System

Abstract

An object of the system according to the embodiment is to provide a personalized study plan based on a study history of a student and to perform feedback in real time.SOLUTION: A system includes a proposal unit, a feedback unit, an answer unit, and a generation unit. The proposal unit proposes a personalized study plan based on the study history of the student. The feedback unit provides feedback in real time based on the learning plan proposed by the proposal unit. The answering component answers the student's question based on the feedback provided by the feedback component. The generation part generates learning contents individualized on the basis of the information obtained by the answer part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide personalized learning plans that address students' individual learning needs, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized learning plan based on the student's learning history and provide feedback in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a feedback unit, an answering unit, and a generation unit. The suggestion unit proposes a personalized learning plan based on the student's learning history. The feedback unit provides real-time feedback based on the learning plan proposed by the suggestion unit. The answering unit answers the student's questions based on the feedback provided by the feedback unit. The generation unit generates personalized learning content based on the information obtained by the answering unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized learning plan based on the student's learning history and provide feedback in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention is a system for proposing a personalized learning plan based on a student's learning history, providing real-time feedback, answering questions, and generating custom learning content. The learning support system improves learning effectiveness by proposing a personalized learning plan based on a student's learning history, providing real-time feedback, answering questions, and generating custom learning content. For example, the learning support system proposes a personalized learning plan based on a student's learning history. For example, the learning support system analyzes the student's learning history and proposes an optimal learning plan for each individual student. Then, the learning support system provides real-time feedback based on the proposed learning plan. For example, the learning support system monitors the student's learning progress and provides feedback as needed. Then, the learning support system answers the student's questions based on the provided feedback. For example, the learning support system provides appropriate answers to the student's questions. Then, the learning support system generates custom learning content based on the obtained information. For example, the learning support system generates problem sets and study materials tailored to the student's level and interests. This allows the learning support system to provide an individualized learning experience. This allows the learning support system to propose a personalized learning plan based on the student's learning history, providing real-time feedback, answering questions, and generating custom learning content. For example, learning support systems can reduce the burden on teachers and improve students' learning outcomes. They also allow students to track their own learning progress in real time and receive feedback as needed, which can help promote students' learning outcomes.

[0029] A learning support system according to an embodiment includes a suggestion unit, a feedback unit, an answering unit, and a generation unit. The suggestion unit proposes a personalized study plan based on a student's learning history. The suggestion unit analyzes, for example, the student's learning history, such as past test results, study time, and learning content, and proposes an optimal study plan for each student. The suggestion unit can also use a generation AI to propose a personalized study plan based on the student's learning history. For example, the generation AI takes the student's learning history as input and outputs a personalized study plan. The feedback unit provides feedback in real time based on the study plan proposed by the suggestion unit. For example, the feedback unit monitors the student's learning progress and provides feedback as needed. The feedback unit can also provide feedback in real time using the generation AI. For example, the generation AI takes the student's learning progress as input and outputs feedback. The answering unit answers the student's question based on the feedback provided by the feedback unit. For example, the answering unit provides an appropriate answer to the student's question. The answering unit can also use the generation AI to answer the student's question. For example, the generation AI takes the student's question as input and outputs an answer. The generation unit generates customized learning content based on the information obtained by the answering unit. The generation unit generates, for example, problem sets and study materials tailored to the student's level and interests. The generation unit can also generate customized learning content using a generation AI. For example, the generation AI inputs the student's level and interests and outputs customized learning content. This allows the learning support system according to the embodiment to propose personalized learning plans based on the student's learning history, provide feedback in real time, answer questions, and generate customized learning content. For example, the learning support system reduces the burden on teachers and improves students' learning effectiveness. Furthermore, students can understand their own learning progress in real time and receive feedback as needed. This can promote students' learning outcomes.

[0030] The suggestion unit can analyze the student's past learning history and select an appropriate learning plan. For example, the suggestion unit can have the generation AI propose a learning plan that focuses on areas in which the student was weak in the past. The suggestion unit can also have the generation AI propose a learning plan to strengthen areas in which the student is strong. The suggestion unit can also have the generation AI propose a balanced learning plan based on the student's past grades. This makes it possible to provide a personalized learning experience by selecting an optimal learning plan based on the student's past learning history. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the student's past learning history into the generation AI and have the generation AI select an optimal learning plan.

[0031] When proposing a study plan, the suggestion unit can filter based on the student's current learning progress and level of understanding. For example, the suggestion unit can cause the generation AI to exclude content that the student currently understands and propose a study plan focusing on content that the student does not yet understand. The suggestion unit can also cause the generation AI to propose a study plan with an adjusted progress level based on the student's progress. The suggestion unit can also cause the generation AI to propose a study plan that includes questions of appropriate difficulty based on the student's level of understanding. This makes it possible to support effective learning by filtering the study plan based on the student's current learning progress and level of understanding. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input the student's current learning progress and level of understanding into the generation AI and cause the generation AI to filter the study plan.

[0032] When proposing a study plan, the suggestion unit can select appropriate study content according to the student's interests. For example, the suggestion unit causes the generation AI to propose a study plan that includes topics that interest the student. The suggestion unit can also cause the generation AI to propose a study plan that includes many questions related to the student's areas of interest. The suggestion unit can also cause the generation AI to propose a study plan with customized study content based on the student's hobbies and interests. This can increase the student's motivation to study by selecting study content that matches the student's interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's interests into the generation AI and cause the generation AI to select appropriate study content.

[0033] When proposing a study plan, the suggestion unit can prioritize suggesting highly relevant study content by taking into account the student's geographical location information. For example, if the student lives in a specific area, the suggestion unit can have the generation AI suggest study content related to that area. Furthermore, if the student attends a specific school, the suggestion unit can have the generation AI suggest study content related to that school's curriculum. Furthermore, if the student is interested in the history or culture of a specific area, the suggestion unit can have the generation AI suggest study content related to that area. In this way, highly relevant study content can be provided by taking geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the student's geographical location information into the generation AI and cause the generation AI to suggest highly relevant study content.

[0034] The suggestion unit can analyze the student's social media activity and suggest related learning content when proposing a learning plan. For example, the suggestion unit causes the generation AI to suggest learning content related to topics the student has shown interest in on social media. The suggestion unit can also cause the generation AI to suggest learning content that the student is likely to be interested in based on the student's social media activity. The suggestion unit can also cause the generation AI to suggest related learning content based on the activity of the student's friends on social media. In this way, related learning content can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the student's social media activity into the generation AI and cause the generation AI to suggest related learning content.

[0035] When proposing a study plan, the suggestion unit can customize the suggestion method by reflecting the student's past feedback. In the suggestion unit, for example, the generation AI adjusts the suggestion method for the study plan based on feedback provided by the student in the past. The suggestion unit can also have the generation AI propose a study plan that reflects the student's preferred learning style based on the student's past feedback. The suggestion unit can also analyze the student's past feedback and have the generation AI propose a study plan that reflects areas for improvement. In this way, by reflecting past feedback, the suggestion method can be customized to support effective learning. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input the student's past feedback into the generation AI and have the generation AI customize the suggestion method.

[0036] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the learning plan. For example, the feedback unit can have the generation AI provide detailed feedback for an important learning plan. The feedback unit can also have the generation AI provide concise feedback for a less important learning plan. The feedback unit can also have the generation AI adjust the level of detail of the feedback according to the importance of the learning plan. This makes it possible to provide effective feedback by adjusting the level of detail of the feedback according to the importance of the learning plan. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or can be performed without using AI. For example, the feedback unit can input the importance of the learning plan to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0037] When providing feedback, the feedback unit can apply different feedback algorithms depending on the category of the learning content. For example, for mathematics learning content, the feedback unit may have the generation AI provide feedback using formulas and graphs. For literature learning content, the feedback unit may have the generation AI provide feedback using sentences and quotations. For science learning content, the feedback unit may have the generation AI provide feedback using experimental results and diagrams. This allows for effective learning support by providing feedback according to the category of the learning content. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the category of the learning content into the generation AI and cause the generation AI to apply a feedback algorithm.

[0038] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the student's past feedback results. In the feedback unit, for example, the generation AI adjusts the content of the feedback based on feedback the student has received in the past. The feedback unit can also have the generation AI learn effective feedback methods from the student's past feedback results. The feedback unit can also analyze the student's past feedback results and have the generation AI improve the accuracy of the feedback. In this way, the accuracy of the feedback can be improved by referring to the past feedback results. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the student's past feedback results into the generation AI and have the generation AI improve the accuracy of the feedback.

[0039] When providing feedback, the feedback unit can determine the priority of feedback based on the submission date of the learning content. For example, the feedback unit allows the generation AI to provide feedback preferentially for learning content with an approaching deadline. The feedback unit can also allow the generation AI to provide feedback later for learning content with a distant submission date. The feedback unit can also allow the generation AI to adjust the priority of feedback according to the submission date of the learning content. In this way, by determining the priority of feedback based on the submission date, it is possible to provide effective feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the submission date of the learning content to the generation AI and have the generation AI determine the priority of feedback.

[0040] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the learning content. For example, the feedback unit allows the generation AI to provide feedback preferentially for important learning content. The feedback unit can also allow the generation AI to provide feedback later for learning content with low relevance. The feedback unit can also allow the generation AI to adjust the order of feedback according to the relevance of the learning content. In this way, by adjusting the order of feedback based on relevance, effective feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the relevance of the learning content to the generation AI and cause the generation AI to adjust the order of feedback.

[0041] When providing feedback, the feedback unit can adjust the use of technical terminology in the feedback depending on the student's level of expertise. For example, if the student is a beginner, the feedback unit can cause the generation AI to provide feedback in simple language. Furthermore, if the student is an intermediate learner, the feedback unit can also cause the generation AI to provide feedback containing appropriate technical terminology. Furthermore, if the student is an advanced learner, the feedback unit can also cause the generation AI to provide feedback containing advanced technical terminology. In this way, by adjusting the technical terminology in the feedback depending on the level of expertise, effective feedback can be provided. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input the student's level of expertise into the generation AI and cause the generation AI to use technical terminology in the feedback.

[0042] When providing an answer, the answering unit can adjust the level of detail of the answer based on the importance of the question. For example, in the answering unit, the generation AI provides a detailed answer to an important question. In addition, in the answering unit, the generation AI can also provide a concise answer to a less important question. In addition, in the answering unit, the generation AI can adjust the level of detail of the answer according to the importance of the question. In this way, by adjusting the level of detail of the answer according to the importance of the question, it is possible to provide an effective answer. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the importance of the question to the generation AI and have the generation AI adjust the level of detail of the answer.

[0043] When providing an answer, the answering unit can apply different answering algorithms depending on the question category. For example, in the answering unit, for a mathematics question, the generation AI can provide an answer using a formula or a graph. In addition, in the answering unit, for a literature question, the generation AI can provide an answer using a sentence or a quote. In addition, in the answering unit, for a science question, the generation AI can provide an answer using experimental results or diagrams. In this way, by providing an answer according to the question category, effective learning support can be provided. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the question category to the generation AI and have the generation AI apply the answering algorithm.

[0044] When providing an answer, the answering unit can improve the accuracy of the answer by referring to the student's past question results. In the answering unit, for example, the generation AI adjusts the content of the answer based on the answers the student received in the past. The answering unit can also have the generation AI learn effective answering methods from the student's past question results. The answering unit can also analyze the student's past question results and have the generation AI improve the accuracy of the answer. In this way, the accuracy of the answer can be improved by referring to the past question results. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the student's past question results into the generation AI and have the generation AI improve the accuracy of the answer.

[0045] When providing an answer, the answering unit can determine the priority of the answers based on the time of submission of the question. For example, the answering unit has the generation AI provide answers preferentially to questions whose deadline is approaching. The answering unit can also have the generation AI provide answers later to questions whose submission date is further away. The answering unit can also have the generation AI adjust the priority of the answers depending on the time of submission of the question. In this way, by determining the priority of answers based on the time of submission, it is possible to provide effective answers. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the time of submission of the question to the generation AI and have the generation AI determine the priority of the answers.

[0046] When providing an answer, the answering unit can adjust the order of answers based on the relevance of the question. For example, the answering unit can have the generation AI provide answers preferentially to important questions. Also, the answering unit can have the generation AI provide answers later to questions with low relevance. Also, the answering unit can have the generation AI adjust the order of answers according to the relevance of the question. In this way, by adjusting the order of answers based on relevance, it is possible to provide effective answers. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the relevance of questions to the generation AI and have the generation AI adjust the order of answers.

[0047] When providing an answer, the answering unit can adjust the use of technical terminology in the answer depending on the student's level of expertise. For example, if the student is a beginner, the generating AI can provide an answer in simple language. Furthermore, if the student is an intermediate learner, the answering unit can also provide an answer containing appropriate technical terminology. Furthermore, if the student is an advanced learner, the answering unit can also provide an answer containing advanced technical terminology. In this way, by adjusting the technical terminology in the answer depending on the level of expertise, an effective answer can be provided. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or without AI. For example, the answering unit can input the student's level of expertise into the generating AI and have the generating AI use technical terminology in the answer.

[0048] When generating learning content, the generation unit can analyze the student's past learning history and select appropriate content. For example, the generation unit uses a generation AI to generate learning content that focuses on areas in which the student was weak in the past. The generation unit can also use a generation AI to generate learning content that strengthens areas in which the student is strong. The generation unit can also use a generation AI to generate balanced learning content based on the student's past grades. This allows for the selection of optimal learning content based on the student's past learning history, thereby providing a personalized learning experience. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the student's past learning history into the generation AI and have the generation AI select appropriate content.

[0049] When generating learning content, the generation unit can filter the learning content based on the student's current learning progress and level of understanding. For example, the generation unit can exclude content that the student currently understands and generate learning content using the generation AI focusing on content that the student does not yet understand. The generation unit can also generate learning content with the generation AI adjusted for the student's progress. The generation unit can also generate learning content including questions of appropriate difficulty based on the student's level of understanding. This allows for effective learning support by filtering learning content based on the student's current learning progress 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 student's current learning progress and level of understanding into the generation AI and have the generation AI filter the learning content.

[0050] When generating learning content, the generation unit can select appropriate learning content based on the student's interests. For example, the generation unit uses a generation AI to generate learning content that includes topics that interest the student. The generation unit can also use the generation AI to generate learning content that includes many questions related to the student's areas of interest. The generation unit can also use the generation AI to generate learning content with customized learning content based on the student's hobbies and interests. This can increase the student's motivation to learn by selecting learning content that matches the student's interests. 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 the student's interests into the generation AI and have the generation AI select appropriate learning content.

[0051] When generating learning content, the generation unit can prioritize generating highly relevant content by taking into account the student's geographical location information. For example, if the student lives in a specific area, the generation unit can cause the generation AI to generate learning content related to that area. Furthermore, if the student attends a specific school, the generation unit can also cause the generation AI to generate learning content related to the school's curriculum. Furthermore, if the student is interested in the history or culture of a specific area, the generation unit can also cause the generation AI to generate learning content related to that area. This makes it possible to provide highly relevant learning content by taking geographical location information into account. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the student's geographical location information into the generation AI and cause the generation AI to generate highly relevant learning content.

[0052] The generation unit can analyze the student's social media activity and generate related content when generating learning content. For example, the generation unit uses a generation AI to generate learning content related to topics that the student has shown interest in on social media. The generation unit can also use the generation AI to generate learning content that is likely to interest the student based on the student's social media activity. The generation unit can also use the generation AI to generate related learning content based on the activity of the student's friends on social media. This makes it possible to provide related learning content by analyzing social media activity. 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 student's social media activity into the generation AI and cause the generation AI to generate related learning content.

[0053] When generating learning content, the generation unit can customize the generation method by reflecting the student's past feedback. For example, the generation unit adjusts the generation method of the learning content based on feedback provided by the student in the past. The generation unit can also generate learning content that reflects the student's preferred learning style based on the student's past feedback. The generation unit can also analyze the student's past feedback and generate learning content that reflects areas for improvement. This allows the generation method to be customized by reflecting past feedback, thereby supporting effective learning. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the student's past feedback into the generation AI and have the generation AI customize the generation method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The suggestion unit can also propose a study plan taking into consideration the student's learning history as well as their learning style preferences. For example, for a student who prefers visual learning, it can propose a study plan that makes extensive use of diagrams and graphs. For a student who prefers auditory learning, it can also propose a study plan that makes extensive use of audio and video. Furthermore, for a student who prefers practical learning, it can also propose a study plan that includes many experiments and practical training. In this way, it is possible to improve learning effectiveness by providing a study plan that suits the student's learning style.

[0056] The suggestion unit can also appropriately insert breaks into the study plan based on the student's learning history. For example, if a student has been studying for a long time, the generation AI can suggest a break at an appropriate time. Also, if the student is losing concentration, the generation AI can suggest a short break. Furthermore, if the student is tired, the generation AI can suggest a longer break. By inserting appropriate breaks, learning efficiency can be improved.

[0057] The suggestion module can also incorporate review sessions into the lesson plan based on the student's current learning progress and level of understanding. For example, if a student does not understand a particular topic, the generative AI can suggest reviewing that topic. Also, if a student has forgotten what they learned in the past, the generative AI can suggest reviewing that content. Furthermore, it can suggest that students review related past content before learning a new topic. This incorporates review, which can help solidify learning.

[0058] The suggestion module can also incorporate project-based learning into lesson plans based on students' interests. For example, if a student is interested in science, the generative AI can suggest a science project. If a student is interested in history, the generative AI can suggest a history project. Furthermore, if a student is interested in art, the generative AI can suggest an art project. In this way, incorporating project-based learning can pique students' interest and increase their motivation to learn.

[0059] The suggestion unit can also incorporate fieldwork into the learning plan based on the student's geographic location information. For example, if a student lives in a natural area, the generating AI can suggest nature observation fieldwork. If a student lives in a historical area, the generating AI can suggest historical exploration fieldwork. Furthermore, if a student lives in an urban area, the generating AI can suggest urban exploration fieldwork. This makes it possible to support practical learning by incorporating fieldwork that takes geographic location information into account.

[0060] The suggestion module can also analyze students' social media activity and incorporate social learning into their lesson plans. For example, if a student is discussing a particular topic on social media, the generative AI can suggest a lesson plan related to that topic. The generative AI can also suggest related lesson plans based on content shared by students on social media. Furthermore, the generative AI can suggest lesson plans based on the activities of experts and influencers the student follows on social media. This can increase motivation to study by providing lesson plans that utilize social media activity.

[0061] The suggestion unit can also incorporate self-assessment sessions into the learning plan by reflecting the student's past feedback. For example, the generation AI can suggest the timing of self-assessment based on the student's past feedback. The generation AI can also customize the content of self-assessment based on the student's past feedback. Furthermore, the generation AI can analyze the student's past feedback and suggest a method of self-assessment. Incorporating self-assessment in this way can increase students' self-awareness and improve learning outcomes.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The suggestion unit proposes a personalized study plan based on the student's learning history. The suggestion unit analyzes the student's learning history, including past test results, study time, and learning content, and proposes the optimal study plan for each individual student. It can also use generative AI to propose personalized study plans based on the student's learning history. Step 2: The feedback unit provides real-time feedback based on the learning plan proposed by the suggestion unit. The feedback unit monitors the student's learning progress and provides feedback as needed. It can also provide real-time feedback using generative AI. Step 3: The answering unit answers the student's question based on the feedback provided by the feedback unit. The answering unit provides an appropriate answer to the student's question. It can also use generative AI to answer the student's question. Step 4: The generator generates custom learning content based on the information obtained by the answerer. The generator generates question sets and learning materials tailored to the student's level and interests. Generative AI can also be used to generate custom learning content.

[0064] (Example 2) A learning support system according to an embodiment of the present invention is a system for proposing a personalized learning plan based on a student's learning history, providing real-time feedback, answering questions, and generating custom learning content. The learning support system improves learning effectiveness by proposing a personalized learning plan based on a student's learning history, providing real-time feedback, answering questions, and generating custom learning content. For example, the learning support system proposes a personalized learning plan based on a student's learning history. For example, the learning support system analyzes the student's learning history and proposes an optimal learning plan for each individual student. Then, the learning support system provides real-time feedback based on the proposed learning plan. For example, the learning support system monitors the student's learning progress and provides feedback as needed. Then, the learning support system answers the student's questions based on the provided feedback. For example, the learning support system provides appropriate answers to the student's questions. Then, the learning support system generates custom learning content based on the obtained information. For example, the learning support system generates problem sets and study materials tailored to the student's level and interests. This allows the learning support system to provide an individualized learning experience. This allows the learning support system to propose a personalized learning plan based on the student's learning history, providing real-time feedback, answering questions, and generating custom learning content. For example, learning support systems can reduce the burden on teachers and improve students' learning outcomes. They also allow students to track their own learning progress in real time and receive feedback as needed, which can help promote students' learning outcomes.

[0065] A learning support system according to an embodiment includes a suggestion unit, a feedback unit, an answering unit, and a generation unit. The suggestion unit proposes a personalized study plan based on a student's learning history. The suggestion unit analyzes, for example, the student's learning history, such as past test results, study time, and learning content, and proposes an optimal study plan for each student. The suggestion unit can also use a generation AI to propose a personalized study plan based on the student's learning history. For example, the generation AI takes the student's learning history as input and outputs a personalized study plan. The feedback unit provides feedback in real time based on the study plan proposed by the suggestion unit. For example, the feedback unit monitors the student's learning progress and provides feedback as needed. The feedback unit can also provide feedback in real time using the generation AI. For example, the generation AI takes the student's learning progress as input and outputs feedback. The answering unit answers the student's question based on the feedback provided by the feedback unit. For example, the answering unit provides an appropriate answer to the student's question. The answering unit can also use the generation AI to answer the student's question. For example, the generation AI takes the student's question as input and outputs an answer. The generation unit generates customized learning content based on the information obtained by the answering unit. The generation unit generates, for example, problem sets and study materials tailored to the student's level and interests. The generation unit can also generate customized learning content using a generation AI. For example, the generation AI inputs the student's level and interests and outputs customized learning content. This allows the learning support system according to the embodiment to propose personalized learning plans based on the student's learning history, provide feedback in real time, answer questions, and generate customized learning content. For example, the learning support system reduces the burden on teachers and improves students' learning effectiveness. Furthermore, students can understand their own learning progress in real time and receive feedback as needed. This can promote students' learning outcomes.

[0066] The suggestion unit can estimate the student's emotions and adjust the difficulty of the lesson plan based on the estimated student's emotions. For example, if the student is feeling stressed, the suggestion unit can cause the generation AI to propose a lesson plan that includes many easy questions. Furthermore, if the student is relaxed, the suggestion unit can cause the generation AI to propose a lesson plan that includes difficult questions. Furthermore, if the student is excited, the suggestion unit can cause the generation AI to propose a lesson plan that includes challenging questions. This adjusts the difficulty of the lesson plan according to the student's emotions, thereby reducing the burden of learning and promoting effective learning. 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 these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the student's emotion data into the generation AI and cause the generation AI to adjust the difficulty of the lesson plan.

[0067] The suggestion unit can analyze the student's past learning history and select an appropriate learning plan. For example, the suggestion unit can have the generation AI propose a learning plan that focuses on areas in which the student was weak in the past. The suggestion unit can also have the generation AI propose a learning plan to strengthen areas in which the student is strong. The suggestion unit can also have the generation AI propose a balanced learning plan based on the student's past grades. This makes it possible to provide a personalized learning experience by selecting an optimal learning plan based on the student's past learning history. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the student's past learning history into the generation AI and have the generation AI select an optimal learning plan.

[0068] When proposing a study plan, the suggestion unit can filter based on the student's current learning progress and level of understanding. For example, the suggestion unit can cause the generation AI to exclude content that the student currently understands and propose a study plan focusing on content that the student does not yet understand. The suggestion unit can also cause the generation AI to propose a study plan with an adjusted progress level based on the student's progress. The suggestion unit can also cause the generation AI to propose a study plan that includes questions of appropriate difficulty based on the student's level of understanding. This makes it possible to support effective learning by filtering the study plan based on the student's current learning progress and level of understanding. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input the student's current learning progress and level of understanding into the generation AI and cause the generation AI to filter the study plan.

[0069] When proposing a study plan, the suggestion unit can select appropriate study content according to the student's interests. For example, the suggestion unit causes the generation AI to propose a study plan that includes topics that interest the student. The suggestion unit can also cause the generation AI to propose a study plan that includes many questions related to the student's areas of interest. The suggestion unit can also cause the generation AI to propose a study plan with customized study content based on the student's hobbies and interests. This can increase the student's motivation to study by selecting study content that matches the student's interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the student's interests into the generation AI and cause the generation AI to select appropriate study content.

[0070] The suggestion unit can estimate the student's emotions and prioritize the study plans based on the estimated student's emotions. For example, if the student is feeling stressed, the suggestion unit can cause the generation AI to propose a study plan that prioritizes relaxing content. Furthermore, if the student is relaxed, the suggestion unit can cause the generation AI to propose a study plan that prioritizes difficult content. Furthermore, if the student is excited, the suggestion unit can cause the generation AI to propose a study plan that prioritizes challenging content. This allows for effective study by prioritizing study plans according to the student'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 suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the student's emotion data into the generation AI and have the generation AI determine the priorities of the study plans.

[0071] When proposing a study plan, the suggestion unit can prioritize suggesting highly relevant study content by taking into account the student's geographical location information. For example, if the student lives in a specific area, the suggestion unit can have the generation AI suggest study content related to that area. Furthermore, if the student attends a specific school, the suggestion unit can have the generation AI suggest study content related to that school's curriculum. Furthermore, if the student is interested in the history or culture of a specific area, the suggestion unit can have the generation AI suggest study content related to that area. In this way, highly relevant study content can be provided by taking geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the student's geographical location information into the generation AI and cause the generation AI to suggest highly relevant study content.

[0072] The suggestion unit can analyze the student's social media activity and suggest related learning content when proposing a learning plan. For example, the suggestion unit causes the generation AI to suggest learning content related to topics the student has shown interest in on social media. The suggestion unit can also cause the generation AI to suggest learning content that the student is likely to be interested in based on the student's social media activity. The suggestion unit can also cause the generation AI to suggest related learning content based on the activity of the student's friends on social media. In this way, related learning content can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the student's social media activity into the generation AI and cause the generation AI to suggest related learning content.

[0073] When proposing a study plan, the suggestion unit can customize the suggestion method by reflecting the student's past feedback. In the suggestion unit, for example, the generation AI adjusts the suggestion method for the study plan based on feedback provided by the student in the past. The suggestion unit can also have the generation AI propose a study plan that reflects the student's preferred learning style based on the student's past feedback. The suggestion unit can also analyze the student's past feedback and have the generation AI propose a study plan that reflects areas for improvement. In this way, by reflecting past feedback, the suggestion method can be customized to support effective learning. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input the student's past feedback into the generation AI and have the generation AI customize the suggestion method.

[0074] The feedback unit can estimate the student's emotions and adjust the way the feedback is expressed based on the estimated student's emotions. For example, if the student is stressed, the generation AI can provide gentle feedback. If the student is relaxed, the feedback unit can provide detailed feedback. If the student is excited, the feedback unit can provide encouraging feedback. This allows for effective feedback to be provided by adjusting the way the feedback is expressed based on the student's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 these examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the student's emotion data into the generation AI and have the generation AI adjust the way the feedback is expressed.

[0075] When providing feedback, the feedback unit can adjust the level of detail of the feedback based on the importance of the learning plan. For example, the feedback unit can have the generation AI provide detailed feedback for an important learning plan. The feedback unit can also have the generation AI provide concise feedback for a less important learning plan. The feedback unit can also have the generation AI adjust the level of detail of the feedback according to the importance of the learning plan. This makes it possible to provide effective feedback by adjusting the level of detail of the feedback according to the importance of the learning plan. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or can be performed without using AI. For example, the feedback unit can input the importance of the learning plan to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0076] When providing feedback, the feedback unit can apply different feedback algorithms depending on the category of the learning content. For example, for mathematics learning content, the feedback unit may have the generation AI provide feedback using formulas and graphs. For literature learning content, the feedback unit may have the generation AI provide feedback using sentences and quotations. For science learning content, the feedback unit may have the generation AI provide feedback using experimental results and diagrams. This allows for effective learning support by providing feedback according to the category of the learning content. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the category of the learning content into the generation AI and cause the generation AI to apply a feedback algorithm.

[0077] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the student's past feedback results. In the feedback unit, for example, the generation AI adjusts the content of the feedback based on feedback the student has received in the past. The feedback unit can also have the generation AI learn effective feedback methods from the student's past feedback results. The feedback unit can also analyze the student's past feedback results and have the generation AI improve the accuracy of the feedback. In this way, the accuracy of the feedback can be improved by referring to the past feedback results. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the student's past feedback results into the generation AI and have the generation AI improve the accuracy of the feedback.

[0078] The feedback unit can estimate the student's emotions and adjust the length of the feedback based on the estimated student's emotions. For example, if the student is stressed, the feedback unit can cause the generation AI to provide short, to-the-point feedback. If the student is relaxed, the feedback unit can also cause the generation AI to provide detailed feedback. If the student is excited, the feedback unit can also cause the generation AI to provide encouraging feedback. This allows for effective feedback to be provided by adjusting the length of the feedback according to the student'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or without an AI. For example, the feedback unit can input the student's emotion data into the generation AI and cause the generation AI to adjust the length of the feedback.

[0079] When providing feedback, the feedback unit can determine the priority of feedback based on the submission date of the learning content. For example, the feedback unit allows the generation AI to provide feedback preferentially for learning content with an approaching deadline. The feedback unit can also allow the generation AI to provide feedback later for learning content with a distant submission date. The feedback unit can also allow the generation AI to adjust the priority of feedback according to the submission date of the learning content. In this way, by determining the priority of feedback based on the submission date, it is possible to provide effective feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the submission date of the learning content to the generation AI and have the generation AI determine the priority of feedback.

[0080] When providing feedback, the feedback unit can adjust the order of feedback based on the relevance of the learning content. For example, the feedback unit allows the generation AI to provide feedback preferentially for important learning content. The feedback unit can also allow the generation AI to provide feedback later for learning content with low relevance. The feedback unit can also allow the generation AI to adjust the order of feedback according to the relevance of the learning content. In this way, by adjusting the order of feedback based on relevance, effective feedback can be provided. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the relevance of the learning content to the generation AI and cause the generation AI to adjust the order of feedback.

[0081] When providing feedback, the feedback unit can adjust the use of technical terminology in the feedback depending on the student's level of expertise. For example, if the student is a beginner, the feedback unit can cause the generation AI to provide feedback in simple language. Furthermore, if the student is an intermediate learner, the feedback unit can also cause the generation AI to provide feedback containing appropriate technical terminology. Furthermore, if the student is an advanced learner, the feedback unit can also cause the generation AI to provide feedback containing advanced technical terminology. In this way, by adjusting the technical terminology in the feedback depending on the level of expertise, effective feedback can be provided. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input the student's level of expertise into the generation AI and cause the generation AI to use technical terminology in the feedback.

[0082] The answering unit can estimate the student's emotions and adjust the way the answer is expressed based on the estimated student's emotions. For example, if the student is stressed, the generation AI can provide a gentle answer. If the student is relaxed, the generation AI can provide a detailed answer. If the student is excited, the generation AI can provide an answer that includes encouraging words. This allows for an effective answer to be provided by adjusting the way the answer is expressed according to the student'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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the answering unit can be performed using, for example, AI, or without AI. For example, the answering unit can input the student's emotion data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0083] When providing an answer, the answering unit can adjust the level of detail of the answer based on the importance of the question. For example, in the answering unit, the generation AI provides a detailed answer to an important question. In addition, in the answering unit, the generation AI can also provide a concise answer to a less important question. In addition, in the answering unit, the generation AI can adjust the level of detail of the answer according to the importance of the question. In this way, by adjusting the level of detail of the answer according to the importance of the question, it is possible to provide an effective answer. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the importance of the question to the generation AI and have the generation AI adjust the level of detail of the answer.

[0084] When providing an answer, the answering unit can apply different answering algorithms depending on the question category. For example, in the answering unit, for a mathematics question, the generation AI can provide an answer using a formula or a graph. In addition, in the answering unit, for a literature question, the generation AI can provide an answer using a sentence or a quote. In addition, in the answering unit, for a science question, the generation AI can provide an answer using experimental results or diagrams. In this way, by providing an answer according to the question category, effective learning support can be provided. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input the question category to the generation AI and have the generation AI apply the answering algorithm.

[0085] When providing an answer, the answering unit can improve the accuracy of the answer by referring to the student's past question results. In the answering unit, for example, the generation AI adjusts the content of the answer based on the answers the student received in the past. The answering unit can also have the generation AI learn effective answering methods from the student's past question results. The answering unit can also analyze the student's past question results and have the generation AI improve the accuracy of the answer. In this way, the accuracy of the answer can be improved by referring to the past question results. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the student's past question results into the generation AI and have the generation AI improve the accuracy of the answer.

[0086] The answering unit can estimate the student's emotions and adjust the length of the answer based on the estimated student's emotions. For example, if the student is stressed, the generation AI of the answering unit can provide a short, to-the-point answer. If the student is relaxed, the generation AI of the answering unit can provide a detailed answer. If the student is excited, the generation AI of the answering unit can provide an answer including words of encouragement. This allows for effective answers to be provided by adjusting the length of the answer according to the student'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 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 answering unit can be performed using, for example, an AI, or without an AI. For example, the answering unit can input the student's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0087] When providing an answer, the answering unit can determine the priority of the answers based on the time of submission of the question. For example, the answering unit has the generation AI provide answers preferentially to questions whose deadline is approaching. The answering unit can also have the generation AI provide answers later to questions whose submission date is further away. The answering unit can also have the generation AI adjust the priority of the answers depending on the time of submission of the question. In this way, by determining the priority of answers based on the time of submission, it is possible to provide effective answers. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the time of submission of the question to the generation AI and have the generation AI determine the priority of the answers.

[0088] When providing an answer, the answering unit can adjust the order of answers based on the relevance of the question. For example, the answering unit can have the generation AI provide answers preferentially to important questions. Also, the answering unit can have the generation AI provide answers later to questions with low relevance. Also, the answering unit can have the generation AI adjust the order of answers according to the relevance of the question. In this way, by adjusting the order of answers based on relevance, it is possible to provide effective answers. Some or all of the above-mentioned processing in the answering unit may be performed using AI, for example, or may be performed without using AI. For example, the answering unit can input the relevance of questions to the generation AI and have the generation AI adjust the order of answers.

[0089] When providing an answer, the answering unit can adjust the use of technical terminology in the answer depending on the student's level of expertise. For example, if the student is a beginner, the generating AI can provide an answer in simple language. Furthermore, if the student is an intermediate learner, the answering unit can also provide an answer containing appropriate technical terminology. Furthermore, if the student is an advanced learner, the answering unit can also provide an answer containing advanced technical terminology. In this way, by adjusting the technical terminology in the answer depending on the level of expertise, an effective answer can be provided. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or without AI. For example, the answering unit can input the student's level of expertise into the generating AI and have the generating AI use technical terminology in the answer.

[0090] The generation unit can estimate the student's emotions and adjust the difficulty of the generated learning content based on the estimated student's emotions. For example, if the student is feeling stressed, the generation AI can generate learning content that includes many easy questions. Furthermore, if the student is relaxed, the generation AI can generate learning content that includes difficult questions. Furthermore, if the student is excited, the generation AI can generate learning content that includes challenging questions. This allows for adjusting the difficulty of the learning content according to the student's emotions, thereby supporting effective learning. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. 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 student's emotion data into the generation AI and cause the generation AI to adjust the difficulty of the learning content.

[0091] When generating learning content, the generation unit can analyze the student's past learning history and select appropriate content. For example, the generation unit uses a generation AI to generate learning content that focuses on areas in which the student was weak in the past. The generation unit can also use a generation AI to generate learning content that strengthens areas in which the student is strong. The generation unit can also use a generation AI to generate balanced learning content based on the student's past grades. This allows for the selection of optimal learning content based on the student's past learning history, thereby providing a personalized learning experience. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the student's past learning history into the generation AI and have the generation AI select appropriate content.

[0092] When generating learning content, the generation unit can filter the learning content based on the student's current learning progress and level of understanding. For example, the generation unit can exclude content that the student currently understands and generate learning content using the generation AI focusing on content that the student does not yet understand. The generation unit can also generate learning content with the generation AI adjusted for the student's progress. The generation unit can also generate learning content including questions of appropriate difficulty based on the student's level of understanding. This allows for effective learning support by filtering learning content based on the student's current learning progress 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 student's current learning progress and level of understanding into the generation AI and have the generation AI filter the learning content.

[0093] When generating learning content, the generation unit can select appropriate learning content based on the student's interests. For example, the generation unit uses a generation AI to generate learning content that includes topics that interest the student. The generation unit can also use the generation AI to generate learning content that includes many questions related to the student's areas of interest. The generation unit can also use the generation AI to generate learning content with customized learning content based on the student's hobbies and interests. This can increase the student's motivation to learn by selecting learning content that matches the student's interests. 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 the student's interests into the generation AI and have the generation AI select appropriate learning content.

[0094] The generation unit can estimate the student's emotions and prioritize the learning content to be generated based on the estimated student's emotions. For example, if the student is feeling stressed, the generation unit generates learning content in which the generation AI prioritizes relaxing content. Furthermore, if the student is relaxed, the generation unit can generate learning content in which the generation AI prioritizes more difficult content. Furthermore, if the student is excited, the generation unit can generate learning content in which the generation AI prioritizes more challenging content. This allows for effective learning by prioritizing learning content according to the student'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 student's emotion data into the generation AI and have the generation AI determine the priority of the learning content.

[0095] When generating learning content, the generation unit can prioritize generating highly relevant content by taking into account the student's geographical location information. For example, if the student lives in a specific area, the generation unit can cause the generation AI to generate learning content related to that area. Furthermore, if the student attends a specific school, the generation unit can also cause the generation AI to generate learning content related to the school's curriculum. Furthermore, if the student is interested in the history or culture of a specific area, the generation unit can also cause the generation AI to generate learning content related to that area. This makes it possible to provide highly relevant learning content by taking geographical location information into account. Some or all of the above-described processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input the student's geographical location information into the generation AI and cause the generation AI to generate highly relevant learning content.

[0096] The generation unit can analyze the student's social media activity and generate related content when generating learning content. For example, the generation unit uses a generation AI to generate learning content related to topics that the student has shown interest in on social media. The generation unit can also use the generation AI to generate learning content that is likely to interest the student based on the student's social media activity. The generation unit can also use the generation AI to generate related learning content based on the activity of the student's friends on social media. This makes it possible to provide related learning content by analyzing social media activity. 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 student's social media activity into the generation AI and cause the generation AI to generate related learning content.

[0097] When generating learning content, the generation unit can customize the generation method by reflecting the student's past feedback. For example, the generation unit adjusts the generation method of the learning content based on feedback provided by the student in the past. The generation unit can also generate learning content that reflects the student's preferred learning style based on the student's past feedback. The generation unit can also analyze the student's past feedback and generate learning content that reflects areas for improvement. This allows the generation method to be customized by reflecting past feedback, thereby supporting effective learning. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the student's past feedback into the generation AI and have the generation AI customize the generation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned suggestion unit, feedback unit, answer unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the answer unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned suggestion unit, feedback unit, answering unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned suggestion unit, feedback unit, answering unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned suggestion unit, feedback unit, answer unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the feedback unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the answer unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[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] The suggestion unit can also propose a study plan taking into consideration the student's learning history as well as their learning style preferences. For example, for a student who prefers visual learning, it can propose a study plan that makes extensive use of diagrams and graphs. For a student who prefers auditory learning, it can also propose a study plan that makes extensive use of audio and video. Furthermore, for a student who prefers practical learning, it can also propose a study plan that includes many experiments and practical training. In this way, it is possible to improve learning effectiveness by providing a study plan that suits the student's learning style.

[0100] The suggestion unit can also estimate the student's emotions and adjust the progress rate of the lesson plan based on the estimated student's emotions. For example, if the student is feeling stressed, the generation AI can slow down the progress rate of the lesson plan. Also, if the student is relaxed, the generation AI can speed up the progress rate of the lesson plan. Furthermore, if the student is excited, the generation AI can moderately adjust the progress rate of the lesson plan. In this way, by adjusting the progress rate of the lesson plan according to the student's emotions, it is possible to support effective learning.

[0101] The suggestion unit can also appropriately insert breaks into the study plan based on the student's learning history. For example, if a student has been studying for a long time, the generation AI can suggest a break at an appropriate time. Also, if the student is losing concentration, the generation AI can suggest a short break. Furthermore, if the student is tired, the generation AI can suggest a longer break. By inserting appropriate breaks, learning efficiency can be improved.

[0102] The suggestion module can also incorporate review sessions into the lesson plan based on the student's current learning progress and level of understanding. For example, if a student does not understand a particular topic, the generative AI can suggest reviewing that topic. Also, if a student has forgotten what they learned in the past, the generative AI can suggest reviewing that content. Furthermore, it can suggest that students review related past content before learning a new topic. This incorporates review, which can help solidify learning.

[0103] The suggestion module can also incorporate project-based learning into lesson plans based on students' interests. For example, if a student is interested in science, the generative AI can suggest a science project. If a student is interested in history, the generative AI can suggest a history project. Furthermore, if a student is interested in art, the generative AI can suggest an art project. In this way, incorporating project-based learning can pique students' interest and increase their motivation to learn.

[0104] The suggestion unit can also estimate the student's emotions and adjust the content of the lesson plan based on the estimated student's emotions. For example, if a student is feeling stressed, the generation AI can suggest a lesson plan that includes a lot of relaxing content. Also, if a student is relaxed, the generation AI can suggest a lesson plan that includes a lot of challenging content. Furthermore, if a student is excited, the generation AI can suggest a lesson plan that includes balanced content. In this way, by adjusting the content of the lesson plan according to the student's emotions, it is possible to support effective learning.

[0105] The suggestion unit can also incorporate fieldwork into the learning plan based on the student's geographic location information. For example, if a student lives in a natural area, the generating AI can suggest nature observation fieldwork. If a student lives in a historical area, the generating AI can suggest historical exploration fieldwork. Furthermore, if a student lives in an urban area, the generating AI can suggest urban exploration fieldwork. This makes it possible to support practical learning by incorporating fieldwork that takes geographic location information into account.

[0106] The suggestion module can also analyze students' social media activity and incorporate social learning into their lesson plans. For example, if a student is discussing a particular topic on social media, the generative AI can suggest a lesson plan related to that topic. The generative AI can also suggest related lesson plans based on content shared by students on social media. Furthermore, the generative AI can suggest lesson plans based on the activities of experts and influencers the student follows on social media. This can increase motivation to study by providing lesson plans that utilize social media activity.

[0107] The suggestion unit can also incorporate self-assessment sessions into the learning plan by reflecting the student's past feedback. For example, the generation AI can suggest the timing of self-assessment based on the student's past feedback. The generation AI can also customize the content of self-assessment based on the student's past feedback. Furthermore, the generation AI can analyze the student's past feedback and suggest a method of self-assessment. Incorporating self-assessment in this way can increase students' self-awareness and improve learning outcomes.

[0108] The feedback unit can also estimate the student's emotions and adjust the timing of feedback based on the estimated student emotions. For example, if a student is feeling stressed, the generation AI can delay the timing of feedback. Alternatively, if the student is relaxed, the generation AI can advance the timing of feedback. Furthermore, if the student is excited, the generation AI can adjust the timing of feedback appropriately. This makes it possible to provide effective feedback by adjusting the timing of feedback according to the student's emotions.

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

[0110] Step 1: The suggestion unit proposes a personalized study plan based on the student's learning history. The suggestion unit analyzes the student's learning history, including past test results, study time, and learning content, and proposes the optimal study plan for each individual student. It can also use generative AI to propose personalized study plans based on the student's learning history. Step 2: The feedback unit provides real-time feedback based on the learning plan proposed by the suggestion unit. The feedback unit monitors the student's learning progress and provides feedback as needed. It can also provide real-time feedback using generative AI. Step 3: The answering unit answers the student's question based on the feedback provided by the feedback unit. The answering unit provides an appropriate answer to the student's question. It can also use generative AI to answer the student's question. Step 4: The generator generates custom learning content based on the information obtained by the answerer. The generator generates question sets and learning materials tailored to the student's level and interests. Generative AI can also be used to generate custom learning content.

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

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

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

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

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

[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, in order to avoid confusion and to 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 proposal department that proposes personalized learning plans based on students' learning history; a feedback unit that provides real-time feedback based on the learning plan proposed by the suggestion unit; an answering unit that answers student questions based on the feedback provided by the feedback unit; a generation unit that generates personalized learning content based on the information obtained by the answering unit; Equipped with A system characterized by:

2. The proposal unit Estimate student emotions and adjust the difficulty of lesson plans based on the estimated student emotions 2. The system of claim 1.

3. The proposal unit Analyze students' past learning history and select appropriate learning plans 2. The system of claim 1.

4. The proposal unit Filter suggested learning plans based on the student's current learning progress and understanding 2. The system of claim 1.

5. The proposal unit When proposing a learning plan, select appropriate learning content based on students' interests and concerns.

2. The system of claim 1.

6. The proposal unit Estimate student emotions and prioritize lesson plans based on the estimated student emotions 2. The system of claim 1.

7. The proposal unit When suggesting learning plans, the app takes into account the student's geographic location to prioritize relevant learning content.

2. The system of claim 1.

8. The proposal unit When proposing lesson plans, analyze students' social media activity and suggest relevant learning content.

2. The system of claim 1.

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