optimal learning environment. The learning history analysis unit can also consider the time of day the user studies (morning person or night person) and suggest the optimal study time. This can improve the effectiveness of learning by providing the optimal learning plan based on the user's learning environment.

[0049] The learning history analysis unit can take into account not only the user's lifestyle and interests, but also the user's social network. For example, if the user finds it effective to study with friends or family, it can suggest a study plan that reflects this. The learning history analysis unit can also utilize online communities and forums in which the user participates and reflect this in the study plan. This can improve the effectiveness of learning by providing a study plan that utilizes the user's social network.

[0050] The learning history analysis unit can be applied not only to users with different educational levels and specialties, but also to users with different cultural backgrounds. For example, it can provide culturally appropriate learning plans to users with different cultural backgrounds. The learning history analysis unit can also provide language-specific learning plans to users who speak different languages. This can improve the effectiveness of learning by providing learning plans that are appropriate for users with different cultural backgrounds and languages.

[0051] The learning history analysis unit can not only share the analysis results of learning history and goals with parents and teachers, but also provide feedback to the user himself / herself. For example, it can provide a dashboard where the user can check the analysis results of his / her learning history and goals. The learning history analysis unit can also provide a tool that allows the user to self-evaluate his / her learning progress. This allows the user to understand his / her own learning situation and manage it himself / herself, thereby improving the effectiveness of his / her learning.

[0052] The study plan generation unit not only takes into account the user's learning pace and concentration duration, but also periodically evaluates the user's degree of achievement of their study goals and adjusts the study plan as necessary. For example, if the user is falling behind in their progress toward their set goals, the unit can analyze the cause and reconstruct the study plan. The study plan generation unit can also propose a specific action plan for the user to achieve their goals. This can improve the effectiveness of learning by providing an optimal study plan based on the user's study goals.

[0053] The study plan generator can incorporate study methods and approaches that have been successful for the user in the past, as well as avoid study methods and approaches that have failed the user. For example, it can identify study methods that have been ineffective in the past and propose a plan that avoids them. The study plan generator can also analyze the causes of the user's failures and propose improvements. This can improve the effectiveness of learning by avoiding the user's past failures and recreating successful experiences.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The learning history analysis unit analyzes the user's learning history and goals. For example, it collects data such as the content the user has studied, test results, and study time, and uses this data to identify the user's strengths and weaknesses. It also takes into account the learning goals set by the user. Step 2: The study plan generator generates an optimal study plan based on the data analyzed by the study history analyzer. For example, if the user is studying for a math exam, the generator will suggest specific study content and schedules to strengthen the user's weak points. Step 3: The progress check unit checks the user's learning progress and level of understanding based on the learning plan generated by the learning plan generation unit. For example, the progress check unit periodically tests the content the user has learned and analyzes the results to evaluate the user's level of understanding. Step 4: The feedback provider provides feedback based on the results checked by the progress checker. For example, if the user lacks understanding in a particular area, the feedback provider may suggest additional study materials or practice questions for that area. Also, if the user is making good progress toward their goal, the feedback provider may send an encouraging message.

[0056] (Example 2) A learning support system according to an embodiment of the present invention utilizes a generative AI to analyze a user's learning history and goals, and provides an optimal learning plan. Furthermore, the system continuously checks the user's learning progress and level of understanding, and provides feedback to improve the efficiency and effectiveness of learning. This allows the learning support system to analyze a user's learning history and goals, and provide an optimal learning plan, thereby improving the efficiency and effectiveness of learning.

[0057] A learning assistance system according to an embodiment includes a learning history analysis unit, a learning plan generation unit, a progress check unit, and a feedback provision unit. The learning history analysis unit analyzes a user's learning history and goals. For example, the learning history analysis unit collects data such as the user's previous learning content, test results, and study time, and identifies the user's strengths and weaknesses based on the collected data. The learning history analysis unit also generates an optimal learning plan taking into account the user's set learning goals. The learning plan generation unit generates an optimal learning plan based on the data analyzed by the learning history analysis unit. For example, if a user is studying for a mathematics exam, the learning plan generation unit suggests specific learning content and a schedule to address the user's weaknesses. The progress check unit checks the user's learning progress and level of understanding based on the learning plan generated by the learning plan generation unit. For example, the progress check unit periodically tests the user's learning content and analyzes the results to evaluate the user's level of understanding. The feedback provision unit provides feedback based on the results of the check by the progress check unit. For example, if the user's understanding is lacking in a particular area, the feedback provision unit suggests additional learning materials or practice questions related to that area. Furthermore, if the user is making steady progress toward the goal, the feedback providing unit sends an encouraging message. As a result, the learning assistance system according to the embodiment can analyze the user's learning history and goals, and provide an optimal learning plan, thereby improving the efficiency and effectiveness of learning.

[0058] The learning history analysis unit can identify the user's learning style and generate a learning plan accordingly. For example, the learning history analysis unit analyzes the user's past learning data and identifies which learning style is most effective: visual, auditory, or experiential. For example, the learning history analysis unit can suggest learning materials that make extensive use of diagrams and graphs to visual users. The learning history analysis unit can also suggest audio learning materials to auditory users. The learning history analysis unit can also suggest practical exercises to experiential users. This can improve the effectiveness of learning by providing an optimal learning plan according to the user's learning style.

[0059] The learning history analysis unit can provide a more personalized learning plan by taking into account the user's lifestyle habits and interests. For example, the learning history analysis unit collects lifestyle habit data (e.g., sleep time and exercise habits) in addition to the user's learning history and reflects this in the learning plan. For example, it can suggest nighttime study times for a night-owl user. The learning history analysis unit also takes into account the user's interests and reflects this in the learning plan. For example, if the user is interested in a particular field, it can prioritize suggesting learning content related to that field. This can improve the effectiveness of learning by providing a learning plan based on the user's lifestyle habits and interests.

[0060] The learning history analysis unit can use the emotion estimation function to analyze the stress and satisfaction the user felt during past learning and propose a learning plan that reduces stress and increases satisfaction. The learning history analysis unit, for example, uses the emotion estimation function to analyze the stress and satisfaction felt during past learning based on the user's learning history. For example, it can propose a plan that avoids learning content that caused high stress. The learning history analysis unit can also prioritize suggesting learning content that caused high satisfaction. The learning history analysis unit can also use the emotion estimation function to monitor the stress the user felt during learning in real time and adjust the learning plan as necessary. This can improve the effectiveness of learning by providing a learning plan that reduces the user's stress and increases satisfaction.

[0061] The learning history analysis unit can be applied to different educational levels and fields of expertise, and can accommodate a wide range of users. The learning history analysis unit adjusts the analysis algorithm for learning history and goals to accommodate different educational levels, for example, from elementary school to university. For example, elementary school students are provided with a learning plan that focuses on basic content. The learning history analysis unit also provides learning plans according to specialized fields. For example, a science student can be provided with a learning plan specialized in science subjects. A humanities student can also be provided with a learning plan specialized in humanities subjects. This makes it possible to provide learning plans that are suitable for a wide range of users, thereby improving the effectiveness of learning.

[0062] The learning history analysis unit can share the results of the analysis of learning history and goals with parents and teachers, thereby strengthening support at home and at school. The learning history analysis unit, for example, builds a system for sharing the results of the analysis of learning history and goals with parents and teachers. For example, it can automatically generate regular reports and send them by email. The learning history analysis unit can also provide a dashboard that allows parents and teachers to check the results of the analysis of learning history and goals in real time. This can strengthen support at home and at school, thereby improving the effectiveness of learning.

[0063] The study plan generation unit can propose optimal study session lengths and break timings, taking into account the user's study pace and concentration duration. The study plan generation unit, for example, analyzes the user's study pace and concentration duration and proposes optimal study session lengths. For example, it can set sessions in 30-minute increments, which allows for easier concentration maintenance. The study plan generation unit can also propose appropriate break timings when the user's concentration begins to wane. The study plan generation unit can also adjust the progress rate of the study content according to the user's study pace. This can improve the effectiveness of learning by providing optimal study sessions according to the user's study pace and concentration.

[0064] The study plan generation unit can incorporate study methods and approaches that have been successful for the user in the past to recreate the user's successful experience. The study plan generation unit, for example, analyzes the user's past study history to identify successful study methods and approaches. For example, it re-suggests a study method that has earned the user a high score in the past. The study plan generation unit can also incorporate approaches that have been successful for the user in the past into the study plan. The study plan generation unit can also suggest specific study content and schedules for recreating the user's successful experience. In this way, the effectiveness of learning can be improved by recreating the user's successful experience.

[0065] The study plan generation unit can use the emotion estimation function to analyze the expectations and anxieties the user feels about the study plan and provide a plan that elicits positive emotions. The study plan generation unit, for example, uses the emotion estimation function to analyze the expectations and anxieties the user feels about the study plan in real time. For example, it can prioritize and suggest plans with high expectations. The study plan generation unit can also suggest plans that avoid study content that makes the user anxious. The study plan generation unit can also adjust the study plan based on the user's emotions and provide a plan that elicits positive emotions. In this way, by providing a positive study plan based on the user's emotions, the effectiveness of learning can be improved.

[0066] The learning plan generation unit can work in conjunction with an online learning platform and an educational app to create an environment in which a user can study anytime, anywhere. The learning plan generation unit, for example, works in conjunction with an online learning platform to provide an environment in which a user can study anytime, anywhere. For example, it provides a learning plan that can be accessed via a smartphone or tablet. The learning plan generation unit can also work in conjunction with an educational app to create an environment in which a user can easily execute the learning plan. The learning plan generation unit can also provide the resources necessary for a user to execute the learning plan. This can improve the effectiveness of learning by providing an environment in which a user can study anytime, anywhere.

[0067] The learning plan generation unit can incorporate elements of group learning and paired learning to promote cooperation and competition with other users. The learning plan generation unit, for example, incorporates elements of group learning into the learning plan to promote cooperation with other users. For example, it can propose online discussions and joint projects. The learning plan generation unit can also incorporate elements of paired learning to promote competition with other users. The learning plan generation unit can also suggest specific methods for a user to cooperate with other users to advance their learning. This can enhance the effectiveness of learning by promoting cooperation and competition with other users.

[0068] The study plan generation unit can use the emotion estimation function to monitor the user's emotions in real time when executing the study plan and adjust the plan as needed. The study plan generation unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when executing the study plan. For example, it can suggest taking a break when stress increases. The study plan generation unit can also adjust the study plan based on the user's emotions to maximize the effectiveness of the study. The study plan generation unit can also monitor the user's emotions in real time when executing the study plan and suggest specific methods for adjusting the plan as needed. In this way, the effectiveness of the study can be improved by adjusting the study plan in real time based on the user's emotions.

[0069] The progress check unit can take into consideration the user's learning environment and recommend studying in the optimal environment. The progress check unit, for example, collects learning environment data for the user and suggests the optimal learning environment. For example, for a user who finds it effective to study in a quiet place, the progress check unit can recommend a library or a study room. The progress check unit can also suggest appropriate music for a user who finds it effective to study while listening to music. The progress check unit can also adjust the learning plan based on the user's learning environment. This can improve the effectiveness of learning by providing the optimal learning environment based on the user's learning environment.

[0070] The progress check unit can analyze patterns of questions that the user got wrong and identify common misunderstandings and weaknesses. For example, the progress check unit can analyze patterns of questions that the user got wrong and identify common misunderstandings and weaknesses. For example, if there are many misunderstandings about a particular concept, the progress check unit can propose a plan to focus on studying that concept. The progress check unit can also identify the user's misunderstandings and weaknesses and adjust the study plan based on that. The progress check unit can also analyze patterns of questions that the user got wrong and propose specific methods for identifying common misunderstandings and weaknesses. In this way, the effectiveness of learning can be improved by identifying the user's misunderstandings and weaknesses.

[0071] The progress check unit can use the emotion estimation function to analyze the tension and anxiety felt by the user during the test, allowing the user to take the test in a relaxed state. The progress check unit, for example, uses the emotion estimation function to analyze the tension and anxiety felt by the user during the test in real time. For example, the progress check unit can suggest breathing techniques to help the user relax when tension builds up. The progress check unit can also suggest specific methods to reduce the user's tension and anxiety. The progress check unit can also create an environment that allows the user to take the test in a relaxed state. This allows the user to take the test in a relaxed state, thereby improving the effectiveness of learning.

[0072] The progress check unit can compare the checks of the user's learning progress and understanding with those of other users to grasp the relative progress. The progress check unit, for example, compares the user's learning progress and understanding with those of other users to build a system for grasping the relative progress. For example, the progress check unit makes a comparison with classmates or users who have the same goal. The progress check unit can also suggest a specific method for grasping the user's relative progress. The progress check unit can also adjust the study plan based on the comparison with other users. This makes it possible to improve the effectiveness of learning by grasping the relative progress through comparison with other users.

[0073] The progress check unit can visualize the learning progress and provide it to the user, allowing for intuitive understanding. The progress check unit, for example, builds a system that visualizes the learning progress and provides it to the user. For example, the progress is displayed in a graph or chart. The progress check unit can also suggest a specific method for visualizing the progress so that the user can intuitively understand it. The progress check unit can also provide tools and resources for visualizing the learning progress. In this way, the learning progress can be visualized to allow the user to intuitively understand it.

[0074] The progress check unit can use the emotion estimation function to analyze the emotion a user feels when checking their learning progress and provide positive feedback. The progress check unit, for example, uses the emotion estimation function to analyze the emotion a user feels when checking their learning progress in real time. For example, it can provide praise when the user feels a strong positive emotion. The progress check unit can also adjust the content of the feedback based on the user's emotion. The progress check unit can also analyze the emotion a user feels when checking their learning progress and suggest a specific method for providing positive feedback. In this way, the effectiveness of learning can be improved by providing positive feedback based on the user's emotion.

[0075] The feedback providing unit can customize the feedback to suit the user's learning style and preferences, and provide more effective advice. The feedback providing unit customizes the content of the feedback to suit the user's learning style and preferences, for example. For example, feedback that makes extensive use of diagrams and graphs is provided to a visual user. The feedback providing unit can also provide audio feedback to an auditory user. The feedback providing unit can also provide practical advice to an experiential user. In this way, the effectiveness of learning can be improved by providing feedback customized based on the user's learning style and preferences.

[0076] The feedback providing unit can provide consistent advice based on a history of feedback the user has received in the past. The feedback providing unit, for example, considers the history of feedback the user has received in the past and builds a system that provides consistent advice. For example, the feedback providing unit provides new advice by referring to the content of past feedback. The feedback providing unit can also suggest a specific method for providing consistent advice based on the user's feedback history. The feedback providing unit can also consider the history of feedback the user has received in the past and provide tools and resources for providing consistent advice. This can improve the effectiveness of learning by providing consistent advice based on the user's feedback history.

[0077] The feedback providing unit can provide the feedback in an audio or video format to make it easier to understand visually or aurally. For example, the feedback providing unit can provide the feedback in an audio format to make it easier to understand aurally. For example, the feedback providing unit can provide the feedback as an audio message. The feedback providing unit can also provide the feedback in a video format to make it easier to understand visually. The feedback providing unit can also suggest a specific method for providing the feedback in a format that is easier for the user to understand visually or aurally. Thus, providing the feedback in an audio or video format can make it easier for the user to understand.

[0078] The feedback providing unit can also share the feedback with the user's parents and teachers, thereby strengthening support at home and at school. The feedback providing unit, for example, builds a system for sharing feedback with the user's parents and teachers. For example, it can automatically generate regular reports and send them by email. The feedback providing unit can also provide a dashboard that allows parents and teachers to check the feedback in real time. The feedback providing unit can also suggest specific methods for strengthening support at home and at school. In this way, strengthening support at home and at school can improve the effectiveness of learning.

[0079] The feedback providing unit can use the emotion estimation function to monitor the emotion of the user when receiving feedback in real time and adjust the content of the feedback as needed. The feedback providing unit, for example, uses the emotion estimation function to monitor the emotion of the user when receiving feedback in real time. For example, the feedback content is adjusted if the user's negative emotion is strong. The feedback providing unit can also suggest a specific method for adjusting the content of the feedback based on the user's emotion. The feedback providing unit can also provide tools and resources for monitoring the emotion of the user when receiving feedback in real time and adjusting the content of the feedback as needed. In this way, the effectiveness of learning can be improved by adjusting the content of the feedback based on the user's emotion.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The learning history analysis unit can not only analyze the user's learning history and goals, but also take into account the user's health data. For example, it can analyze the user's sleep patterns and exercise habits and reflect them in the learning plan. For users who are sleep-deprived, it can suggest a plan that adjusts study time and prioritizes rest. For users who exercise regularly, it can also suggest a learning plan that makes use of the refreshment time after exercise. This can improve the effectiveness of learning by providing an optimal learning plan based on the user's health condition.

[0082] The learning history analysis unit can not only identify the user's learning style, but also take into account the user's learning environment. For example, it can compare the effectiveness of a user studying at home with that of studying at a cafe and suggest the optimal learning environment. The learning history analysis unit can also consider the time of day the user studies (morning person or night person) and suggest the optimal study time. This can improve the effectiveness of learning by providing the optimal learning plan based on the user's learning environment.

[0083] The learning history analysis unit can take into account not only the user's lifestyle and interests, but also the user's social network. For example, if the user finds it effective to study with friends or family, it can suggest a study plan that reflects this. The learning history analysis unit can also utilize online communities and forums in which the user participates and reflect this in the study plan. This can improve the effectiveness of learning by providing a study plan that utilizes the user's social network.

[0084] The learning history analysis unit uses the emotion estimation function to not only analyze the stress and satisfaction the user felt during past learning, but also take into account fluctuations in the user's motivation. For example, if the user's motivation tends to decrease at certain times, the unit can propose a special learning plan to increase motivation at that time. The learning history analysis unit can also propose specific methods for the user to maintain their motivation (for example, a reward system for achieving goals). This can maintain the user's motivation and improve the effectiveness of their learning.

[0085] The learning history analysis unit can be applied not only to users with different educational levels and specialties, but also to users with different cultural backgrounds. For example, it can provide culturally appropriate learning plans to users with different cultural backgrounds. The learning history analysis unit can also provide language-specific learning plans to users who speak different languages. This can improve the effectiveness of learning by providing learning plans that are appropriate for users with different cultural backgrounds and languages.

[0086] The learning history analysis unit can not only share the analysis results of learning history and goals with parents and teachers, but also provide feedback to the user himself / herself. For example, it can provide a dashboard where the user can check the analysis results of his / her learning history and goals. The learning history analysis unit can also provide a tool that allows the user to self-evaluate his / her learning progress. This allows the user to understand his / her own learning situation and manage it himself / herself, thereby improving the effectiveness of his / her learning.

[0087] The study plan generator can take into account not only the user's learning pace and concentration duration, but also the user's emotional state. For example, it can monitor the stress and fatigue the user feels while studying in real time and suggest taking a break as needed. The study plan generator can also suggest specific methods for creating a relaxing environment for the user (for example, providing relaxing music). This can improve the effectiveness of learning by providing an optimal study session based on the user's emotional state.

[0088] The study plan generation unit not only takes into account the user's learning pace and concentration duration, but also periodically evaluates the user's degree of achievement of their study goals and adjusts the study plan as necessary. For example, if the user is falling behind in their progress toward their set goals, the unit can analyze the cause and reconstruct the study plan. The study plan generation unit can also propose a specific action plan for the user to achieve their goals. This can improve the effectiveness of learning by providing an optimal study plan based on the user's study goals.

[0089] The study plan generator can incorporate study methods and approaches that have been successful for the user in the past, as well as avoid study methods and approaches that have failed the user. For example, it can identify study methods that have been ineffective in the past and propose a plan that avoids them. The study plan generator can also analyze the causes of the user's failures and propose improvements. This can improve the effectiveness of learning by avoiding the user's past failures and recreating successful experiences.

[0090] The study plan generation unit can use the emotion estimation function to not only analyze the user's expectations and anxieties about the study plan, but also suggest specific methods to increase the user's motivation. For example, if the user has high expectations about the study plan, it can provide an encouraging message to help the user maintain those expectations. The study plan generation unit can also suggest a step-by-step method for tackling study content that the user is anxious about. This can improve the effectiveness of learning by providing a positive study plan based on the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The learning history analysis unit analyzes the user's learning history and goals. For example, it collects data such as the content the user has studied, test results, and study time, and uses this data to identify the user's strengths and weaknesses. It also takes into account the learning goals set by the user. Step 2: The study plan generator generates an optimal study plan based on the data analyzed by the study history analyzer. For example, if the user is studying for a math exam, the generator will suggest specific study content and schedules to strengthen the user's weak points. Step 3: The progress check unit checks the user's learning progress and level of understanding based on the learning plan generated by the learning plan generation unit. For example, the progress check unit periodically tests the content the user has learned and analyzes the results to evaluate the user's level of understanding. Step 4: The feedback provider provides feedback based on the results checked by the progress checker. For example, if the user lacks understanding in a particular area, the feedback provider may suggest additional study materials or practice questions for that area. Also, if the user is making good progress toward their goal, the feedback provider may send an encouraging message.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 user's learning history and goals; a learning plan generation unit that generates an optimal learning plan based on the data analyzed by the learning history analysis unit; a progress check unit that checks the progress and understanding of the learning based on the learning plan generated by the learning plan generation unit; a feedback providing unit that provides feedback based on the results of the check by the progress check unit. A system characterized by:

2. The learning history analysis unit Identify your learning style and generate a learning plan accordingly 2. The system of claim 1.

3. The learning history analysis unit Applicable to a wide range of users across different educational levels and specialties 2. The system of claim 1.

4. The progress check unit Considering the user's learning environment, recommending the most suitable learning environment 2. The system of claim 1.

5. The feedback providing unit: Provide more effective advice by tailoring it to your learning style and preferences 2. The system of claim 1.

6. The learning history analysis unit Analyzes the stress and satisfaction experienced by users during past learning sessions and proposes learning plans that reduce stress and increase satisfaction.

2. The system of claim 1.

7. The learning plan generation unit Analyze the expectations and anxieties that users have about their learning plans and provide plans that evoke positive emotions 2. The system of claim 1.

8. The progress check section Analyze users' emotions when checking their learning progress and provide positive feedback 2. The system of claim 1.

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