System

Abstract

An object of a system according to an embodiment is to provide learning content in an optimal format for a learner.SOLUTION: A system includes a registration unit, a designation unit, a generation unit, and a provision unit. The registration unit registers learning contents. The designation unit designates a format or a design based on the learning content registered by the registration unit. The generation unit generates learning content on the basis of the information designated by the designation unit. The provision unit provides the learning content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide learning content in a format that is optimal for each individual learner, and there is a risk that the learning effect will not be fully realized.

[0005] The system according to the embodiment aims to provide learning content to learners in a format that is most suitable for them. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a designation unit, a generation unit, and a provision unit. The registration unit registers learning content. The designation unit designates a format or design based on the learning content registered by the registration unit. The generation unit generates learning content based on the information designated by the designation unit. The provision unit provides the learning content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning content in a format that is most suitable for the learner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention allows users to register learning content, specify their own format and design through an image generation AI, and proceed with learning using content that is most easily understood by the user. In the learning support system, a user registers learning content and specifies their preferred format and design. The generation AI then analyzes this information and generates optimal learning content for the user. This content is created based on the user-specified format and design, allowing the user to proceed with learning in a format that is most easily understood by the user. For example, in the learning support system, a user registers learning content. For example, the learning support system inputs the subjects or topics the user wants to study. For example, specific content such as differential calculus in mathematics or Japanese history in history can be registered. This information is input to the generation AI. Next, the learning support system allows the user to specify their preferred format and design. For example, if the user wants to study in a game format or an anime-style design, the user can specify this based on their preferences. This information is also input to the generation AI. Next, the learning support system uses the generation AI to analyze the input learning content, format, and design information and generate optimal learning content for the user. For example, if learning calculus in a game format, a generative AI could generate content that presents calculus problems in the form of a game quest, progressing the game as the user answers them. Similarly, if learning Japanese history in an anime-style design, a generative AI could generate content in which anime characters explain important events in Japanese history. This allows the learning support system to provide learning content tailored to the user's preferences, improving learning efficiency. For example, even if a subject is difficult, learning in a format or design of the user's choice can encourage interest and engagement. Furthermore, using visually easy-to-understand content can help deepen understanding. Furthermore, the learning support system can use generative AI to monitor the user's learning progress and update content as needed. For example, if a user is struggling with a particular topic, the generative AI can generate supplementary content related to that topic and provide it to the user.This allows users to study at their own pace. This allows the learning support system to proceed with learning in a way that is easiest for the user. For example, learning efficiency improves when users use learning content that suits their preferences. For example, even if a subject is difficult, they can study it with interest by studying it in their preferred format or design. Furthermore, using visually easy-to-understand content makes it easier to deepen understanding.

[0029] A learning assistance system according to an embodiment includes a registration unit, a designation unit, a generation unit, and a provision unit. The registration unit registers learning content. The learning content includes, but is not limited to, subjects and topics such as mathematics, science, and history. The registration unit inputs, for example, subjects and topics that a user wants to study. For example, specific content such as differential and integral calculus in mathematics or Japanese history in history can be registered. The designation unit specifies a format or design based on the learning content registered by the registration unit. Formats and designs include, but are not limited to, text format, visual format, and interactive format. The designation unit specifies, for example, a user's preferred format or design. For example, a user can specify a format or design based on their preference, such as a game format or an anime-style design. The generation unit uses a generation AI to generate learning content based on the information specified by the designation unit. The generation AI generates the learning content using technologies such as natural language processing, image generation, and speech synthesis. The generation unit analyzes the input learning content, format, and design information to generate learning content optimal for the user. For example, if a user is learning calculus in a game format, the generation AI generates content in which calculus problems are presented in the form of a game quest, and the user progresses through the game as they answer them. Furthermore, if a user is learning Japanese history in an anime-style design, the generation AI generates content in which anime characters explain important events in Japanese history. The provision unit provides the learning content generated by the generation unit. The provision unit provides the learning content using means such as an online platform, a mobile app, or email. The provision unit provides the generated learning content to the user, for example, allowing the user to advance their learning. This allows the learning support system according to the embodiment to allow the user to advance their learning in a format that is easiest for them. For example, by using learning content tailored to the user's preferences, learning efficiency can be improved. For example, even if a user is not good at a subject, they can become interested in studying it by studying it in a format or design they like.In addition, using visually easy-to-understand content makes it easier to deepen understanding.

[0030] The generation unit analyzes the learning content using a generation AI and generates learning content based on the learning content. The generation unit analyzes the learning content using a generation AI and generates learning content based on the learning content. The generation AI generates learning content using technologies such as natural language processing, image generation, and speech synthesis. For example, the generation AI analyzes mathematical calculus and generates content in which calculus problems are presented in the form of a game quest, and the game progresses as the user answers them. The generation AI also analyzes Japanese history and generates content in which anime characters explain important events in Japanese history. This allows the generation AI to generate optimal learning content based on the learning content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without a generation AI. For example, the generation unit can generate learning content using a generation AI model that inputs learning content and outputs learning content.

[0031] The generation unit generates game-style learning content based on the learning content using a generation AI. The generation unit generates game-style learning content based on the learning content using the generation AI. The generation AI generates the learning content using technologies such as natural language processing, image generation, and speech synthesis. For example, the generation AI analyzes mathematical calculus and presents calculus problems in the form of a game quest, generating content in which the user progresses through the game as they answer them. The generation AI also analyzes Japanese history and generates content that allows users to learn about important events in Japanese history in a game format. This allows users to enjoy learning by generating game-style learning content. Some or all of the above-described processing by the generation unit may be performed using, for example, a generation AI, or may be performed without a generation AI. For example, the generation unit may generate learning content using a generation AI model that inputs learning content and outputs game-style learning content.

[0032] The generation unit generates anime-style learning content based on the learning content using a generation AI. The generation unit generates anime-style learning content based on the learning content using a generation AI. The generation AI generates learning content using technologies such as natural language processing, image generation, and voice synthesis. For example, the generation AI analyzes Japanese history and generates content in which anime characters explain important events in Japanese history. The generation AI also analyzes mathematical calculus and generates learning content with an anime-style design. By generating anime-style learning content, this makes it easier to understand visually. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without a generation AI. For example, the generation unit can generate learning content using a generation AI model that inputs learning content and outputs anime-style learning content.

[0033] The providing unit provides the generated learning content to a user. The providing unit provides the learning content generated by the generating unit to a user. The providing unit provides the learning content using means such as an online platform, a mobile app, or email. For example, the providing unit provides the generated learning content to a user, thereby allowing the user to advance their learning. As a result, by providing the generated learning content to a user, the user can advance their learning. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may provide the learning content to a user using an AI model that uses the learning content as input.

[0034] The provision unit monitors the user's learning progress and updates the content as necessary. The provision unit monitors the user's learning progress and updates the content as necessary. Monitoring includes, but is not limited to, methods for tracking learning progress and means for collecting data. For example, the provision unit monitors the user's learning progress, and if the user struggles with a particular topic, it generates supplemental content related to that topic and provides it to the user. This allows the user to study at their own pace by updating the content according to the user's learning progress. Some or all of the above-described processing by the provision unit may be performed, for example, using AI or without AI. For example, the provision unit may update the learning content using an AI model that uses learning progress data as input and updates the content.

[0035] The registration unit analyzes the user's past learning history and selects a registration method based on the user's past learning history. The registration unit analyzes the user's past learning history and selects the optimal registration method. The past learning history includes, but is not limited to, study time, learning content, and grades. For example, the registration unit preferentially suggests a registration method (audio, text, etc.) that the user has frequently used in the past. The registration unit can also suggest the optimal registration method for a specific time period based on the user's past learning history. The registration unit can also analyze the user's past learning history and suggest the most efficient registration method. In this way, the optimal registration method can be suggested by analyzing the user's past learning history. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can select a registration method using an AI model that inputs past learning history data and selects a registration method.

[0036] The registration unit filters the learning content based on the user's current learning progress and areas of interest when registering the learning content. The registration unit filters the learning content based on the user's current learning progress and areas of interest when registering the learning content. Examples of filtering include, but are not limited to, evaluation criteria for learning progress and methods for identifying areas of interest. For example, the registration unit preferentially registers related learning content based on the user's current learning progress. The registration unit can also suggest interesting learning content based on the user's areas of interest. The registration unit can also filter optimal learning content by combining the user's learning progress and areas of interest. This allows optimal learning content to be registered based on the user's learning progress and areas of interest. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can filter the learning content using an AI model that inputs learning progress data and area of ​​interest data and filters the learning content.

[0037] The registration unit selects a registration means based on the user's input method when registering learning content. The registration unit selects a registration means based on the user's input method when registering learning content. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the registration unit may preferentially suggest voice input. Furthermore, if the user prefers text input, the registration unit may preferentially suggest text input. Furthermore, if the user prefers image input, the registration unit may preferentially suggest image input. This enables efficient registration of learning content by selecting the optimal registration means according to the user's input method. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit may select a registration means using an AI model that receives input method data as input and selects a registration means.

[0038] When registering learning content, the registration unit prioritizes registering highly relevant content taking into account the user's geographical location information. When registering learning content, the registration unit prioritizes registering highly relevant content taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the registration unit prioritizes registering learning content related to that area. Furthermore, if the user is traveling, the registration unit can also suggest learning content related to the travel destination. Furthermore, the registration unit can filter optimal learning content based on the user's geographical location information. In this way, highly relevant learning content can be prioritized and registered by taking the user's geographical location information into account. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can filter the learning content using an AI model that uses geographical location information data as input and filters the learning content.

[0039] The registration unit analyzes the user's social media activity and registers the related learning content when registering the learning content. The registration unit analyzes the user's social media activity and registers the related learning content when registering the learning content. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the registration unit suggests learning content related to topics in which the user has shown interest on social media. The registration unit can also analyze the content of the user's social media posts and register the related learning content. The registration unit can also suggest related learning content based on the activities of the user's friends on social media. In this way, related learning content can be registered by analyzing the user's social media activity. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can filter the learning content using an AI model that uses social media data as input and filters the learning content.

[0040] The registration unit customizes the registration method by reflecting the user's past feedback when registering learning content. The registration unit customizes the registration method by reflecting the user's past feedback when registering learning content. Feedback includes, but is not limited to, questionnaires, reviews, and comments, for example. For example, the registration unit suggests an optimal registration method based on feedback previously provided by the user. The registration unit can also preferentially suggest a specific registration method based on the user's past feedback. The registration unit can also customize the registration method by reflecting the user's feedback. In this way, the optimal registration method can be suggested by reflecting the user's past feedback. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can customize the registration method using an AI model that uses feedback data as input and customizes the registration method.

[0041] When specifying a format or design, the specification unit adjusts the level of detail of the specification based on the importance of the learning content. When specifying a format or design, the specification unit adjusts the level of detail of the specification based on the importance of the learning content. The importance level includes, but is not limited to, the difficulty of the learning content and the degree of achievement of the learning goal. For example, the specification unit suggests a detailed format and design for important learning content. The specification unit can also suggest a concise format and design for less important learning content. The specification unit can also adjust the level of detail of the format and design according to the importance of the learning content. This enables efficient learning by adjusting the level of detail of the format and design according to the importance of the learning content. Some or all of the above-described processing by the specification unit may be performed using, for example, AI, or may be performed without using AI. For example, the specification unit can adjust the level of detail of the format and design using an AI model that inputs importance data of the learning content and adjusts the level of detail of the format and design.

[0042] The designation unit applies different designation algorithms depending on the category of the learning content when designating the format or design. The designation unit applies different designation algorithms depending on the category of the learning content when designating the format or design. Categories include, but are not limited to, by subject, by topic, by difficulty level, etc. For example, the designation unit may propose a format that emphasizes formulas and graphs for mathematics learning content. The designation unit may also propose a design that emphasizes timelines and maps for history learning content. The designation unit may also propose an optimal format and design depending on the category of the learning content. This makes it possible to propose an optimal format and design depending on the category of the learning content. Some or all of the above-described processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit may input learning content category data and design the format or design using an AI model that applies a designation algorithm.

[0043] The designation unit improves the accuracy of the designation by referring to the user's past designations when designating a format or design. The designation unit improves the accuracy of the designation by referring to the user's past designations when designating a format or design. Designation results include, but are not limited to, user satisfaction and learning effects. For example, the designation unit suggests an optimal designation method based on the user's past designations. The designation unit can also preferentially suggest specific formats and designs based on the user's past designations. The designation unit can also improve the accuracy of the designation by reflecting the user's past designations. In this way, the accuracy of the designation can be improved by referring to the user's past designations. Some or all of the above-described processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit can specify a format or design using an AI model that uses past designation result data as input and improves the accuracy of the designation.

[0044] When specifying a format or design, the designation unit determines the priority of the designation based on the submission date of the learning content. When specifying a format or design, the designation unit determines the priority of the designation based on the submission date of the learning content. The submission date includes, but is not limited to, a deadline or the end of the semester. For example, the designation unit may prioritize the designation of a format and design for learning content with an upcoming submission deadline. The designation unit may also postpone the designation of a format and design for learning content with a distant submission deadline. The designation unit may also adjust the priority of the designation based on the submission date of the learning content. This enables efficient learning by adjusting the priority of the designation based on the submission date of the learning content. Some or all of the above-described processing by the designation unit may be performed using, for example, AI, or may be performed without AI. For example, the designation unit may specify a format or design using an AI model that inputs submission date data and determines the priority of the designation.

[0045] When specifying a format or design, the designation unit adjusts the order of designation based on the relevance of the learning content. When specifying a format or design, the designation unit adjusts the order of designation based on the relevance of the learning content. Relevance includes, but is not limited to, for example, the relevance of the learning content and the topic relevance. For example, the designation unit prioritizes designating a format and design for highly relevant learning content. The designation unit can also postpone designating a format and design for less relevant learning content. The designation unit can also adjust the order of designation based on the relevance of the learning content. This enables efficient learning by adjusting the order of designation based on the relevance of the learning content. Some or all of the above-described processing in the designation unit may be performed, for example, using AI, or may be performed without using AI. For example, the designation unit can specify a format or design using an AI model that inputs relevance data and adjusts the order of designation.

[0046] The designation unit adjusts the use of specified terminology according to the user's level of expertise when designating a format or design. The designation unit adjusts the use of specified terminology according to the user's level of expertise when designating a format or design. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the designation unit may suggest a format that avoids terminology. If the user is an intermediate user, the designation unit may also suggest a design that uses a moderate amount of terminology. If the user is an advanced user, the designation unit may also suggest a format that uses a lot of terminology. This enables more effective learning by adjusting the use of specified terminology according to the user's level of expertise. Some or all of the above-described processing by the designation unit may be performed using, for example, AI, or may be performed without AI. For example, the designation unit may specify a format or design using an AI model that adjusts the use of terminology using expertise level data as input.

[0047] When generating learning content, the generation unit adjusts the level of detail of the generated content based on the importance of the learning content. When generating learning content, the generation unit adjusts the level of detail of the generated content based on the importance of the learning content. The level of detail includes, but is not limited to, the depth of information and the detail of the explanation. For example, the generation unit generates detailed content for important learning content. The generation unit can also generate concise content for less important learning content. The generation unit can also adjust the level of detail of the generated content based on the importance of the learning content. This enables efficient learning by adjusting the level of detail of the generated content based on the importance of the learning content. Some or all of the above-described processing by the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate learning content using an AI model that inputs importance data of the learning content and adjusts the level of detail of the generated content.

[0048] When generating learning content, the generation unit applies different generation algorithms depending on the category of the learning content. When generating learning content, the generation unit applies different generation algorithms depending on the category of the learning content. Categories include, but are not limited to, by subject, topic, difficulty level, etc. For example, the generation unit may generate content that emphasizes formulas and graphs for mathematics learning content. The generation unit may also generate content that emphasizes timelines and maps for history learning content. The generation unit may also apply an optimal generation algorithm depending on the category of the learning content. This enables efficient learning by applying an optimal generation algorithm depending on the category of the learning content. Some or all of the above-mentioned processing by the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate learning content using an AI model that receives learning content category data and applies a generation algorithm.

[0049] When generating study content, the generation unit improves the accuracy of generation by referring to the user's past generation results. When generating study content, the generation unit improves the accuracy of generation by referring to the user's past generation results. Generation results include, but are not limited to, user satisfaction and learning effect. For example, the generation unit suggests an optimal generation method based on content previously generated by the user. The generation unit can also preferentially suggest a specific generation method based on the user's past generation results. The generation unit can also improve the accuracy of generation by reflecting the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate study content using an AI model that uses past generation result data as input and improves generation accuracy.

[0050] When generating learning content, the generation unit determines generation priorities based on the submission dates of the learning content. When generating learning content, the generation unit determines generation priorities based on the submission dates of the learning content. Submission dates include, but are not limited to, deadlines and the end of the semester. For example, the generation unit prioritizes content generation for learning content with an upcoming submission deadline. The generation unit can also postpone content generation for learning content with a distant submission deadline. The generation unit can also adjust generation priorities based on the submission dates of the learning content. This enables efficient learning by adjusting generation priorities based on the submission dates of the learning content. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without a generation AI. For example, the generation unit can generate learning content using an AI model that uses submission date data as input and determines generation priorities.

[0051] When generating learning content, the generation unit adjusts the generation order based on the relevance of the learning content. When generating learning content, the generation unit adjusts the generation order based on the relevance of the learning content. Relevance includes, but is not limited to, the relevance of the learning content and the topic relevance. For example, the generation unit prioritizes generating content for highly relevant learning content. The generation unit can also postpone generating content for less relevant learning content. The generation unit can also adjust the generation order based on the relevance of the learning content. This enables efficient learning by adjusting the generation order based on the relevance of the learning content. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate learning content using an AI model that inputs relevance data and adjusts the generation order.

[0052] When generating learning content, the generation unit adjusts the use of technical terminology in the generated content according to the user's level of expertise. When generating learning content, the generation unit adjusts the use of technical terminology in the generated content according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the generation unit may generate content that avoids technical terminology. If the user is an intermediate user, the generation unit may also generate content that uses technical terminology moderately. If the user is an advanced user, the generation unit may also generate content that uses a lot of technical terminology. This enables more effective learning by adjusting the use of technical terminology in the generated content according to the user's level of expertise. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate learning content using an AI model that adjusts the use of technical terminology using expertise level data as input.

[0053] When providing learning content, the provision unit selects the optimal delivery method by referring to the user's past learning history. When providing learning content, the provision unit selects the optimal delivery method by referring to the user's past learning history. The learning history includes, but is not limited to, examples of study time, learning content, and grades. For example, the provision unit preferentially suggests delivery methods (e.g., videos, texts) that the user has previously preferred. The provision unit can also suggest the optimal delivery method for a specific time period based on the user's past learning history. The provision unit can also analyze the user's past learning history and suggest the most efficient delivery method. In this way, the optimal delivery method can be suggested by referring to the user's past learning history. Some or all of the above-described processing by the provision unit may be performed, for example, using AI, or may be performed without AI. For example, the provision unit can provide learning content using an AI model that inputs past learning history data and selects a delivery method.

[0054] When providing learning content, the provision unit customizes the content to be provided based on the user's current learning progress. When providing learning content, the provision unit customizes the content to be provided based on the user's current learning progress. Learning progress includes, but is not limited to, learning achievement level and progress status. For example, the provision unit prioritizes providing related learning content based on the user's current learning progress. The provision unit can also provide content with adjusted difficulty level according to the user's learning progress. The provision unit can also customize and provide optimal learning content based on the user's learning progress. This makes it possible to provide optimal learning content according to the user's learning progress. Some or all of the above-described processing by the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can provide learning content using an AI model that uses learning progress data as input and customizes the content to be provided.

[0055] The provision unit improves the delivery method by reflecting user feedback when providing learning content. The provision unit improves the delivery method by reflecting user feedback when providing learning content. Feedback includes, but is not limited to, questionnaires, reviews, and comments. For example, the provision unit suggests an optimal delivery method based on feedback provided by the user. The provision unit can also preferentially suggest a specific delivery method based on user feedback. The provision unit can also improve the delivery method by reflecting user feedback. In this way, the optimal delivery method can be suggested by reflecting user feedback. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide learning content using an AI model that uses feedback data as input and improves the delivery method.

[0056] The provision unit selects the optimal delivery method when providing learning content, taking into account the user's geographical location information. The provision unit selects the optimal delivery method when providing learning content, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the provision unit prioritizes providing learning content related to that area. Furthermore, if the user is traveling, the provision unit can also suggest learning content related to the user's travel destination. Furthermore, the provision unit can filter optimal learning content based on the user's geographical location information. This allows for providing highly relevant learning content by taking the user's geographical location information into account. Some or all of the above-described processing by the provision unit may be performed, for example, using AI, or may be performed without AI. For example, the provision unit may provide learning content using an AI model that inputs geographical location information and selects a delivery method.

[0057] The provision unit, when providing learning content, analyzes the user's social media activity to customize the content to be provided. The provision unit, when providing learning content, analyzes the user's social media activity to customize the content to be provided. Social media activity includes, but is not limited to, post content, number of likes, number of followers, etc. For example, the provision unit suggests learning content related to topics in which the user has shown interest on social media. The provision unit can also analyze the user's social media posts to provide related learning content. The provision unit can also suggest related learning content based on the activity of the user's friends on social media. In this way, related learning content can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide learning content using an AI model that uses social media data as input and customizes the content to be provided.

[0058] The provision unit customizes the delivery method by reflecting the user's past feedback when providing the learning content. The provision unit customizes the delivery method by reflecting the user's past feedback when providing the learning content. Feedback includes, but is not limited to, questionnaires, reviews, and comments. For example, the provision unit suggests an optimal delivery method based on feedback previously provided by the user. The provision unit can also preferentially suggest a specific delivery method based on the user's past feedback. The provision unit can also customize the delivery method by reflecting the user's feedback. In this way, the optimal delivery method can be suggested by reflecting the user's past feedback. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide the learning content using an AI model that uses feedback data as input and customizes the delivery method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The registration unit can also analyze the user's learning history and automate the registration of learning content based on past learning patterns. For example, if the user has studied during a specific time period in the past, new learning content can be automatically registered for that time period. Also, if the user has shown a high interest in a particular subject, new learning content related to that subject can be preferentially registered. Furthermore, it is possible to automatically register supplementary learning content for topics that the user has previously struggled with. This makes it possible to provide optimal learning content based on the user's learning history, improving learning efficiency.

[0061] The designation unit can also analyze the user's learning style and suggest the optimal format and design. For example, if the user prefers visual learning, visual content can be preferentially suggested. If the user prefers auditory learning, audio content can be suggested. Furthermore, if the user prefers interactive learning, interactive content can be suggested. This makes it possible to provide the optimal format and design according to the user's learning style, improving the effectiveness of learning.

[0062] The generator can also monitor the user's learning progress in real time and dynamically adjust the learning content according to the progress. For example, if the user has difficulty with a particular topic, it can automatically generate supplemental content related to that topic. Also, if the user quickly understands a particular topic, it can generate content to help the user move on to the next topic. It can also adjust the difficulty of the content according to the user's learning speed. This allows the system to provide optimal learning content according to the user's learning progress, improving learning efficiency.

[0063] The providing unit can also provide learning content taking into account the user's learning environment. For example, if the user is on the move, content optimized for mobile devices can be provided. If the user is studying at home, content optimized for large-screen devices can be provided. Furthermore, if the user is studying in a public place, quiet content without audio can be provided. This allows the unit to provide optimal learning content according to the user's learning environment, improving the effectiveness of learning.

[0064] The provision unit can also customize learning content based on the user's learning goals. For example, if the user's goal is to pass a specific exam, learning content related to that exam can be provided preferentially. Also, if the user's goal is to acquire a specific skill, learning content related to that skill can be provided. Furthermore, if the user's goal is to complete a specific project, learning content related to that project can be provided. This allows the provision of optimal learning content according to the user's learning goals, improving the effectiveness of learning.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The registration unit registers the learning content. Learning content includes subjects and topics such as mathematics, science, and history. Users can input the subjects and topics they want to study and register specific content. For example, differential calculus in mathematics or Japanese history in history can be registered. Step 2: The specification unit specifies a format or design based on the learning content registered by the registration unit. Formats and designs include text format, visual format, interactive format, etc. The user can specify their preferred format or design. For example, if they want to learn in a game format or with an anime-style design, they can specify it according to their preferences. Step 3: The generation unit uses the generation AI to generate learning content based on the information specified by the specification unit. The generation AI generates learning content using technologies such as natural language processing, image generation, and speech synthesis. For example, the generation AI analyzes the input learning content, format, and design information to generate learning content that is optimal for the user. Specifically, if learning mathematical calculus in a game format, the generation AI generates content in which calculus problems are presented in the form of game quests, and the game progresses as the user answers them. Similarly, if learning Japanese history in an anime-style design, the generation AI generates content in which anime characters explain important events in Japanese history. Step 4: The providing unit provides the learning content generated by the generating unit. The providing unit provides the learning content using means such as an online platform, a mobile app, or email. By providing the generated learning content to the user, the user can advance their learning. This allows the user to advance their learning in a way that is easiest for them, improving learning efficiency.

[0067] (Example 2) A learning support system according to an embodiment of the present invention allows users to register learning content, specify their own format and design through an image generation AI, and proceed with learning using content that is most easily understood by the user. In the learning support system, a user registers learning content and specifies their preferred format and design. The generation AI then analyzes this information and generates optimal learning content for the user. This content is created based on the user-specified format and design, allowing the user to proceed with learning in a format that is most easily understood by the user. For example, in the learning support system, a user registers learning content. For example, the learning support system inputs the subjects or topics the user wants to study. For example, specific content such as differential calculus in mathematics or Japanese history in history can be registered. This information is input to the generation AI. Next, the learning support system allows the user to specify their preferred format and design. For example, if the user wants to study in a game format or an anime-style design, the user can specify this based on their preferences. This information is also input to the generation AI. Next, the learning support system uses the generation AI to analyze the input learning content, format, and design information and generate optimal learning content for the user. For example, if learning calculus in a game format, a generative AI could generate content that presents calculus problems in the form of a game quest, progressing the game as the user answers them. Similarly, if learning Japanese history in an anime-style design, a generative AI could generate content in which anime characters explain important events in Japanese history. This allows the learning support system to provide learning content tailored to the user's preferences, improving learning efficiency. For example, even if a subject is difficult, learning in a format or design of the user's choice can encourage interest and engagement. Furthermore, using visually easy-to-understand content can help deepen understanding. Furthermore, the learning support system can use generative AI to monitor the user's learning progress and update content as needed. For example, if a user is struggling with a particular topic, the generative AI can generate supplementary content related to that topic and provide it to the user.This allows users to study at their own pace. This allows the learning support system to proceed with learning in a way that is easiest for the user. For example, learning efficiency improves when users use learning content that suits their preferences. For example, even if a subject is difficult, they can study it with interest by studying it in their preferred format or design. Furthermore, using visually easy-to-understand content makes it easier to deepen understanding.

[0068] A learning assistance system according to an embodiment includes a registration unit, a designation unit, a generation unit, and a provision unit. The registration unit registers learning content. The learning content includes, but is not limited to, subjects and topics such as mathematics, science, and history. The registration unit inputs, for example, subjects and topics that a user wants to study. For example, specific content such as differential and integral calculus in mathematics or Japanese history in history can be registered. The designation unit specifies a format or design based on the learning content registered by the registration unit. Formats and designs include, but are not limited to, text format, visual format, and interactive format. The designation unit specifies, for example, a user's preferred format or design. For example, a user can specify a format or design based on their preference, such as a game format or an anime-style design. The generation unit uses a generation AI to generate learning content based on the information specified by the designation unit. The generation AI generates the learning content using technologies such as natural language processing, image generation, and speech synthesis. The generation unit analyzes the input learning content, format, and design information to generate learning content optimal for the user. For example, if a user is learning calculus in a game format, the generation AI generates content in which calculus problems are presented in the form of a game quest, and the user progresses through the game as they answer them. Furthermore, if a user is learning Japanese history in an anime-style design, the generation AI generates content in which anime characters explain important events in Japanese history. The provision unit provides the learning content generated by the generation unit. The provision unit provides the learning content using means such as an online platform, a mobile app, or email. The provision unit provides the generated learning content to the user, for example, allowing the user to advance their learning. This allows the learning support system according to the embodiment to allow the user to advance their learning in a format that is easiest for them. For example, by using learning content tailored to the user's preferences, learning efficiency can be improved. For example, even if a user is not good at a subject, they can become interested in studying it by studying it in a format or design they like.In addition, using visually easy-to-understand content makes it easier to deepen understanding.

[0069] The generation unit analyzes the learning content using a generation AI and generates learning content based on the learning content. The generation unit analyzes the learning content using a generation AI and generates learning content based on the learning content. The generation AI generates learning content using technologies such as natural language processing, image generation, and speech synthesis. For example, the generation AI analyzes mathematical calculus and generates content in which calculus problems are presented in the form of a game quest, and the game progresses as the user answers them. The generation AI also analyzes Japanese history and generates content in which anime characters explain important events in Japanese history. This allows the generation AI to generate optimal learning content based on the learning content. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without a generation AI. For example, the generation unit can generate learning content using a generation AI model that inputs learning content and outputs learning content.

[0070] The generation unit generates game-style learning content based on the learning content using a generation AI. The generation unit generates game-style learning content based on the learning content using the generation AI. The generation AI generates the learning content using technologies such as natural language processing, image generation, and speech synthesis. For example, the generation AI analyzes mathematical calculus and presents calculus problems in the form of a game quest, generating content in which the user progresses through the game as they answer them. The generation AI also analyzes Japanese history and generates content that allows users to learn about important events in Japanese history in a game format. This allows users to enjoy learning by generating game-style learning content. Some or all of the above-described processing by the generation unit may be performed using, for example, a generation AI, or may be performed without a generation AI. For example, the generation unit may generate learning content using a generation AI model that inputs learning content and outputs game-style learning content.

[0071] The generation unit generates anime-style learning content based on the learning content using a generation AI. The generation unit generates anime-style learning content based on the learning content using a generation AI. The generation AI generates learning content using technologies such as natural language processing, image generation, and voice synthesis. For example, the generation AI analyzes Japanese history and generates content in which anime characters explain important events in Japanese history. The generation AI also analyzes mathematical calculus and generates learning content with an anime-style design. By generating anime-style learning content, this makes it easier to understand visually. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without a generation AI. For example, the generation unit can generate learning content using a generation AI model that inputs learning content and outputs anime-style learning content.

[0072] The providing unit provides the generated learning content to a user. The providing unit provides the learning content generated by the generating unit to a user. The providing unit provides the learning content using means such as an online platform, a mobile app, or email. For example, the providing unit provides the generated learning content to a user, thereby allowing the user to advance their learning. As a result, by providing the generated learning content to a user, the user can advance their learning. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may provide the learning content to a user using an AI model that uses the learning content as input.

[0073] The provision unit monitors the user's learning progress and updates the content as necessary. The provision unit monitors the user's learning progress and updates the content as necessary. Monitoring includes, but is not limited to, methods for tracking learning progress and means for collecting data. For example, the provision unit monitors the user's learning progress, and if the user struggles with a particular topic, it generates supplemental content related to that topic and provides it to the user. This allows the user to study at their own pace by updating the content according to the user's learning progress. Some or all of the above-described processing by the provision unit may be performed, for example, using AI or without AI. For example, the provision unit may update the learning content using an AI model that uses learning progress data as input and updates the content.

[0074] The registration unit estimates the user's emotions and adjusts the timing of registering the learning content based on the estimated user emotions. The registration unit estimates the user's emotions and adjusts the timing of registering the learning content based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the registration unit may encourage the user to register the learning content at a time when the user is able to relax. Furthermore, if the user is concentrating, the registration unit may also encourage the user to register the learning content at that time. Furthermore, if the user is tired, the registration unit may suggest registering the learning content after a break. This enables more effective learning by adjusting the timing of registering the learning content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can adjust the timing of registering learning content using an AI model that takes user emotional data as input and adjusts the timing of registering learning content.

[0075] The registration unit analyzes the user's past learning history and selects a registration method based on the user's past learning history. The registration unit analyzes the user's past learning history and selects the optimal registration method. The past learning history includes, but is not limited to, study time, learning content, and grades. For example, the registration unit preferentially suggests a registration method (audio, text, etc.) that the user has frequently used in the past. The registration unit can also suggest the optimal registration method for a specific time period based on the user's past learning history. The registration unit can also analyze the user's past learning history and suggest the most efficient registration method. In this way, the optimal registration method can be suggested by analyzing the user's past learning history. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can select a registration method using an AI model that inputs past learning history data and selects a registration method.

[0076] The registration unit filters the learning content based on the user's current learning progress and areas of interest when registering the learning content. The registration unit filters the learning content based on the user's current learning progress and areas of interest when registering the learning content. Examples of filtering include, but are not limited to, evaluation criteria for learning progress and methods for identifying areas of interest. For example, the registration unit preferentially registers related learning content based on the user's current learning progress. The registration unit can also suggest interesting learning content based on the user's areas of interest. The registration unit can also filter optimal learning content by combining the user's learning progress and areas of interest. This allows optimal learning content to be registered based on the user's learning progress and areas of interest. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can filter the learning content using an AI model that inputs learning progress data and area of ​​interest data and filters the learning content.

[0077] The registration unit selects a registration means based on the user's input method when registering learning content. The registration unit selects a registration means based on the user's input method when registering learning content. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the registration unit may preferentially suggest voice input. Furthermore, if the user prefers text input, the registration unit may preferentially suggest text input. Furthermore, if the user prefers image input, the registration unit may preferentially suggest image input. This enables efficient registration of learning content by selecting the optimal registration means according to the user's input method. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit may select a registration means using an AI model that receives input method data as input and selects a registration means.

[0078] The registration unit estimates the user's emotions and determines the priority of the learning content to be registered based on the estimated user emotions. The registration unit estimates the user's emotions and determines the priority of the learning content to be registered based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the registration unit may preferentially register relaxing content. Furthermore, if the user is concentrating, the registration unit may preferentially register difficult content. Furthermore, if the user is tired, the registration unit may preferentially register easy content. This enables more effective learning by prioritizing the learning content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the registration unit can input the user's emotional data and use an AI model that determines the priority of learning content to determine the priority of learning content.

[0079] When registering learning content, the registration unit prioritizes registering highly relevant content taking into account the user's geographical location information. When registering learning content, the registration unit prioritizes registering highly relevant content taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the registration unit prioritizes registering learning content related to that area. Furthermore, if the user is traveling, the registration unit can also suggest learning content related to the travel destination. Furthermore, the registration unit can filter optimal learning content based on the user's geographical location information. In this way, highly relevant learning content can be prioritized and registered by taking the user's geographical location information into account. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can filter the learning content using an AI model that uses geographical location information data as input and filters the learning content.

[0080] The registration unit analyzes the user's social media activity and registers the related learning content when registering the learning content. The registration unit analyzes the user's social media activity and registers the related learning content when registering the learning content. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the registration unit suggests learning content related to topics in which the user has shown interest on social media. The registration unit can also analyze the content of the user's social media posts and register the related learning content. The registration unit can also suggest related learning content based on the activities of the user's friends on social media. In this way, related learning content can be registered by analyzing the user's social media activity. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can filter the learning content using an AI model that uses social media data as input and filters the learning content.

[0081] The registration unit customizes the registration method by reflecting the user's past feedback when registering learning content. The registration unit customizes the registration method by reflecting the user's past feedback when registering learning content. Feedback includes, but is not limited to, questionnaires, reviews, and comments, for example. For example, the registration unit suggests an optimal registration method based on feedback previously provided by the user. The registration unit can also preferentially suggest a specific registration method based on the user's past feedback. The registration unit can also customize the registration method by reflecting the user's feedback. In this way, the optimal registration method can be suggested by reflecting the user's past feedback. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can customize the registration method using an AI model that uses feedback data as input and customizes the registration method.

[0082] The designation unit estimates the user's emotions and adjusts the format and design design method based on the estimated user emotions. The designation unit estimates the user's emotions and adjusts the format and design design method based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the designation unit may suggest a design with soft colors if the user is relaxed. The designation unit may also suggest a simple, highly visible format if the user is concentrating. The designation unit may also suggest a less visually stimulating design if the user is tired. This enables more effective learning by adjusting the format and design design method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the designation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the designation unit can adjust the method of designating the format or design using an AI model that takes user emotion data as input and adjusts the method of designating the format or design.

[0083] When specifying a format or design, the specification unit adjusts the level of detail of the specification based on the importance of the learning content. When specifying a format or design, the specification unit adjusts the level of detail of the specification based on the importance of the learning content. The importance level includes, but is not limited to, the difficulty of the learning content and the degree of achievement of the learning goal. For example, the specification unit suggests a detailed format and design for important learning content. The specification unit can also suggest a concise format and design for less important learning content. The specification unit can also adjust the level of detail of the format and design according to the importance of the learning content. This enables efficient learning by adjusting the level of detail of the format and design according to the importance of the learning content. Some or all of the above-described processing by the specification unit may be performed using, for example, AI, or may be performed without using AI. For example, the specification unit can adjust the level of detail of the format and design using an AI model that inputs importance data of the learning content and adjusts the level of detail of the format and design.

[0084] The designation unit applies different designation algorithms depending on the category of the learning content when designating the format or design. The designation unit applies different designation algorithms depending on the category of the learning content when designating the format or design. Categories include, but are not limited to, by subject, by topic, by difficulty level, etc. For example, the designation unit may propose a format that emphasizes formulas and graphs for mathematics learning content. The designation unit may also propose a design that emphasizes timelines and maps for history learning content. The designation unit may also propose an optimal format and design depending on the category of the learning content. This makes it possible to propose an optimal format and design depending on the category of the learning content. Some or all of the above-described processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit may input learning content category data and design the format or design using an AI model that applies a designation algorithm.

[0085] The designation unit improves the accuracy of the designation by referring to the user's past designations when designating a format or design. The designation unit improves the accuracy of the designation by referring to the user's past designations when designating a format or design. Designation results include, but are not limited to, user satisfaction and learning effects. For example, the designation unit suggests an optimal designation method based on the user's past designations. The designation unit can also preferentially suggest specific formats and designs based on the user's past designations. The designation unit can also improve the accuracy of the designation by reflecting the user's past designations. In this way, the accuracy of the designation can be improved by referring to the user's past designations. Some or all of the above-described processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit can specify a format or design using an AI model that uses past designation result data as input and improves the accuracy of the designation.

[0086] The designation unit estimates the user's emotion and adjusts the length of the format and design based on the estimated user emotion. The designation unit estimates the user's emotion and adjusts the length of the format and design based on the estimated user emotion. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the designation unit may suggest a longer format if the user is relaxed. The designation unit may also suggest a shorter format if the user is in a hurry. The designation unit may also suggest a concise design if the user is tired. This enables more effective learning by adjusting the length of the format and design according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the designation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the specification unit can adjust the length of the format or design using an AI model that takes user emotion data as input and adjusts the length of the format or design.

[0087] When specifying a format or design, the designation unit determines the priority of the designation based on the submission date of the learning content. When specifying a format or design, the designation unit determines the priority of the designation based on the submission date of the learning content. The submission date includes, but is not limited to, a deadline or the end of the semester. For example, the designation unit may prioritize the designation of a format and design for learning content with an upcoming submission deadline. The designation unit may also postpone the designation of a format and design for learning content with a distant submission deadline. The designation unit may also adjust the priority of the designation based on the submission date of the learning content. This enables efficient learning by adjusting the priority of the designation based on the submission date of the learning content. Some or all of the above-described processing by the designation unit may be performed using, for example, AI, or may be performed without AI. For example, the designation unit may specify a format or design using an AI model that inputs submission date data and determines the priority of the designation.

[0088] When specifying a format or design, the designation unit adjusts the order of designation based on the relevance of the learning content. When specifying a format or design, the designation unit adjusts the order of designation based on the relevance of the learning content. Relevance includes, but is not limited to, for example, the relevance of the learning content and the topic relevance. For example, the designation unit prioritizes designating a format and design for highly relevant learning content. The designation unit can also postpone designating a format and design for less relevant learning content. The designation unit can also adjust the order of designation based on the relevance of the learning content. This enables efficient learning by adjusting the order of designation based on the relevance of the learning content. Some or all of the above-described processing in the designation unit may be performed, for example, using AI, or may be performed without using AI. For example, the designation unit can specify a format or design using an AI model that inputs relevance data and adjusts the order of designation.

[0089] The designation unit adjusts the use of specified terminology according to the user's level of expertise when designating a format or design. The designation unit adjusts the use of specified terminology according to the user's level of expertise when designating a format or design. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the designation unit may suggest a format that avoids terminology. If the user is an intermediate user, the designation unit may also suggest a design that uses a moderate amount of terminology. If the user is an advanced user, the designation unit may also suggest a format that uses a lot of terminology. This enables more effective learning by adjusting the use of specified terminology according to the user's level of expertise. Some or all of the above-described processing by the designation unit may be performed using, for example, AI, or may be performed without AI. For example, the designation unit may specify a format or design using an AI model that adjusts the use of terminology using expertise level data as input.

[0090] The generation unit estimates the user's emotions and adjusts the presentation of the generated learning content based on the estimated user emotions. The generation unit estimates the user's emotions and adjusts the presentation of the generated learning content based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit generates a design with soft colors when the user is relaxed. The generation unit can also generate a simple, highly visible design when the user is concentrating. The generation unit can also generate a less visually stimulating design when the user is tired. This enables more effective learning by adjusting the presentation of the learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generator can adjust the presentation of the learning content using an AI model that takes user emotional data as input and adjusts the presentation of the learning content.

[0091] When generating learning content, the generation unit adjusts the level of detail of the generated content based on the importance of the learning content. When generating learning content, the generation unit adjusts the level of detail of the generated content based on the importance of the learning content. The level of detail includes, but is not limited to, the depth of information and the detail of the explanation. For example, the generation unit generates detailed content for important learning content. The generation unit can also generate concise content for less important learning content. The generation unit can also adjust the level of detail of the generated content based on the importance of the learning content. This enables efficient learning by adjusting the level of detail of the generated content based on the importance of the learning content. Some or all of the above-described processing by the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate learning content using an AI model that inputs importance data of the learning content and adjusts the level of detail of the generated content.

[0092] When generating learning content, the generation unit applies different generation algorithms depending on the category of the learning content. When generating learning content, the generation unit applies different generation algorithms depending on the category of the learning content. Categories include, but are not limited to, by subject, topic, difficulty level, etc. For example, the generation unit may generate content that emphasizes formulas and graphs for mathematics learning content. The generation unit may also generate content that emphasizes timelines and maps for history learning content. The generation unit may also apply an optimal generation algorithm depending on the category of the learning content. This enables efficient learning by applying an optimal generation algorithm depending on the category of the learning content. Some or all of the above-mentioned processing by the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate learning content using an AI model that receives learning content category data and applies a generation algorithm.

[0093] When generating study content, the generation unit improves the accuracy of generation by referring to the user's past generation results. When generating study content, the generation unit improves the accuracy of generation by referring to the user's past generation results. Generation results include, but are not limited to, user satisfaction and learning effect. For example, the generation unit suggests an optimal generation method based on content previously generated by the user. The generation unit can also preferentially suggest a specific generation method based on the user's past generation results. The generation unit can also improve the accuracy of generation by reflecting the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate study content using an AI model that uses past generation result data as input and improves generation accuracy.

[0094] The generation unit estimates the user's emotions and adjusts the length of the generated learning content based on the estimated user emotions. The generation unit estimates the user's emotions and adjusts the length of the generated learning content based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit generates longer content when the user is relaxed. The generation unit can also generate shorter content when the user is in a hurry. The generation unit can also generate concise content when the user is tired. This enables more effective learning by adjusting the length of the learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generator can adjust the length of the learning content using an AI model that takes user emotional data as input and adjusts the length of the learning content.

[0095] When generating learning content, the generation unit determines generation priorities based on the submission dates of the learning content. When generating learning content, the generation unit determines generation priorities based on the submission dates of the learning content. Submission dates include, but are not limited to, deadlines and the end of the semester. For example, the generation unit prioritizes content generation for learning content with an upcoming submission deadline. The generation unit can also postpone content generation for learning content with a distant submission deadline. The generation unit can also adjust generation priorities based on the submission dates of the learning content. This enables efficient learning by adjusting generation priorities based on the submission dates of the learning content. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without a generation AI. For example, the generation unit can generate learning content using an AI model that uses submission date data as input and determines generation priorities.

[0096] When generating learning content, the generation unit adjusts the generation order based on the relevance of the learning content. When generating learning content, the generation unit adjusts the generation order based on the relevance of the learning content. Relevance includes, but is not limited to, the relevance of the learning content and the topic relevance. For example, the generation unit prioritizes generating content for highly relevant learning content. The generation unit can also postpone generating content for less relevant learning content. The generation unit can also adjust the generation order based on the relevance of the learning content. This enables efficient learning by adjusting the generation order based on the relevance of the learning content. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate learning content using an AI model that inputs relevance data and adjusts the generation order.

[0097] When generating learning content, the generation unit adjusts the use of technical terminology in the generated content according to the user's level of expertise. When generating learning content, the generation unit adjusts the use of technical terminology in the generated content according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the generation unit may generate content that avoids technical terminology. If the user is an intermediate user, the generation unit may also generate content that uses technical terminology moderately. If the user is an advanced user, the generation unit may also generate content that uses a lot of technical terminology. This enables more effective learning by adjusting the use of technical terminology in the generated content according to the user's level of expertise. Some or all of the above-described processing by the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate learning content using an AI model that adjusts the use of technical terminology using expertise level data as input.

[0098] The providing unit estimates the user's emotions and adjusts the method of providing the learning content based on the estimated user emotions. The providing unit estimates the user's emotions and adjusts the method of providing the learning content based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the providing unit may provide content with a soft color design. If the user is concentrating, the providing unit may also provide content with a simple, highly visible design. If the user is tired, the providing unit may also provide content with a less visually stimulating design. This enables more effective learning by adjusting the method of providing the learning content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the provision unit can adjust the way the learning content is provided using an AI model that takes user emotional data as input and adjusts the way the learning content is provided.

[0099] When providing learning content, the provision unit selects the optimal delivery method by referring to the user's past learning history. When providing learning content, the provision unit selects the optimal delivery method by referring to the user's past learning history. The learning history includes, but is not limited to, examples of study time, learning content, and grades. For example, the provision unit preferentially suggests delivery methods (e.g., videos, texts) that the user has previously preferred. The provision unit can also suggest the optimal delivery method for a specific time period based on the user's past learning history. The provision unit can also analyze the user's past learning history and suggest the most efficient delivery method. In this way, the optimal delivery method can be suggested by referring to the user's past learning history. Some or all of the above-described processing by the provision unit may be performed, for example, using AI, or may be performed without AI. For example, the provision unit can provide learning content using an AI model that inputs past learning history data and selects a delivery method.

[0100] When providing learning content, the provision unit customizes the content to be provided based on the user's current learning progress. When providing learning content, the provision unit customizes the content to be provided based on the user's current learning progress. Learning progress includes, but is not limited to, learning achievement level and progress status. For example, the provision unit prioritizes providing related learning content based on the user's current learning progress. The provision unit can also provide content with adjusted difficulty level according to the user's learning progress. The provision unit can also customize and provide optimal learning content based on the user's learning progress. This makes it possible to provide optimal learning content according to the user's learning progress. Some or all of the above-described processing by the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can provide learning content using an AI model that uses learning progress data as input and customizes the content to be provided.

[0101] The provision unit improves the delivery method by reflecting user feedback when providing learning content. The provision unit improves the delivery method by reflecting user feedback when providing learning content. Feedback includes, but is not limited to, questionnaires, reviews, and comments. For example, the provision unit suggests an optimal delivery method based on feedback provided by the user. The provision unit can also preferentially suggest a specific delivery method based on user feedback. The provision unit can also improve the delivery method by reflecting user feedback. In this way, the optimal delivery method can be suggested by reflecting user feedback. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide learning content using an AI model that uses feedback data as input and improves the delivery method.

[0102] The providing unit estimates the user's emotions and determines the priority of providing learning content based on the estimated user emotions. The providing unit estimates the user's emotions and determines the priority of providing learning content based on the estimated user emotions. Emotion estimation includes, but is not limited to, techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is relaxed, the providing unit may prioritize providing relaxing content. Furthermore, if the user is concentrating, the providing unit may prioritize providing difficult content. Furthermore, if the user is tired, the providing unit may prioritize providing easy content. This enables more effective learning by determining the priority of providing learning content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit may provide learning content using an AI model that inputs user emotion data and determines the priority of provision.

[0103] The provision unit selects the optimal delivery method when providing learning content, taking into account the user's geographical location information. The provision unit selects the optimal delivery method when providing learning content, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the provision unit prioritizes providing learning content related to that area. Furthermore, if the user is traveling, the provision unit can also suggest learning content related to the user's travel destination. Furthermore, the provision unit can filter optimal learning content based on the user's geographical location information. This allows for providing highly relevant learning content by taking the user's geographical location information into account. Some or all of the above-described processing by the provision unit may be performed, for example, using AI, or may be performed without AI. For example, the provision unit may provide learning content using an AI model that inputs geographical location information and selects a delivery method.

[0104] The provision unit, when providing learning content, analyzes the user's social media activity to customize the content to be provided. The provision unit, when providing learning content, analyzes the user's social media activity to customize the content to be provided. Social media activity includes, but is not limited to, post content, number of likes, number of followers, etc. For example, the provision unit suggests learning content related to topics in which the user has shown interest on social media. The provision unit can also analyze the user's social media posts to provide related learning content. The provision unit can also suggest related learning content based on the activity of the user's friends on social media. In this way, related learning content can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide learning content using an AI model that uses social media data as input and customizes the content to be provided.

[0105] The provision unit customizes the delivery method by reflecting the user's past feedback when providing the learning content. The provision unit customizes the delivery method by reflecting the user's past feedback when providing the learning content. Feedback includes, but is not limited to, questionnaires, reviews, and comments. For example, the provision unit suggests an optimal delivery method based on feedback previously provided by the user. The provision unit can also preferentially suggest a specific delivery method based on the user's past feedback. The provision unit can also customize the delivery method by reflecting the user's feedback. In this way, the optimal delivery method can be suggested by reflecting the user's past feedback. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide the learning content using an AI model that uses feedback data as input and customizes the delivery method. === Hard Collateral 1-1 === For example, each of the multiple elements including the registration unit, designation unit, generation unit, and provision unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14, and the user registers the learning content. The designation unit is realized by the control unit 46A of the smart device 14, and the user specifies the format and design they prefer. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates learning content using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14, and provides the generated learning content to the user. === Hard Collateral 1-2 === For example, each of the multiple elements including the registration unit, designation unit, generation unit, and provision unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214, and the user registers the learning content. The designation unit is realized by the control unit 46A of the smart glasses 214, and the user designates the format and design they prefer. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates the learning content using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides the generated learning content to the user. === Hard Collateral 1-3 === For example, each of the multiple elements including the registration unit, designation unit, generation unit, and provision unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314, and the user registers the learning content. The designation unit is realized by the control unit 46A of the headset type terminal 314, and the user specifies the format and design they prefer. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates learning content using a generation AI. The provision unit is realized by the control unit 46A of the headset type terminal 314, and provides the generated learning content to the user. === Hard Collateral 1-4 === For example, each of the multiple elements including the registration unit, designation unit, generation unit, and provision unit is realized by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414, and the user registers the learning content. The designation unit is realized by the control unit 46A of the robot 414, and the user designates the format and design they prefer. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates learning content using a generation AI. The provision unit is realized by the control unit 46A of the robot 414, and provides the generated learning content to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The registration unit can also analyze the user's learning history and automate the registration of learning content based on past learning patterns. For example, if the user has studied during a specific time period in the past, new learning content can be automatically registered for that time period. Also, if the user has shown a high interest in a particular subject, new learning content related to that subject can be preferentially registered. Furthermore, it is possible to automatically register supplementary learning content for topics that the user has previously struggled with. This makes it possible to provide optimal learning content based on the user's learning history, improving learning efficiency.

[0108] The designation unit can also analyze the user's learning style and suggest the optimal format and design. For example, if the user prefers visual learning, visual content can be preferentially suggested. If the user prefers auditory learning, audio content can be suggested. Furthermore, if the user prefers interactive learning, interactive content can be suggested. This makes it possible to provide the optimal format and design according to the user's learning style, improving the effectiveness of learning.

[0109] The generator can also monitor the user's learning progress in real time and dynamically adjust the learning content according to the progress. For example, if the user has difficulty with a particular topic, it can automatically generate supplemental content related to that topic. Also, if the user quickly understands a particular topic, it can generate content to help the user move on to the next topic. It can also adjust the difficulty of the content according to the user's learning speed. This allows the system to provide optimal learning content according to the user's learning progress, improving learning efficiency.

[0110] The providing unit can also provide learning content taking into account the user's learning environment. For example, if the user is on the move, content optimized for mobile devices can be provided. If the user is studying at home, content optimized for large-screen devices can be provided. Furthermore, if the user is studying in a public place, quiet content without audio can be provided. This allows the unit to provide optimal learning content according to the user's learning environment, improving the effectiveness of learning.

[0111] The provision unit can also customize learning content based on the user's learning goals. For example, if the user's goal is to pass a specific exam, learning content related to that exam can be provided preferentially. Also, if the user's goal is to acquire a specific skill, learning content related to that skill can be provided. Furthermore, if the user's goal is to complete a specific project, learning content related to that project can be provided. This allows the provision of optimal learning content according to the user's learning goals, improving the effectiveness of learning.

[0112] The registration unit can also estimate the user's emotions and adjust the difficulty of the learning content based on the estimated emotions. For example, if the user is feeling stressed, learning content with low difficulty can be provided. On the other hand, if the user is relaxed, learning content with high difficulty can be provided. Furthermore, if the user is concentrating, learning content with high difficulty can be provided. This makes it possible to provide optimal learning content according to the user's emotions, improving the effectiveness of learning.

[0113] The designation unit can also estimate the user's emotions and adjust the presentation order of the learning content based on the estimated emotions. For example, if the user is tired, it can start with easy content and gradually increase the difficulty level. Alternatively, if the user is concentrating, it can start with more difficult content. Furthermore, if the user is relaxed, it can start with an interesting topic. This makes it possible to provide the optimal presentation order of learning content according to the user's emotions, improving the effectiveness of learning.

[0114] The generator can also estimate the user's emotions and adjust the format of the learning content based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxing visual content. If the user is concentrating, it can provide detailed text content. If the user is tired, it can provide short video content. This allows the generator to provide the optimal learning content format according to the user's emotions, improving the effectiveness of learning.

[0115] The provision unit can also estimate the user's emotions and adjust the timing of providing learning content based on the estimated emotions. For example, if the user is relaxed, the timing of providing learning content can be advanced. Also, if the user is feeling stressed, the timing of providing learning content can be delayed. Furthermore, if the user is concentrating, learning content can be provided continuously. This allows the timing of providing learning content to be optimized according to the user's emotions, improving the effectiveness of learning.

[0116] The feedback unit can also estimate the user's emotions and adjust the feedback method for the learning content based on the estimated emotions. For example, if the user is relaxed, positive feedback can be provided. If the user is feeling stressed, encouraging feedback can be provided. Furthermore, if the user is concentrating, feedback indicating specific areas for improvement can be provided. This makes it possible to provide the optimal feedback method according to the user's emotions, improving the effectiveness of learning.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The registration unit registers the learning content. Learning content includes subjects and topics such as mathematics, science, and history. Users can input the subjects and topics they want to study and register specific content. For example, differential calculus in mathematics or Japanese history in history can be registered. Step 2: The specification unit specifies a format or design based on the learning content registered by the registration unit. Formats and designs include text format, visual format, interactive format, etc. The user can specify their preferred format or design. For example, if they want to learn in a game format or with an anime-style design, they can specify it according to their preferences. Step 3: The generation unit uses the generation AI to generate learning content based on the information specified by the specification unit. The generation AI generates learning content using technologies such as natural language processing, image generation, and speech synthesis. For example, the generation AI analyzes the input learning content, format, and design information to generate learning content that is optimal for the user. Specifically, if learning mathematical calculus in a game format, the generation AI generates content in which calculus problems are presented in the form of game quests, and the game progresses as the user answers them. Similarly, if learning Japanese history in an anime-style design, the generation AI generates content in which anime characters explain important events in Japanese history. Step 4: The providing unit provides the learning content generated by the generating unit. The providing unit provides the learning content using means such as an online platform, a mobile app, or email. By providing the generated learning content to the user, the user can advance their learning. This allows the user to advance their learning in a way that is easiest for them, improving learning efficiency.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] 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.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 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 registration section for registering learning content; a designation unit that designates a format or design based on the learning content registered by the registration unit; a generation unit that generates study content based on the information specified by the specification unit; a provision unit that provides the study content generated by the generation unit. A system characterized by:

2. The generation unit Analyze learning content using generative AI and generate learning content based on the learning content 2. The system of claim 1.

3. The generation unit Generative AI generates game-style learning content based on learning content 2. The system of claim 1.

4. The generation unit Generative AI generates anime-style learning content based on learning content 2. The system of claim 1.

5. The providing unit Providing generated learning content to users 2. The system of claim 1.

6. The providing unit Monitor user progress and update content as needed 2. The system of claim 1.

7. The registration unit Estimates user emotions and adjusts the timing of learning content registration based on the estimated user emotions.

2. The system of claim 1.

8. The registration unit Analyze the user's past learning history and select a registration method based on the user's past learning history.

2. The system of claim 1.

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