system

Abstract

The system according to the embodiment aims to optimize learning content and promote learning efficiently. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a learning unit. The receiving unit receives input of learning content from a user. The generating unit analyzes the information received by the receiving unit and generates an appropriate learning plan. The learning unit proceeds with learning based on the learning plan generated by the generating unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately generate optimal plans for learning content and efficiently advance learning, so there is room for improvement.

[0005] The system according to the embodiment aims to optimize learning content and promote learning efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a learning unit. The receiving unit receives input of learning content from a user. The generating unit analyzes the information received by the receiving unit and generates an appropriate learning plan. The learning unit proceeds with learning based on the learning plan generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the learning content and progress the learning efficiently. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The learning optimization system according to an embodiment of the present invention is an optimization tool for efficiently performing arithmetic operations and memorization, which are the foundation of exam preparation. This learning optimization system improves upon outdated study methods, such as using vocabulary books and dictation, by utilizing modern technology to achieve efficient learning. Specifically, a user inputs the content they want to study, and a generation AI analyzes the information to generate an optimal study plan. The generation AI considers the user's learning history and level of understanding and proposes questions and memorization methods that are optimal for each individual user. For example, it may present questions that focus on the user's weak areas and suggest efficient memorization methods. Based on the generated study plan, the user progresses with their studies, for example, by solving the problems presented by the generation AI and practicing the memorization methods. During this process, the generation AI monitors the user's learning progress in real time and adjusts the study plan as needed. This mechanism allows users to efficiently solidify the foundations of exam preparation. For example, when solving arithmetic operations, the generation AI presents questions that are appropriate for the user's level of understanding and provides feedback on the answers. Furthermore, when memorizing English vocabulary, the generation AI suggests efficient memorization methods to help the user solidify their memory. Furthermore, the generative AI accumulates the user's learning data and provides a long-term learning plan, allowing the user to continue consistent learning and maximize the results of their exam preparation. This allows the learning optimization system to efficiently accept and analyze the user's learning content and provide the optimal learning plan.

[0029] A learning optimization system according to an embodiment includes a reception unit, a generation unit, and a learning unit. The reception unit receives input of learning content from a user. The learning content may include, but is not limited to, mathematical arithmetic operations, basic scientific concepts, and vocabulary memorization for language learning. The reception unit, for example, receives text data input by the user and transmits it to the generation AI. The reception unit can also support multiple input methods, such as voice input and image input. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. The reception unit can also convert handwritten notes and diagrams into digital data using image recognition technology. The generation unit uses the generation AI to analyze the information received by the reception unit and generate an optimal learning plan. The generation AI, for example, considers the user's learning history and level of understanding to suggest questions and memorization methods that are optimal for each individual user. For example, the generation AI can repeat questions the user previously answered incorrectly or generate a learning plan that focuses on the user's weak areas. The generation AI can also customize the learning plan based on the user's learning style and preferences. For example, if the user prefers visual learning, the generation AI generates a study plan that makes extensive use of charts and illustrations. The learning unit proceeds with the study based on the study plan generated by the generation unit. For example, the learning unit presents questions presented by the generation AI to the user and accepts the answers. The learning unit also provides feedback on the questions the user has answered to improve the user's understanding. For example, the learning unit displays the correct answer and explanation for a question the user got wrong. The learning unit can also monitor the user's study progress in real time and adjust the study plan as needed. For example, if the user repeatedly gets a particular question wrong, the learning unit has the user re-study basic content related to that question. This allows the learning optimization system according to the embodiment to efficiently accept and analyze the user's study content and provide an optimal study plan.

[0030] The monitoring unit can monitor the user's learning progress in real time. For example, the monitoring unit can record the correct answer rate and answer time of questions answered by the user in real time to monitor the learning progress. The monitoring unit can also evaluate the progress of the learning plan based on the user's learning history and level of understanding. For example, if the user repeatedly makes mistakes on a particular question, the monitoring unit can have the user re-study basic content related to that question. The monitoring unit can also visually display the user's learning progress as a graph or chart. For example, the monitoring unit can display the user's learning progress in chronological order, allowing the user to check the results of their learning at a glance. This can improve the effectiveness of learning by monitoring the user's learning progress in real time. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's learning data into a generation AI and cause the generation AI to monitor the learning progress.

[0031] The adjustment unit can adjust the study plan. For example, the adjustment unit adjusts the study plan in real time based on the user's learning progress and level of understanding. For example, if the user repeatedly makes mistakes on a particular problem, the adjustment unit has the user re-learn basic content related to that problem. The adjustment unit can also customize the study plan according to the user's learning style and preferences. For example, if the user prefers visual learning, the adjustment unit generates a study plan that makes extensive use of diagrams and illustrations. The adjustment unit can also monitor the user's learning progress in real time and adjust the study plan as needed. For example, if the user repeatedly makes mistakes on a particular problem, the adjustment unit has the user re-learn basic content related to that problem. By adjusting the study plan in this way, an optimal learning environment can be provided for the user. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's learning data into the generation AI and cause the generation AI to adjust the study plan.

[0032] The provision unit can provide a long-term study plan. The provision unit generates a long-term study plan based on, for example, the user's learning history and level of understanding. For example, the provision unit may re-ask questions that the user previously got wrong or generate a study plan that focuses on areas in which the user is weak. The provision unit can also customize the study plan according to the user's learning style and preferences. For example, if the user prefers visual learning, the provision unit may generate a study plan that makes extensive use of diagrams and illustrations. The provision unit can also monitor the user's learning progress in real time and adjust the study plan as needed. For example, if the user repeatedly gets a particular question wrong, the provision unit may have the user re-study basic content related to that question. This allows for consistent support of the user's learning by providing a long-term study plan. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit may input the user's learning data into a generation AI and cause the generation AI to generate a long-term study plan.

[0033] The generation unit can propose appropriate questions and memorization methods by taking into account the user's learning history and level of understanding. The generation unit can propose questions and memorization methods optimal for each individual user, for example, based on the user's learning history and level of understanding. For example, the generation unit can re-ask questions that the user previously got wrong or generate a study plan that focuses on the user's weak areas. The generation unit can also customize the study plan based on the user's learning style and preferences. For example, if the user prefers visual learning, the generation unit can generate a study plan that makes extensive use of diagrams and illustrations. The generation unit can also monitor the user's learning progress in real time and adjust the study plan as needed. For example, if the user repeatedly gets a particular question wrong, the generation unit can have the user re-learn basic content related to that question. This can provide a more effective study plan by taking into account the user's learning history and level of understanding. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's learning data into the generation AI and have the AI ​​generate a study plan.

[0034] The learning unit can solve problems presented by the generation AI and practice memorization methods. For example, the learning unit presents problems presented by the generation AI to the user and accepts answers. The learning unit also provides feedback on problems answered by the user to improve the user's understanding. For example, the learning unit displays the correct answer and explanation for a problem the user got wrong. The learning unit can also monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if the user repeatedly gets a particular problem wrong, the learning unit can have the user re-study basic content related to that problem. This allows the user to practice the problems and memorization methods presented by the generation AI, thereby improving the learning effect. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's learning data into the generation AI and have the generation AI execute the learning progress.

[0035] The reception unit can analyze the user's past learning history and select an appropriate input method. The reception unit selects the optimal input method based on the user's past learning history, for example. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past learning history. Furthermore, the reception unit can select the optimal input method based on learning methods that the user has used successfully in the past. In this way, the optimal input method can be provided by analyzing the user's past learning history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data into a generation AI and have the generation AI select the optimal input method.

[0036] When inputting learning content, the reception unit can filter the learning content based on the user's current learning status and areas of interest. For example, the reception unit prioritizes input of content related to the area the user is currently studying. The reception unit can also filter and input related learning content based on the user's areas of interest. Furthermore, the reception unit can also input learning content of an appropriate level of difficulty according to the user's learning progress. This can improve learning efficiency by filtering the learning content based on the user's learning status and areas of interest. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's learning status data to a generation AI and have the generation AI filter the learning content.

[0037] When inputting learning content, the reception unit can prioritize inputting highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting learning content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting learning content related to the travel destination. Furthermore, if the user is at a specific school or facility, the reception unit can prioritize inputting learning content related to that location. In this way, highly relevant learning content can be provided by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant learning content.

[0038] When inputting learning content, the reception unit can analyze the user's social media activity and input related content. For example, the reception unit can input learning content related to topics in which the user has shown interest on social media. The reception unit can also input learning content related to accounts the user follows on social media. Furthermore, the reception unit can also input learning content related to content shared by the user on social media. This makes it possible to provide related learning content by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related learning content.

[0039] When generating a study plan, the generation unit can adjust the level of detail of the plan based on the priority of the study content. For example, the generation unit generates a plan that includes detailed explanations for study content with high importance. The generation unit can also generate a concise plan for study content with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the plan according to the importance of the study content. This allows for improving study efficiency by adjusting the level of detail of the plan based on the importance of the study content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the study content into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0040] When generating a learning plan, the generation unit can apply different generation algorithms depending on the type of learning content. For example, the generation unit can apply a generation algorithm specialized for calculation problems to a mathematics learning plan. The generation unit can also apply a generation algorithm specialized for memory consolidation to an English vocabulary memorization plan. Furthermore, the generation unit can apply a generation algorithm specialized for experiment procedures to a science experiment plan. In this way, by applying different generation algorithms depending on the type of learning content, learning efficiency can be improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the type of learning content into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0041] When generating a study plan, the generation unit can determine the priority of the plan based on the submission deadline of the study content. For example, the generation unit prioritizes study content with an upcoming submission deadline into the plan. The generation unit can also postpone study content with a distant submission deadline. Furthermore, the generation unit can gradually adjust the priority of the study plan according to the submission time. This allows for improving study efficiency by determining the priority of the plan based on the submission time of the study content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input study content submission deadline data into the generation AI and have the generation AI determine the priority of the plan.

[0042] When generating a learning plan, the generation unit can adjust the order of the plan based on the relationships between the learning contents. For example, the generation unit incorporates highly relevant learning contents consecutively into the plan. The generation unit can also incorporate less relevant learning contents dispersedly into the plan. Furthermore, the generation unit can gradually adjust the order of the plan according to the relevance of the learning contents. This allows for improving learning efficiency by adjusting the order of the plan based on the relevance of the learning contents. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the learning contents into the generation AI and have the generation AI adjust the order of the plan.

[0043] The learning unit can select an appropriate learning method by analyzing the user's past learning behavior. For example, the learning unit selects the optimal learning method based on learning methods that the user has used successfully in the past. The learning unit can also suggest the optimal learning method for a specific time period based on the user's past learning behavior. Furthermore, the learning unit can preferentially suggest learning methods (audio, text, etc.) that the user has used in the past. This makes it possible to provide the optimal learning method by analyzing the user's past learning behavior. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past learning behavior data into a generation AI and have the generation AI select the optimal learning method.

[0044] The learning unit can adjust the learning means based on the user's current living situation during learning. For example, if the user is busy, the learning unit can provide a learning means that can be completed in a short time. Furthermore, if the user is relaxed, the learning unit can also provide a learning means that takes a long time. Furthermore, if the user is on the move, the learning unit can provide a learning means optimized for a mobile device. This allows for customizing the learning means based on the user's current living situation, thereby improving learning efficiency. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the user's living situation data into the generation AI and have the generation AI adjust the learning means.

[0045] The learning unit can select an appropriate learning method during learning by taking into account the user's geographical location information. For example, if the user is in a specific area, the learning unit can provide a learning method related to that area. Furthermore, if the user is traveling, the learning unit can provide a learning method related to the travel destination. Furthermore, if the user is at a specific school or facility, the learning unit can provide a learning method related to that location. In this way, the optimal learning method can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's geographical location information data into the generation AI and cause the generation AI to select an appropriate learning method.

[0046] During learning, the learning unit can analyze the user's social media activity and provide learning means. For example, the learning unit can provide learning means related to topics in which the user has shown interest on social media. The learning unit can also provide learning means related to accounts the user follows on social media. Furthermore, the learning unit can also provide learning means related to content the user has shared on social media. In this way, by analyzing the user's social media activity, related learning means can be provided. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's social media activity data into the generation AI and cause the generation AI to select appropriate learning means.

[0047] When monitoring learning progress, the monitoring unit can predict current progress based on past learning data. The monitoring unit predicts current progress based on, for example, the user's past learning data. The monitoring unit can also analyze the user's past learning patterns and predict progress. Furthermore, the monitoring unit can also predict progress by referring to the user's past learning history. In this way, current progress can be predicted by referring to past learning data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past learning data into a generation AI and have the generation AI predict current progress.

[0048] When monitoring learning progress, the monitoring unit can analyze changes in progress based on the deadline for submission of learning content. For example, the monitoring unit prioritizes monitoring the progress of learning content with an upcoming submission deadline. The monitoring unit can also postpone progress of learning content with a distant submission deadline. Furthermore, the monitoring unit can analyze changes in progress in stages according to the submission time. This makes it possible to improve learning efficiency by analyzing changes in progress based on the submission time of learning content. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input deadline data for submission of learning content into the generation AI and cause the generation AI to analyze changes in progress.

[0049] When adjusting the study plan, the adjustment unit can make appropriate adjustments based on the user's past study history. The adjustment unit, for example, makes optimal adjustments based on the user's past study history. The adjustment unit can also analyze the user's past study patterns and make adjustments. Furthermore, the adjustment unit can also make adjustments by referring to the user's past study data. This allows optimal adjustments to be made by referring to the user's past study history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past study history data into the generation AI and cause the generation AI to perform appropriate adjustments.

[0050] When adjusting a study plan, the adjustment unit can make appropriate adjustments by taking into account the user's geographical location information. For example, if the user is in a specific area, the adjustment unit can adjust a study plan related to that area. Furthermore, if the user is traveling, the adjustment unit can also adjust a study plan related to a travel destination. Furthermore, if the user is at a specific school or facility, the adjustment unit can also adjust a study plan related to that location. In this way, by taking the user's geographical location information into account, an optimal study plan can be adjusted. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform appropriate adjustments.

[0051] When providing a long-term study plan, the providing unit can provide an appropriate plan based on the user's past study data. The providing unit, for example, provides an optimal long-term study plan based on the user's past study data. The providing unit can also analyze the user's past study patterns and provide a long-term study plan. Furthermore, the providing unit can also provide a long-term study plan by referring to the user's past study history. In this way, by referring to the user's past study data, an optimal long-term study plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past study data into a generation AI and cause the generation AI to provide an appropriate plan.

[0052] When providing a long-term study plan, the providing unit can provide an appropriate plan by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a long-term study plan related to that area. Furthermore, if the user is traveling, the providing unit can provide a long-term study plan related to the travel destination. Furthermore, if the user is at a specific school or facility, the providing unit can provide a long-term study plan related to that location. In this way, an optimal long-term study plan can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to provide an appropriate plan.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When the user inputs their learning content, the reception unit can suggest the optimal input method based on the user's past learning history. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user is concentrating on studying during a specific time period, the reception unit can suggest an input method suited to that time period. Furthermore, the reception unit can select the optimal input method based on the learning methods that the user has used successfully in the past. In this way, the reception unit can provide the optimal input method by analyzing the user's past learning history.

[0055] The monitoring unit can not only monitor the user's learning progress in real time, but also the user's learning environment. For example, it can check whether the user is studying in a quiet environment and notify the user if there is a lot of noise. It can also monitor the user's posture and line of sight and advise the user to maintain an appropriate learning posture. Furthermore, it can monitor the temperature and lighting conditions of the user's learning environment and make suggestions to maintain an optimal learning environment. This can improve learning efficiency by optimizing the user's learning environment.

[0056] The adjustment unit can take the user's lifestyle into account when adjusting the user's study plan. For example, if the user is a nocturnal person, the adjustment unit can suggest a study plan that allows the user to concentrate at night. Alternatively, if the user is a morning person, the adjustment unit can generate a study plan that emphasizes morning hours. Furthermore, the adjustment unit can also adjust the study plan taking into account the user's weekend and holiday schedule. This can improve study efficiency by providing a study plan that matches the user's lifestyle.

[0057] When providing a long-term study plan, the provision unit can evaluate the user's level of goal achievement. For example, it can evaluate the degree to which the user has achieved the goals they set and adjust the study plan according to the level of achievement. In addition, if the user achieves a goal, it can set a new goal and provide a study plan based on that. Furthermore, if the user fails to achieve a goal, it can analyze the cause and propose improvements. In this way, by evaluating the user's level of goal achievement, it is possible to provide a more effective long-term study plan.

[0058] The generation unit can generate a study plan that not only takes into account the user's learning history and level of understanding, but also suits the user's learning style. For example, if the user prefers visual learning, a study plan that makes extensive use of charts and illustrations can be generated. If the user prefers auditory learning, a study plan that makes extensive use of audio and video can be generated. Furthermore, if the user prefers practical learning, a study plan that includes experiments and practical training can be generated. This makes it possible to improve learning efficiency by providing a study plan that suits the user's learning style.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit accepts learning content input from the user. Learning content includes the four arithmetic operations in mathematics, basic scientific concepts, and memorizing vocabulary for language learning. The reception unit receives the text data entered by the user and sends it to the generation AI. It also supports multiple input methods, such as voice input and image input. For example, it uses voice recognition technology to convert the user's voice input into text data, and image recognition technology to convert handwritten notes and diagrams into digital data. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate an optimal study plan. The generation AI considers the user's learning history and level of understanding and suggests questions and memorization methods that are optimal for each individual user. For example, it may re-ask questions that the user got wrong in the past or generate a study plan that focuses on areas in which the user is weak. It can also customize study plans based on the user's learning style and preferences. For example, for users who prefer visual learning, it generates a study plan that makes extensive use of charts and illustrations. Step 3: The learning unit proceeds with learning based on the learning plan generated by the generation unit. The learning unit presents questions presented by the generation AI to the user and accepts their answers. It also provides feedback on the questions the user has answered to improve their understanding. For example, it displays the correct answer and explanation for questions the user got wrong. Furthermore, the learning unit monitors the user's learning progress in real time and adjusts the learning plan as necessary. For example, if the user repeatedly gets a particular question wrong, it will have the user re-learn the basic content related to that question.

[0061] (Example 2) The learning optimization system according to an embodiment of the present invention is an optimization tool for efficiently performing arithmetic operations and memorization, which are the foundation of exam preparation. This learning optimization system improves upon outdated study methods, such as using vocabulary books and dictation, by utilizing modern technology to achieve efficient learning. Specifically, a user inputs the content they want to study, and a generation AI analyzes the information to generate an optimal study plan. The generation AI considers the user's learning history and level of understanding and proposes questions and memorization methods that are optimal for each individual user. For example, it may present questions that focus on the user's weak areas and suggest efficient memorization methods. Based on the generated study plan, the user progresses with their studies, for example, by solving the problems presented by the generation AI and practicing the memorization methods. During this process, the generation AI monitors the user's learning progress in real time and adjusts the study plan as needed. This mechanism allows users to efficiently solidify the foundations of exam preparation. For example, when solving arithmetic operations, the generation AI presents questions that are appropriate for the user's level of understanding and provides feedback on the answers. Furthermore, when memorizing English vocabulary, the generation AI suggests efficient memorization methods to help the user solidify their memory. Furthermore, the generative AI accumulates the user's learning data and provides a long-term learning plan, allowing the user to continue consistent learning and maximize the results of their exam preparation. This allows the learning optimization system to efficiently accept and analyze the user's learning content and provide the optimal learning plan.

[0062] A learning optimization system according to an embodiment includes a reception unit, a generation unit, and a learning unit. The reception unit receives input of learning content from a user. The learning content may include, but is not limited to, mathematical arithmetic operations, basic scientific concepts, and vocabulary memorization for language learning. The reception unit, for example, receives text data input by the user and transmits it to the generation AI. The reception unit can also support multiple input methods, such as voice input and image input. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. The reception unit can also convert handwritten notes and diagrams into digital data using image recognition technology. The generation unit uses the generation AI to analyze the information received by the reception unit and generate an optimal learning plan. The generation AI, for example, considers the user's learning history and level of understanding to suggest questions and memorization methods that are optimal for each individual user. For example, the generation AI can repeat questions the user previously answered incorrectly or generate a learning plan that focuses on the user's weak areas. The generation AI can also customize the learning plan based on the user's learning style and preferences. For example, if the user prefers visual learning, the generation AI generates a study plan that makes extensive use of charts and illustrations. The learning unit proceeds with the study based on the study plan generated by the generation unit. For example, the learning unit presents questions presented by the generation AI to the user and accepts the answers. The learning unit also provides feedback on the questions the user has answered to improve the user's understanding. For example, the learning unit displays the correct answer and explanation for a question the user got wrong. The learning unit can also monitor the user's study progress in real time and adjust the study plan as needed. For example, if the user repeatedly gets a particular question wrong, the learning unit has the user re-study basic content related to that question. This allows the learning optimization system according to the embodiment to efficiently accept and analyze the user's study content and provide an optimal study plan.

[0063] The monitoring unit can monitor the user's learning progress in real time. For example, the monitoring unit can record the correct answer rate and answer time of questions answered by the user in real time to monitor the learning progress. The monitoring unit can also evaluate the progress of the learning plan based on the user's learning history and level of understanding. For example, if the user repeatedly makes mistakes on a particular question, the monitoring unit can have the user re-study basic content related to that question. The monitoring unit can also visually display the user's learning progress as a graph or chart. For example, the monitoring unit can display the user's learning progress in chronological order, allowing the user to check the results of their learning at a glance. This can improve the effectiveness of learning by monitoring the user's learning progress in real time. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's learning data into a generation AI and cause the generation AI to monitor the learning progress.

[0064] The adjustment unit can adjust the study plan. For example, the adjustment unit adjusts the study plan in real time based on the user's learning progress and level of understanding. For example, if the user repeatedly makes mistakes on a particular problem, the adjustment unit has the user re-learn basic content related to that problem. The adjustment unit can also customize the study plan according to the user's learning style and preferences. For example, if the user prefers visual learning, the adjustment unit generates a study plan that makes extensive use of diagrams and illustrations. The adjustment unit can also monitor the user's learning progress in real time and adjust the study plan as needed. For example, if the user repeatedly makes mistakes on a particular problem, the adjustment unit has the user re-learn basic content related to that problem. By adjusting the study plan in this way, an optimal learning environment can be provided for the user. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's learning data into the generation AI and cause the generation AI to adjust the study plan.

[0065] The provision unit can provide a long-term study plan. The provision unit generates a long-term study plan based on, for example, the user's learning history and level of understanding. For example, the provision unit may re-ask questions that the user previously got wrong or generate a study plan that focuses on areas in which the user is weak. The provision unit can also customize the study plan according to the user's learning style and preferences. For example, if the user prefers visual learning, the provision unit may generate a study plan that makes extensive use of diagrams and illustrations. The provision unit can also monitor the user's learning progress in real time and adjust the study plan as needed. For example, if the user repeatedly gets a particular question wrong, the provision unit may have the user re-study basic content related to that question. This allows for consistent support of the user's learning by providing a long-term study plan. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit may input the user's learning data into a generation AI and cause the generation AI to generate a long-term study plan.

[0066] The generation unit can propose appropriate questions and memorization methods by taking into account the user's learning history and level of understanding. The generation unit can propose questions and memorization methods optimal for each individual user, for example, based on the user's learning history and level of understanding. For example, the generation unit can re-ask questions that the user previously got wrong or generate a study plan that focuses on the user's weak areas. The generation unit can also customize the study plan based on the user's learning style and preferences. For example, if the user prefers visual learning, the generation unit can generate a study plan that makes extensive use of diagrams and illustrations. The generation unit can also monitor the user's learning progress in real time and adjust the study plan as needed. For example, if the user repeatedly gets a particular question wrong, the generation unit can have the user re-learn basic content related to that question. This can provide a more effective study plan by taking into account the user's learning history and level of understanding. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's learning data into the generation AI and have the AI ​​generate a study plan.

[0067] The learning unit can solve problems presented by the generation AI and practice memorization methods. For example, the learning unit presents problems presented by the generation AI to the user and accepts answers. The learning unit also provides feedback on problems answered by the user to improve the user's understanding. For example, the learning unit displays the correct answer and explanation for a problem the user got wrong. The learning unit can also monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if the user repeatedly gets a particular problem wrong, the learning unit can have the user re-study basic content related to that problem. This allows the user to practice the problems and memorization methods presented by the generation AI, thereby improving the learning effect. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's learning data into the generation AI and have the generation AI execute the learning progress.

[0068] The reception unit can estimate the user's emotions and adjust the timing of inputting the learning content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input the learning content during a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can also encourage the user to input the learning content at that time. Furthermore, if the user is tired, the reception unit can also prompt the user to input the learning content after a break. This allows for adjusting the timing of inputting the learning content according to the user's emotions, thereby improving learning efficiency. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception unit can analyze the user's past learning history and select an appropriate input method. The reception unit selects the optimal input method based on the user's past learning history, for example. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past learning history. Furthermore, the reception unit can select the optimal input method based on learning methods that the user has used successfully in the past. In this way, the optimal input method can be provided by analyzing the user's past learning history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's learning history data into a generation AI and have the generation AI select the optimal input method.

[0070] When inputting learning content, the reception unit can filter the learning content based on the user's current learning status and areas of interest. For example, the reception unit prioritizes input of content related to the area the user is currently studying. The reception unit can also filter and input related learning content based on the user's areas of interest. Furthermore, the reception unit can also input learning content of an appropriate level of difficulty according to the user's learning progress. This can improve learning efficiency by filtering the learning content based on the user's learning status and areas of interest. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's learning status data to a generation AI and have the generation AI filter the learning content.

[0071] The reception unit can estimate the user's emotions and determine the priority of the learning content to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize input of easy learning content. Furthermore, when the user is concentrating, the reception unit can prioritize input of difficult learning content. Furthermore, when the user is relaxed, the reception unit can prioritize input of learning content that the user is interested in. This can improve learning efficiency by determining the priority of learning content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0072] When inputting learning content, the reception unit can prioritize inputting highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting learning content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting learning content related to the travel destination. Furthermore, if the user is at a specific school or facility, the reception unit can prioritize inputting learning content related to that location. In this way, highly relevant learning content can be provided by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant learning content.

[0073] When inputting learning content, the reception unit can analyze the user's social media activity and input related content. For example, the reception unit can input learning content related to topics in which the user has shown interest on social media. The reception unit can also input learning content related to accounts the user follows on social media. Furthermore, the reception unit can also input learning content related to content shared by the user on social media. This makes it possible to provide related learning content by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related learning content.

[0074] The generation unit can estimate the user's emotions and adjust the presentation method of the study plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a study plan with a visually relaxing design. Furthermore, if the user is nervous, the generation unit can generate a simple, highly visible study plan. Furthermore, if the user is excited, the generation unit can generate a study plan with a visually stimulating design. This allows for improving learning efficiency by adjusting the presentation method of the study plan according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the study plan.

[0075] When generating a study plan, the generation unit can adjust the level of detail of the plan based on the priority of the study content. For example, the generation unit generates a plan that includes detailed explanations for study content with high importance. The generation unit can also generate a concise plan for study content with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the plan according to the importance of the study content. This allows for improving study efficiency by adjusting the level of detail of the plan based on the importance of the study content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input importance data of the study content into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0076] When generating a learning plan, the generation unit can apply different generation algorithms depending on the type of learning content. For example, the generation unit can apply a generation algorithm specialized for calculation problems to a mathematics learning plan. The generation unit can also apply a generation algorithm specialized for memory consolidation to an English vocabulary memorization plan. Furthermore, the generation unit can apply a generation algorithm specialized for experiment procedures to a science experiment plan. In this way, by applying different generation algorithms depending on the type of learning content, learning efficiency can be improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the type of learning content into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0077] The generation unit can estimate the user's emotions and adjust the duration of the study plan based on the estimated user emotions. For example, if the user is tired, the generation unit generates a study plan that can be completed in a short time. Furthermore, if the user is concentrating, the generation unit can also generate a long study plan. Furthermore, if the user is relaxed, the generation unit can also generate a study plan of an appropriate length. This allows for adjusting the length of the study plan according to the user's emotions, thereby improving study efficiency. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the duration of the study plan.

[0078] When generating a study plan, the generation unit can determine the priority of the plan based on the submission deadline of the study content. For example, the generation unit prioritizes study content with an upcoming submission deadline into the plan. The generation unit can also postpone study content with a distant submission deadline. Furthermore, the generation unit can gradually adjust the priority of the study plan according to the submission time. This allows for improving study efficiency by determining the priority of the plan based on the submission time of the study content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input study content submission deadline data into the generation AI and have the generation AI determine the priority of the plan.

[0079] When generating a learning plan, the generation unit can adjust the order of the plan based on the relationships between the learning contents. For example, the generation unit incorporates highly relevant learning contents consecutively into the plan. The generation unit can also incorporate less relevant learning contents dispersedly into the plan. Furthermore, the generation unit can gradually adjust the order of the plan according to the relevance of the learning contents. This allows for improving learning efficiency by adjusting the order of the plan based on the relevance of the learning contents. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the learning contents into the generation AI and have the generation AI adjust the order of the plan.

[0080] The learning unit can estimate the user's emotions and adjust the learning method based on the estimated user emotions. For example, if the user is relaxed, the learning unit can provide a visually relaxing learning method. Furthermore, if the user is nervous, the learning unit can provide a simple and highly visible learning method. Furthermore, if the user is excited, the learning unit can provide a visually stimulating learning method. This allows for improving learning efficiency by adjusting the learning method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI adjust the learning method.

[0081] The learning unit can select an appropriate learning method by analyzing the user's past learning behavior. For example, the learning unit selects the optimal learning method based on learning methods that the user has used successfully in the past. The learning unit can also suggest the optimal learning method for a specific time period based on the user's past learning behavior. Furthermore, the learning unit can preferentially suggest learning methods (audio, text, etc.) that the user has used in the past. This makes it possible to provide the optimal learning method by analyzing the user's past learning behavior. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's past learning behavior data into a generation AI and have the generation AI select the optimal learning method.

[0082] The learning unit can adjust the learning means based on the user's current living situation during learning. For example, if the user is busy, the learning unit can provide a learning means that can be completed in a short time. Furthermore, if the user is relaxed, the learning unit can also provide a learning means that takes a long time. Furthermore, if the user is on the move, the learning unit can provide a learning means optimized for a mobile device. This allows for customizing the learning means based on the user's current living situation, thereby improving learning efficiency. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the user's living situation data into the generation AI and have the generation AI adjust the learning means.

[0083] The learning unit can estimate the user's emotions and determine learning priorities based on the estimated user emotions. For example, when the user is feeling stressed, the learning unit can prioritize providing easy learning content. Furthermore, when the user is concentrating, the learning unit can prioritize providing more difficult learning content. Furthermore, when the user is relaxed, the learning unit can prioritize providing learning content that the user is interested in. This improves learning efficiency by determining learning priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI determine the learning priorities.

[0084] The learning unit can select an appropriate learning method during learning by taking into account the user's geographical location information. For example, if the user is in a specific area, the learning unit can provide a learning method related to that area. Furthermore, if the user is traveling, the learning unit can provide a learning method related to the travel destination. Furthermore, if the user is at a specific school or facility, the learning unit can provide a learning method related to that location. In this way, the optimal learning method can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the user's geographical location information data into the generation AI and cause the generation AI to select an appropriate learning method.

[0085] During learning, the learning unit can analyze the user's social media activity and provide learning means. For example, the learning unit can provide learning means related to topics in which the user has shown interest on social media. The learning unit can also provide learning means related to accounts the user follows on social media. Furthermore, the learning unit can also provide learning means related to content the user has shared on social media. In this way, by analyzing the user's social media activity, related learning means can be provided. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's social media activity data into the generation AI and cause the generation AI to select appropriate learning means.

[0086] The monitoring unit can estimate the user's emotions and adjust the learning progress monitoring method based on the estimated user emotions. For example, the monitoring unit can provide a detailed progress report when the user is relaxed. The monitoring unit can also provide a concise progress report when the user is nervous. Furthermore, the monitoring unit can provide a visually stimulating progress report when the user is excited. This allows for improving learning efficiency by adjusting the learning progress monitoring method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the learning progress monitoring method.

[0087] When monitoring learning progress, the monitoring unit can predict current progress based on past learning data. The monitoring unit predicts current progress based on, for example, the user's past learning data. The monitoring unit can also analyze the user's past learning patterns and predict progress. Furthermore, the monitoring unit can also predict progress by referring to the user's past learning history. In this way, current progress can be predicted by referring to past learning data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past learning data into a generation AI and have the generation AI predict current progress.

[0088] The monitoring unit can estimate the user's emotions and adjust the importance of progress based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can set the importance of progress to low. Furthermore, if the user is concentrating, the monitoring unit can also set the importance of progress to high. Furthermore, if the user is relaxed, the monitoring unit can also set the importance of progress to medium. This allows for adjusting the importance of progress according to the user's emotions, thereby improving learning efficiency. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the importance of progress.

[0089] When monitoring learning progress, the monitoring unit can analyze changes in progress based on the deadline for submission of learning content. For example, the monitoring unit prioritizes monitoring the progress of learning content with an upcoming submission deadline. The monitoring unit can also postpone progress of learning content with a distant submission deadline. Furthermore, the monitoring unit can analyze changes in progress in stages according to the submission time. This makes it possible to improve learning efficiency by analyzing changes in progress based on the submission time of learning content. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input deadline data for submission of learning content into the generation AI and cause the generation AI to analyze changes in progress.

[0090] The adjustment unit can estimate the user's emotions and determine how to adjust the study plan based on the estimated user emotions. For example, the adjustment unit can make detailed adjustments when the user is relaxed. Furthermore, the adjustment unit can also make simple adjustments when the user is nervous. Furthermore, the adjustment unit can also make visually stimulating adjustments when the user is excited. This improves learning efficiency by determining how to adjust the study plan according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and have the generation AI determine how to adjust the study plan.

[0091] When adjusting the study plan, the adjustment unit can make appropriate adjustments based on the user's past study history. The adjustment unit, for example, makes optimal adjustments based on the user's past study history. The adjustment unit can also analyze the user's past study patterns and make adjustments. Furthermore, the adjustment unit can also make adjustments by referring to the user's past study data. This allows optimal adjustments to be made by referring to the user's past study history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past study history data into the generation AI and cause the generation AI to perform appropriate adjustments.

[0092] The adjustment unit can estimate the user's emotions and determine the frequency of adjustment of the study plan based on the estimated user emotions. For example, the adjustment unit can set the adjustment frequency low when the user is stressed. The adjustment unit can also set the adjustment frequency high when the user is concentrating. Furthermore, the adjustment unit can also set the adjustment frequency to medium when the user is relaxed. This allows for determining the frequency of adjustment of the study plan according to the user's emotions, thereby improving study efficiency. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and have the generation AI determine the frequency of adjustment of the study plan.

[0093] When adjusting a study plan, the adjustment unit can make appropriate adjustments by taking into account the user's geographical location information. For example, if the user is in a specific area, the adjustment unit can adjust a study plan related to that area. Furthermore, if the user is traveling, the adjustment unit can also adjust a study plan related to a travel destination. Furthermore, if the user is at a specific school or facility, the adjustment unit can also adjust a study plan related to that location. In this way, by taking the user's geographical location information into account, an optimal study plan can be adjusted. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform appropriate adjustments.

[0094] The providing unit can estimate the user's emotions and adjust the method of providing the long-term study plan based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a detailed long-term study plan. Furthermore, if the user is nervous, the providing unit can provide a concise long-term study plan. Furthermore, if the user is excited, the providing unit can provide a visually stimulating long-term study plan. This can improve learning efficiency by adjusting the method of providing the long-term study plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing the long-term study plan.

[0095] When providing a long-term study plan, the providing unit can provide an appropriate plan based on the user's past study data. The providing unit, for example, provides an optimal long-term study plan based on the user's past study data. The providing unit can also analyze the user's past study patterns and provide a long-term study plan. Furthermore, the providing unit can also provide a long-term study plan by referring to the user's past study history. In this way, by referring to the user's past study data, an optimal long-term study plan can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past study data into a generation AI and cause the generation AI to provide an appropriate plan.

[0096] The providing unit can estimate the user's emotions and determine the priorities of the long-term study plan based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize easy study content. Furthermore, when the user is concentrating, the providing unit can prioritize difficult study content. Furthermore, when the user is relaxed, the providing unit can prioritize interesting study content. This improves learning efficiency by determining the priorities of the long-term study plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priorities of the long-term study plan.

[0097] When providing a long-term study plan, the providing unit can provide an appropriate plan by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a long-term study plan related to that area. Furthermore, if the user is traveling, the providing unit can provide a long-term study plan related to the travel destination. Furthermore, if the user is at a specific school or facility, the providing unit can provide a long-term study plan related to that location. In this way, an optimal long-term study plan can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to provide an appropriate plan. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's learning content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's learning history and level of understanding to generate an optimal learning plan. The learning unit is realized, for example, by the control unit 46A of the smart device 14 and progresses learning based on the generated learning plan. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's learning content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's learning history and level of understanding to generate an optimal learning plan. The learning unit is realized, for example, by the control unit 46A of the smart glasses 214 and progresses learning based on the generated learning plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and learning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the user's learning content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's learning history and level of understanding to generate an optimal learning plan. The learning unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and progresses learning based on the generated learning plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's learning content. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's learning history and level of understanding to generate an optimal learning plan. The learning unit is realized, for example, by the control unit 46A of the robot 414 and progresses learning based on the generated learning plan.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When the user inputs their learning content, the reception unit can suggest the optimal input method based on the user's past learning history. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user is concentrating on studying during a specific time period, the reception unit can suggest an input method suited to that time period. Furthermore, the reception unit can select the optimal input method based on the learning methods that the user has used successfully in the past. In this way, the reception unit can provide the optimal input method by analyzing the user's past learning history.

[0100] The monitoring unit can not only monitor the user's learning progress in real time, but also the user's learning environment. For example, it can check whether the user is studying in a quiet environment and notify the user if there is a lot of noise. It can also monitor the user's posture and line of sight and advise the user to maintain an appropriate learning posture. Furthermore, it can monitor the temperature and lighting conditions of the user's learning environment and make suggestions to maintain an optimal learning environment. This can improve learning efficiency by optimizing the user's learning environment.

[0101] The adjustment unit can take the user's lifestyle into account when adjusting the user's study plan. For example, if the user is a nocturnal person, the adjustment unit can suggest a study plan that allows the user to concentrate at night. Alternatively, if the user is a morning person, the adjustment unit can generate a study plan that emphasizes morning hours. Furthermore, the adjustment unit can also adjust the study plan taking into account the user's weekend and holiday schedule. This can improve study efficiency by providing a study plan that matches the user's lifestyle.

[0102] When providing a long-term study plan, the provision unit can evaluate the user's level of goal achievement. For example, it can evaluate the degree to which the user has achieved the goals they set and adjust the study plan according to the level of achievement. In addition, if the user achieves a goal, it can set a new goal and provide a study plan based on that. Furthermore, if the user fails to achieve a goal, it can analyze the cause and propose improvements. In this way, by evaluating the user's level of goal achievement, it is possible to provide a more effective long-term study plan.

[0103] The generation unit can generate a study plan that not only takes into account the user's learning history and level of understanding, but also suits the user's learning style. For example, if the user prefers visual learning, a study plan that makes extensive use of charts and illustrations can be generated. If the user prefers auditory learning, a study plan that makes extensive use of audio and video can be generated. Furthermore, if the user prefers practical learning, a study plan that includes experiments and practical training can be generated. This makes it possible to improve learning efficiency by providing a study plan that suits the user's learning style.

[0104] The learning unit can not only solve problems presented by the generation AI and practice memorization methods, but also be equipped with functions to maintain the user's motivation to learn. For example, the learning unit can provide rewards when the user achieves a goal. It can also display encouraging messages to encourage the user to continue learning. Furthermore, if the user loses interest in learning, it can analyze the cause and make suggestions to restore motivation. This can maintain the user's motivation to learn and improve the effectiveness of learning.

[0105] The reception unit can estimate the user's emotions and adjust the timing of inputting study content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input study content at a time when the user is able to relax. Also, if the user is concentrating, the reception unit can encourage the user to input study content at that time. Furthermore, if the user is tired, the reception unit can encourage the user to input study content after a break. In this way, the efficiency of learning can be improved by adjusting the timing of inputting study content according to the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the presentation method of the study plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a study plan with a visually relaxing design. If the user is nervous, the generation unit can generate a simple study plan with high visibility. Furthermore, if the user is excited, the generation unit can generate a study plan with a visually stimulating design. In this way, by adjusting the presentation method of the study plan according to the user's emotions, the efficiency of study can be improved.

[0107] The learning unit can estimate the user's emotions and adjust the learning method based on the estimated user's emotions. For example, if the user is relaxed, a visually relaxing learning method can be provided. If the user is nervous, a simple and highly visible learning method can be provided. Furthermore, if the user is excited, a visually stimulating learning method can be provided. In this way, the efficiency of learning can be improved by adjusting the learning method according to the user's emotions.

[0108] The provision unit can estimate the user's emotions and adjust the method of providing the long-term study plan based on the estimated user's emotions. For example, if the user is relaxed, a detailed long-term study plan can be provided. If the user is nervous, a concise long-term study plan can be provided. Furthermore, if the user is excited, a visually stimulating long-term study plan can be provided. In this way, by adjusting the method of providing the long-term study plan according to the user's emotions, it is possible to improve the efficiency of learning.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit accepts learning content input from the user. Learning content includes the four arithmetic operations in mathematics, basic scientific concepts, and memorizing vocabulary for language learning. The reception unit receives the text data entered by the user and sends it to the generation AI. It also supports multiple input methods, such as voice input and image input. For example, it uses voice recognition technology to convert the user's voice input into text data, and image recognition technology to convert handwritten notes and diagrams into digital data. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate an optimal study plan. The generation AI considers the user's learning history and level of understanding and suggests questions and memorization methods that are optimal for each individual user. For example, it may re-ask questions that the user got wrong in the past or generate a study plan that focuses on areas in which the user is weak. It can also customize study plans based on the user's learning style and preferences. For example, for users who prefer visual learning, it generates a study plan that makes extensive use of charts and illustrations. Step 3: The learning unit proceeds with learning based on the learning plan generated by the generation unit. The learning unit presents questions presented by the generation AI to the user and accepts their answers. It also provides feedback on the questions the user has answered to improve their understanding. For example, it displays the correct answer and explanation for questions the user got wrong. Furthermore, the learning unit monitors the user's learning progress in real time and adjusts the learning plan as necessary. For example, if the user repeatedly gets a particular question wrong, it will have the user re-learn the basic content related to that question.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

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

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0134] The 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.

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

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

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

[0138] Fig. 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.

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

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

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

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

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

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

[0150] The 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.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 reception unit that receives input of learning content from a user; a generation unit that analyzes the information received by the reception unit and generates an appropriate study plan; a learning unit that advances learning based on the learning plan generated by the generation unit; A system characterized by:

2. Equipped with a monitoring unit that monitors the user's learning progress in real time The system of claim 1 .

3. Equipping a coordination department to adjust study plans The system of claim 1 .

4. Have a department that provides continuous learning plans The system of claim 1 .

5. The generation unit Suggests appropriate questions and memorization methods based on the user's learning history and level of understanding The system of claim 1 .

6. The learning unit Solve problems presented by the generative AI and practice memorization methods The system of claim 1 .

7. The reception unit Estimates the user's emotions and adjusts the timing of learning content input based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past learning history and select the appropriate input method The system of claim 1 .

Images