System

Abstract

To provide a system creating an individualized learning plan on the basis of an understanding degree of a student, and providing a proper learning content.SOLUTION: A system comprises: measuring part; a creating part; and a providing part. The measuring part measures an understanding degree of a student. The creating part creates a learning plan of a student on the basis of the understanding degree measured by the measuring part. The providing part provides a learning content to the student, on the basis of the learning plan created by the creating 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the problem of not being able to adequately create individualized learning plans based on students' level of understanding and provide appropriate learning content.

[0005] The system according to the embodiment aims to create an individualized learning plan based on the student's level of understanding and provide appropriate learning content. [Means for solving the problem]

[0006] The system according to the embodiment includes a measurement unit, a creation unit, and a provision unit. The measurement unit measures a student's level of understanding. The creation unit creates a learning plan for the student based on the level of understanding measured by the measurement unit. The provision unit provides learning content to the student based on the learning plan created by the creation unit. Effect of the Invention

[0007] The system according to the embodiment can create an individualized learning plan based on the student's level of understanding and provide appropriate learning content. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The learning support system according to the embodiment of the present invention is a system in which AI periodically measures the level of understanding of an individual in elementary school studies or qualification studies, and presents optimal learning plans and contents. This learning support system is first provided with a measurement unit for measuring the level of understanding of a student, and this measurement unit periodically measures the level of understanding of the student using AI. Next, there is a creation unit that creates a learning plan based on the measured level of understanding, and this creation unit also uses AI to create an optimal learning plan according to the student's level of understanding. Furthermore, there is a provision unit that provides learning content based on the created learning plan, and this provision unit provides appropriate learning content to the student based on the learning plan created by AI. In addition, there is also a response unit that answers questions about the content of the learning content by voice. For example, when a student asks a question by voice, such as "Why is it XX?", the AI ​​answers the question by voice. This eliminates the need to search through text, and makes it easy to resolve questions. In addition, there is also a collection unit that collects information about the student, and this collection unit collects information such as the student's seat position and learning history. Based on the collected information, the measurement unit periodically measures the students' level of understanding, and the creation unit creates a learning plan based on the level of understanding and seating position. This service can be provided as an app, and can also be deployed as a physical AI speaker for elementary school students. This allows optimal learning support to be provided to students of a wide range of ages. This allows the learning support system to efficiently measure students' level of understanding and provide optimal learning plans and content.

[0029] The learning support system according to the embodiment includes a measurement unit, a creation unit, and a provision unit. The measurement unit measures the level of understanding of the student. The level of understanding of the student includes, for example, a test score, a correct answer rate of a quiz, a questionnaire result, and the like, but is not limited to such examples. The measurement unit evaluates the level of understanding based on, for example, the test score. The measurement unit can also evaluate the level of understanding based on the correct answer rate of the quiz. The measurement unit can also evaluate the level of understanding based on the questionnaire result. For example, the measurement unit inputs the test score to the AI, and the AI ​​evaluates the level of understanding based on the score. The correct answer rate of the quiz is also input to the AI, and the AI ​​evaluates the level of understanding based on the correct answer rate. The questionnaire result is also input to the AI, and the AI ​​evaluates the level of understanding based on the result. The creation unit creates a learning plan based on the level of understanding measured by the measurement unit. The learning plan includes, for example, a learning goal, a learning schedule, a type of teaching material, and the like, but is not limited to such examples. The creation unit sets, for example, a learning goal, and creates a learning schedule based on the goal. The creation unit can also select a type of teaching material based on the learning schedule. For example, the creation unit inputs learning goals into the AI, and the AI ​​creates a learning schedule based on the goals. The learning schedule is also input into the AI, and the AI ​​selects the type of teaching material based on the schedule. The provision unit provides learning content based on the learning plan created by the creation unit. The learning content includes, for example, video lectures, text teaching materials, interactive exercises, and the like, but is not limited to such examples. The provision unit can provide, for example, video lectures, text teaching materials, and interactive exercises. For example, the provision unit inputs video lectures into the AI, and the AI ​​provides the lectures to students. The text teaching materials are also input into the AI, and the AI ​​provides the teaching materials to students. The interactive exercises are also input into the AI, and the AI ​​provides the exercises to students. As a result, the learning support system according to the embodiment can efficiently measure the students' understanding and provide optimal learning plans and content.

[0030] The measurement unit has the function of measuring the students' level of understanding from multiple angles. Specifically, it collects data such as test scores, quiz correct answer rates, and questionnaire results, and evaluates them comprehensively. For example, test scores are important indicators that show how much a student understands a particular subject or topic. The measurement unit inputs these scores into the AI, and the AI ​​evaluates the level of understanding based on the scores. By comparing past test results and data from other students, the AI ​​can more accurately evaluate the level of understanding of each student. The correct answer rate of the quiz is also an important indicator, and is particularly effective in capturing changes in the level of understanding over a short period of time. The measurement unit inputs the correct answer rate of the quiz into the AI, and the AI ​​evaluates the level of understanding based on the correct answer rate. The AI ​​can dynamically evaluate the level of understanding of students while taking into account the difficulty of the quiz and the range of questions. In addition, the survey results are also used to evaluate the level of understanding. The survey is intended to measure the student's own subjective level of understanding, motivation to learn, and satisfaction with the learning environment. The measurement unit inputs the survey results into the AI, and the AI ​​evaluates the level of understanding based on the results. AI can analyze questionnaire response patterns and past response history to capture changes in students' understanding and motivation to learn. This allows the measurement department to provide basic data for evaluating students' understanding from multiple angles and providing optimal learning plans for individual students.

[0031] The creation unit creates an optimal learning plan for each student based on the level of understanding measured by the measurement unit. The learning plan includes learning goals, learning schedules, types of teaching materials, etc. Specifically, the creation unit first sets the learning goals of the student. This learning goal is set based on the student's current level of understanding and future goals. For example, a student may aim to achieve a high score in a specific subject, or may aim to master a specific skill. The creation unit inputs these goals into the AI, which then creates a learning schedule based on the goals. The learning schedule is set flexibly to suit the student's life rhythm and learning style. For example, a student can study for a short period of time on weekdays and study intensively on weekends. The AI ​​proposes an optimal learning schedule by referring to the student's past learning data and the data of other students. The creation unit also selects the type of teaching materials based on the learning schedule. Teaching materials include video lectures, text teaching materials, interactive exercises, etc. The AI ​​selects the optimal teaching materials according to the student's level of understanding and learning style. For example, video lectures are provided to students who prefer visual learning, and text teaching materials are provided to students who want to improve their reading comprehension. In addition, AI can dynamically revise the learning plan according to the learning progress and changes in the level of understanding, allowing the creation department to provide the optimal learning plan for each student and support efficient learning.

[0032] The provision unit provides appropriate learning content to students based on the learning plan created by the creation unit. The learning content includes video lectures, text materials, interactive exercises, etc. Specifically, the provision unit first provides video lectures. Video lectures are videos in which specialized lecturers give explanations, and students can deepen their understanding by watching them. The provision unit inputs the video lectures into the AI, which then provides the lectures to students. The AI ​​can deliver video lectures at appropriate times according to the students' level of understanding and learning progress. Next, the provision unit provides text materials. The text materials include detailed explanations and examples, and students can deepen their knowledge by reading them. The provision unit inputs the text materials into the AI, which then provides the materials to students. The AI ​​can select and provide the optimal text materials according to the students' learning style and level of understanding. Furthermore, the provision unit provides interactive exercises. Interactive exercises allow students to deepen their understanding by actually solving problems and performing simulations. The provision unit inputs interactive exercises into the AI, which then provides the exercises to students. The AI ​​can provide exercises of appropriate difficulty and provide feedback according to the student's level of understanding and learning progress. This allows the provider to provide a variety of learning content to students and support efficient learning. Furthermore, the provider can monitor the student's learning situation in real time and revise the learning plan and content as necessary. This allows the provider to always provide optimal learning support based on the latest information.

[0033] The learning support system includes a response unit that answers questions about the contents of the learning content by voice. When a student asks a question by voice, such as "Why is it like this?", the response unit uses voice recognition technology to understand the student's question and generates an answer by voice using voice synthesis technology. For example, when a student asks "Why is the Earth round?", the response unit uses AI to understand the question and answers "The Earth maintains its round shape due to gravity." When a student asks "Why is water transparent?", the response unit can also understand the question and answer "Water appears transparent because it has the property of transmitting light." When a student asks "Why is the sky blue?", the response unit can also understand the question and answer "The sky appears blue because sunlight is scattered by molecules in the atmosphere." This allows students to easily resolve their questions without having to search for text. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can convert a student's question into text using voice recognition technology, input that text into a generation AI, and the generation AI can generate an answer.

[0034] The learning support system includes a collection unit that collects information about students, and a measurement unit that periodically measures the level of understanding based on the information collected by the collection unit. The collection unit collects information about students. The information about students includes, for example, learning history, test results, and questionnaire responses, but is not limited to such examples. The collection unit can, for example, collect learning history, collect test results, and collect questionnaire responses. For example, the collection unit inputs the learning history into an AI, and the AI ​​collects information based on the history. The test results are also input into the AI, and the AI ​​collects information based on the results. The questionnaire responses are also input into the AI, and the AI ​​collects information based on the responses. The measurement unit periodically measures the level of understanding based on the information collected by the collection unit. The measurement of the level of understanding includes, for example, a frequency of every week, every month, every semester, and the like, but is not limited to such examples. The measurement unit can, for example, measure the level of understanding every week, every month, and every semester. For example, the measurement unit inputs the information collected by the collection unit into an AI, and the AI ​​measures the level of understanding based on the information. This makes it possible to accurately measure the level of understanding based on the collected information. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learning history of the student to the generation AI, and the generation AI can collect information based on the history.

[0035] The creation unit can create a learning plan based on the degree of understanding measured by the measurement unit and the seat position of the student. The creation unit creates a learning plan, for example, based on the degree of understanding measured by the measurement unit and the seat position of the student. The seat position includes, for example, a seating arrangement in a classroom, a seat number, etc., but is not limited to such examples. The creation unit creates a learning plan, for example, based on a seating arrangement in a classroom. The creation unit can also create a learning plan based on a seat number. For example, the creation unit inputs a seating arrangement into an AI, and the AI ​​creates a learning plan based on the arrangement. The seat number is also input into the AI, and the AI ​​creates a learning plan based on the number. This makes it possible to create an optimal learning plan based on the student's seat position. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit can input a seat position into a generation AI, and the generation AI can create a learning plan based on the position.

[0036] The measurement unit can analyze the student's past learning history and select the optimal measurement method. For example, for subjects in which the student has previously scored high, the measurement unit measures the student's level of understanding by asking difficult questions. In addition, for subjects in which the student is weak, the measurement unit can also measure the student's level of understanding from basic questions. Furthermore, the measurement unit can analyze the student's learning history to find that concentration increases during a specific time period and measure the student's level of understanding during that time period. For example, the measurement unit inputs the student's past test results into the AI, and the AI ​​selects the optimal measurement method based on the results. The student's learning history is also input into the AI, and the AI ​​selects the optimal measurement method based on the history. This makes it possible to select the optimal measurement method based on the past learning history. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the measurement unit inputs the student's past learning history into the generation AI, and the generation AI can select the optimal measurement method based on the history.

[0037] The measurement unit can perform filtering based on the student's current learning situation and field of interest when measuring the level of understanding. For example, the measurement unit measures the level of understanding by asking questions related to the subject the student is currently studying. The measurement unit can also measure the level of understanding by asking questions related to the field in which the student is interested. Furthermore, the measurement unit can measure the level of understanding by asking questions of appropriate difficulty according to the student's learning progress. For example, the measurement unit inputs the student's current learning situation into the AI, and the AI ​​performs filtering based on that situation. The student's field of interest is also input into the AI, and the AI ​​performs filtering based on that field. This makes it possible to measure the level of understanding based on the current learning situation and field of interest. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit inputs the student's current learning situation into the generation AI, and the generation AI can perform filtering based on that situation.

[0038] The measurement unit can prioritize measuring highly relevant data in consideration of the geographical location information of the student when measuring the level of understanding. For example, if the student lives in a specific area, the measurement unit measures the level of understanding by asking questions related to that area. In addition, if the student is traveling, the measurement unit can also measure the level of understanding by asking questions related to the travel destination. In addition, if the student is at school, the measurement unit can also measure the level of understanding by asking questions related to the school curriculum. For example, the measurement unit inputs the geographical location information of the student to the AI, and the AI ​​measures highly relevant data on a priority basis based on the information. This makes it possible to measure highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the measurement unit inputs the geographical location information of the student to the generation AI, and the generation AI can measure highly relevant data on a priority basis based on the information.

[0039] The measurement unit can analyze the student's social media activity and measure related data when measuring the level of understanding. For example, the measurement unit measures the level of understanding by asking questions related to a topic in which the student is interested on social media. The measurement unit can also measure the level of understanding based on the learning content shared by the student on social media. Furthermore, the measurement unit can measure the level of understanding at an optimal timing, taking into account the student's social media activity time. For example, the measurement unit inputs the student's social media activity into the AI, and the AI ​​measures related data based on the activity. This makes it possible to measure related data based on the social media activity. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the measurement unit can input the student's social media activity into the generation AI, and the generation AI can measure related data based on the activity.

[0040] The creation unit can adjust the level of detail of the plan based on the importance of the learning content when creating the learning plan. For example, the creation unit creates a detailed learning plan for important subjects. The creation unit can also create a concise learning plan for supplementary subjects. Furthermore, the creation unit can create a learning plan that focuses on the main points for subjects immediately before an exam. For example, the creation unit inputs the importance of the learning content to an AI, and the AI ​​adjusts the level of detail of the plan based on the importance. This makes it possible to adjust the level of detail of the plan based on the importance of the learning content. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit inputs the importance of the learning content to a generation AI, and the generation AI can adjust the level of detail of the plan based on the importance.

[0041] When creating a learning plan, the creation unit can apply different plan creation algorithms depending on the category of the learning content. For example, for science subjects, the creation unit creates a learning plan including experiments. For mathematics subjects, the creation unit can also create a learning plan centered on exercise problems. Furthermore, for social studies subjects, the creation unit can create a learning plan including historical background. For example, the creation unit inputs the category of the learning content to the AI, and the AI ​​applies different plan creation algorithms depending on the category. This makes it possible to create an optimal plan depending on the category of the learning content. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit inputs the category of the learning content to the generation AI, and the generation AI can apply different plan creation algorithms based on the category.

[0042] The creation unit can determine the priority of the plan based on the submission time of the learning content when creating the learning plan. For example, the creation unit creates a learning plan with priority for assignments with a deadline approaching. The creation unit can also create a step-by-step learning plan for a long-term project. Furthermore, the creation unit can also create a learning plan for a regular exam with priority. For example, the creation unit inputs the submission time of the learning content to the AI, and the AI ​​determines the priority of the plan based on the submission time. This makes it possible to determine the priority of the plan based on the submission time. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit inputs the submission time of the learning content to the generation AI, and the generation AI can determine the priority of the plan based on the submission time.

[0043] The creation unit can adjust the order of the plan based on the relevance of the learning contents when creating the learning plan. For example, the creation unit creates a plan in which related subjects are studied consecutively. In addition, when the learning contents are consecutive, the creation unit can also create a plan taking into account the order. Furthermore, the creation unit can create an efficient learning plan based on the relevance of the learning contents. For example, the creation unit inputs the relevance of the learning contents to an AI, and the AI ​​adjusts the order of the plan based on the relevance. This makes it possible to create an efficient plan based on the relevance of the learning contents. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit inputs the relevance of the learning contents to a generation AI, and the generation AI can adjust the order of the plan based on the relevance.

[0044] When providing learning content, the providing unit can select the optimal content by referring to the student's past learning history. For example, the providing unit can provide content with a high level of difficulty for a subject in which the student has previously scored high. The providing unit can also provide basic content for a subject in which the student is weak. Furthermore, the providing unit can analyze that the student's concentration is high during a specific time period from the student's learning history, and provide the optimal content for that time period. For example, the providing unit inputs the student's past learning history into an AI, and the AI ​​selects the optimal content based on the history. This makes it possible to provide the optimal content based on the past learning history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the student's past learning history into a generating AI, and the generating AI can select the optimal content based on the history.

[0045] The provision unit can customize the content provision means based on the student's current learning situation when providing the learning content. The provision unit, for example, provides content related to a subject that the student is currently studying. The provision unit can also provide content related to a field in which the student is interested. Furthermore, the provision unit can provide content of an appropriate level of difficulty according to the student's learning progress. For example, the provision unit inputs the student's current learning situation into an AI, and the AI ​​customizes the content provision means based on that situation. This makes it possible to provide optimal content based on the current learning situation. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provision unit inputs the student's current learning situation into a generation AI, and the generation AI can customize the content provision means based on that situation.

[0046] The provision unit can select optimal content in consideration of the geographical location information of the student when providing the learning content. For example, if the student lives in a specific area, the provision unit can provide content related to the area. In addition, if the student is traveling, the provision unit can also provide content related to the travel destination. In addition, if the student is at school, the provision unit can also provide content related to the school curriculum. For example, the provision unit inputs the geographical location information of the student to an AI, and the AI ​​selects optimal content based on the information. This makes it possible to provide optimal content based on the geographical location information. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provision unit inputs the geographical location information of the student to a generation AI, and the generation AI can select optimal content based on the information.

[0047] When providing learning content, the providing unit can analyze the student's social media activity and suggest a means of providing the content. For example, the providing unit provides content related to a topic in which the student is interested on social media. The providing unit can also provide content based on the learning content shared by the student on social media. Furthermore, the providing unit can provide content at an optimal timing, taking into account the student's social media activity time. For example, the providing unit inputs the student's social media activity into an AI, and the AI ​​suggests a means of providing the content based on the activity. This makes it possible to provide optimal content based on the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the student's social media activity into a generating AI, and the generating AI can suggest a means of providing the content based on the activity.

[0048] The response unit can provide an optimal response by referring to the student's past question history when responding. The response unit can provide related information based on, for example, the content of the student's past questions. The response unit can also re-explain the content that the student did not understand in the past. Furthermore, the response unit can provide a response to a frequently asked question from the student's question history. For example, the response unit inputs the student's past question history into an AI, and the AI ​​provides an optimal response based on the history. This makes it possible to provide an optimal response based on the past question history. A part or all of the above-mentioned processing in the response unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the response unit inputs the student's past question history into a generation AI, and the generation AI can provide an optimal response based on the history.

[0049] The response unit can provide an optimal response in consideration of the device information of the student when responding. For example, when the student is using a smartphone, the response unit provides a response that matches the screen size. In addition, when the student is using a tablet, the response unit can also provide a response optimized for a large screen. Furthermore, when the student is using a smartwatch, the response unit can provide a concise and highly visible response. For example, the response unit inputs the device information of the student to the AI, and the AI ​​provides an optimal response based on the information. This makes it possible to provide an optimal response based on the device information. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit inputs the device information of the student to the generation AI, and the generation AI can provide an optimal response based on the information.

[0050] The collection unit can optimize the collection algorithm by referring to the past collected data at the time of collection. The collection unit, for example, selects an optimal collection method based on the data collected in the past. The collection unit can also adjust the frequency of collection from the past data. Furthermore, the collection unit can analyze the past data and improve the accuracy of collection. For example, the collection unit inputs the past collected data to an AI, and the AI ​​optimizes the collection algorithm based on the data. This makes it possible to optimize the collection algorithm based on the past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit inputs the past collected data to a generation AI, and the generation AI can optimize the collection algorithm based on the data.

[0051] The collection unit can weight the collected data based on the seat position of the student at the time of collection. For example, when the student is sitting in the front row, the collection unit collects detailed data. Also, when the student is sitting in the back row, the collection unit can collect data that covers the main points. Furthermore, the collection unit can adjust the weighting of the collected data based on the seat position of the student. For example, the collection unit inputs the seat position of the student to the AI, and the AI ​​weights the collected data based on the position. This makes it possible to adjust the weighting of the collected data based on the seat position. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the seat position of the student to the generation AI, and the generation AI weights the collected data based on the position.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The learning support system may further include an analysis unit that analyzes the learning style of the student. The analysis unit determines whether the student is a visual learner, an auditory learner, or a tactile learner, and creates an optimal learning plan based on the result. For example, visual learners may be provided with learning materials that make extensive use of diagrams and graphs, while auditory learners may be provided with learning materials that focus on audio and video lectures. Tactile learners may be provided with learning materials that include many experiments and exercises that allow students to learn by actually doing things. This makes it possible to provide optimal learning support according to the student's learning style.

[0054] The learning support system can also be equipped with a gamification function to improve students' motivation. For example, a system can be introduced that allows students to earn points and badges according to the degree of achievement of the learning plan. A ranking function that allows students to compete with other students can also be provided. Furthermore, virtual rewards and titles can be given when certain goals are achieved. This can increase students' motivation to learn and encourage continuous learning.

[0055] The learning support system can further include a health management unit that monitors the health of students. The health management unit uses sensors to collect the student's heart rate and sleep pattern and evaluates the student's health condition. For example, if the student's heart rate is high, the system can suggest taking a break to relax, and if the student continues to be sleep-deprived, the system can advise the student to go to bed earlier. This makes it possible to provide learning support that takes into account the student's health condition.

[0056] The learning support system may further include an environment adjustment unit for optimizing the learning environment for students. The environment adjustment unit monitors the learning environment for students (e.g., lighting, temperature, noise level, etc.) and provides an optimal environment. For example, when the lighting is dim, the brightness can be adjusted, and when the temperature is high, the air conditioning can be adjusted. In addition, when the noise level is high, a noise canceling function can be provided. This makes it possible to create an environment where students can concentrate on their studies.

[0057] The learning support system may further include a progress display unit for visualizing the learning progress of students. The progress display unit displays the learning progress of students in graphs and charts, visually showing the degree of goal achievement. For example, the achievement rate for learning goals may be displayed in a pie chart, and the progress status of each subject may be displayed in a bar graph. In addition, past learning history may be displayed in a line graph, allowing students to grasp learning trends. This allows students to grasp their own learning status at a glance and maintain their motivation.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The measurement unit measures the student's level of understanding. Measurement of comprehension includes test scores, quiz correct answer rates, survey results, etc. For example, the measurement unit inputs test scores into the AI, and the AI ​​evaluates the level of comprehension based on those scores. The measurement unit also inputs the quiz correct answer rates and survey results into the AI, and the AI ​​evaluates the level of comprehension based on those. Step 2: The creation unit creates a learning plan based on the level of understanding measured by the measurement unit. The learning plan includes learning goals, a learning schedule, and types of teaching materials. For example, the creation unit inputs learning goals into the AI, which then creates a learning schedule based on the goals. In addition, the creation unit selects the type of teaching materials based on the learning schedule. Step 3: The provision unit provides learning content based on the learning plan created by the creation unit. The learning content includes video lectures, text materials, interactive exercises, etc. For example, the provision unit inputs video lectures into the AI, which then provides the lectures to students. Text materials and interactive exercises are also input into the AI, which then provides them to students.

[0060] (Example 2) The learning support system according to the embodiment of the present invention is a system in which AI periodically measures the level of understanding of an individual in elementary school studies or qualification studies, and presents optimal learning plans and contents. This learning support system is first provided with a measurement unit for measuring the level of understanding of a student, and this measurement unit periodically measures the level of understanding of the student using AI. Next, there is a creation unit that creates a learning plan based on the measured level of understanding, and this creation unit also uses AI to create an optimal learning plan according to the student's level of understanding. Furthermore, there is a provision unit that provides learning content based on the created learning plan, and this provision unit provides appropriate learning content to the student based on the learning plan created by AI. In addition, there is also a response unit that answers questions about the content of the learning content by voice. For example, when a student asks a question by voice, such as "Why is it XX?", the AI ​​answers the question by voice. This eliminates the need to search through text, and makes it easy to resolve questions. In addition, there is also a collection unit that collects information about the student, and this collection unit collects information such as the student's seat position and learning history. Based on the collected information, the measurement unit periodically measures the students' level of understanding, and the creation unit creates a learning plan based on the level of understanding and seating position. This service can be provided as an app, and can also be deployed as a physical AI speaker for elementary school students. This allows optimal learning support to be provided to students of a wide range of ages. This allows the learning support system to efficiently measure students' level of understanding and provide optimal learning plans and content.

[0061] The learning support system according to the embodiment includes a measurement unit, a creation unit, and a provision unit. The measurement unit measures the level of understanding of the student. The level of understanding of the student includes, for example, a test score, a correct answer rate of a quiz, a questionnaire result, and the like, but is not limited to such examples. The measurement unit evaluates the level of understanding based on, for example, the test score. The measurement unit can also evaluate the level of understanding based on the correct answer rate of the quiz. The measurement unit can also evaluate the level of understanding based on the questionnaire result. For example, the measurement unit inputs the test score to the AI, and the AI ​​evaluates the level of understanding based on the score. The correct answer rate of the quiz is also input to the AI, and the AI ​​evaluates the level of understanding based on the correct answer rate. The questionnaire result is also input to the AI, and the AI ​​evaluates the level of understanding based on the result. The creation unit creates a learning plan based on the level of understanding measured by the measurement unit. The learning plan includes, for example, a learning goal, a learning schedule, a type of teaching material, and the like, but is not limited to such examples. The creation unit sets, for example, a learning goal, and creates a learning schedule based on the goal. The creation unit can also select a type of teaching material based on the learning schedule. For example, the creation unit inputs learning goals into the AI, and the AI ​​creates a learning schedule based on the goals. The learning schedule is also input into the AI, and the AI ​​selects the type of teaching material based on the schedule. The provision unit provides learning content based on the learning plan created by the creation unit. The learning content includes, for example, video lectures, text teaching materials, interactive exercises, and the like, but is not limited to such examples. The provision unit can provide, for example, video lectures, text teaching materials, and interactive exercises. For example, the provision unit inputs video lectures into the AI, and the AI ​​provides the lectures to students. The text teaching materials are also input into the AI, and the AI ​​provides the teaching materials to students. The interactive exercises are also input into the AI, and the AI ​​provides the exercises to students. As a result, the learning support system according to the embodiment can efficiently measure the students' understanding and provide optimal learning plans and content.

[0062] The measurement unit has the function of measuring the students' level of understanding from multiple angles. Specifically, it collects data such as test scores, quiz correct answer rates, and questionnaire results, and evaluates them comprehensively. For example, test scores are important indicators that show how much a student understands a particular subject or topic. The measurement unit inputs these scores into the AI, and the AI ​​evaluates the level of understanding based on the scores. By comparing past test results and data from other students, the AI ​​can more accurately evaluate the level of understanding of each student. The correct answer rate of the quiz is also an important indicator, and is particularly effective in capturing changes in the level of understanding over a short period of time. The measurement unit inputs the correct answer rate of the quiz into the AI, and the AI ​​evaluates the level of understanding based on the correct answer rate. The AI ​​can dynamically evaluate the level of understanding of students while taking into account the difficulty of the quiz and the range of questions. In addition, the survey results are also used to evaluate the level of understanding. The survey is intended to measure the student's own subjective level of understanding, motivation to learn, and satisfaction with the learning environment. The measurement unit inputs the survey results into the AI, and the AI ​​evaluates the level of understanding based on the results. AI can analyze questionnaire response patterns and past response history to capture changes in students' understanding and motivation to learn. This allows the measurement department to provide basic data for evaluating students' understanding from multiple angles and providing optimal learning plans for individual students.

[0063] The creation unit creates an optimal learning plan for each student based on the level of understanding measured by the measurement unit. The learning plan includes learning goals, learning schedules, types of teaching materials, etc. Specifically, the creation unit first sets the learning goals of the student. This learning goal is set based on the student's current level of understanding and future goals. For example, a student may aim to achieve a high score in a specific subject, or may aim to master a specific skill. The creation unit inputs these goals into the AI, which then creates a learning schedule based on the goals. The learning schedule is set flexibly to suit the student's life rhythm and learning style. For example, a student can study for a short period of time on weekdays and study intensively on weekends. The AI ​​proposes an optimal learning schedule by referring to the student's past learning data and the data of other students. The creation unit also selects the type of teaching materials based on the learning schedule. Teaching materials include video lectures, text teaching materials, interactive exercises, etc. The AI ​​selects the optimal teaching materials according to the student's level of understanding and learning style. For example, video lectures are provided to students who prefer visual learning, and text teaching materials are provided to students who want to improve their reading comprehension. In addition, AI can dynamically revise the learning plan according to the learning progress and changes in the level of understanding, allowing the creation department to provide the optimal learning plan for each student and support efficient learning.

[0064] The provision unit provides appropriate learning content to students based on the learning plan created by the creation unit. The learning content includes video lectures, text materials, interactive exercises, etc. Specifically, the provision unit first provides video lectures. Video lectures are videos in which specialized lecturers give explanations, and students can deepen their understanding by watching them. The provision unit inputs the video lectures into the AI, which then provides the lectures to students. The AI ​​can deliver video lectures at appropriate times according to the students' level of understanding and learning progress. Next, the provision unit provides text materials. The text materials include detailed explanations and examples, and students can deepen their knowledge by reading them. The provision unit inputs the text materials into the AI, which then provides the materials to students. The AI ​​can select and provide the optimal text materials according to the students' learning style and level of understanding. Furthermore, the provision unit provides interactive exercises. Interactive exercises allow students to deepen their understanding by actually solving problems and performing simulations. The provision unit inputs interactive exercises into the AI, which then provides the exercises to students. The AI ​​can provide exercises of appropriate difficulty and provide feedback according to the student's level of understanding and learning progress. This allows the provider to provide a variety of learning content to students and support efficient learning. Furthermore, the provider can monitor the student's learning situation in real time and revise the learning plan and content as necessary. This allows the provider to always provide optimal learning support based on the latest information.

[0065] The learning support system includes a response unit that answers questions about the contents of the learning content by voice. When a student asks a question by voice, such as "Why is it like this?", the response unit uses voice recognition technology to understand the student's question and generates an answer by voice using voice synthesis technology. For example, when a student asks "Why is the Earth round?", the response unit uses AI to understand the question and answers "The Earth maintains its round shape due to gravity." When a student asks "Why is water transparent?", the response unit can also understand the question and answer "Water appears transparent because it has the property of transmitting light." When a student asks "Why is the sky blue?", the response unit can also understand the question and answer "The sky appears blue because sunlight is scattered by molecules in the atmosphere." This allows students to easily resolve their questions without having to search for text. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can convert a student's question into text using voice recognition technology, input that text into a generation AI, and the generation AI can generate an answer.

[0066] The learning support system includes a collection unit that collects information about students, and a measurement unit that periodically measures the level of understanding based on the information collected by the collection unit. The collection unit collects information about students. The information about students includes, for example, learning history, test results, and questionnaire responses, but is not limited to such examples. The collection unit can, for example, collect learning history, collect test results, and collect questionnaire responses. For example, the collection unit inputs the learning history into an AI, and the AI ​​collects information based on the history. The test results are also input into the AI, and the AI ​​collects information based on the results. The questionnaire responses are also input into the AI, and the AI ​​collects information based on the responses. The measurement unit periodically measures the level of understanding based on the information collected by the collection unit. The measurement of the level of understanding includes, for example, a frequency of every week, every month, every semester, and the like, but is not limited to such examples. The measurement unit can, for example, measure the level of understanding every week, every month, and every semester. For example, the measurement unit inputs the information collected by the collection unit into an AI, and the AI ​​measures the level of understanding based on the information. This makes it possible to accurately measure the level of understanding based on the collected information. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learning history of the student to the generation AI, and the generation AI can collect information based on the history.

[0067] The creation unit can create a learning plan based on the degree of understanding measured by the measurement unit and the seat position of the student. The creation unit creates a learning plan, for example, based on the degree of understanding measured by the measurement unit and the seat position of the student. The seat position includes, for example, a seating arrangement in a classroom, a seat number, etc., but is not limited to such examples. The creation unit creates a learning plan, for example, based on a seating arrangement in a classroom. The creation unit can also create a learning plan based on a seat number. For example, the creation unit inputs a seating arrangement into an AI, and the AI ​​creates a learning plan based on the arrangement. The seat number is also input into the AI, and the AI ​​creates a learning plan based on the number. This makes it possible to create an optimal learning plan based on the student's seat position. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit can input a seat position into a generation AI, and the generation AI can create a learning plan based on the position.

[0068] The measurement unit can estimate the student's emotion and adjust the timing of measuring the level of understanding based on the estimated emotion of the student. For example, when the student is feeling stressed, the measurement unit measures the level of understanding after providing time for the student to relax. Furthermore, when the student is concentrating, the measurement unit can also measure the level of understanding at that timing. Furthermore, when the student is tired, the measurement unit can measure the level of understanding after a break. For example, the measurement unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the measurement unit calculates an emotion score based on a change in facial expression. Furthermore, the measurement unit can record the student's voice and estimate the emotion using a voice analysis technique. For example, the measurement unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the measurement unit can collect the student's biological data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the measurement unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to measure the level of understanding at an optimal timing according to the student's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the measurement unit may be performed, for example, using an AI or may be performed without using an AI. For example, the measurement unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0069] The measurement unit can analyze the student's past learning history and select the optimal measurement method. For example, for subjects in which the student has previously scored high, the measurement unit measures the student's level of understanding by asking difficult questions. In addition, for subjects in which the student is weak, the measurement unit can also measure the student's level of understanding from basic questions. Furthermore, the measurement unit can analyze the student's learning history to find that concentration increases during a specific time period and measure the student's level of understanding during that time period. For example, the measurement unit inputs the student's past test results into the AI, and the AI ​​selects the optimal measurement method based on the results. The student's learning history is also input into the AI, and the AI ​​selects the optimal measurement method based on the history. This makes it possible to select the optimal measurement method based on the past learning history. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the measurement unit inputs the student's past learning history into the generation AI, and the generation AI can select the optimal measurement method based on the history.

[0070] The measurement unit can perform filtering based on the student's current learning situation and field of interest when measuring the level of understanding. For example, the measurement unit measures the level of understanding by asking questions related to the subject the student is currently studying. The measurement unit can also measure the level of understanding by asking questions related to the field in which the student is interested. Furthermore, the measurement unit can measure the level of understanding by asking questions of appropriate difficulty according to the student's learning progress. For example, the measurement unit inputs the student's current learning situation into the AI, and the AI ​​performs filtering based on that situation. The student's field of interest is also input into the AI, and the AI ​​performs filtering based on that field. This makes it possible to measure the level of understanding based on the current learning situation and field of interest. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit inputs the student's current learning situation into the generation AI, and the generation AI can perform filtering based on that situation.

[0071] The measurement unit can estimate the emotion of the student and determine the priority of the degree of understanding to be measured based on the estimated emotion of the student. For example, when the student is excited, the measurement unit preferentially measures the degree of understanding of a subject in which the student is interested. Furthermore, when the student is relaxed, the measurement unit can also preferentially measure the degree of understanding of a subject that the student is weak at. Furthermore, when the student is concentrating, the measurement unit can also preferentially measure the degree of understanding of an important subject. For example, the measurement unit captures the facial expression of the student with a camera and estimates the emotion using an emotion estimation algorithm. For example, the measurement unit calculates an emotion score based on a change in the facial expression. Furthermore, the measurement unit can record the voice of the student and estimate the emotion using a voice analysis technology. For example, the measurement unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the measurement unit can collect the biological data of the student (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the measurement unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to determine the priority of the degree of understanding according to the emotion of the student. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the measurement unit may be performed, for example, using an AI or may be performed without using an AI. For example, the measurement unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0072] The measurement unit can prioritize measuring highly relevant data in consideration of the geographical location information of the student when measuring the level of understanding. For example, if the student lives in a specific area, the measurement unit measures the level of understanding by asking questions related to that area. In addition, if the student is traveling, the measurement unit can also measure the level of understanding by asking questions related to the travel destination. In addition, if the student is at school, the measurement unit can also measure the level of understanding by asking questions related to the school curriculum. For example, the measurement unit inputs the geographical location information of the student to the AI, and the AI ​​measures highly relevant data on a priority basis based on the information. This makes it possible to measure highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the measurement unit inputs the geographical location information of the student to the generation AI, and the generation AI can measure highly relevant data on a priority basis based on the information.

[0073] The measurement unit can analyze the student's social media activity and measure related data when measuring the level of understanding. For example, the measurement unit measures the level of understanding by asking questions related to a topic in which the student is interested on social media. The measurement unit can also measure the level of understanding based on the learning content shared by the student on social media. Furthermore, the measurement unit can measure the level of understanding at an optimal timing, taking into account the student's social media activity time. For example, the measurement unit inputs the student's social media activity into the AI, and the AI ​​measures related data based on the activity. This makes it possible to measure related data based on the social media activity. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the measurement unit can input the student's social media activity into the generation AI, and the generation AI can measure related data based on the activity.

[0074] The creation unit can estimate the student's emotion and adjust the way of expressing the learning plan based on the estimated emotion of the student. For example, when the student is relaxed, the creation unit creates a learning plan including detailed explanations. Furthermore, when the student is in a hurry, the creation unit can also create a concise learning plan that focuses on the main points. Furthermore, when the student is excited, the creation unit can create a learning plan with visually stimulating effects added. For example, the creation unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on the change in facial expression. Furthermore, the creation unit can record the student's voice and estimate the emotion using a voice analysis technology. For example, the creation unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the creation unit can collect the student's biological data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the way of expressing the learning plan according to the student's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the creation unit may be performed, for example, using AI or may be performed without using AI. For example, the creation unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0075] The creation unit can adjust the level of detail of the plan based on the importance of the learning content when creating the learning plan. For example, the creation unit creates a detailed learning plan for important subjects. The creation unit can also create a concise learning plan for supplementary subjects. Furthermore, the creation unit can create a learning plan that focuses on the main points for subjects immediately before an exam. For example, the creation unit inputs the importance of the learning content to an AI, and the AI ​​adjusts the level of detail of the plan based on the importance. This makes it possible to adjust the level of detail of the plan based on the importance of the learning content. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit inputs the importance of the learning content to a generation AI, and the generation AI can adjust the level of detail of the plan based on the importance.

[0076] When creating a learning plan, the creation unit can apply different plan creation algorithms depending on the category of the learning content. For example, for science subjects, the creation unit creates a learning plan including experiments. For mathematics subjects, the creation unit can also create a learning plan centered on exercise problems. Furthermore, for social studies subjects, the creation unit can create a learning plan including historical background. For example, the creation unit inputs the category of the learning content to the AI, and the AI ​​applies different plan creation algorithms depending on the category. This makes it possible to create an optimal plan depending on the category of the learning content. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using AI, or may be performed without using AI. For example, the creation unit inputs the category of the learning content to the generation AI, and the generation AI can apply different plan creation algorithms based on the category.

[0077] The creation unit can estimate the student's emotion and adjust the length of the learning plan based on the estimated student's emotion. For example, when the student is tired, the creation unit creates a learning plan that can be completed in a short time. Furthermore, when the student is relaxed, the creation unit can also create a longer learning plan including detailed explanations. Furthermore, when the student is excited, the creation unit can also create a learning plan with visually stimulating effects added. For example, the creation unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on the change in facial expression. Furthermore, the creation unit can record the student's voice and estimate the emotion using a voice analysis technology. For example, the creation unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the creation unit can collect the student's biological data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the length of the learning plan according to the student's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the creation unit may be performed, for example, using AI or may be performed without using AI. For example, the creation unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0078] The creation unit can determine the priority of the plan based on the submission time of the learning content when creating the learning plan. For example, the creation unit creates a learning plan with priority for assignments with a deadline approaching. The creation unit can also create a step-by-step learning plan for a long-term project. Furthermore, the creation unit can also create a learning plan for a regular exam with priority. For example, the creation unit inputs the submission time of the learning content to the AI, and the AI ​​determines the priority of the plan based on the submission time. This makes it possible to determine the priority of the plan based on the submission time. Part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit inputs the submission time of the learning content to the generation AI, and the generation AI can determine the priority of the plan based on the submission time.

[0079] The creation unit can adjust the order of the plan based on the relevance of the learning contents when creating the learning plan. For example, the creation unit creates a plan in which related subjects are studied consecutively. In addition, when the learning contents are consecutive, the creation unit can also create a plan taking into account the order. Furthermore, the creation unit can create an efficient learning plan based on the relevance of the learning contents. For example, the creation unit inputs the relevance of the learning contents to an AI, and the AI ​​adjusts the order of the plan based on the relevance. This makes it possible to create an efficient plan based on the relevance of the learning contents. A part or all of the above-mentioned processing in the creation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the creation unit inputs the relevance of the learning contents to a generation AI, and the generation AI can adjust the order of the plan based on the relevance.

[0080] The providing unit can estimate the student's emotion and adjust the method of providing the learning content based on the estimated student's emotion. For example, when the student is relaxed, the providing unit provides learning content including detailed explanations. Furthermore, when the student is in a hurry, the providing unit can provide concise learning content with the main points. Furthermore, when the student is excited, the providing unit can provide learning content with visually stimulating effects. For example, the providing unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in facial expression. Furthermore, the providing unit can record the student's voice and estimate the emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the providing unit can collect the student's biological data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to select the optimal provision method according to the student's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the providing unit may be performed, for example, using an AI or may be performed without using an AI. For example, the providing unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0081] When providing learning content, the providing unit can select the optimal content by referring to the student's past learning history. For example, the providing unit can provide content with a high level of difficulty for a subject in which the student has previously scored high. The providing unit can also provide basic content for a subject in which the student is weak. Furthermore, the providing unit can analyze that the student's concentration is high during a specific time period from the student's learning history, and provide the optimal content for that time period. For example, the providing unit inputs the student's past learning history into an AI, and the AI ​​selects the optimal content based on the history. This makes it possible to provide the optimal content based on the past learning history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the student's past learning history into a generating AI, and the generating AI can select the optimal content based on the history.

[0082] The provision unit can customize the content provision means based on the student's current learning situation when providing the learning content. The provision unit, for example, provides content related to a subject that the student is currently studying. The provision unit can also provide content related to a field in which the student is interested. Furthermore, the provision unit can provide content of an appropriate level of difficulty according to the student's learning progress. For example, the provision unit inputs the student's current learning situation into an AI, and the AI ​​customizes the content provision means based on that situation. This makes it possible to provide optimal content based on the current learning situation. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provision unit inputs the student's current learning situation into a generation AI, and the generation AI can customize the content provision means based on that situation.

[0083] The providing unit can estimate the emotion of the student and determine the priority of the learning content based on the estimated emotion of the student. For example, when the student is excited, the providing unit can provide the content of the subject that the student is interested in with priority. Furthermore, when the student is relaxed, the providing unit can provide the content of the subject that the student is weak in with priority. Furthermore, when the student is concentrating, the providing unit can provide the content of the important subject with priority. For example, the providing unit captures the facial expression of the student with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the change in the facial expression. Furthermore, the providing unit can record the voice of the student and estimate the emotion using a voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the providing unit can collect the biological data of the student (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to determine the priority of the content according to the emotion of the student. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the providing unit may be performed, for example, using an AI or may be performed without using an AI. For example, the providing unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0084] The provision unit can select optimal content in consideration of the geographical location information of the student when providing the learning content. For example, if the student lives in a specific area, the provision unit can provide content related to the area. In addition, if the student is traveling, the provision unit can also provide content related to the travel destination. In addition, if the student is at school, the provision unit can also provide content related to the school curriculum. For example, the provision unit inputs the geographical location information of the student to an AI, and the AI ​​selects optimal content based on the information. This makes it possible to provide optimal content based on the geographical location information. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the provision unit inputs the geographical location information of the student to a generation AI, and the generation AI can select optimal content based on the information.

[0085] When providing learning content, the providing unit can analyze the student's social media activity and suggest a means of providing the content. For example, the providing unit provides content related to a topic in which the student is interested on social media. The providing unit can also provide content based on the learning content shared by the student on social media. Furthermore, the providing unit can provide content at an optimal timing, taking into account the student's social media activity time. For example, the providing unit inputs the student's social media activity into an AI, and the AI ​​suggests a means of providing the content based on the activity. This makes it possible to provide optimal content based on the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit inputs the student's social media activity into a generating AI, and the generating AI can suggest a means of providing the content based on the activity.

[0086] The response unit can estimate the student's emotion and adjust the way of expressing the response based on the estimated emotion of the student. For example, when the student is relaxed, the response unit provides a response including a detailed explanation. Furthermore, when the student is in a hurry, the response unit can provide a concise response that focuses on the main points. Furthermore, when the student is excited, the response unit can provide a response with a visually stimulating effect. For example, the response unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the response unit calculates an emotion score based on the change in facial expression. Furthermore, the response unit can record the student's voice and estimate the emotion using a voice analysis technology. For example, the response unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the response unit can collect the student's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the response unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to provide an optimal response according to the student's emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the response unit may be performed, for example, using AI or may be performed without using AI. For example, the response unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0087] The response unit can provide an optimal response by referring to the student's past question history when responding. The response unit can provide related information based on, for example, the content of the student's past questions. The response unit can also re-explain the content that the student did not understand in the past. Furthermore, the response unit can provide a response to a frequently asked question from the student's question history. For example, the response unit inputs the student's past question history into an AI, and the AI ​​provides an optimal response based on the history. This makes it possible to provide an optimal response based on the past question history. A part or all of the above-mentioned processing in the response unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the response unit inputs the student's past question history into a generation AI, and the generation AI can provide an optimal response based on the history.

[0088] The response unit can estimate the emotion of the student and determine the priority of the response based on the estimated emotion of the student. For example, when the student is excited, the response unit can provide a response related to a topic that the student is interested in preferentially. Furthermore, when the student is relaxed, the response unit can provide a response related to a topic that the student is not good at preferentially. Furthermore, when the student is concentrating, the response unit can provide a response related to an important topic preferentially. For example, the response unit captures the facial expression of the student with a camera and estimates the emotion using an emotion estimation algorithm. For example, the response unit calculates an emotion score based on the change in the facial expression. Furthermore, the response unit can record the voice of the student and estimate the emotion using a voice analysis technology. For example, the response unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the response unit can collect the biological data of the student (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the response unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to determine the priority of the response according to the emotion of the student. The estimation of emotions is realized, for example, by using an emotion estimation function using 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-mentioned processing in the response unit may be performed, for example, using AI or may be performed without using AI. For example, the response unit inputs facial expression data of a student to the generation AI, and the generation AI can estimate emotions based on the data.

[0089] The response unit can provide an optimal response in consideration of the device information of the student when responding. For example, when the student is using a smartphone, the response unit provides a response that matches the screen size. In addition, when the student is using a tablet, the response unit can also provide a response optimized for a large screen. Furthermore, when the student is using a smartwatch, the response unit can provide a concise and highly visible response. For example, the response unit inputs the device information of the student to the AI, and the AI ​​provides an optimal response based on the information. This makes it possible to provide an optimal response based on the device information. Part or all of the above-mentioned processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit inputs the device information of the student to the generation AI, and the generation AI can provide an optimal response based on the information.

[0090] The collection unit can estimate the student's emotion and select collected data based on the estimated student's emotion. For example, when the student is relaxed, the collection unit collects detailed data. When the student is in a hurry, the collection unit can also collect data that covers the main points. When the student is excited, the collection unit can also collect visually stimulating data. For example, the collection unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the change in facial expression. The collection unit can also record the student's voice and estimate the emotion using a voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. The collection unit can also collect the student's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to collect optimal data according to the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs facial expression data of students to the generation AI, and the generation AI can estimate emotions based on the data.

[0091] The collection unit can optimize the collection algorithm by referring to the past collected data at the time of collection. The collection unit, for example, selects an optimal collection method based on the data collected in the past. The collection unit can also adjust the frequency of collection from the past data. Furthermore, the collection unit can analyze the past data and improve the accuracy of collection. For example, the collection unit inputs the past collected data to an AI, and the AI ​​optimizes the collection algorithm based on the data. This makes it possible to optimize the collection algorithm based on the past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit inputs the past collected data to a generation AI, and the generation AI can optimize the collection algorithm based on the data.

[0092] The collection unit can estimate the student's emotion and adjust the frequency of collection based on the estimated student's emotion. For example, when the student is relaxed, the collection unit collects data frequently. In addition, when the student is in a hurry, the collection unit can reduce the frequency of collection. In addition, when the student is excited, the collection unit can adjust the frequency of collection. For example, the collection unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the change in facial expression. In addition, the collection unit can record the student's voice and estimate the emotion using a voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates the emotion score. In addition, the collection unit can collect the student's biodata (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuation of the heart rate. This makes it possible to adjust the frequency of collection according to the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may 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-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs facial expression data of students to the generation AI, and the generation AI can estimate emotions based on the data.

[0093] The collection unit can weight the collected data based on the seat position of the student at the time of collection. For example, when the student is sitting in the front row, the collection unit collects detailed data. Also, when the student is sitting in the back row, the collection unit can collect data that covers the main points. Furthermore, the collection unit can adjust the weighting of the collected data based on the seat position of the student. For example, the collection unit inputs the seat position of the student to the AI, and the AI ​​weights the collected data based on the position. This makes it possible to adjust the weighting of the collected data based on the seat position. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the seat position of the student to the generation AI, and the generation AI weights the collected data based on the position.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The learning support system may further include an analysis unit that analyzes the learning style of the student. The analysis unit determines whether the student is a visual learner, an auditory learner, or a tactile learner, and creates an optimal learning plan based on the result. For example, visual learners may be provided with learning materials that make extensive use of diagrams and graphs, while auditory learners may be provided with learning materials that focus on audio and video lectures. Tactile learners may be provided with learning materials that include many experiments and exercises that allow students to learn by actually doing things. This makes it possible to provide optimal learning support according to the student's learning style.

[0096] The learning support system can also be equipped with a gamification function to improve students' motivation. For example, a system can be introduced that allows students to earn points and badges according to the degree of achievement of the learning plan. A ranking function that allows students to compete with other students can also be provided. Furthermore, virtual rewards and titles can be given when certain goals are achieved. This can increase students' motivation to learn and encourage continuous learning.

[0097] The learning support system can further include a health management unit that monitors the health of students. The health management unit uses sensors to collect the student's heart rate and sleep pattern and evaluates the student's health condition. For example, if the student's heart rate is high, the system can suggest taking a break to relax, and if the student continues to be sleep-deprived, the system can advise the student to go to bed earlier. This makes it possible to provide learning support that takes into account the student's health condition.

[0098] The learning support system may further include an environment adjustment unit for optimizing the learning environment for students. The environment adjustment unit monitors the learning environment for students (e.g., lighting, temperature, noise level, etc.) and provides an optimal environment. For example, when the lighting is dim, the brightness can be adjusted, and when the temperature is high, the air conditioning can be adjusted. In addition, when the noise level is high, a noise canceling function can be provided. This makes it possible to create an environment where students can concentrate on their studies.

[0099] The learning support system may further include a progress display unit for visualizing the learning progress of students. The progress display unit displays the learning progress of students in graphs and charts, visually showing the degree of goal achievement. For example, the achievement rate for learning goals may be displayed in a pie chart, and the progress status of each subject may be displayed in a bar graph. In addition, past learning history may be displayed in a line graph, allowing students to grasp learning trends. This allows students to grasp their own learning status at a glance and maintain their motivation.

[0100] The learning support system can further estimate the student's emotions and adjust the difficulty of the learning content based on the estimated student's emotions. For example, if the student is stressed, it can provide low-difficulty questions, and if the student is relaxed, it can provide high-difficulty questions. It can also provide challenging questions when the student is excited, and questions that require deep thinking when the student is focused. In this way, it is possible to provide optimal learning content according to the student's emotions.

[0101] The learning support system can further estimate the student's emotions and adjust the timing of study based on the estimated student's emotions. For example, if the student is tired, it can suggest a break, and if the student is relaxed, it can suggest the timing to start studying. Also, if the student is concentrating, it can encourage them to proceed with their studies at that timing. In this way, it is possible to provide the optimal study timing according to the student's emotions.

[0102] The learning support system can further estimate the student's emotions and adjust the way in which it provides feedback on the progress of learning based on the estimated student's emotions. For example, if the student is relaxed, detailed feedback can be provided, and if the student is in a hurry, concise feedback that focuses on the main points can be provided. Also, if the student is excited, feedback with visually stimulating effects can be provided. In this way, it is possible to provide optimal feedback according to the student's emotions.

[0103] The learning support system can further estimate the student's emotions and adjust the way of reporting the learning progress based on the estimated student's emotions. For example, if the student is relaxed, a detailed progress report can be provided, and if the student is in a hurry, a concise progress report with the main points can be provided. Also, if the student is excited, a progress report with visually stimulating effects can be provided. In this way, the optimal progress report can be provided according to the student's emotions.

[0104] The learning support system can further estimate the student's emotions and adjust the learning goal setting based on the estimated student's emotions. For example, if the student is relaxed, a detailed goal can be set, and if the student is in a hurry, a concise goal that focuses on the main points can be set. Also, if the student is excited, a goal with visually stimulating effects can be set. In this way, it is possible to provide optimal goal setting according to the student's emotions.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The measurement unit measures the student's level of understanding. Measurement of comprehension includes test scores, quiz correct answer rates, survey results, etc. For example, the measurement unit inputs test scores into the AI, and the AI ​​evaluates the level of comprehension based on those scores. The measurement unit also inputs the quiz correct answer rates and survey results into the AI, and the AI ​​evaluates the level of comprehension based on those. Step 2: The creation unit creates a learning plan based on the level of understanding measured by the measurement unit. The learning plan includes learning goals, a learning schedule, and types of teaching materials. For example, the creation unit inputs learning goals into the AI, which then creates a learning schedule based on the goals. In addition, the creation unit selects the type of teaching materials based on the learning schedule. Step 3: The provision unit provides learning content based on the learning plan created by the creation unit. The learning content includes video lectures, text materials, interactive exercises, etc. For example, the provision unit inputs video lectures into the AI, which then provides the lectures to students. Text materials and interactive exercises are also input into the AI, which then provides them to students.

[0107] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0108] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] For example, the measurement unit measures the student's understanding level using the camera 42 and microphone 38B of the smart device 14, and analyzes the data using the specific processing unit 290 of the data processing device 12. The creation unit creates a learning plan using the specific processing unit 290 of the data processing device 12, and provides it to the student through the control unit 46A of the smart device 14. The provision unit displays learning content using the output device 40 of the smart device 14, and responds by voice through the speaker 40B of the smart device 14. The collection unit collects information about the student using the camera 42 and microphone 38B of the smart device 14, and analyzes the information using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 4 shows an example of 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.

[0119] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] For example, the measurement unit measures the student's understanding level using the camera 42 and microphone 238 of the smart glasses 214, and analyzes the data using the specific processing unit 290 of the data processing device 12. The creation unit creates a learning plan using the specific processing unit 290 of the data processing device 12, and provides it to the student through the control unit 46A of the smart glasses 214. The provision unit provides learning content by voice using the speaker 240 of the smart glasses 214, and responses are also made in the same manner. The collection unit collects student information using the camera 42 and microphone 238 of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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, the RAM 30, and the 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the measurement unit measures the student's level of understanding using the camera 42 and microphone 238 of the headset terminal 314, and analyzes the data using the specific processing unit 290 of the data processing device 12. The creation unit creates a learning plan using the specific processing unit 290 of the data processing device 12, and provides it to the student through the control unit 46A of the headset terminal 314. The provision unit displays learning content using the display 343 of the headset terminal 314, and audio responses are made through the speaker 240 of the headset terminal 314. The collection unit collects information about the student using the camera 42 and microphone 238 of the headset terminal 314, and analyzes the information using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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, the RAM 30, and the 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] 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 performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0155] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] 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 a voice indicating a user input for 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.

[0157] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0159] For example, the measurement unit measures the student's level of understanding using the camera 42 and microphone 238 of the robot 414, and analyzes the data using the specific processing unit 290 of the data processing device 12. The creation unit creates a learning plan using the specific processing unit 290 of the data processing device 12, and provides it to the student through the control unit 46A of the robot 414. The provision unit provides learning content by voice using the speaker 240 of the robot 414, and responses are made in the same manner. The collection unit collects information about the student using the camera 42 and microphone 238 of the robot 414, and analyzes the information using the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] 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 Universal Serial Bus (USB) 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.

[0169] In addition, 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 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0173] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A measurement section that measures students' understanding; A creation unit that creates a learning plan for the student based on the level of understanding measured by the measurement unit; A provision unit that provides learning content to the student based on the learning plan created by the creation unit. A system characterized by: (Appendix 2) Equipped with a voice response section that answers questions about the content of the learning content 2. The system of claim 1, (Appendix 3) It has a collection department that collects student information. The measurement unit includes: The level of understanding is periodically measured based on the information collected by the collection unit. 2. The system of claim 1, (Appendix 4) The creation unit is A learning plan is created for the student based on the level of understanding measured by the measuring unit and the seat position of the student. 2. The system of claim 1, (Appendix 5) The measurement unit includes: Estimate the student's emotions and adjust the timing of measuring comprehension based on the estimated student emotions. 2. The system of claim 1, (Appendix 6) The measurement unit includes: Analyze students' past learning histories and select the most appropriate measurement method 2. The system of claim 1, (Appendix 7) The measurement unit includes: Filtering comprehension assessments based on students' current learning status and areas of interest 2. The system of claim 1, (Appendix 8) The measurement unit includes: Estimate students' emotions and prioritize the levels of understanding to be measured based on the estimated emotions of students. 2. The system of claim 1, (Appendix 9) The measurement unit includes: When measuring comprehension, prioritize the most relevant data by taking into account students' geographic location 2. The system of claim 1, (Appendix 10) The measurement unit includes: Analyze students' social media activity and measure relevant data when assessing comprehension 2. The system of claim 1, (Appendix 11) The creation unit is Infer student emotions and adjust the presentation of lesson plans based on the inferred student emotions 2. The system of claim 1, (Appendix 12) The creation unit is When creating a study plan, adjust the level of detail based on the importance of the content 2. The system of claim 1, (Appendix 13) The creation unit is When creating a study plan, different planning algorithms are applied depending on the content category 2. The system of claim 1, (Appendix 14) The creation unit is Estimate student emotions and adjust the length of the lesson plan based on the estimated student emotions 2. The system of claim 1, (Appendix 15) The creation unit is When creating your study plan, prioritize your plan based on when your learning will be submitted 2. The system of claim 1, (Appendix 16) The creation unit is When creating a study plan, rearrange the order of your plan based on the relevance of the learning content 2. The system of claim 1, (Appendix 17) The providing unit is Estimate student emotions and adjust the delivery of learning content based on the estimated student emotions 2. The system of claim 1, (Appendix 18) The providing unit is When providing learning content, select the most appropriate content by referring to the student's past learning history 2. The system of claim 1, (Appendix 19) The providing unit is Customize content delivery based on the student's current learning context 2. The system of claim 1, (Appendix 20) The providing unit is Estimate student emotions and prioritize learning content based on estimated student emotions 2. The system of claim 1, (Appendix 21) The providing unit is When providing learning content, select the most appropriate content by taking into account the student's geographic location information 2. The system of claim 1, (Appendix 22) The providing unit is When providing learning content, analyze students' social media activity and suggest ways to provide the content 2. The system of claim 1, (Appendix 23) The response unit: Infer student emotions and adjust the way responses are phrased based on the inferred student emotions 3. The system of claim 2, (Appendix 24) The response unit: When responding, it references the student's past question history to provide the best response 3. The system of claim 2, (Appendix 25) The response unit: Estimate student emotions and prioritize responses based on the estimated student emotions 3. The system of claim 2, (Appendix 26) The response unit: When responding, consider student device information to provide the best response 3. The system of claim 2, (Appendix 27) The collecting unit includes: Estimate the student's emotions and select the data to be collected based on the estimated student emotions. 4. The system of claim 3. (Appendix 28) The collecting unit includes: When collecting, the collection algorithm is optimized by referencing past collected data. 4. The system of claim 3. (Appendix 29) The collecting unit includes: Estimate student emotions and adjust collection frequency based on estimated student emotions 4. The system of claim 3. (Appendix 30) The collecting unit includes: At the time of collection, the collected data is weighted based on the student's seating position. 4. The system of claim 3. [Explanation of symbols]

[0179] 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 measurement section that measures students' understanding; A creation unit that creates a learning plan for the student based on the level of understanding measured by the measurement unit; A provision unit that provides learning content to the student based on the learning plan created by the creation unit. A system characterized by:

2. Equipped with a voice response section that answers questions about the content of the learning content 2. The system of claim 1 .

3. A collection unit for collecting information of the students, The measurement unit includes: The level of understanding is periodically measured based on the information collected by the collection unit.

2. The system of claim 1 .

4. The creation unit is A learning plan is created for the student based on the level of understanding measured by the measuring unit and the seat position of the student.

2. The system of claim 1 .

5. The measurement unit includes: The student's feelings are estimated, and the timing of measuring the level of understanding is adjusted based on the estimated feelings of the student.

2. The system of claim 1 .

6. The measurement unit includes: Analyze the student's past learning history and select the most appropriate measurement method.

2. The system of claim 1 .

7. The measurement unit includes: When measuring the level of understanding, filtering is performed based on the student's current learning situation and areas of interest.

2. The system of claim 1 .

8. The measurement unit includes: Estimating the student's feelings and determining a priority of the comprehension level to be measured based on the estimated student's feelings.

2. The system of claim 1 .

9. The measurement unit includes: When measuring the level of understanding, the geographic location information of the student is taken into consideration to preferentially measure data that is highly relevant.

2. The system of claim 1 .

10. The measurement unit includes: When measuring the level of understanding, analyze the social media activity of the student and measure related data.

2. The system of claim 1 .

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