System

Abstract

An object of the system according to the embodiment is to deepen understanding by displaying related entertainment contents when a learner solves problems.SOLUTION: A system includes a problem solving part and a content display part. The problem solving unit receives an answer of the user. The content display unit displays the entertainment content related to the answer received by the question answering 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] Conventional technology has the drawback that learners are limited to solving problems and lack opportunities to deepen their understanding through related entertainment content.

[0005] The system according to the embodiment aims to deepen a learner's understanding by displaying related entertainment content as the learner solves problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a question answering unit and a content display unit. The question answering unit accepts answers from users. The content display unit displays entertainment content related to the answers accepted by the question answering unit. [Effects of the Invention]

[0007] The system according to the embodiment allows learners to solve problems and thereby display related entertainment content, thereby deepening their understanding. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 app according to an embodiment of the present invention is a system that allows users to encounter related entertainment content by solving problems and deepen their understanding, thereby enabling users to understand the learning content in a real context and obtain a rich learning experience.

[0029] A learning app according to an embodiment includes a question answering unit and a content display unit. The question answering unit accepts answers from a user. For example, the question answering unit accepts answers in a text input format. The question answering unit can also accept answers in a multiple-choice format. The question answering unit can also accept answers in a voice input format. For example, the user may input an answer by voice, convert it into text, and accept it. The content display unit displays entertainment content related to the answer accepted by the question answering unit. For example, when a user answers a question about kanji or proverbs, information about novels or movies in which the answer is used is displayed. When a user answers a history question, manga related to the question can be displayed. The content display unit can also display entertainment content related to the answer in video format. For example, a trailer for a movie related to the answer is displayed. This allows the learning app according to an embodiment to allow the user to understand the learning content in a real context and have a rich learning experience.

[0030] The problem-answering unit uses a generation AI to analyze the user's answer history and recommend entertainment content optimal for each individual learning style. For example, the problem-answering unit uses a generation AI to perform a detailed analysis of the user's past answer history to identify their learning style and interests. For example, if a user has solved many kanji problems, the unit can recommend novels and movies that use those kanji. The problem-answering unit also uses a generation AI to dynamically select entertainment content optimal for each individual learning style based on the user's answer history. For example, if a user has solved many history problems, the unit can recommend manga and documentaries related to that era. The problem-answering unit also uses a generation AI to recommend entertainment content appropriate to the user's learning progress based on the user's answer history. For example, if a user has solved many problems on a specific topic, the unit can recommend movies and dramas related to that topic. This can improve learning effectiveness by recommending entertainment content optimal for the user's learning style.

[0031] The content display unit can automatically extract scenes and episodes with particularly high educational value from entertainment content related to the answer and present them to the user. The content display unit, for example, uses a generation AI to automatically extract scenes and episodes with high educational value from entertainment content. For example, it identifies scenes depicting important historical events in a historical drama and presents them to the user. The content display unit also uses a generation AI to automatically extract scenes and episodes related to the learning goal from entertainment content. For example, when a user solves a proverb question, it identifies a movie scene in which the proverb is used and presents it to the user. The content display unit also uses a generation AI to automatically extract episodes containing educational messages from entertainment content. For example, it identifies episodes with high educational value in a historical fiction drama and presents them to the user. This automatically extracting scenes and episodes with high educational value can improve learning effectiveness.

[0032] The content display unit can provide a learning experience in multimedia format, including music or podcasts, as entertainment content related to the answer. The content display unit, for example, provides a learning experience in multimedia format, including music or podcasts, as entertainment content related to the answer. For example, when a history question is answered, music or podcasts related to that era are played. The content display unit also provides a learning experience in multimedia format, including videos and animations, as entertainment content related to the answer. For example, when a kanji question is answered, an animation using that kanji is displayed. The content display unit also provides a learning experience in multimedia format, including interactive experiences, as entertainment content related to the answer. For example, when a proverb question is answered, an interactive game using that proverb is displayed. In this way, the enjoyment of learning can be maximized by providing a learning experience in multimedia format, including music and podcasts.

[0033] The content display unit can display entertainment content related to answers in different academic fields in a crossover format. For example, the content display unit builds a system in which, when a user solves problems in different academic fields, entertainment content related to each field is displayed in a crossover format. For example, after solving a history problem, related literary works are displayed. The content display unit also develops an algorithm in which, when a user solves problems in different academic fields, entertainment content related to each field is displayed in a crossover format. For example, after solving a science problem, related documentary videos are displayed. The content display unit also dynamically adjusts a system in which, when a user solves problems in different academic fields, entertainment content related to each field is displayed in a crossover format. For example, after solving a math problem, related artwork is displayed. This crossover of entertainment content from different academic fields broadens the scope of learning.

[0034] The content display unit can automatically extract at least one scene with particularly high educational value from related manga based on the answer to the history question. The content display unit, for example, uses a generation AI to build a system that automatically extracts scenes with particularly high educational value from related manga based on the answer to the history question. For example, when a question about the Sengoku period is answered, important scenes from manga depicting that period are displayed. The content display unit also uses a generation AI to develop an algorithm that automatically extracts scenes with particularly high educational value from related manga based on the answer to the history question. For example, when a question about the end of the Edo period is answered, important scenes from manga depicting that period are displayed. The content display unit also uses a generation AI to dynamically adjust the system that automatically extracts scenes with particularly high educational value from related manga based on the answer to the history question. For example, when a question about the Meiji period is answered, important scenes from manga depicting that period are displayed. This automatically extracting scenes with high educational value can improve learning effectiveness.

[0035] The content display unit can provide detailed explanations of manga characters or episodes related to the answer to the history question. For example, the content display unit adds a function to provide detailed explanations of manga characters and episodes related to the answer to the history question. For example, when a question about the Sengoku period is answered, detailed explanations of manga characters and episodes depicting that period are displayed. The content display unit also develops an algorithm to provide detailed explanations of manga characters and episodes related to the answer to the history question. For example, when a question about the end of the Edo period is answered, detailed explanations of manga characters and episodes depicting that period are displayed. The content display unit also dynamically adjusts a system to provide detailed explanations of manga characters and episodes related to the answer to the history question. For example, when a question about the Meiji period is answered, detailed explanations of manga characters and episodes depicting that period are displayed. This allows for a deeper understanding of the learning content by providing detailed explanations of manga characters and episodes.

[0036] The content display unit can display not only manga related to the answers to history questions, but also documentary footage or historical dramas. The content display unit, for example, builds a system that displays not only manga related to the answers to history questions, but also documentary footage and historical dramas. For example, when a question about the Sengoku period is answered, manga and documentary footage depicting that period are displayed. The content display unit also develops an algorithm that displays not only manga related to the answers to history questions, but also documentary footage and historical dramas. For example, when a question about the end of the Edo period is answered, manga and historical dramas depicting that period are displayed. The content display unit also dynamically adjusts the system that displays not only manga related to the answers to history questions, but also documentary footage and historical dramas. For example, when a question about the Meiji period is answered, manga and documentary footage depicting that period are displayed. This allows a student to deepen their understanding of the learning content by displaying not only manga, but also documentary footage and historical dramas.

[0037] The content display unit can display manga related to each era or region in a crossover format when a history question from a different era or region is answered. The content display unit, for example, builds a system in which manga related to each era or region is displayed in a crossover format when a history question from a different era or region is answered. For example, after answering a question from the Sengoku period, manga from the end of the Edo period is displayed. The content display unit also develops an algorithm in which manga related to each era or region is displayed in a crossover format when a history question from a different era or region is answered. For example, after answering a question from the Meiji period, manga from the Taisho period is displayed. The content display unit also dynamically adjusts the system in which manga related to each era or region is displayed in a crossover format when a history question from a different era or region is answered. For example, after answering a question from the Showa period, manga from the Heisei period is displayed. This crossover of manga from different eras and regions broadens the scope of learning.

[0038] The content display unit can use the generation AI to analyze the user's learning history and dynamically select entertainment content to provide the optimal context. For example, the content display unit uses the generation AI to perform a detailed analysis of the user's learning history and dynamically select entertainment content to provide the optimal context. For example, if the user has solved many history problems, movies and dramas related to that era are recommended. The content display unit also uses the generation AI to develop an algorithm that dynamically selects entertainment content to provide the optimal context based on the user's learning history. For example, if the user has solved many science problems, documentaries related to that field are recommended. The content display unit also uses the generation AI to dynamically adjust the system that dynamically selects entertainment content to provide the optimal context based on the user's learning history. For example, if the user has solved many literature problems, novels and movies related to that field are recommended. This can improve learning effectiveness by providing the optimal context based on the user's learning history.

[0039] The content display unit can provide a visual learning experience, including artwork or photographs, as entertainment content related to the learning content. The content display unit, for example, builds a system that provides a visual learning experience, including artwork and photographs, as entertainment content related to the learning content. For example, when solving a history problem, artworks and photographs related to that era are displayed. The content display unit also develops an algorithm that provides a visual learning experience, including artwork and photographs, as entertainment content related to the learning content. For example, when solving a science problem, artworks and photographs related to that field are displayed. The content display unit also dynamically adjusts the system that provides a visual learning experience, including artwork and photographs, as entertainment content related to the learning content. For example, when solving a literature problem, artworks and photographs related to that field are displayed. This maximizes the enjoyment of learning by providing a visual learning experience that includes artwork and photographs.

[0040] The content display unit can display entertainment content related to learning content from different academic fields in a crossover format. For example, the content display unit builds a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a history problem, related literary works are displayed. The content display unit also develops an algorithm in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a science problem, related documentary videos are displayed. The content display unit also dynamically adjusts a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a math problem, related artwork is displayed. This crossover of entertainment content from different academic fields broadens the scope of learning.

[0041] The content display unit uses the generation AI to analyze the user's learning history and recommend optimal entertainment content, thereby maximizing the enjoyment of learning. For example, the content display unit uses the generation AI to perform a detailed analysis of the user's past learning history to identify their learning style and interests. For example, if the user has solved many kanji problems, the content display unit recommends novels and movies that use those kanji. The content display unit also uses the generation AI to dynamically select optimal entertainment content based on the user's learning history. For example, if the user has solved many history problems, the content display unit recommends manga and documentaries related to that era. The content display unit also uses the generation AI to recommend entertainment content according to the user's learning progress based on their learning history. For example, if the user has solved many problems on a specific topic, the content display unit recommends movies and dramas related to that topic. This maximizes the enjoyment of learning by recommending optimal entertainment content based on the user's learning history.

[0042] The content display unit can automatically extract scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content and present them to the user. For example, the content display unit uses a generation AI to build a system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a historical drama and presents them to the user. The content display unit also uses a generation AI to develop an algorithm that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies empathetic episodes in literary works and presents them to the user. The content display unit also uses a generation AI to dynamically adjust the system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a science documentary and presents them to the user. This automatically extracting scenes that are particularly likely to evoke emotional empathy can improve learning effectiveness.

[0043] The content display unit can provide a more diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. The content display unit, for example, builds a system that provides a diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. For example, when a student solves a history problem, a game set in that era is displayed. The content display unit also develops an algorithm that provides a diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. For example, when a student solves a science problem, an interactive simulation related to that field is displayed. The content display unit also dynamically adjusts the system to provide a diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. For example, when a student solves a literature problem, an interactive storytelling related to that field is displayed. This maximizes the enjoyment of learning by providing a diverse learning experience, including games and interactive experiences.

[0044] The content display unit can display entertainment content related to learning content from different academic fields in a crossover format. For example, the content display unit builds a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a history problem, related literary works are displayed. The content display unit also develops an algorithm in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a science problem, related documentary videos are displayed. The content display unit also dynamically adjusts a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a math problem, related artwork is displayed. This crossover of entertainment content from different academic fields broadens the scope of learning.

[0045] The content display unit uses the generation AI to analyze the user's learning history and recommend optimal entertainment content, thereby maximizing the enjoyment of learning. For example, the content display unit uses the generation AI to perform a detailed analysis of the user's past learning history to identify their learning style and interests. For example, if the user has solved many kanji problems, the content display unit recommends novels and movies that use those kanji. The content display unit also uses the generation AI to dynamically select optimal entertainment content based on the user's learning history. For example, if the user has solved many history problems, the content display unit recommends manga and documentaries related to that era. The content display unit also uses the generation AI to recommend entertainment content according to the user's learning progress based on their learning history. For example, if the user has solved many problems on a specific topic, the content display unit recommends movies and dramas related to that topic. This maximizes the enjoyment of learning by recommending optimal entertainment content based on the user's learning history.

[0046] The content display unit can automatically extract scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content and present them to the user. For example, the content display unit uses a generation AI to build a system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a historical drama and presents them to the user. The content display unit also uses a generation AI to develop an algorithm that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies empathetic episodes in literary works and presents them to the user. The content display unit also uses a generation AI to dynamically adjust the system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a science documentary and presents them to the user. This automatically extracting scenes that are particularly likely to evoke emotional empathy can improve learning effectiveness.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] A learning app can also include a break suggestion unit that suggests appropriate break times based on the user's learning progress. For example, if a user has been studying continuously for a certain period of time or more, the break suggestion unit can display relaxing music or short entertainment content. Furthermore, if the break suggestion unit determines that the user's concentration is declining, it can provide guidance for light stretching or meditation. Furthermore, the break suggestion unit can analyze the user's learning history and dynamically adjust the optimal break timing. This allows the user to study efficiently.

[0049] The learning app may also include a difficulty adjustment unit that adjusts the difficulty of the next question based on the difficulty of the question answered by the user. For example, if the user correctly answers easy questions in succession, the difficulty adjustment unit may increase the difficulty of the next question. Alternatively, if the user is struggling with a difficult question, the difficulty adjustment unit may lower the difficulty of the next question. Furthermore, the difficulty adjustment unit may analyze the user's learning history and dynamically select questions of the optimal difficulty level. This allows the user to progress through their studies at their own pace.

[0050] Learning apps can also include a real-world connection module that provides real-world examples and news articles related to the questions the user answers. For example, if a user answers a question about environmental issues, the real-world connection module can display the latest environmental news and related documentaries. Or, if a user answers a question about economics, the real-world connection module can provide recent economic news and analysis articles. Furthermore, the real-world connection module can dynamically select the most appropriate real-world examples based on the user's interests and learning style. This allows users to understand the learning content in a real-world context.

[0051] A learning app can provide a multimedia learning experience, including music or podcasts, as entertainment content related to the questions the user answers. For example, when a user answers a history question, music or podcasts related to that period are played. The content display unit also provides a multimedia learning experience, including videos and animations, as entertainment content related to the answer. For example, when a user answers a kanji question, an animation using that kanji is displayed. The content display unit also provides a multimedia learning experience, including interactive experiences, as entertainment content related to the answer. For example, when a user answers a proverb question, an interactive game using that proverb is displayed. This maximizes the enjoyment of learning by providing a multimedia learning experience, including music and podcasts.

[0052] The learning app can automatically extract scenes and episodes with particularly high educational value from entertainment content related to the questions the user answered and present them to the user. For example, it can identify scenes depicting important historical events in a historical drama and present them to the user. The content display unit also uses generative AI to automatically extract scenes and episodes related to the learning goal from entertainment content. For example, when a user solves a proverb question, it can identify a movie scene in which that proverb is used and present it to the user. The content display unit also uses generative AI to automatically extract episodes with educational messages from entertainment content. For example, it can identify episodes with high educational value in a historical fiction drama and present them to the user. This automatically extracting scenes and episodes with high educational value can improve learning effectiveness.

[0053] A learning app can provide a multimedia learning experience, including music or podcasts, as entertainment content related to the questions the user answers. For example, when a user answers a history question, music or podcasts related to that period are played. The content display unit also provides a multimedia learning experience, including videos and animations, as entertainment content related to the answer. For example, when a user answers a kanji question, an animation using that kanji is displayed. The content display unit also provides a multimedia learning experience, including interactive experiences, as entertainment content related to the answer. For example, when a user answers a proverb question, an interactive game using that proverb is displayed. This maximizes the enjoyment of learning by providing a multimedia learning experience, including music and podcasts.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The question answering unit accepts the user's answer. For example, answers can be accepted in text input format, multiple choice format, or voice input format. In the voice input format, the user inputs the answer by voice, which is converted into text and accepted. Step 2: The content display unit displays entertainment content related to the answer accepted by the question answering unit. For example, when a question about kanji or proverbs is answered, information about novels or movies in which the question is used is displayed. Also, when a history question is answered, a manga related to the question can be displayed. Furthermore, entertainment content related to the answer can be displayed in video format; for example, a trailer for a movie related to the answer can be displayed.

[0056] (Example 2) A learning app according to an embodiment of the present invention is a system that allows users to encounter related entertainment content by solving problems and deepen their understanding, thereby enabling users to understand the learning content in a real context and obtain a rich learning experience.

[0057] A learning app according to an embodiment includes a question answering unit and a content display unit. The question answering unit accepts answers from a user. For example, the question answering unit accepts answers in a text input format. The question answering unit can also accept answers in a multiple-choice format. The question answering unit can also accept answers in a voice input format. For example, the user may input an answer by voice, convert it into text, and accept it. The content display unit displays entertainment content related to the answer accepted by the question answering unit. For example, when a user answers a question about kanji or proverbs, information about novels or movies in which the answer is used is displayed. When a user answers a history question, manga related to the question can be displayed. The content display unit can also display entertainment content related to the answer in video format. For example, a trailer for a movie related to the answer is displayed. This allows the learning app according to an embodiment to allow the user to understand the learning content in a real context and have a rich learning experience.

[0058] The problem-answering unit uses a generation AI to analyze the user's answer history and recommend entertainment content optimal for each individual learning style. For example, the problem-answering unit uses a generation AI to perform a detailed analysis of the user's past answer history to identify their learning style and interests. For example, if a user has solved many kanji problems, the unit can recommend novels and movies that use those kanji. The problem-answering unit also uses a generation AI to dynamically select entertainment content optimal for each individual learning style based on the user's answer history. For example, if a user has solved many history problems, the unit can recommend manga and documentaries related to that era. The problem-answering unit also uses a generation AI to recommend entertainment content appropriate to the user's learning progress based on the user's answer history. For example, if a user has solved many problems on a specific topic, the unit can recommend movies and dramas related to that topic. This can improve learning effectiveness by recommending entertainment content optimal for the user's learning style.

[0059] The content display unit can automatically extract scenes and episodes with particularly high educational value from entertainment content related to the answer and present them to the user. The content display unit, for example, uses a generation AI to automatically extract scenes and episodes with high educational value from entertainment content. For example, it identifies scenes depicting important historical events in a historical drama and presents them to the user. The content display unit also uses a generation AI to automatically extract scenes and episodes related to the learning goal from entertainment content. For example, when a user solves a proverb question, it identifies a movie scene in which the proverb is used and presents it to the user. The content display unit also uses a generation AI to automatically extract episodes containing educational messages from entertainment content. For example, it identifies episodes with high educational value in a historical fiction drama and presents them to the user. This automatically extracting scenes and episodes with high educational value can improve learning effectiveness.

[0060] The content display unit can use the emotion estimation function to analyze the emotions felt by the user when answering questions and select entertainment content that elicits positive emotions. For example, the content display unit uses the emotion estimation function to analyze the emotions felt by the user when answering questions in real time and select entertainment content that elicits positive emotions. For example, if the user felt stressed when answering questions, the content display unit recommends a relaxing movie. The content display unit also uses the emotion estimation function to dynamically select entertainment content that elicits positive emotions based on the emotions felt by the user when answering questions. For example, if the user felt anxious when answering questions, the content display unit recommends music that gives a sense of security. The content display unit also uses the emotion estimation function to develop an algorithm that selects entertainment content that elicits positive emotions based on the emotions felt by the user when answering questions. For example, if the user felt joy when answering questions, the content display unit recommends entertainment content that further enhances that emotion. This allows for an improved learning experience by selecting entertainment content based on the user's emotions.

[0061] The content display unit can provide a learning experience in multimedia format, including music or podcasts, as entertainment content related to the answer. The content display unit, for example, provides a learning experience in multimedia format, including music or podcasts, as entertainment content related to the answer. For example, when a history question is answered, music or podcasts related to that era are played. The content display unit also provides a learning experience in multimedia format, including videos and animations, as entertainment content related to the answer. For example, when a kanji question is answered, an animation using that kanji is displayed. The content display unit also provides a learning experience in multimedia format, including interactive experiences, as entertainment content related to the answer. For example, when a proverb question is answered, an interactive game using that proverb is displayed. In this way, the enjoyment of learning can be maximized by providing a learning experience in multimedia format, including music and podcasts.

[0062] The content display unit can display entertainment content related to answers in different academic fields in a crossover format. For example, the content display unit builds a system in which, when a user solves problems in different academic fields, entertainment content related to each field is displayed in a crossover format. For example, after solving a history problem, related literary works are displayed. The content display unit also develops an algorithm in which, when a user solves problems in different academic fields, entertainment content related to each field is displayed in a crossover format. For example, after solving a science problem, related documentary videos are displayed. The content display unit also dynamically adjusts a system in which, when a user solves problems in different academic fields, entertainment content related to each field is displayed in a crossover format. For example, after solving a math problem, related artwork is displayed. This crossover of entertainment content from different academic fields broadens the scope of learning.

[0063] The content display unit can use the emotion estimation function to identify the genre of entertainment content in which the user is most interested and prioritize questions related to that genre. For example, the content display unit uses the emotion estimation function to identify the genre of entertainment content in which the user is most interested and build a system that prioritizes questions related to that genre. For example, if the user is interested in movies, questions related to movies are displayed. Furthermore, the content display unit uses the emotion estimation function to identify the genre of entertainment content in which the user is most interested and develops an algorithm that prioritizes questions related to that genre. For example, if the user is interested in music, questions related to music are displayed. Furthermore, the content display unit uses the emotion estimation function to identify the genre of entertainment content in which the user is most interested and dynamically adjusts the system to prioritize questions related to that genre. For example, if the user is interested in games, questions related to games are displayed. This allows questions to be displayed based on the user's interests, thereby increasing learning motivation.

[0064] The content display unit can automatically extract at least one scene with particularly high educational value from related manga based on the answer to the history question. The content display unit, for example, uses a generation AI to build a system that automatically extracts scenes with particularly high educational value from related manga based on the answer to the history question. For example, when a question about the Sengoku period is answered, important scenes from manga depicting that period are displayed. The content display unit also uses a generation AI to develop an algorithm that automatically extracts scenes with particularly high educational value from related manga based on the answer to the history question. For example, when a question about the end of the Edo period is answered, important scenes from manga depicting that period are displayed. The content display unit also uses a generation AI to dynamically adjust the system that automatically extracts scenes with particularly high educational value from related manga based on the answer to the history question. For example, when a question about the Meiji period is answered, important scenes from manga depicting that period are displayed. This automatically extracting scenes with high educational value can improve learning effectiveness.

[0065] The content display unit can provide detailed explanations of manga characters or episodes related to the answer to the history question. For example, the content display unit adds a function to provide detailed explanations of manga characters and episodes related to the answer to the history question. For example, when a question about the Sengoku period is answered, detailed explanations of manga characters and episodes depicting that period are displayed. The content display unit also develops an algorithm to provide detailed explanations of manga characters and episodes related to the answer to the history question. For example, when a question about the end of the Edo period is answered, detailed explanations of manga characters and episodes depicting that period are displayed. The content display unit also dynamically adjusts a system to provide detailed explanations of manga characters and episodes related to the answer to the history question. For example, when a question about the Meiji period is answered, detailed explanations of manga characters and episodes depicting that period are displayed. This allows for a deeper understanding of the learning content by providing detailed explanations of manga characters and episodes.

[0066] The content display unit can use the emotion estimation function to analyze the emotions felt by the user when solving history questions and recommend manga that elicit positive emotions. For example, the content display unit uses the emotion estimation function to analyze the emotions felt by the user when solving history questions in real time and recommend manga that elicit positive emotions. For example, if the user feels stressed while answering the questions, the content display unit recommends manga that elicits relaxing emotions. The content display unit also uses the emotion estimation function to dynamically select manga that elicits positive emotions based on the emotions felt by the user when solving history questions. For example, if the user feels anxious while answering the questions, the content display unit recommends manga that gives a sense of security. The content display unit also uses the emotion estimation function to develop an algorithm that recommends manga that elicits positive emotions based on the emotions felt by the user when solving history questions. For example, if the user feels joy while answering the questions, the content display unit recommends manga that further enhances that emotion. This makes it possible to improve the learning experience by recommending manga based on the user's emotions.

[0067] The content display unit can display not only manga related to the answers to history questions, but also documentary footage or historical dramas. The content display unit, for example, builds a system that displays not only manga related to the answers to history questions, but also documentary footage and historical dramas. For example, when a question about the Sengoku period is answered, manga and documentary footage depicting that period are displayed. The content display unit also develops an algorithm that displays not only manga related to the answers to history questions, but also documentary footage and historical dramas. For example, when a question about the end of the Edo period is answered, manga and historical dramas depicting that period are displayed. The content display unit also dynamically adjusts the system that displays not only manga related to the answers to history questions, but also documentary footage and historical dramas. For example, when a question about the Meiji period is answered, manga and documentary footage depicting that period are displayed. This allows a student to deepen their understanding of the learning content by displaying not only manga, but also documentary footage and historical dramas.

[0068] The content display unit can display manga related to each era or region in a crossover format when a history question from a different era or region is answered. The content display unit, for example, builds a system in which manga related to each era or region is displayed in a crossover format when a history question from a different era or region is answered. For example, after answering a question from the Sengoku period, manga from the end of the Edo period is displayed. The content display unit also develops an algorithm in which manga related to each era or region is displayed in a crossover format when a history question from a different era or region is answered. For example, after answering a question from the Meiji period, manga from the Taisho period is displayed. The content display unit also dynamically adjusts the system in which manga related to each era or region is displayed in a crossover format when a history question from a different era or region is answered. For example, after answering a question from the Showa period, manga from the Heisei period is displayed. This crossover of manga from different eras and regions broadens the scope of learning.

[0069] The content display unit can use the emotion estimation function to identify the historical period or theme in which the user is most interested and prioritize questions related to that theme. For example, the content display unit uses the emotion estimation function to identify the historical period or theme in which the user is most interested and build a system that prioritizes questions related to that theme. For example, if the user is interested in the Sengoku period, questions related to that period are displayed. The content display unit also uses the emotion estimation function to identify the historical period or theme in which the user is most interested and develop an algorithm that prioritizes questions related to that theme. For example, if the user is interested in the end of the Edo period, questions related to that period are displayed. The content display unit also uses the emotion estimation function to identify the historical period or theme in which the user is most interested and dynamically adjust the system to prioritize questions related to that theme. For example, if the user is interested in the Meiji period, questions related to that period are displayed. This allows questions to be displayed based on the user's interests, thereby increasing learning motivation.

[0070] The content display unit can use the generation AI to analyze the user's learning history and dynamically select entertainment content to provide the optimal context. For example, the content display unit uses the generation AI to perform a detailed analysis of the user's learning history and dynamically select entertainment content to provide the optimal context. For example, if the user has solved many history problems, movies and dramas related to that era are recommended. The content display unit also uses the generation AI to develop an algorithm that dynamically selects entertainment content to provide the optimal context based on the user's learning history. For example, if the user has solved many science problems, documentaries related to that field are recommended. The content display unit also uses the generation AI to dynamically adjust the system that dynamically selects entertainment content to provide the optimal context based on the user's learning history. For example, if the user has solved many literature problems, novels and movies related to that field are recommended. This can improve learning effectiveness by providing the optimal context based on the user's learning history.

[0071] The content display unit can use the emotion estimation function to analyze the emotions felt by the user while studying and provide a context that elicits positive emotions. For example, the content display unit uses the emotion estimation function to analyze the emotions felt by the user while studying in real time and provide a context that elicits positive emotions. For example, if the user feels stressed while studying, the content display unit recommends a relaxing movie. The content display unit also uses the emotion estimation function to dynamically select a context that elicits positive emotions based on the emotions felt by the user while studying. For example, if the user feels anxious while studying, the content display unit recommends music that gives a sense of security. The content display unit also uses the emotion estimation function to develop an algorithm that provides a context that elicits positive emotions based on the emotions felt by the user while studying. For example, if the user feels joy while studying, the content display unit recommends entertainment content that further enhances those emotions. This makes it possible to improve the learning experience by providing context based on the user's emotions.

[0072] The content display unit can provide a visual learning experience, including artwork or photographs, as entertainment content related to the learning content. The content display unit, for example, builds a system that provides a visual learning experience, including artwork and photographs, as entertainment content related to the learning content. For example, when solving a history problem, artworks and photographs related to that era are displayed. The content display unit also develops an algorithm that provides a visual learning experience, including artwork and photographs, as entertainment content related to the learning content. For example, when solving a science problem, artworks and photographs related to that field are displayed. The content display unit also dynamically adjusts the system that provides a visual learning experience, including artwork and photographs, as entertainment content related to the learning content. For example, when solving a literature problem, artworks and photographs related to that field are displayed. This maximizes the enjoyment of learning by providing a visual learning experience that includes artwork and photographs.

[0073] The content display unit can display entertainment content related to learning content from different academic fields in a crossover format. For example, the content display unit builds a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a history problem, related literary works are displayed. The content display unit also develops an algorithm in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a science problem, related documentary videos are displayed. The content display unit also dynamically adjusts a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a math problem, related artwork is displayed. This crossover of entertainment content from different academic fields broadens the scope of learning.

[0074] The content display unit can use the emotion estimation function to identify the context in which the user is most interested and prioritize questions related to that context. For example, the content display unit uses the emotion estimation function to identify the context in which the user is most interested and build a system that prioritizes questions related to that context. For example, if the user is interested in movies, questions related to movies are displayed. The content display unit also uses the emotion estimation function to identify the context in which the user is most interested and develops an algorithm that prioritizes questions related to that context. For example, if the user is interested in music, questions related to music are displayed. The content display unit also uses the emotion estimation function to identify the context in which the user is most interested and dynamically adjusts the system to prioritize questions related to that context. For example, if the user is interested in games, questions related to games are displayed. This allows questions to be displayed based on the user's interests, thereby increasing learning motivation.

[0075] The content display unit uses the generation AI to analyze the user's learning history and recommend optimal entertainment content, thereby maximizing the enjoyment of learning. For example, the content display unit uses the generation AI to perform a detailed analysis of the user's past learning history to identify their learning style and interests. For example, if the user has solved many kanji problems, the content display unit recommends novels and movies that use those kanji. The content display unit also uses the generation AI to dynamically select optimal entertainment content based on the user's learning history. For example, if the user has solved many history problems, the content display unit recommends manga and documentaries related to that era. The content display unit also uses the generation AI to recommend entertainment content according to the user's learning progress based on their learning history. For example, if the user has solved many problems on a specific topic, the content display unit recommends movies and dramas related to that topic. This maximizes the enjoyment of learning by recommending optimal entertainment content based on the user's learning history.

[0076] The content display unit can automatically extract scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content and present them to the user. For example, the content display unit uses a generation AI to build a system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a historical drama and presents them to the user. The content display unit also uses a generation AI to develop an algorithm that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies empathetic episodes in literary works and presents them to the user. The content display unit also uses a generation AI to dynamically adjust the system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a science documentary and presents them to the user. This automatically extracting scenes that are particularly likely to evoke emotional empathy can improve learning effectiveness.

[0077] The content display unit can provide a more diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. The content display unit, for example, builds a system that provides a diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. For example, when a student solves a history problem, a game set in that era is displayed. The content display unit also develops an algorithm that provides a diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. For example, when a student solves a science problem, an interactive simulation related to that field is displayed. The content display unit also dynamically adjusts the system to provide a diverse learning experience, including games and interactive experiences, as entertainment content related to the learning content. For example, when a student solves a literature problem, an interactive storytelling related to that field is displayed. This maximizes the enjoyment of learning by providing a diverse learning experience, including games and interactive experiences.

[0078] The content display unit can display entertainment content related to learning content from different academic fields in a crossover format. For example, the content display unit builds a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a history problem, related literary works are displayed. The content display unit also develops an algorithm in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a science problem, related documentary videos are displayed. The content display unit also dynamically adjusts a system in which, when learning content from different academic fields is solved, entertainment content related to each field is displayed in a crossover format. For example, after solving a math problem, related artwork is displayed. This crossover of entertainment content from different academic fields broadens the scope of learning.

[0079] The content display unit can use the emotion estimation function to identify the genre of entertainment content in which the user is most interested and prioritize questions related to that genre. For example, the content display unit uses the emotion estimation function to identify the genre of entertainment content in which the user is most interested and build a system that prioritizes questions related to that genre. For example, if the user is interested in movies, questions related to movies are displayed. Furthermore, the content display unit uses the emotion estimation function to identify the genre of entertainment content in which the user is most interested and develops an algorithm that prioritizes questions related to that genre. For example, if the user is interested in music, questions related to music are displayed. Furthermore, the content display unit uses the emotion estimation function to identify the genre of entertainment content in which the user is most interested and dynamically adjusts the system to prioritize questions related to that genre. For example, if the user is interested in games, questions related to games are displayed. This allows questions to be displayed based on the user's interests, thereby increasing learning motivation.

[0080] The content display unit uses the generation AI to analyze the user's learning history and recommend optimal entertainment content, thereby maximizing the enjoyment of learning. For example, the content display unit uses the generation AI to perform a detailed analysis of the user's past learning history to identify their learning style and interests. For example, if the user has solved many kanji problems, the content display unit recommends novels and movies that use those kanji. The content display unit also uses the generation AI to dynamically select optimal entertainment content based on the user's learning history. For example, if the user has solved many history problems, the content display unit recommends manga and documentaries related to that era. The content display unit also uses the generation AI to recommend entertainment content according to the user's learning progress based on their learning history. For example, if the user has solved many problems on a specific topic, the content display unit recommends movies and dramas related to that topic. This maximizes the enjoyment of learning by recommending optimal entertainment content based on the user's learning history.

[0081] The content display unit can automatically extract scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content and present them to the user. For example, the content display unit uses a generation AI to build a system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a historical drama and presents them to the user. The content display unit also uses a generation AI to develop an algorithm that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies empathetic episodes in literary works and presents them to the user. The content display unit also uses a generation AI to dynamically adjust the system that automatically extracts scenes that are particularly likely to evoke emotional empathy from entertainment content related to the learning content. For example, it identifies moving scenes in a science documentary and presents them to the user. This automatically extracting scenes that are particularly likely to evoke emotional empathy can improve learning effectiveness.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] A learning app can also include a break suggestion unit that suggests appropriate break times based on the user's learning progress. For example, if a user has been studying continuously for a certain period of time or more, the break suggestion unit can display relaxing music or short entertainment content. Furthermore, if the break suggestion unit determines that the user's concentration is declining, it can provide guidance for light stretching or meditation. Furthermore, the break suggestion unit can analyze the user's learning history and dynamically adjust the optimal break timing. This allows the user to study efficiently.

[0084] The learning app may also include a difficulty adjustment unit that adjusts the difficulty of the next question based on the difficulty of the question answered by the user. For example, if the user correctly answers easy questions in succession, the difficulty adjustment unit may increase the difficulty of the next question. Alternatively, if the user is struggling with a difficult question, the difficulty adjustment unit may lower the difficulty of the next question. Furthermore, the difficulty adjustment unit may analyze the user's learning history and dynamically select questions of the optimal difficulty level. This allows the user to progress through their studies at their own pace.

[0085] Learning apps can also include a real-world connection module that provides real-world examples and news articles related to the questions the user answers. For example, if a user answers a question about environmental issues, the real-world connection module can display the latest environmental news and related documentaries. Or, if a user answers a question about economics, the real-world connection module can provide recent economic news and analysis articles. Furthermore, the real-world connection module can dynamically select the most appropriate real-world examples based on the user's interests and learning style. This allows users to understand the learning content in a real-world context.

[0086] The learning app can automatically extract scenes that are particularly likely to resonate emotionally from entertainment content related to the questions the user has answered and present them to the user. For example, it could identify moving scenes in a historical drama and present them to the user. The content display unit also uses generative AI to develop an algorithm that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify empathetic episodes in literary works and present them to the user. The content display unit also uses generative AI to dynamically adjust a system that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify moving scenes in a science documentary and present them to the user. This automatically extracting scenes that are particularly likely to resonate emotionally can improve learning effectiveness.

[0087] A learning app can provide a multimedia learning experience, including music or podcasts, as entertainment content related to the questions the user answers. For example, when a user answers a history question, music or podcasts related to that period are played. The content display unit also provides a multimedia learning experience, including videos and animations, as entertainment content related to the answer. For example, when a user answers a kanji question, an animation using that kanji is displayed. The content display unit also provides a multimedia learning experience, including interactive experiences, as entertainment content related to the answer. For example, when a user answers a proverb question, an interactive game using that proverb is displayed. This maximizes the enjoyment of learning by providing a multimedia learning experience, including music and podcasts.

[0088] The learning app can automatically extract scenes and episodes with particularly high educational value from entertainment content related to the questions the user answered and present them to the user. For example, it can identify scenes depicting important historical events in a historical drama and present them to the user. The content display unit also uses generative AI to automatically extract scenes and episodes related to the learning goal from entertainment content. For example, when a user solves a proverb question, it can identify a movie scene in which that proverb is used and present it to the user. The content display unit also uses generative AI to automatically extract episodes with educational messages from entertainment content. For example, it can identify episodes with high educational value in a historical fiction drama and present them to the user. This automatically extracting scenes and episodes with high educational value can improve learning effectiveness.

[0089] The learning app can automatically extract scenes that are particularly likely to resonate emotionally from entertainment content related to the questions the user has answered and present them to the user. For example, it could identify moving scenes in a historical drama and present them to the user. The content display unit also uses generative AI to develop an algorithm that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify empathetic episodes in literary works and present them to the user. The content display unit also uses generative AI to dynamically adjust a system that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify moving scenes in a science documentary and present them to the user. This automatically extracting scenes that are particularly likely to resonate emotionally can improve learning effectiveness.

[0090] A learning app can provide a multimedia learning experience, including music or podcasts, as entertainment content related to the questions the user answers. For example, when a user answers a history question, music or podcasts related to that period are played. The content display unit also provides a multimedia learning experience, including videos and animations, as entertainment content related to the answer. For example, when a user answers a kanji question, an animation using that kanji is displayed. The content display unit also provides a multimedia learning experience, including interactive experiences, as entertainment content related to the answer. For example, when a user answers a proverb question, an interactive game using that proverb is displayed. This maximizes the enjoyment of learning by providing a multimedia learning experience, including music and podcasts.

[0091] The learning app can automatically extract scenes that are particularly likely to resonate emotionally from entertainment content related to the questions the user has answered and present them to the user. For example, it could identify moving scenes in a historical drama and present them to the user. The content display unit also uses generative AI to develop an algorithm that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify empathetic episodes in literary works and present them to the user. The content display unit also uses generative AI to dynamically adjust a system that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify moving scenes in a science documentary and present them to the user. This automatically extracting scenes that are particularly likely to resonate emotionally can improve learning effectiveness.

[0092] The learning app can automatically extract scenes that are particularly likely to resonate emotionally from entertainment content related to the questions the user has answered and present them to the user. For example, it could identify moving scenes in a historical drama and present them to the user. The content display unit also uses generative AI to develop an algorithm that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify empathetic episodes in literary works and present them to the user. The content display unit also uses generative AI to dynamically adjust a system that automatically extracts scenes that are particularly likely to resonate emotionally from entertainment content related to the learning content. For example, it could identify moving scenes in a science documentary and present them to the user. This automatically extracting scenes that are particularly likely to resonate emotionally can improve learning effectiveness.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The question answering unit accepts the user's answer. For example, answers can be accepted in text input format, multiple choice format, or voice input format. In the voice input format, the user inputs the answer by voice, which is converted into text and accepted. Step 2: The content display unit displays entertainment content related to the answer accepted by the question answering unit. For example, when a question about kanji or proverbs is answered, information about novels or movies in which the question is used is displayed. Also, when a history question is answered, a manga related to the question can be displayed. Furthermore, entertainment content related to the answer can be displayed in video format; for example, a trailer for a movie related to the answer can be displayed.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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. [Explanation of symbols]

[0162] 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 question answering unit that accepts answers from users; a content display unit that displays entertainment content related to the answer received by the question answer unit. A system characterized by:

2. The problem solving unit Using generative AI to analyze the user's answer history and recommend the entertainment content that is best suited to each individual learning style.

2. The system of claim 1.

3. The content display unit The entertainment content related to the answer may include music or podcasts, providing a multimedia learning experience.

2. The system of claim 1.

4. The content display unit Based on the answer to the history question, at least one scene with particularly high educational value is automatically extracted from the related manga.

2. The system of claim 1.

5. The content display unit Using generative AI to analyze the user's learning history and dynamically select the entertainment content to provide the optimal context.

2. The system of claim 1.

6. The content display unit Analyzing the emotions felt by the user when answering questions and selecting the entertainment content that elicits positive emotions 2. The system of claim 1.

7. The content display unit Analyze the emotions felt by the user when solving history questions and recommend manga that evoke positive emotions.

2. The system of claim 1.

8. The content display unit Identifying the context in which the user is most interested and giving questions related to that context priority 2. The system of claim 1.

Images