System

Abstract

To provide a system configured to formulate learning plans in line with goals of individual learners and manage the progress.SOLUTION: A system includes a planning unit, an answering unit, a creating unit, a reflecting unit, and a guidance unit. The planning unit formulates a learning plan in line with the goal of a user. The answering unit answers a question from a student based on the learning plan formulated by the planning unit. The creating unit creates problems based on the question to which the answering unit answered. The reflecting unit reflects a degree of understanding based on a result of the problem created by the creating unit. The guidance unit guides a lessen video based on the degree of understanding reflected by the reflecting unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 do not adequately develop learning plans or manage progress tailored to the goals of individual learners, and there is room for improvement.

[0005] The system according to the embodiment aims to formulate a learning plan tailored to the goals of each individual learner and to manage the progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a planning unit, an answering unit, a creating unit, a reflecting unit, and a guidance unit. The planning unit creates a study plan that matches the user's goals. The answering unit answers questions from students based on the study plan created by the planning unit. The creating unit creates questions based on the questions answered by the answering unit. The reflecting unit reflects the level of understanding based on the results of the questions created by the creating unit. The guidance unit guides students through lesson videos based on the level of understanding reflected by the reflecting unit. Effect of the Invention

[0007] The system according to the embodiment can formulate a learning plan tailored to the goals of each individual learner and manage the progress. [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 learning support system for students and working adults who are aiming to obtain qualifications. This learning support system is a system that creates a learning plan according to the user's goals, answers questions from students, creates questions to determine the learning level, reflects the level of understanding in future learning plans, and introduces appropriate lesson videos, etc. For example, the learning support system creates a learning plan according to the user's goals. For example, the generation AI creates a learning plan according to the goals of each user, such as a high school student aiming to take a university entrance exam or a working adult aiming to take a qualification exam. At this time, the user's current learning situation and the period until the goal is achieved are taken into consideration. Next, the learning support system answers questions from students. If the user has a question during learning, the generation AI provides an appropriate answer to the question. For example, detailed answers are provided to specific questions, such as how to solve a math problem or an explanation of English grammar. Furthermore, the learning support system creates questions and determines the learning level. The generation AI automatically creates questions according to the user's learning situation and provides them to the user. When the user solves the problems, the generation AI determines the learning level based on the results. For example, the user's level of understanding is evaluated as a result of solving a math problem and reflected in the next learning plan. The learning support system reflects the level of understanding in future study plans. The generation AI automatically adjusts the next study plan based on the user's learning level. For example, if there is a lack of understanding in a particular area, a study plan focusing on that area will be created. Finally, the learning support system guides users to appropriate lesson videos, etc. The generation AI provides appropriate lesson videos and reference materials based on the user's study plan. For example, it guides users to lesson videos on a specific math unit or audio files for English listening practice. This system allows people living in rural areas to receive high-quality education, correcting regional disparities and contributing to local communities. For example, high school students in rural areas can take classes at famous prep schools in urban areas, which is expected to improve their academic ability. In addition, working adults who are aiming to obtain qualifications can study efficiently between work. This allows people living in rural areas to receive high-quality education, correcting regional disparities and contributing to local communities.

[0029] The learning support system according to the embodiment includes a planning unit, an answering unit, a creating unit, a reflecting unit, and a guidance unit. The planning unit makes a learning plan according to the user's goal. The planning unit can make a learning plan based on the user's past learning history and test results, for example, using a generating AI. For example, the planning unit generates an optimal learning plan in consideration of the user's current learning situation and the period until the goal is achieved. The answering unit answers questions from students based on the learning plan made by the planning unit. The answering unit can use a generating AI, for example, to answer questions using natural language processing technology. For example, when the user has a question during learning, the answering unit provides an appropriate answer to the question. The creating unit creates questions based on the questions answered by the answering unit. The creating unit can adjust the difficulty of the questions according to the user's level of understanding, for example, using a generating AI. For example, the creating unit automatically creates questions according to the user's learning situation and provides them to the user. The reflecting unit reflects the level of understanding based on the results of the questions created by the creating unit. For example, the reflecting unit can use a generating AI to evaluate the user's learning level and reflect it in the next learning plan. For example, if the user lacks understanding in a particular field, the reflection unit creates a study plan that focuses on that field. The guidance unit guides the user to an appropriate lesson video based on the level of understanding reflected by the reflection unit. The guidance unit can provide appropriate lesson videos and reference materials based on the user's study plan, for example, using a generation AI. For example, the guidance unit guides the user to a lesson video related to a particular mathematics unit or an audio file for English listening practice. This allows the learning support system according to the embodiment to plan a study plan tailored to the user's goals, answer questions, create questions, reflect the user's level of understanding, and guide the user to lesson videos.

[0030] The planning unit creates a study plan according to the user's goal. For example, the planning unit can use the generation AI to create a study plan based on the user's past learning history and test results. Specifically, the generation AI analyzes the user's past learning data and identifies the user's learning progress and strengths and weaknesses. This generates an optimal study plan taking into account the user's current learning situation and the period until the goal is achieved. For example, the generation AI can create a plan to focus on problems and areas that the user was weak at in the past, while creating a plan centered on reviewing areas in which the user is good. In addition, the generation AI can flexibly adjust the study plan to suit the user's learning style and pace. For example, if the user is the type who studies intensively in a short period of time, a short-term intensive study plan can be created, and if the user is the type who studies little by little over a long period of time, a long-term study plan can be created. Furthermore, the generation AI can set the study plan in stages, taking into account the period until the user achieves the goal. For example, if the period until the goal is achieved is set to several months, the goals and study contents to be achieved for each month can be specifically indicated. This allows the user to always grasp his / her own study progress and efficiently study toward the achievement of the goal.

[0031] The answering section answers questions from students based on the learning plan established by the planning section. The answering section can use, for example, a generation AI to answer questions using natural language processing technology. Specifically, the generation AI analyzes questions from users and understands the intent and content of the questions. For example, if a user asks a question about a math problem, the generation AI can analyze the content of the problem and provide an appropriate solution and explanation. The generation AI can also provide more appropriate answers by taking into account the user's past question history and learning situation. For example, if a user has asked a similar question in the past, the generation AI can provide more detailed explanations and additional information by referring to past answers to that question. Furthermore, the generation AI can adjust the difficulty and detail of the answer according to the user's level of understanding. For example, if the user is a beginner, the generation AI can provide basic concepts and simple explanations, and if the user is an advanced learner, the generation AI can provide more advanced explanations and application examples. This allows the answering section to support the user's learning and resolve doubts, thereby improving the efficiency of learning.

[0032] The creation unit creates questions based on the questions answered by the answer unit. The creation unit can adjust the difficulty of the questions according to the user's level of understanding, for example, by using a generation AI. Specifically, the generation AI analyzes the user's past learning data and question history in the answer unit to evaluate the user's level of understanding. This makes it possible to automatically create questions according to the user's level of understanding and provide them to the user. For example, if the user lacks understanding in a particular field, basic questions related to that field are created and practice is provided for the user to deepen their understanding. In addition, if the user shows a high level of understanding in a particular field, application questions and advanced questions related to that field can be created to further deepen the user's understanding. Furthermore, the generation AI can adjust the frequency and difficulty of the questions according to the user's learning pace and goals. For example, if the user studies intensively in a short period of time, many questions can be asked in a short period of time to quickly evaluate the user's level of understanding. On the other hand, if the user studies little by little over a long period of time, questions can be asked in stages over a long period of time to continuously evaluate the user's level of understanding. This allows the creation unit to provide questions according to the user's learning situation and improve the user's level of understanding.

[0033] The reflection unit reflects the degree of understanding based on the results of the questions created by the creation unit. The reflection unit can use, for example, the generation AI to evaluate the user's learning level and reflect it in the next study plan. Specifically, the generation AI analyzes the results of the questions answered by the user and evaluates the user's level of understanding. For example, if the user shows a high rate of correct answers in a specific field, the generation AI can evaluate the user's level of understanding in that field as being high, and the next study plan can reduce the study of that field. On the other hand, if the user shows a low rate of correct answers in a specific field, the generation AI can evaluate the user's level of understanding in that field as being low, and the next study plan can increase the study of that field. Furthermore, the generation AI can create a long-term study plan taking into account the user's learning history and past evaluation results. For example, the generation AI can create a study plan to regularly review areas that the user was weak in in the past. The generation AI can also flexibly adjust the study plan to suit the user's learning pace and goals. For example, if the user wants to achieve a goal in a short period of time, the generation AI can create a plan to study intensively in a short period of time, and if the user wants to study little by little over a long period of time, the generation AI can create a long-term study plan. This allows the reflection unit to accurately evaluate the user's level of understanding and reflect the evaluation in the next study plan, thereby improving the user's study efficiency.

[0034] The guidance unit guides the user to an appropriate lesson video based on the level of understanding reflected by the reflection unit. The guidance unit can provide the user with appropriate lesson videos and reference materials based on the user's learning plan, for example, using a generation AI. Specifically, the generation AI analyzes the user's learning plan and level of understanding, and selects the most suitable lesson videos and reference materials for the user. For example, if the user has a low level of understanding of a particular unit of mathematics, lesson videos and explanatory materials related to that unit can be provided. In addition, if the user needs English listening practice, audio files and videos for listening practice can be provided. Furthermore, the generation AI can customize lesson videos and reference materials according to the user's learning style and preferences. For example, if the user prefers visual learning, lesson videos that make extensive use of figures and graphs can be provided, and if the user prefers auditory learning, lesson videos that focus on audio commentary can be provided. This allows the guidance unit to maximize the user's learning efficiency and support effective learning. Furthermore, the guidance unit can collect user feedback and continuously improve the quality of the lesson videos and reference materials provided. For example, if the user provides evaluations and comments on the lesson videos, the content and format of the lesson videos can be improved based on the feedback. This allows the guidance section to always provide the latest and most appropriate study content, and effectively support the user's studies.

[0035] The planning unit can create a study plan based on the user's past learning history and test results. The planning unit creates a study plan based on the user's past learning history and test results, for example. For example, the planning unit generates an optimal study plan taking into account the user's past test results, study time, study content, etc. This makes it possible to create a study plan based on the user's past learning history and test results. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's past learning history and test results into the generation AI and cause the generation AI to generate a study plan.

[0036] The answering unit can answer questions using natural language processing technology. The answering unit answers questions using natural language processing technology, for example, using a generation AI. For example, when a user has a question while studying, the answering unit provides an appropriate answer to the question. For example, the answering unit provides a detailed answer to a specific question, such as how to solve a math problem or an explanation of English grammar. This makes it possible to provide an appropriate answer to a question by using natural language processing technology. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input a user's question to the generation AI and have the generation AI execute an answer to the question.

[0037] The creation unit can adjust the difficulty of the problem according to the user's level of understanding. The creation unit adjusts the difficulty of the problem according to the user's level of understanding, for example, using a generation AI. For example, the creation unit automatically creates problems according to the user's learning situation and provides them to the user. For example, the creation unit determines the learning level based on the result of the user solving the problem and reflects it in the next learning plan. This makes it possible to adjust the difficulty of the problem according to the user's level of understanding. 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 user's learning situation to the generation AI and causes the generation AI to create the problem.

[0038] The reflection unit can evaluate the user's learning level and reflect it in the next learning plan. The reflection unit can use, for example, a generation AI to evaluate the user's learning level and reflect it in the next learning plan. For example, if there is a lack of understanding in a specific field, the reflection unit creates a learning plan that focuses on that field. This makes it possible to evaluate the user's learning level and reflect it in the next learning plan. A part or all of the above-mentioned processing in the reflection unit can be performed, for example, using AI or can be performed without using AI. For example, the reflection unit can input the user's learning level into the generation AI and cause the generation AI to generate the next learning plan.

[0039] The guidance unit can provide appropriate lesson videos and reference materials based on the user's study plan. The guidance unit provides appropriate lesson videos and reference materials based on the user's study plan, for example, using a generation AI. For example, the guidance unit guides the user to lesson videos on a specific math unit, audio files for English listening practice, and the like. This makes it possible to provide lesson videos and reference materials based on the user's study plan. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the guidance unit can input the user's study plan into the generation AI and cause the generation AI to perform guidance on appropriate lesson videos and reference materials.

[0040] The planning unit can create a plan tailored to a specific learning style based on the user's past learning history and test results. The planning unit, for example, uses a generation AI to create a plan tailored to a specific learning style (visual, auditory, tactile) based on the user's past learning history and test results. For example, if the user has a visual learning style, the planning unit creates a learning plan that includes a lot of visual materials. Also, if the user has an auditory learning style, the planning unit can create a learning plan that includes a lot of audio materials and podcasts. Also, if the user has a tactile learning style, the planning unit can create a learning plan that includes a lot of practical exercises and hands-on activities. This makes it possible to create a learning plan tailored to a specific learning style. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the planning unit can input the user's learning style data into the generation AI and cause the generation AI to generate a learning plan.

[0041] The planning unit can propose a study time period in consideration of the user's life rhythm and daily schedule. The planning unit proposes an optimal study time period in consideration of the user's life rhythm and daily schedule, for example, using a generation AI. For example, if the user has a morning-type life rhythm, the planning unit makes a plan to concentrate study in the morning hours. Also, if the user has a night-type life rhythm, the planning unit can make a plan to concentrate study in the evening hours. Also, if the user has an irregular schedule, the planning unit can propose a flexible study time period and proceed with study according to the user's convenience. This makes it possible to propose an optimal study time period based on the user's life rhythm and daily schedule. Part or all of the above-mentioned processing in the planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the planning unit can input the user's life rhythm data to the generation AI and cause the generation AI to execute the proposal of a study time period.

[0042] The planning unit can incorporate region-specific learning resources into the plan, taking into account the geographical location information of the user. The planning unit, for example, uses a generation AI to incorporate region-specific learning resources into the plan, taking into account the geographical location information of the user. For example, if the user lives in an urban area, the planning unit creates a plan that includes many urban learning resources. Also, if the user lives in a rural area, the planning unit can create a plan that includes many regional learning resources. Also, if the user lives overseas, the planning unit can incorporate learning resources that match the language and culture of that area into the plan. This makes it possible to create a learning plan that incorporates region-specific learning resources. Part or all of the above-mentioned processing in the planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the planning unit can input the user's geographical location information into the generation AI and cause the generation AI to incorporate the learning resources.

[0043] The planning unit can analyze the user's social media activities and add related learning topics to the plan. The planning unit can, for example, use a generation AI to analyze the user's social media activities and add related learning topics to the plan. For example, the planning unit can add topics in which the user is interested on social media to the learning plan. The planning unit can also reflect information obtained from education-related accounts followed by the user in the learning plan. The planning unit can also incorporate topics of online communities in which the user participates into the learning plan. This makes it possible to add learning topics based on social media activities. Some or all of the above-mentioned processing in the planning unit can be performed, for example, using AI or can be performed without using AI. For example, the planning unit can input the user's social media activity data to the generation AI and cause the generation AI to add learning topics.

[0044] The answering unit can generate a plurality of answer patterns according to the content of the question and provide the user with options. The answering unit can generate a plurality of answer patterns according to the content of the question using, for example, a generation AI and provide the user with options. For example, the answering unit can provide a plurality of answer patterns showing different solutions to a math problem. The answering unit can also provide an answer pattern using different example sentences to a question about English grammar. The answering unit can also provide an answer pattern from a different perspective to a question about history. This makes it possible to provide a plurality of answer patterns according to the content of the question. A part or all of the above-mentioned processing in the answering unit can be performed using, for example, an AI or can be performed without using an AI. For example, the answering unit can input the content of the question to the generation AI and cause the generation AI to generate a plurality of answer patterns.

[0045] The answering unit can adjust the level of detail of the answer according to the difficulty of the question. The answering unit adjusts the level of detail of the answer according to the difficulty of the question, for example, by using a generation AI. For example, the answering unit provides a concise answer to an easy question. Furthermore, the answering unit can provide an answer including a detailed explanation to a question of medium difficulty. Furthermore, the answering unit can provide a detailed step-by-step answer to a difficult question. This makes it possible to adjust the level of detail of the answer according to the difficulty of the question. A part or all of the above-mentioned processing in the answering unit may be performed, for example, by using an AI, or may be performed without using an AI. For example, the answering unit can input the difficulty data of the question to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0046] The answering unit can provide related reference materials and links according to the content of the question. The answering unit can provide related reference materials and links according to the content of the question, for example, using a generation AI. For example, the answering unit can provide links to related reference books and websites for mathematics questions. Also, the answering unit can provide links to related grammar books and online resources for English questions. Also, the answering unit can provide links to related documentaries and articles for history questions. This makes it possible to provide related reference materials and links according to the content of the question. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or can be performed without using AI. For example, the answering unit can input the content of the question to the generation AI and cause the generation AI to provide related reference materials and links.

[0047] The answering unit can present similar questions and their answers from other users based on the content of the question. The answering unit, for example, uses a generation AI to present similar questions and their answers from other users based on the content of the question. For example, the answering unit presents an answer to a math question when another user asks the same question. Also, the answering unit can present an answer to an English question when another user asks the same question about grammar. Also, the answering unit can present an answer to a history question when another user asks a question about the same era. This makes it possible to present similar questions and their answers from other users. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the answering unit can input the content of the question to the generation AI and cause the generation AI to execute the presentation of similar questions and their answers.

[0048] The creation unit can create questions to strengthen a specific weak point based on the user's past answer history. The creation unit creates questions to strengthen a specific weak point based on the user's past answer history, for example, using a generation AI. For example, the creation unit creates similar questions based on questions that the user got wrong in the past. The creation unit can also create a question set that includes many questions related to areas that the user was weak in in the past. The creation unit can also create a question set that includes few questions related to areas that the user was good at in the past. This makes it possible to create questions to strengthen a specific weak point. 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 the user's past answer history into the generation AI and cause the generation AI to create questions.

[0049] The creation unit can adjust the question format to suit the user's learning style. The creation unit adjusts the question format (multiple choice, written, practical) to suit the user's learning style, for example, using a generation AI. For example, if the user has a visual learning style, the creation unit creates a question set that includes many multiple choice questions. Also, if the user has an auditory learning style, the creation unit can create a question set that includes many written questions. Also, if the user has a tactile learning style, the creation unit can create a question set that includes many practical questions. This makes it possible to adjust the question format to suit the user's learning style. A part 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 can input the user's learning style data to the generation AI and cause the generation AI to adjust the question format.

[0050] The creation unit can provide related examples and case studies according to the content of the problem. The creation unit can provide related examples and case studies according to the content of the problem, for example, using a generation AI. For example, the creation unit provides related examples and case studies for mathematics problems. The creation unit can also provide related examples and case studies for English problems. The creation unit can also provide related examples and case studies for history problems. This makes it possible to provide related examples and case studies according to the content of the problem. 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 the content of the problem to the generation AI and cause the generation AI to provide related examples and case studies.

[0051] The creation unit can present example answers of other users based on the content of the problem. The creation unit presents example answers of other users based on the content of the problem, for example, using a generation AI. For example, the creation unit presents example answers of other users to a math problem. The creation unit can also present example answers of other users to an English problem. The creation unit can also present example answers of other users to a history problem. This makes it possible to present example answers of other users. 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 the content of the problem to the generation AI and cause the generation AI to present example answers of other users.

[0052] The reflection unit can evaluate the level of understanding in a specific field in detail based on the user's answer result. The reflection unit, for example, uses a generation AI to evaluate the level of understanding in a specific field in detail based on the user's answer result. For example, the reflection unit evaluates the level of understanding in a specific unit based on the mathematics answer result. The reflection unit can also evaluate the level of understanding of grammar and vocabulary based on the English answer result. The reflection unit can also evaluate the level of understanding of a specific era or event based on the history answer result. This makes it possible to evaluate the level of understanding in a specific field in detail. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's answer result to the generation AI and cause the generation AI to execute the evaluation of the level of understanding.

[0053] The reflection unit can monitor the user's learning progress in real time and adjust the learning plan immediately as necessary. The reflection unit can monitor the user's learning progress in real time using, for example, a generation AI and adjust the learning plan immediately as necessary. For example, if the user is progressing faster than planned, the reflection unit can adjust the learning plan ahead of schedule. Also, if the user is behind schedule, the reflection unit can adjust the learning plan more loosely. Also, if the user is struggling in a particular area, the reflection unit can immediately adjust the learning plan to one that focuses on that area. This makes it possible to monitor the learning progress in real time and immediately adjust the learning plan. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's learning progress data into the generation AI and cause the generation AI to adjust the learning plan.

[0054] The reflection unit can suggest related additional learning resources based on the user's answer result. The reflection unit, for example, uses a generation AI to suggest related additional learning resources based on the user's answer result. For example, the reflection unit can suggest related reference books and online resources based on the mathematics answer result. The reflection unit can also suggest related grammar books and listening materials based on the English answer result. The reflection unit can also suggest related documentaries and articles based on the history answer result. This makes it possible to suggest additional learning resources based on the answer result. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's answer result to the generation AI and cause the generation AI to execute the suggestion of additional learning resources.

[0055] The reflection unit can compare the user's learning progress with other users and evaluate the relative level of understanding. The reflection unit can compare the user's learning progress with other users, for example, using a generation AI, and evaluate the relative level of understanding. For example, the reflection unit can compare the user's mathematics progress with other users and evaluate the relative level of understanding. The reflection unit can also compare the user's English progress with other users and evaluate the relative level of understanding. The reflection unit can also compare the user's history progress with other users and evaluate the relative level of understanding. This makes it possible to evaluate the relative level of understanding by comparison with other users. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reflection unit can input the user's learning progress data to the generation AI and cause the generation AI to execute an evaluation of the relative level of understanding.

[0056] The guidance unit can provide a lesson video specialized in a specific field based on the user's study plan. The guidance unit can provide a lesson video specialized in a specific field based on the user's study plan, for example, using a generation AI. For example, when the user has a study plan for mathematics, the guidance unit can provide a lesson video specialized in mathematics. Also, when the user has a study plan for English, the guidance unit can provide a lesson video specialized in English. Also, when the user has a study plan for history, the guidance unit can provide a lesson video specialized in history. This makes it possible to provide a lesson video specialized in a specific field. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's study plan into the generation AI and cause the generation AI to provide a lesson video specialized in a specific field.

[0057] The guidance unit can automatically suggest the next lesson video to be viewed according to the user's learning progress. The guidance unit automatically suggests the next lesson video to be viewed according to the user's learning progress, for example, using a generation AI. For example, when the user completes a specific math unit, the guidance unit suggests a lesson video for the next unit. Also, when the user understands a specific English grammar, the guidance unit can suggest a lesson video for the next grammar. Also, when the user studies a specific historical period, the guidance unit can suggest a lesson video for the next period. This makes it possible to suggest the next lesson video according to the learning progress. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's learning progress data into the generation AI and cause the generation AI to execute the suggestion of the next lesson video to be viewed.

[0058] The guidance unit can provide related reference materials and links according to the content of the lesson video. The guidance unit can provide related reference materials and links according to the content of the lesson video, for example, using a generation AI. For example, the guidance unit can provide links to related reference books and websites for a math lesson video. The guidance unit can also provide links to related grammar books and online resources for an English lesson video. The guidance unit can also provide links to related documentaries and articles for a history lesson video. This makes it possible to provide related reference materials and links according to the content of the lesson video. Some or all of the above-mentioned processing in the guidance unit can be performed, for example, using AI, or can be performed without using AI. For example, the guidance unit can input the content of the lesson video into the generation AI and cause the generation AI to provide related reference materials and links.

[0059] The guidance unit can present the viewing history and evaluation of other users based on the content of the lesson video. The guidance unit presents the viewing history and evaluation of other users based on the content of the lesson video, for example, using a generation AI. For example, the guidance unit presents the viewing history and evaluation of other users for a math lesson video. The guidance unit can also present the viewing history and evaluation of other users for an English lesson video. The guidance unit can also present the viewing history and evaluation of other users for a history lesson video. This makes it possible to present the viewing history and evaluation of other users. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the guidance unit can input the content of the lesson video to the generation AI and cause the generation AI to execute the presentation of the viewing history and evaluation of other users.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The planner can adjust the way the lesson plan progresses based on the user's learning style. For example, for a user with a visual learning style, a plan can be created that includes more visual materials. For a user with an auditory learning style, a plan can be created that includes more audio materials and podcasts. Furthermore, for a user with a tactile learning style, a plan can be created that includes more practical exercises and hands-on activities. This allows the way the lesson plan progresses to be adjusted to suit the user's learning style.

[0062] The answering section can provide relevant examples and case studies for a user's question. For example, relevant examples and case studies can be provided for a math problem. Also, relevant examples and case studies can be provided for a question about English grammar. Furthermore, relevant examples and case studies can be provided for a question about history. This makes it possible to provide relevant examples and case studies according to the content of the question.

[0063] The creation unit can create questions to strengthen specific weak points based on the user's past answer history. For example, similar questions can be created based on questions that the user got wrong in the past. Also, a question set can be created that includes many questions related to areas that the user was weak in in the past. Furthermore, a question set can be created that includes few questions related to areas that the user was strong in in the past. This makes it possible to create questions to strengthen specific weak points.

[0064] The reflection unit can monitor the user's learning progress in real time and instantly adjust the learning plan as necessary. For example, if the user is progressing faster than planned, the learning plan can be adjusted ahead of schedule. Also, if the user is falling behind schedule, the learning plan can be adjusted more slowly. Furthermore, if the user is struggling in a particular area, the learning plan can be instantly adjusted to focus on that area. This makes it possible to monitor the learning progress in real time and instantly adjust the learning plan.

[0065] The guidance unit can provide lesson videos specialized in a specific field based on the user's study plan. For example, if the user has a mathematics study plan, lesson videos specialized in mathematics can be provided. Also, if the user has an English study plan, lesson videos specialized in English can be provided. Furthermore, if the user has a history study plan, lesson videos specialized in history can be provided. This makes it possible to provide lesson videos specialized in a specific field.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The planning unit creates a study plan tailored to the user's goals. Using a generation AI, the planning unit can create a study plan based on the user's past learning history and test results. For example, the planning unit generates an optimal study plan taking into account the user's current learning situation and the time it takes to achieve the goal. Step 2: The answering unit answers questions from students based on the learning plan created by the planning unit. The answering unit can use generative AI to answer questions using natural language processing technology. For example, if a user has a question while studying, it provides an appropriate answer to that question. Step 3: The creation unit creates questions based on the questions answered by the answering unit. The creation unit can use a generation AI to adjust the difficulty of the questions according to the user's level of understanding. For example, the creation unit can automatically create questions according to the user's learning status and provide them to the user. Step 4: The reflection unit reflects the degree of understanding based on the results of the questions created by the creation unit. The reflection unit can use the generation AI to evaluate the user's learning level and reflect it in the next study plan. For example, if there is a lack of understanding in a specific area, a study plan focusing on that area will be created. Step 5: The guidance unit guides the user to appropriate lesson videos based on the level of understanding reflected by the reflection unit. The guidance unit can provide appropriate lesson videos and reference materials based on the user's study plan using the generation AI. For example, the guidance unit guides the user to lesson videos on a specific math unit or audio files for English listening practice.

[0068] (Example 2) The learning support system according to the embodiment of the present invention is a learning support system for students and working adults who are aiming to obtain qualifications. This learning support system is a system that creates a learning plan according to the user's goals, answers questions from students, creates questions to determine the learning level, reflects the level of understanding in future learning plans, and introduces appropriate lesson videos, etc. For example, the learning support system creates a learning plan according to the user's goals. For example, the generation AI creates a learning plan according to the goals of each user, such as a high school student aiming to take a university entrance exam or a working adult aiming to take a qualification exam. At this time, the user's current learning situation and the period until the goal is achieved are taken into consideration. Next, the learning support system answers questions from students. If the user has a question during learning, the generation AI provides an appropriate answer to the question. For example, detailed answers are provided to specific questions, such as how to solve a math problem or an explanation of English grammar. Furthermore, the learning support system creates questions and determines the learning level. The generation AI automatically creates questions according to the user's learning situation and provides them to the user. When the user solves the problems, the generation AI determines the learning level based on the results. For example, the user's level of understanding is evaluated as a result of solving a math problem and reflected in the next learning plan. The learning support system reflects the level of understanding in future study plans. The generation AI automatically adjusts the next study plan based on the user's learning level. For example, if there is a lack of understanding in a particular area, a study plan focusing on that area will be created. Finally, the learning support system guides users to appropriate lesson videos, etc. The generation AI provides appropriate lesson videos and reference materials based on the user's study plan. For example, it guides users to lesson videos on a specific math unit or audio files for English listening practice. This system allows people living in rural areas to receive high-quality education, correcting regional disparities and contributing to local communities. For example, high school students in rural areas can take classes at famous prep schools in urban areas, which is expected to improve their academic ability. In addition, working adults who are aiming to obtain qualifications can study efficiently between work. This allows people living in rural areas to receive high-quality education, correcting regional disparities and contributing to local communities.

[0069] The learning support system according to the embodiment includes a planning unit, an answering unit, a creating unit, a reflecting unit, and a guidance unit. The planning unit makes a learning plan according to the user's goal. The planning unit can make a learning plan based on the user's past learning history and test results, for example, using a generating AI. For example, the planning unit generates an optimal learning plan in consideration of the user's current learning situation and the period until the goal is achieved. The answering unit answers questions from students based on the learning plan made by the planning unit. The answering unit can use a generating AI, for example, to answer questions using natural language processing technology. For example, when the user has a question during learning, the answering unit provides an appropriate answer to the question. The creating unit creates questions based on the questions answered by the answering unit. The creating unit can adjust the difficulty of the questions according to the user's level of understanding, for example, using a generating AI. For example, the creating unit automatically creates questions according to the user's learning situation and provides them to the user. The reflecting unit reflects the level of understanding based on the results of the questions created by the creating unit. For example, the reflecting unit can use a generating AI to evaluate the user's learning level and reflect it in the next learning plan. For example, if the user lacks understanding in a particular field, the reflection unit creates a study plan that focuses on that field. The guidance unit guides the user to an appropriate lesson video based on the level of understanding reflected by the reflection unit. The guidance unit can provide appropriate lesson videos and reference materials based on the user's study plan, for example, using a generation AI. For example, the guidance unit guides the user to a lesson video related to a particular mathematics unit or an audio file for English listening practice. This allows the learning support system according to the embodiment to plan a study plan tailored to the user's goals, answer questions, create questions, reflect the user's level of understanding, and guide the user to lesson videos.

[0070] The planning unit creates a study plan according to the user's goal. For example, the planning unit can use the generation AI to create a study plan based on the user's past learning history and test results. Specifically, the generation AI analyzes the user's past learning data and identifies the user's learning progress and strengths and weaknesses. This generates an optimal study plan taking into account the user's current learning situation and the period until the goal is achieved. For example, the generation AI can create a plan to focus on problems and areas that the user was weak at in the past, while creating a plan centered on reviewing areas in which the user is good. In addition, the generation AI can flexibly adjust the study plan to suit the user's learning style and pace. For example, if the user is the type who studies intensively in a short period of time, a short-term intensive study plan can be created, and if the user is the type who studies little by little over a long period of time, a long-term study plan can be created. Furthermore, the generation AI can set the study plan in stages, taking into account the period until the user achieves the goal. For example, if the period until the goal is achieved is set to several months, the goals and study contents to be achieved for each month can be specifically indicated. This allows the user to always grasp his / her own study progress and efficiently study toward the achievement of the goal.

[0071] The answering section answers questions from students based on the learning plan established by the planning section. The answering section can use, for example, a generation AI to answer questions using natural language processing technology. Specifically, the generation AI analyzes questions from users and understands the intent and content of the questions. For example, if a user asks a question about a math problem, the generation AI can analyze the content of the problem and provide an appropriate solution and explanation. The generation AI can also provide more appropriate answers by taking into account the user's past question history and learning situation. For example, if a user has asked a similar question in the past, the generation AI can provide more detailed explanations and additional information by referring to past answers to that question. Furthermore, the generation AI can adjust the difficulty and detail of the answer according to the user's level of understanding. For example, if the user is a beginner, the generation AI can provide basic concepts and simple explanations, and if the user is an advanced learner, the generation AI can provide more advanced explanations and application examples. This allows the answering section to support the user's learning and resolve doubts, thereby improving the efficiency of learning.

[0072] The creation unit creates questions based on the questions answered by the answer unit. The creation unit can adjust the difficulty of the questions according to the user's level of understanding, for example, by using a generation AI. Specifically, the generation AI analyzes the user's past learning data and question history in the answer unit to evaluate the user's level of understanding. This makes it possible to automatically create questions according to the user's level of understanding and provide them to the user. For example, if the user lacks understanding in a particular field, basic questions related to that field are created and practice is provided for the user to deepen their understanding. In addition, if the user shows a high level of understanding in a particular field, application questions and advanced questions related to that field can be created to further deepen the user's understanding. Furthermore, the generation AI can adjust the frequency and difficulty of the questions according to the user's learning pace and goals. For example, if the user studies intensively in a short period of time, many questions can be asked in a short period of time to quickly evaluate the user's level of understanding. On the other hand, if the user studies little by little over a long period of time, questions can be asked in stages over a long period of time to continuously evaluate the user's level of understanding. This allows the creation unit to provide questions according to the user's learning situation and improve the user's level of understanding.

[0073] The reflection unit reflects the degree of understanding based on the results of the questions created by the creation unit. The reflection unit can use, for example, the generation AI to evaluate the user's learning level and reflect it in the next study plan. Specifically, the generation AI analyzes the results of the questions answered by the user and evaluates the user's level of understanding. For example, if the user shows a high rate of correct answers in a specific field, the generation AI can evaluate the user's level of understanding in that field as being high, and the next study plan can reduce the study of that field. On the other hand, if the user shows a low rate of correct answers in a specific field, the generation AI can evaluate the user's level of understanding in that field as being low, and the next study plan can increase the study of that field. Furthermore, the generation AI can create a long-term study plan taking into account the user's learning history and past evaluation results. For example, the generation AI can create a study plan to regularly review areas that the user was weak in in the past. The generation AI can also flexibly adjust the study plan to suit the user's learning pace and goals. For example, if the user wants to achieve a goal in a short period of time, the generation AI can create a plan to study intensively in a short period of time, and if the user wants to study little by little over a long period of time, the generation AI can create a long-term study plan. This allows the reflection unit to accurately evaluate the user's level of understanding and reflect the evaluation in the next study plan, thereby improving the user's study efficiency.

[0074] The guidance unit guides the user to an appropriate lesson video based on the level of understanding reflected by the reflection unit. The guidance unit can provide the user with appropriate lesson videos and reference materials based on the user's learning plan, for example, using a generation AI. Specifically, the generation AI analyzes the user's learning plan and level of understanding, and selects the most suitable lesson videos and reference materials for the user. For example, if the user has a low level of understanding of a particular unit of mathematics, lesson videos and explanatory materials related to that unit can be provided. In addition, if the user needs English listening practice, audio files and videos for listening practice can be provided. Furthermore, the generation AI can customize lesson videos and reference materials according to the user's learning style and preferences. For example, if the user prefers visual learning, lesson videos that make extensive use of figures and graphs can be provided, and if the user prefers auditory learning, lesson videos that focus on audio commentary can be provided. This allows the guidance unit to maximize the user's learning efficiency and support effective learning. Furthermore, the guidance unit can collect user feedback and continuously improve the quality of the lesson videos and reference materials provided. For example, if the user provides evaluations and comments on the lesson videos, the content and format of the lesson videos can be improved based on the feedback. This allows the guidance section to always provide the latest and most appropriate study content, and effectively support the user's studies.

[0075] The planning unit can create a study plan based on the user's past learning history and test results. The planning unit creates a study plan based on the user's past learning history and test results, for example. For example, the planning unit generates an optimal study plan taking into account the user's past test results, study time, study content, etc. This makes it possible to create a study plan based on the user's past learning history and test results. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's past learning history and test results into the generation AI and cause the generation AI to generate a study plan.

[0076] The answering unit can answer questions using natural language processing technology. The answering unit answers questions using natural language processing technology, for example, using a generation AI. For example, when a user has a question while studying, the answering unit provides an appropriate answer to the question. For example, the answering unit provides a detailed answer to a specific question, such as how to solve a math problem or an explanation of English grammar. This makes it possible to provide an appropriate answer to a question by using natural language processing technology. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input a user's question to the generation AI and have the generation AI execute an answer to the question.

[0077] The creation unit can adjust the difficulty of the problem according to the user's level of understanding. The creation unit adjusts the difficulty of the problem according to the user's level of understanding, for example, using a generation AI. For example, the creation unit automatically creates problems according to the user's learning situation and provides them to the user. For example, the creation unit determines the learning level based on the result of the user solving the problem and reflects it in the next learning plan. This makes it possible to adjust the difficulty of the problem according to the user's level of understanding. 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 user's learning situation to the generation AI and causes the generation AI to create the problem.

[0078] The reflection unit can evaluate the user's learning level and reflect it in the next learning plan. The reflection unit can use, for example, a generation AI to evaluate the user's learning level and reflect it in the next learning plan. For example, if there is a lack of understanding in a specific field, the reflection unit creates a learning plan that focuses on that field. This makes it possible to evaluate the user's learning level and reflect it in the next learning plan. A part or all of the above-mentioned processing in the reflection unit can be performed, for example, using AI or can be performed without using AI. For example, the reflection unit can input the user's learning level into the generation AI and cause the generation AI to generate the next learning plan.

[0079] The guidance unit can provide appropriate lesson videos and reference materials based on the user's study plan. The guidance unit provides appropriate lesson videos and reference materials based on the user's study plan, for example, using a generation AI. For example, the guidance unit guides the user to lesson videos on a specific math unit, audio files for English listening practice, and the like. This makes it possible to provide lesson videos and reference materials based on the user's study plan. Some or all of the above-mentioned processing in the guidance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the guidance unit can input the user's study plan into the generation AI and cause the generation AI to perform guidance on appropriate lesson videos and reference materials.

[0080] The planning unit can estimate the user's emotions and adjust the progress speed of the study plan based on the estimated user's emotions. The planning unit, for example, uses a generation AI to estimate the user's emotions and adjust the progress speed of the study plan based on the estimated user's emotions. For example, when the user is feeling stressed, the planning unit slows down the progress speed of the study plan and adjusts it so that the user can relax. Also, when the user is relaxed, the planning unit sets the progress speed of the study plan to the normal speed, so that the user can proceed with the study efficiently. Also, when the user is in a hurry, the planning unit speeds up the progress speed of the study plan so that the user can achieve the goal in a short period of time. This makes it possible to adjust the progress speed of the study plan based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the planning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the progress speed of the learning plan.

[0081] The planning unit can create a plan tailored to a specific learning style based on the user's past learning history and test results. The planning unit, for example, uses a generation AI to create a plan tailored to a specific learning style (visual, auditory, tactile) based on the user's past learning history and test results. For example, if the user has a visual learning style, the planning unit creates a learning plan that includes a lot of visual materials. Also, if the user has an auditory learning style, the planning unit can create a learning plan that includes a lot of audio materials and podcasts. Also, if the user has a tactile learning style, the planning unit can create a learning plan that includes a lot of practical exercises and hands-on activities. This makes it possible to create a learning plan tailored to a specific learning style. Some or all of the above-mentioned processing in the planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the planning unit can input the user's learning style data into the generation AI and cause the generation AI to generate a learning plan.

[0082] The planning unit can propose a study time period in consideration of the user's life rhythm and daily schedule. The planning unit proposes an optimal study time period in consideration of the user's life rhythm and daily schedule, for example, using a generation AI. For example, if the user has a morning-type life rhythm, the planning unit makes a plan to concentrate study in the morning hours. Also, if the user has a night-type life rhythm, the planning unit can make a plan to concentrate study in the evening hours. Also, if the user has an irregular schedule, the planning unit can propose a flexible study time period and proceed with study according to the user's convenience. This makes it possible to propose an optimal study time period based on the user's life rhythm and daily schedule. Part or all of the above-mentioned processing in the planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the planning unit can input the user's life rhythm data to the generation AI and cause the generation AI to execute the proposal of a study time period.

[0083] The planning unit can estimate the user's emotions and adjust the contents of the study plan based on the estimated user's emotions. The planning unit, for example, uses a generation AI to estimate the user's emotions and adjust the contents of the study plan based on the estimated user's emotions. For example, when the user is feeling stressed, the planning unit makes a study plan including relaxing content. Also, when the user is relaxed, the planning unit can make a plan including normal study content. Also, when the user is in a hurry, the planning unit can make a study plan that focuses on important content. This makes it possible to adjust the contents of the study plan based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the planning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the planning unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the contents of the study plan.

[0084] The planning unit can incorporate region-specific learning resources into the plan, taking into account the geographical location information of the user. The planning unit, for example, uses a generation AI to incorporate region-specific learning resources into the plan, taking into account the geographical location information of the user. For example, if the user lives in an urban area, the planning unit creates a plan that includes many urban learning resources. Also, if the user lives in a rural area, the planning unit can create a plan that includes many regional learning resources. Also, if the user lives overseas, the planning unit can incorporate learning resources that match the language and culture of that area into the plan. This makes it possible to create a learning plan that incorporates region-specific learning resources. Part or all of the above-mentioned processing in the planning unit may be performed, for example, using AI, or may be performed without using AI. For example, the planning unit can input the user's geographical location information into the generation AI and cause the generation AI to incorporate the learning resources.

[0085] The planning unit can analyze the user's social media activities and add related learning topics to the plan. The planning unit can, for example, use a generation AI to analyze the user's social media activities and add related learning topics to the plan. For example, the planning unit can add topics in which the user is interested on social media to the learning plan. The planning unit can also reflect information obtained from education-related accounts followed by the user in the learning plan. The planning unit can also incorporate topics of online communities in which the user participates into the learning plan. This makes it possible to add learning topics based on social media activities. Some or all of the above-mentioned processing in the planning unit can be performed, for example, using AI or can be performed without using AI. For example, the planning unit can input the user's social media activity data to the generation AI and cause the generation AI to add learning topics.

[0086] The answering unit can estimate the user's emotion and adjust the tone and expression of the answer based on the estimated user's emotion. The answering unit can estimate the user's emotion using, for example, a generation AI, and adjust the tone and expression of the answer based on the estimated user's emotion. For example, the answering unit can provide an answer in a calm tone when the user is nervous. Also, the answering unit can provide an answer in a friendly tone when the user is relaxed. Also, the answering unit can provide a concise and quick answer when the user is in a hurry. This makes it possible to adjust the tone and expression of the answer based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the tone and expression of the answer.

[0087] The answering unit can generate a plurality of answer patterns according to the content of the question and provide the user with options. The answering unit can generate a plurality of answer patterns according to the content of the question using, for example, a generation AI and provide the user with options. For example, the answering unit can provide a plurality of answer patterns showing different solutions to a math problem. The answering unit can also provide an answer pattern using different example sentences to a question about English grammar. The answering unit can also provide an answer pattern from a different perspective to a question about history. This makes it possible to provide a plurality of answer patterns according to the content of the question. A part or all of the above-mentioned processing in the answering unit can be performed using, for example, an AI or can be performed without using an AI. For example, the answering unit can input the content of the question to the generation AI and cause the generation AI to generate a plurality of answer patterns.

[0088] The answering unit can adjust the level of detail of the answer according to the difficulty of the question. The answering unit adjusts the level of detail of the answer according to the difficulty of the question, for example, by using a generation AI. For example, the answering unit provides a concise answer to an easy question. Furthermore, the answering unit can provide an answer including a detailed explanation to a question of medium difficulty. Furthermore, the answering unit can provide a detailed step-by-step answer to a difficult question. This makes it possible to adjust the level of detail of the answer according to the difficulty of the question. A part or all of the above-mentioned processing in the answering unit may be performed, for example, by using an AI, or may be performed without using an AI. For example, the answering unit can input the difficulty data of the question to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0089] The answering unit can estimate the user's emotion and adjust the order of the answers based on the estimated user's emotion. The answering unit can estimate the user's emotion using, for example, a generation AI, and adjust the order of the answers based on the estimated user's emotion. For example, the answering unit provides answers in order from easy questions when the user is nervous. Also, the answering unit can answer questions regardless of the difficulty when the user is relaxed. Also, the answering unit can provide answers in order from important questions when the user is in a hurry. This makes it possible to adjust the order of answers based on the user'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 is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the order of the answers.

[0090] The answering unit can provide related reference materials and links according to the content of the question. The answering unit can provide related reference materials and links according to the content of the question, for example, using a generation AI. For example, the answering unit can provide links to related reference books and websites for mathematics questions. Also, the answering unit can provide links to related grammar books and online resources for English questions. Also, the answering unit can provide links to related documentaries and articles for history questions. This makes it possible to provide related reference materials and links according to the content of the question. Some or all of the above-mentioned processing in the answering unit can be performed, for example, using AI or can be performed without using AI. For example, the answering unit can input the content of the question to the generation AI and cause the generation AI to provide related reference materials and links.

[0091] The answering unit can present similar questions and their answers from other users based on the content of the question. The answering unit, for example, uses a generation AI to present similar questions and their answers from other users based on the content of the question. For example, the answering unit presents an answer to a math question when another user asks the same question. Also, the answering unit can present an answer to an English question when another user asks the same question about grammar. Also, the answering unit can present an answer to a history question when another user asks a question about the same era. This makes it possible to present similar questions and their answers from other users. A part or all of the above-mentioned processing in the answering unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the answering unit can input the content of the question to the generation AI and cause the generation AI to execute the presentation of similar questions and their answers.

[0092] The creation unit can estimate the user's emotion and adjust the difficulty of the problem based on the estimated user's emotion. The creation unit, for example, uses a generation AI to estimate the user's emotion and adjust the difficulty of the problem based on the estimated user's emotion. For example, the creation unit creates a problem set including many easy problems when the user is stressed. Also, the creation unit can create a problem set including problems of normal difficulty when the user is relaxed. Also, the creation unit can create a problem set focusing on important problems when the user is in a hurry. This makes it possible to adjust the difficulty of the problem based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the difficulty of the problem.

[0093] The creation unit can create questions to strengthen a specific weak point based on the user's past answer history. The creation unit creates questions to strengthen a specific weak point based on the user's past answer history, for example, using a generation AI. For example, the creation unit creates similar questions based on questions that the user got wrong in the past. The creation unit can also create a question set that includes many questions related to areas that the user was weak in in the past. The creation unit can also create a question set that includes few questions related to areas that the user was good at in the past. This makes it possible to create questions to strengthen a specific weak point. 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 the user's past answer history into the generation AI and cause the generation AI to create questions.

[0094] The creation unit can adjust the question format to suit the user's learning style. The creation unit adjusts the question format (multiple choice, written, practical) to suit the user's learning style, for example, using a generation AI. For example, if the user has a visual learning style, the creation unit creates a question set that includes many multiple choice questions. Also, if the user has an auditory learning style, the creation unit can create a question set that includes many written questions. Also, if the user has a tactile learning style, the creation unit can create a question set that includes many practical questions. This makes it possible to adjust the question format to suit the user's learning style. A part 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 can input the user's learning style data to the generation AI and cause the generation AI to adjust the question format.

[0095] The creation unit can estimate the user's emotion and adjust the question order based on the estimated user's emotion. The creation unit, for example, uses a generation AI to estimate the user's emotion and adjust the question order based on the estimated user's emotion. For example, the creation unit presents questions in order from easy to difficult when the user is nervous. Furthermore, the creation unit can present questions regardless of the difficulty level when the user is relaxed. Furthermore, the creation unit can present questions in order from important to important when the user is in a hurry. This makes it possible to adjust the question order based on the user'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 is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the question order.

[0096] The creation unit can provide related examples and case studies according to the content of the problem. The creation unit can provide related examples and case studies according to the content of the problem, for example, using a generation AI. For example, the creation unit provides related examples and case studies for mathematics problems. The creation unit can also provide related examples and case studies for English problems. The creation unit can also provide related examples and case studies for history problems. This makes it possible to provide related examples and case studies according to the content of the problem. 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 the content of the problem to the generation AI and cause the generation AI to provide related examples and case studies.

[0097] The creation unit can present example answers of other users based on the content of the problem. The creation unit presents example answers of other users based on the content of the problem, for example, using a generation AI. For example, the creation unit presents example answers of other users to a math problem. The creation unit can also present example answers of other users to an English problem. The creation unit can also present example answers of other users to a history problem. This makes it possible to present example answers of other users. 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 the content of the problem to the generation AI and cause the generation AI to present example answers of other users.

[0098] The reflection unit can estimate the user's emotion and adjust the evaluation criteria of the understanding level based on the estimated user's emotion. The reflection unit can estimate the user's emotion using, for example, a generation AI, and adjust the evaluation criteria of the understanding level based on the estimated user's emotion. For example, the reflection unit sets the evaluation criteria of the understanding level to be loose when the user is stressed. Furthermore, the reflection unit can apply normal evaluation criteria when the user is relaxed. Furthermore, the reflection unit can set evaluation criteria that focus on important points when the user is in a hurry. This makes it possible to adjust the evaluation criteria of the understanding level based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the evaluation criteria of the understanding level.

[0099] The reflection unit can evaluate the level of understanding in a specific field in detail based on the user's answer result. The reflection unit, for example, uses a generation AI to evaluate the level of understanding in a specific field in detail based on the user's answer result. For example, the reflection unit evaluates the level of understanding in a specific unit based on the mathematics answer result. The reflection unit can also evaluate the level of understanding of grammar and vocabulary based on the English answer result. The reflection unit can also evaluate the level of understanding of a specific era or event based on the history answer result. This makes it possible to evaluate the level of understanding in a specific field in detail. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's answer result to the generation AI and cause the generation AI to execute the evaluation of the level of understanding.

[0100] The reflection unit can monitor the user's learning progress in real time and adjust the learning plan immediately as necessary. The reflection unit can monitor the user's learning progress in real time using, for example, a generation AI and adjust the learning plan immediately as necessary. For example, if the user is progressing faster than planned, the reflection unit can adjust the learning plan ahead of schedule. Also, if the user is behind schedule, the reflection unit can adjust the learning plan more loosely. Also, if the user is struggling in a particular area, the reflection unit can immediately adjust the learning plan to one that focuses on that area. This makes it possible to monitor the learning progress in real time and immediately adjust the learning plan. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's learning progress data into the generation AI and cause the generation AI to adjust the learning plan.

[0101] The reflection unit can estimate the user's emotion and adjust the feedback method of the understanding level based on the estimated user's emotion. The reflection unit can estimate the user's emotion using, for example, a generation AI, and adjust the feedback method of the understanding level based on the estimated user's emotion. For example, when the user is nervous, the reflection unit provides a method including a lot of positive feedback. Also, when the user is relaxed, the reflection unit can provide a normal feedback method. Also, when the user is in a hurry, the reflection unit can provide a simple and quick feedback method. This makes it possible to adjust the feedback method of the understanding level based on the user'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 is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the feedback method.

[0102] The reflection unit can suggest related additional learning resources based on the user's answer result. The reflection unit, for example, uses a generation AI to suggest related additional learning resources based on the user's answer result. For example, the reflection unit can suggest related reference books and online resources based on the mathematics answer result. The reflection unit can also suggest related grammar books and listening materials based on the English answer result. The reflection unit can also suggest related documentaries and articles based on the history answer result. This makes it possible to suggest additional learning resources based on the answer result. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's answer result to the generation AI and cause the generation AI to execute the suggestion of additional learning resources.

[0103] The reflection unit can compare the user's learning progress with other users and evaluate the relative level of understanding. The reflection unit can compare the user's learning progress with other users, for example, using a generation AI, and evaluate the relative level of understanding. For example, the reflection unit can compare the user's mathematics progress with other users and evaluate the relative level of understanding. The reflection unit can also compare the user's English progress with other users and evaluate the relative level of understanding. The reflection unit can also compare the user's history progress with other users and evaluate the relative level of understanding. This makes it possible to evaluate the relative level of understanding by comparison with other users. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reflection unit can input the user's learning progress data to the generation AI and cause the generation AI to execute an evaluation of the relative level of understanding.

[0104] The guidance unit can estimate the user's emotion and adjust the selection criteria for the lesson video to be guided based on the estimated user's emotion. The guidance unit can estimate the user's emotion using, for example, a generation AI and adjust the selection criteria for the lesson video to be guided based on the estimated user's emotion. For example, when the user is stressed, the guidance unit selects a lesson video with relaxing content. Also, when the user is relaxed, the guidance unit can select a normal lesson video. Also, when the user is in a hurry, the guidance unit can select a lesson video that focuses on important points. This makes it possible to adjust the selection criteria for the lesson video based on the user's emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the selection criteria for the lesson video.

[0105] The guidance unit can provide a lesson video specialized in a specific field based on the user's study plan. The guidance unit can provide a lesson video specialized in a specific field based on the user's study plan, for example, using a generation AI. For example, when the user has a study plan for mathematics, the guidance unit can provide a lesson video specialized in mathematics. Also, when the user has a study plan for English, the guidance unit can provide a lesson video specialized in English. Also, when the user has a study plan for history, the guidance unit can provide a lesson video specialized in history. This makes it possible to provide a lesson video specialized in a specific field. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's study plan into the generation AI and cause the generation AI to provide a lesson video specialized in a specific field.

[0106] The guidance unit can automatically suggest the next lesson video to be viewed according to the user's learning progress. The guidance unit automatically suggests the next lesson video to be viewed according to the user's learning progress, for example, using a generation AI. For example, when the user completes a specific math unit, the guidance unit suggests a lesson video for the next unit. Also, when the user understands a specific English grammar, the guidance unit can suggest a lesson video for the next grammar. Also, when the user studies a specific historical period, the guidance unit can suggest a lesson video for the next period. This makes it possible to suggest the next lesson video according to the learning progress. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's learning progress data into the generation AI and cause the generation AI to execute the suggestion of the next lesson video to be viewed.

[0107] The guidance unit can estimate the user's emotion and adjust the display order of the lesson videos based on the estimated user's emotion. The guidance unit can estimate the user's emotion using, for example, a generation AI, and adjust the display order of the lesson videos based on the estimated user's emotion. For example, when the user is nervous, the guidance unit displays the lesson videos in order starting from easy content. Furthermore, when the user is relaxed, the guidance unit can display the lesson videos regardless of the difficulty level. Furthermore, when the user is in a hurry, the guidance unit can display the lesson videos in order starting from important content. This makes it possible to adjust the display order of the lesson videos based on the user's emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the lesson videos.

[0108] The guidance unit can provide related reference materials and links according to the content of the lesson video. The guidance unit can provide related reference materials and links according to the content of the lesson video, for example, using a generation AI. For example, the guidance unit can provide links to related reference books and websites for a math lesson video. The guidance unit can also provide links to related grammar books and online resources for an English lesson video. The guidance unit can also provide links to related documentaries and articles for a history lesson video. This makes it possible to provide related reference materials and links according to the content of the lesson video. Some or all of the above-mentioned processing in the guidance unit can be performed, for example, using AI, or can be performed without using AI. For example, the guidance unit can input the content of the lesson video into the generation AI and cause the generation AI to provide related reference materials and links.

[0109] The guidance unit can present the viewing history and evaluation of other users based on the content of the lesson video. The guidance unit presents the viewing history and evaluation of other users based on the content of the lesson video, for example, using a generation AI. For example, the guidance unit presents the viewing history and evaluation of other users for a math lesson video. The guidance unit can also present the viewing history and evaluation of other users for an English lesson video. The guidance unit can also present the viewing history and evaluation of other users for a history lesson video. This makes it possible to present the viewing history and evaluation of other users. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the guidance unit can input the content of the lesson video to the generation AI and cause the generation AI to execute the presentation of the viewing history and evaluation of other users.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The planner can adjust the way the lesson plan progresses based on the user's learning style. For example, for a user with a visual learning style, a plan can be created that includes more visual materials. For a user with an auditory learning style, a plan can be created that includes more audio materials and podcasts. Furthermore, for a user with a tactile learning style, a plan can be created that includes more practical exercises and hands-on activities. This allows the way the lesson plan progresses to be adjusted to suit the user's learning style.

[0112] The answering section can provide relevant examples and case studies for a user's question. For example, relevant examples and case studies can be provided for a math problem. Also, relevant examples and case studies can be provided for a question about English grammar. Furthermore, relevant examples and case studies can be provided for a question about history. This makes it possible to provide relevant examples and case studies according to the content of the question.

[0113] The creation unit can create questions to strengthen specific weak points based on the user's past answer history. For example, similar questions can be created based on questions that the user got wrong in the past. Also, a question set can be created that includes many questions related to areas that the user was weak in in the past. Furthermore, a question set can be created that includes few questions related to areas that the user was strong in in the past. This makes it possible to create questions to strengthen specific weak points.

[0114] The reflection unit can monitor the user's learning progress in real time and instantly adjust the learning plan as necessary. For example, if the user is progressing faster than planned, the learning plan can be adjusted ahead of schedule. Also, if the user is falling behind schedule, the learning plan can be adjusted more slowly. Furthermore, if the user is struggling in a particular area, the learning plan can be instantly adjusted to focus on that area. This makes it possible to monitor the learning progress in real time and instantly adjust the learning plan.

[0115] The guidance unit can provide lesson videos specialized in a specific field based on the user's study plan. For example, if the user has a mathematics study plan, lesson videos specialized in mathematics can be provided. Also, if the user has an English study plan, lesson videos specialized in English can be provided. Furthermore, if the user has a history study plan, lesson videos specialized in history can be provided. This makes it possible to provide lesson videos specialized in a specific field.

[0116] The planning unit can estimate the user's emotions and adjust the progress speed of the study plan based on the estimated user's emotions. For example, if the user is feeling stressed, the progress speed of the study plan is slowed down to allow the user to relax. Also, if the user is relaxed, the progress speed of the study plan is set to normal, allowing the user to study efficiently. Furthermore, if the user is in a hurry, the progress speed of the study plan is increased to allow the user to achieve the goal in a short period of time. This makes it possible to adjust the progress speed of the study plan based on the user's emotions.

[0117] The answering unit can estimate the user's emotions and adjust the tone and expression of the answer based on the estimated user's emotions. For example, if the user is nervous, the answer can be provided in a calm tone. If the user is relaxed, the answer can be provided in a friendly tone. Furthermore, if the user is in a hurry, a concise and quick answer can be provided. This makes it possible to adjust the tone and expression of the answer based on the user's emotions.

[0118] The creation unit can estimate the user's emotions and adjust the difficulty of the questions based on the estimated user's emotions. For example, if the user is stressed, a question set including many easy questions can be created. Also, if the user is relaxed, a question set including questions of normal difficulty can be created. Furthermore, if the user is in a hurry, a question set focusing on important questions can be created. This makes it possible to adjust the difficulty of the questions based on the user's emotions.

[0119] The reflection unit can estimate the user's emotions and adjust the evaluation criteria for the degree of understanding based on the estimated user's emotions. For example, if the user is stressed, the evaluation criteria for the degree of understanding can be set to be loose. If the user is relaxed, normal evaluation criteria can be applied. Furthermore, if the user is in a hurry, evaluation criteria that focus on important points can be set. This makes it possible to adjust the evaluation criteria for the degree of understanding based on the user's emotions.

[0120] The guidance unit can estimate the user's emotions and adjust the selection criteria for the lesson videos to be guided based on the estimated user's emotions. For example, if the user is feeling stressed, a lesson video with relaxing content can be selected. If the user is relaxed, a regular lesson video can be selected. Furthermore, if the user is in a hurry, a lesson video that focuses on important points can be selected. This makes it possible to adjust the selection criteria for lesson videos based on the user's emotions.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The planning unit creates a study plan tailored to the user's goals. Using a generation AI, the planning unit can create a study plan based on the user's past learning history and test results. For example, the planning unit generates an optimal study plan taking into account the user's current learning situation and the time it takes to achieve the goal. Step 2: The answering unit answers questions from students based on the learning plan created by the planning unit. The answering unit can use generative AI to answer questions using natural language processing technology. For example, if a user has a question while studying, it provides an appropriate answer to that question. Step 3: The creation unit creates questions based on the questions answered by the answering unit. The creation unit can use a generation AI to adjust the difficulty of the questions according to the user's level of understanding. For example, the creation unit can automatically create questions according to the user's learning status and provide them to the user. Step 4: The reflection unit reflects the degree of understanding based on the results of the questions created by the creation unit. The reflection unit can use the generation AI to evaluate the user's learning level and reflect it in the next study plan. For example, if there is a lack of understanding in a specific area, a study plan focusing on that area will be created. Step 5: The guidance unit guides the user to appropriate lesson videos based on the level of understanding reflected by the reflection unit. The guidance unit can provide appropriate lesson videos and reference materials based on the user's study plan using the generation AI. For example, the guidance unit guides the user to lesson videos on a specific math unit or audio files for English listening practice.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements including the above-mentioned planning unit, answering unit, creation unit, reflection unit, and guidance unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the planning unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The answering unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The creation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reflection unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the smart device 14 or 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-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0142] Each of the multiple elements including the above-mentioned planning unit, answering unit, creation unit, reflection unit, and guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the planning unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The answering unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The creation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The reflection unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the smart glasses 214 or 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-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[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 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.

[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 (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).

[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] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements including the above-mentioned planning unit, answering unit, creation unit, reflection unit, and guidance unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the planning unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The answering unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The creation unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The reflection unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the headset type terminal 314 or 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-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements including the above-mentioned planning unit, answering unit, creation unit, reflection unit, and guidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the planning unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The answering unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The creation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The reflection unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The guidance unit is realized, for example, by the control unit 46A of the robot 414 or 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-mentioned example, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Appendix 1) A planning section that creates a learning plan tailored to the user's goals; an answering unit that answers questions from students based on the learning plan established by the planning unit; a creation unit that creates questions based on the questions answered by the answering unit; a reflection unit that reflects a degree of understanding based on the results of the questions created by the creation unit; A guidance unit that guides the user through a video lesson based on the level of understanding reflected by the reflection unit. A system characterized by: (Appendix 2) The planning unit Create a study plan based on the user's past learning history and test results 2. The system of claim 1, (Appendix 3) The answering section: Answer questions using natural language processing technology 2. The system of claim 1, (Appendix 4) The creation unit is Adjust the difficulty of the questions according to the user's level of understanding 2. The system of claim 1, (Appendix 5) The reflection unit is Evaluate the user's learning level and reflect it in the next learning plan 2. The system of claim 1, (Appendix 6) The guide unit is Providing appropriate lesson videos and reference materials based on the user's learning plan 2. The system of claim 1, (Appendix 7) The planning unit Inferring a user's emotions and adjusting the progress rate of the learning plan based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The planning unit Create a plan tailored to a user's specific learning style based on their past learning history and test results 2. The system of claim 1, (Appendix 9) The planning unit Suggest study times based on the user's daily schedule and lifestyle 2. The system of claim 1, (Appendix 10) The planning unit Inferring user emotions and adjusting the content of the study plan based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The planning unit Consider users' geographic location and incorporate location-specific learning resources into your planning 2. The system of claim 1, (Appendix 12) The planning unit Analyze users' social media activity and add relevant learning topics to your plan 2. The system of claim 1, (Appendix 13) The answering section: Infer user sentiment and adjust the tone and wording of responses based on the inferred sentiment 2. The system of claim 1, (Appendix 14) The answering section: Depending on the content of the question, multiple answer patterns are generated and users are given a choice 2. The system of claim 1, (Appendix 15) The answering section: Adjust the level of detail in your answers depending on the difficulty of the question 2. The system of claim 1, (Appendix 16) The answering section: Infer user sentiment and adjust answer order based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The answering section: Depending on the question, provide relevant references and links 2. The system of claim 1, (Appendix 18) The answering section: Based on the content of your question, it will suggest similar questions and answers from other users 2. The system of claim 1, (Appendix 19) The creation unit is Estimate the user's emotions and adjust the difficulty of the questions based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The creation unit is Create questions to improve specific weak areas based on the user's past answer history 2. The system of claim 1, (Appendix 21) The creation unit is Adapt the question format to suit your users' learning style 2. The system of claim 1, (Appendix 22) The creation unit is Infer the user's emotions and adjust the order of questions based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The creation unit is Provide relevant examples and case studies depending on the problem 2. The system of claim 1, (Appendix 24) The creation unit is Suggest solutions provided by other users based on the content of the question 2. The system of claim 1, (Appendix 25) The reflection unit is Inferring a user's emotions and adjusting an evaluation criterion for understanding based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The reflection unit is Based on the user's answers, we provide a detailed assessment of their understanding in a specific area 2. The system of claim 1, (Appendix 27) The reflection unit is Monitor users' learning progress in real time and instantly adjust their learning plans if necessary 2. The system of claim 1, (Appendix 28) The reflection unit is To estimate a user's emotion and adjust a feedback method of understanding based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The reflection unit is Suggest related additional learning resources based on the user's answers 2. The system of claim 1, (Appendix 30) The reflection unit is Compare your progress with others to assess your relative understanding 2. The system of claim 1, (Appendix 31) The guide unit is Estimate the user's emotions and adjust the selection criteria for the lesson videos to be guided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 32) The guide unit is Providing specialized video lessons based on the user's learning plan 2. The system of claim 1, (Appendix 33) The guide unit is Automatically suggests the next lesson video to watch based on the user's learning progress 2. The system of claim 1, (Appendix 34) The guide unit is Estimating user emotions and adjusting the display order of lesson videos based on the estimated user emotions 2. The system of claim 1, (Appendix 35) The guide unit is Provide related reference materials and links according to the content of the lesson video 2. The system of claim 1, (Appendix 36) The guide unit is Presents other users' viewing history and ratings based on the content of the lesson video 2. The system of claim 1, [Explanation of symbols]

[0195] 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 planning section that creates a learning plan tailored to the user's goals; an answering unit that answers questions from students based on the learning plan established by the planning unit; a creation unit that creates questions based on the questions answered by the answering unit; a reflection unit that reflects a degree of understanding based on the results of the questions created by the creation unit; A guidance unit that guides the user through a video lesson based on the level of understanding reflected by the reflection unit. A system characterized by:

2. The planning unit Create a study plan based on the user's past learning history and test results 2. The system of claim 1 .

3. The answering section: Answer questions using natural language processing technology 2. The system of claim 1 .

4. The creation unit is Adjust the difficulty of the questions according to the user's level of understanding 2. The system of claim 1 .

5. The reflection unit is Evaluate the user's learning level and reflect it in the next learning plan 2. The system of claim 1 .

6. The guide unit is Providing appropriate lesson videos and reference materials based on the user's learning plan 2. The system of claim 1 .

7. The planning unit Inferring a user's emotions and adjusting the progress rate of the learning plan based on the estimated user's emotions 2. The system of claim 1 .

8. The planning unit Create a plan tailored to a user's specific learning style based on their past learning history and test results 2. The system of claim 1 .

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