System

Abstract

An object of the system according to the embodiment is to provide learning content customized based on the age, interest, and learning style of a child.SOLUTION: A system includes a customization unit and a learning content generation unit. The customization unit generates learning content based on the age, interest, and learning style of the child. The learning content generation unit provides the learning content generated by the customization 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 of making it difficult to provide customized learning content tailored to a child's age, interests, and learning style.

[0005] The system according to the embodiment aims to provide customized learning content based on a child's age, interests, and learning style. [Means for solving the problem]

[0006] The system according to the embodiment includes a customization unit and a learning content generation unit. The customization unit generates learning content based on the child's age, interests, and learning style. The learning content generation unit provides the learning content generated by the customization unit. [Effects of the Invention]

[0007] An embodiment of the system can provide customized learning content based on a child's age, interests, and learning style. [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) The learning experience system according to the embodiment of the present invention provides a customized learning experience based on a child's age, interests, and learning style, allowing children to acquire the skills and knowledge necessary for their personal growth while having fun.

[0029] A learning experience system according to an embodiment includes a generation AI, a customization unit, and a learning content generation unit. The generation AI generates learning content based on a child's age, interests, and learning style. For example, the generation AI selects appropriate learning content according to the child's age and interests. The customization unit provides the learning content generated by the generation AI. For example, the customization unit adjusts the content to suit the child's learning style. The learning content generation unit actually displays the learning content provided by the customization unit. For example, the learning content generation unit displays game-style learning content. The learning content generation unit can also display story-style learning content. The learning content generation unit can also display quiz-style learning content. This allows the learning experience system according to an embodiment to provide a customized learning experience based on a child's age, interests, and learning style.

[0030] The customization unit can analyze the child's learning history and automatically suggest the next learning step based on past learning achievements. The customization unit, for example, collects the child's past learning data and analyzes the learning history. For example, it stores in a database the level of understanding in each area. The customization unit also suggests the next learning step based on past learning achievements. For example, if there is a lack of understanding in a particular area, it provides additional learning content related to that area. The customization unit also adjusts the next learning step based on the learning achievements. For example, it provides more advanced content for areas with a high level of understanding. This makes it possible to suggest the next learning step based on past learning achievements.

[0031] The customization unit can detect changes in a child's interests and generate content to stimulate new interests when interest wanes. The customization unit, for example, analyzes the child's learning data to detect changes in interests. For example, if the child's response to specific content decreases, it determines that interest has waned. The customization unit also generates content to stimulate new interests when interest wanes. For example, it provides content based on a new theme or topic. The customization unit also provides interactive content to stimulate interest. For example, it provides game-style content or story-style content. This makes it possible to provide content that stimulates new interests in accordance with changes in a child's interests.

[0032] The customization unit can incorporate feedback from parents and teachers to customize the learning content. For example, the customization unit collects feedback provided by parents and teachers and reflects it in the learning content. For example, the customization unit inputs feedback about a child's weaknesses in a particular area. The customization unit also adjusts the learning content based on the feedback. For example, the customization unit provides content that focuses on a particular area based on the feedback. The customization unit also adjusts the difficulty level of the learning content based on the feedback. For example, the customization unit determines whether to increase or decrease the difficulty level of the content based on the feedback. This makes it possible to customize the learning content based on feedback from parents and teachers.

[0033] The customization unit can refer to the learning data of a child's friends and siblings and generate content that promotes collaborative learning and competition. For example, the customization unit collects learning data of a child's friends and siblings and generates content that promotes collaborative learning and competition. For example, it provides a quiz that allows a child to compete with friends in the same grade. The customization unit also provides content to promote collaborative learning. For example, it provides game-style content that allows a child to study together with friends or siblings. The customization unit also provides a ranking function to promote competition. For example, it displays a ranking that allows a child to compare their learning results with those of their friends or siblings. This makes it possible to provide content that promotes collaborative learning and competition.

[0034] An educational game can analyze a child's learning style and interests based on choices and actions in the game and customize the next game content. For example, an educational game collects choices and actions in the game as data and analyzes a child's learning style and interests. For example, it records which choices were chosen and which actions were taken. The educational game also customizes the next game content based on the analysis results. For example, it changes the content of the next stage based on the choice chosen by the child. The educational game also adjusts the game progress based on the child's actions. For example, if a child takes a specific action, it provides a reward according to that action. In this way, it is possible to provide game content based on a child's learning style and interests.

[0035] The learning game can analyze the progress of the game and, if a particular skill is lacking, insert a mini-game to strengthen that skill. For example, the learning game collects data on the progress of the game and analyzes whether a particular skill is lacking. For example, if a child repeatedly makes mistakes on a particular problem, it can be determined that that skill is lacking. Furthermore, if a particular skill is lacking, the learning game can insert a mini-game to strengthen that skill. For example, if a child is lacking in calculation skills, a mini-game for solving calculation problems can be provided. Furthermore, the learning game can provide practice problems to strengthen that skill. For example, if a child is lacking in grammar skills, a practice problem for solving grammar problems can be provided. In this way, if a particular skill is lacking, a mini-game for strengthening that skill can be provided.

[0036] The learning game can be a multidisciplinary learning game that incorporates different learning fields. For example, the learning game can be a learning game that combines different learning fields. For example, a rhythm game that combines music and mathematics is provided, and calculation problems are solved to the rhythm. The learning game can also be a learning game that combines art and science. For example, basic principles of science are learned through artwork. The learning game can also be a learning game that combines history and geography. For example, geographical knowledge is learned through historical events. In this way, it is possible to provide a multidisciplinary learning game that incorporates different learning fields.

[0037] Learning games can add a feature that allows parents or teachers to monitor game progress and adjust game content based on the feedback. For example, learning games add a feature that allows parents or teachers to monitor game progress in real time. For example, a dashboard is provided that allows parents or teachers to see which problems a child is struggling with. The learning game also collects feedback provided by parents or teachers and reflects it in the game content. For example, the child may enter feedback about areas in which they are weak. The learning game also adjusts the game content based on the feedback. For example, the learning game provides content that focuses on specific areas based on the feedback. The learning game also adjusts the difficulty of the game based on the feedback. For example, the learning game determines whether to increase or decrease the difficulty of the game based on the feedback. This allows parents or teachers to monitor game progress and adjust the game content based on the feedback.

[0038] The story can analyze a child's learning style and interests based on choices and actions within the story and customize the next story content. For example, the story collects choices and actions within the story as data and analyzes a child's learning style and interests. For example, it records which choices were chosen and which actions were taken. The story also customizes the next story content based on the analysis results. For example, it changes the content of the next scene based on the choice chosen by the child. The story also adjusts the progress of the story based on the child's actions. For example, if a child takes a specific action, it provides a development that corresponds to that action. This makes it possible to provide story content based on a child's learning style and interests.

[0039] The story can analyze the progress of the story, and if a particular skill is lacking, insert a scene to strengthen that skill. For example, the story collects the progress of the story as data and analyzes whether a particular skill is lacking. For example, if a child makes repeated mistakes on a particular problem, it determines that that skill is lacking. Furthermore, if a particular skill is lacking, the story inserts a scene to strengthen that skill. For example, if calculation skills are lacking, a scene to solve a calculation problem is provided. Furthermore, the story provides a practice scene to strengthen a particular skill. For example, if grammar skills are lacking, a practice scene to solve a grammar problem is provided. In this way, if a particular skill is lacking, a scene to strengthen that skill can be provided.

[0040] The stories can provide multicultural learning stories that incorporate different cultures and historical backgrounds. For example, stories that incorporate different cultures and historical backgrounds can be provided. For example, stories that teach African history and culture can be provided to help children understand multiple cultures. Furthermore, stories that incorporate cultures from different eras and regions can be provided. For example, stories that teach ancient Egyptian culture can be provided. Furthermore, stories that incorporate different historical events can be provided. For example, stories that teach the history of World War II can be provided. In this way, multicultural learning stories that incorporate different cultures and historical backgrounds can be provided.

[0041] Stories can add a function that allows parents and teachers to monitor the progress of a story and adjust the story content based on the feedback. For example, Stories can add a function that allows parents and teachers to monitor the progress of a story in real time. For example, it can provide a dashboard that allows them to see which scenes a child is struggling with. Stories can also collect feedback provided by parents and teachers and reflect it in the story content. For example, a child can enter feedback about areas in which they are struggling. Stories can also adjust the story content based on the feedback. For example, it can provide scenes that focus on specific areas based on the feedback. Stories can also adjust the difficulty of the story based on the feedback. For example, it can decide whether to increase or decrease the difficulty of the story based on the feedback. This allows parents and teachers to monitor the progress of a story and adjust the story content based on the feedback.

[0042] The quiz analyzes the quiz answer history, and if understanding in a particular field is lacking, additional quizzes related to that field can be provided. For example, the quiz collects the quiz answer history as data and analyzes whether understanding in a particular field is lacking. For example, if a child repeatedly makes mistakes on a particular question, it can be determined that the child does not understand that field. Furthermore, if understanding in a particular field is lacking, the quiz provides additional quizzes related to that field. For example, if understanding of history is lacking, additional quizzes related to history can be provided. Furthermore, the quiz provides practice questions to deepen understanding in a particular field. For example, if understanding of geography is lacking, practice questions related to geography can be provided. In this way, if understanding in a particular field is lacking, additional quizzes related to that field can be provided.

[0043] The quiz can analyze the progress of the quiz, and if a particular skill is lacking, insert a quiz to strengthen that skill. For example, the quiz collects data on the progress of the quiz and analyzes whether a particular skill is lacking. For example, if a child repeatedly makes mistakes on a particular question, it is determined that that skill is lacking. Furthermore, if a particular skill is lacking, the quiz inserts a quiz to strengthen that skill. For example, if a child is lacking in calculation skills, a quiz to solve calculation problems is provided. Furthermore, the quiz provides practice questions to strengthen a particular skill. For example, if a child is lacking in grammar skills, practice questions to solve grammar problems are provided. In this way, if a particular skill is lacking, a quiz to strengthen that skill can be provided.

[0044] The quiz can provide a multidisciplinary quiz that incorporates different learning fields. For example, the quiz can provide a quiz that combines different learning fields. For example, a quiz can be provided that combines music and mathematics, in which calculation problems are solved to the rhythm. Alternatively, the quiz can provide a quiz that combines art and science. For example, a quiz can be provided that teaches basic scientific principles through artwork. Alternatively, the quiz can be provided that combines history and geography. For example, a quiz can be provided that teaches geographical knowledge through historical events. In this way, it is possible to provide a multidisciplinary quiz that incorporates different learning fields.

[0045] The quiz may add a function that allows parents or teachers to monitor the progress of the quiz and adjust the quiz content based on the feedback. For example, the quiz may add a function that allows parents or teachers to monitor the progress of the quiz in real time. For example, it may provide a dashboard that allows them to see which questions a child is struggling with. The quiz may also collect feedback provided by parents or teachers and reflect it in the quiz content. For example, the quiz may input as feedback information about areas in which the child is weak. The quiz may also adjust the quiz content based on the feedback. For example, it may provide questions that focus on specific areas based on the feedback. The quiz may also adjust the difficulty of the quiz based on the feedback. For example, it may decide whether to increase or decrease the difficulty of the quiz based on the feedback. This allows parents and teachers to monitor the progress of the quiz and adjust the quiz content based on the feedback.

[0046] Learning progress can analyze learning progress data and, if understanding in a particular area is lacking, provide additional learning content related to that area. Learning progress, for example, collects learning progress data as data and analyzes whether understanding in a particular area is lacking. For example, if a child repeatedly makes mistakes on a particular problem, it can be determined that the child does not understand that area. Furthermore, if understanding in a particular area is lacking, learning progress can provide additional learning content related to that area. For example, if understanding of history is lacking, additional learning content related to history can be provided. Furthermore, learning progress can provide practice questions to deepen understanding of a particular area. For example, if understanding of geography is lacking, practice questions related to geography can be provided. In this way, if understanding in a particular area is lacking, additional learning content related to that area can be provided.

[0047] Learning progress can analyze a child's learning style and interests based on learning progress data and customize the next learning step. Learning progress, for example, collects learning progress data and analyzes a child's learning style and interests. For example, it stores in a database the level of understanding in each area. Learning progress also customizes the next learning step based on the analysis results. For example, it changes the content of the next step based on the child's learning style. Learning progress also adjusts the content of the next step based on the child's interests. For example, it provides content related to areas that the child is interested in. This makes it possible to provide the next learning step based on the child's learning style and interests.

[0048] Learning progress can add a function that allows parents and teachers to monitor learning progress and adjust learning content based on the feedback. Learning progress, for example, adds a function that allows parents and teachers to monitor learning progress in real time. For example, it provides a dashboard that allows them to see in which areas a child is struggling. Learning progress also collects feedback provided by parents and teachers and reflects it in the learning content. For example, it enters as feedback the content that a child is weak in in a particular area. Learning progress also adjusts the learning content based on the feedback. For example, it provides content that focuses on a particular area based on the feedback. Learning progress also adjusts the difficulty of learning based on the feedback. For example, it decides whether to increase or decrease the difficulty of learning based on the feedback. This allows parents and teachers to monitor learning progress and adjust learning content based on the feedback.

[0049] Learning progress can provide a ranking function that allows learning progress data to be compared with other children and brings out a competitive spirit. Learning progress, for example, provides a function that allows learning progress data to be compared with other children and displays a ranking. For example, it can display the ranking among children in the same grade. Learning progress also provides a reward system that brings out a competitive spirit. For example, it can provide special content to children who rank highly. Learning progress also adjusts the frequency of updating the ranking. For example, it can update the ranking every week to maintain children's competitive spirit. This makes it possible to provide a ranking function that allows learning progress data to be compared with other children and bring out a competitive spirit.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The learning experience system can also monitor a child's health condition and provide learning content appropriate to the child's health. For example, if a child has a cold, light learning content that does not drain the child's energy can be provided. On the other hand, if a child is in good health, more active learning content can be provided. Furthermore, the system can suggest break times based on the child's health condition. For example, encouraging a child to take an appropriate break after a long period of study can help maintain the child's concentration. This makes it possible to provide a learning experience appropriate to the child's health condition.

[0052] The learning experience system can also provide a dashboard that visualizes learning progress based on a child's learning history. For example, it can display the child's level of understanding in each subject using graphs and charts. It can also set goals according to learning progress and display the degree of achievement in real time. It can also introduce a reward system based on learning progress. For example, by awarding badges or points when a specific goal is achieved, it can increase a child's motivation to learn. This makes it possible to provide a progress visualization and reward system based on a child's learning history.

[0053] The learning experience system can also detect changes in a child's interests and, if their interest wanes, provide interactive content to stimulate new interests. For example, if a child loses interest in a particular topic, it can suggest new related topics. It can also re-stimulate interest by providing quizzes or games based on topics that interest children. It can also provide storytelling-style content to stimulate interest. For example, it can stimulate a child's interest by progressing learning through adventure or mystery-themed stories. This makes it possible to provide an interactive learning experience that responds to changes in a child's interests.

[0054] The learning experience system can further customize learning content based on feedback from parents and teachers. For example, it can collect feedback provided by parents and teachers and provide content to improve understanding in a particular area. It can also adjust learning progress based on the feedback. For example, if understanding in a particular area is lacking, it can provide additional learning content related to that area. It can also adjust the difficulty of learning based on the feedback. For example, it can decide whether to make the content more or less difficult based on the feedback. This allows learning content to be customized based on feedback from parents and teachers.

[0055] The learning experience system can further refer to the learning data of a child's friends and siblings to provide content that promotes collaborative learning and competition. For example, it can provide a quiz that allows a child to compete with friends in the same grade. It can also provide game-style content to promote collaborative learning. For example, it can provide interactive content that allows a child to study together with friends or siblings. It can also provide a ranking function to promote competition. For example, it can display a ranking that compares the learning results of friends or siblings. This makes it possible to provide content that promotes collaborative learning and competition.

[0056] The learning experience system can also analyze a child's learning style and interests to customize the next learning step. For example, it can collect a child's learning data and analyze the child's level of understanding in each area. It can also suggest the next learning step based on the analysis results. For example, if a child's understanding in a particular area is lacking, it can provide additional learning content related to that area. It can also adjust the learning progress based on the child's learning style. For example, if a child prefers a visual learning style, it can provide a learning step that includes a lot of visual content. This makes it possible to provide the child with the next learning step based on their learning style and interests.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: Generative AI generates learning content based on the child's age, interests, and learning style. For example, the generative AI selects appropriate learning content based on the child's age and interests. Step 2: The customization unit provides learning content generated by the generative AI. For example, the customization unit adjusts the content to suit the child's learning style. Step 3: The learning content generator actually displays the learning content provided by the customization unit. For example, the learning content generator can display learning content in the form of a game, a story, or a quiz.

[0059] (Example 2) The learning experience system according to the embodiment of the present invention provides a customized learning experience based on a child's age, interests, and learning style, allowing children to acquire the skills and knowledge necessary for their personal growth while having fun.

[0060] A learning experience system according to an embodiment includes a generation AI, a customization unit, and a learning content generation unit. The generation AI generates learning content based on a child's age, interests, and learning style. For example, the generation AI selects appropriate learning content according to the child's age and interests. The customization unit provides the learning content generated by the generation AI. For example, the customization unit adjusts the content to suit the child's learning style. The learning content generation unit actually displays the learning content provided by the customization unit. For example, the learning content generation unit displays game-style learning content. The learning content generation unit can also display story-style learning content. The learning content generation unit can also display quiz-style learning content. This allows the learning experience system according to an embodiment to provide a customized learning experience based on a child's age, interests, and learning style.

[0061] The customization unit includes an emotion estimation unit that estimates the child's emotional state in real time and generates learning content according to the emotion. The customization unit, for example, analyzes the child's facial expressions and vocal tone to estimate the emotional state in real time. For example, a camera and a microphone are used to detect changes in the child's facial expressions and voice and calculate an emotion score. The customization unit also generates learning content based on the emotional state estimated by the emotion estimation unit. For example, if the child is having fun, more difficult content is provided. If the child is tired, relaxing content is provided. This makes it possible to provide learning content according to the child's emotional state.

[0062] The customization unit can analyze the child's learning history and automatically suggest the next learning step based on past learning achievements. The customization unit, for example, collects the child's past learning data and analyzes the learning history. For example, it stores in a database the level of understanding in each area. The customization unit also suggests the next learning step based on past learning achievements. For example, if there is a lack of understanding in a particular area, it provides additional learning content related to that area. The customization unit also adjusts the next learning step based on the learning achievements. For example, it provides more advanced content for areas with a high level of understanding. This makes it possible to suggest the next learning step based on past learning achievements.

[0063] The customization unit can detect changes in a child's interests and generate content to stimulate new interests when interest wanes. The customization unit, for example, analyzes the child's learning data to detect changes in interests. For example, if the child's response to specific content decreases, it determines that interest has waned. The customization unit also generates content to stimulate new interests when interest wanes. For example, it provides content based on a new theme or topic. The customization unit also provides interactive content to stimulate interest. For example, it provides game-style content or story-style content. This makes it possible to provide content that stimulates new interests in accordance with changes in a child's interests.

[0064] The customization unit can incorporate feedback from parents and teachers to customize the learning content. For example, the customization unit collects feedback provided by parents and teachers and reflects it in the learning content. For example, the customization unit inputs feedback about a child's weaknesses in a particular area. The customization unit also adjusts the learning content based on the feedback. For example, the customization unit provides content that focuses on a particular area based on the feedback. The customization unit also adjusts the difficulty level of the learning content based on the feedback. For example, the customization unit determines whether to increase or decrease the difficulty level of the content based on the feedback. This makes it possible to customize the learning content based on feedback from parents and teachers.

[0065] The customization unit can refer to the learning data of a child's friends and siblings and generate content that promotes collaborative learning and competition. For example, the customization unit collects learning data of a child's friends and siblings and generates content that promotes collaborative learning and competition. For example, it provides a quiz that allows a child to compete with friends in the same grade. The customization unit also provides content to promote collaborative learning. For example, it provides game-style content that allows a child to study together with friends or siblings. The customization unit also provides a ranking function to promote competition. For example, it displays a ranking that allows a child to compare their learning results with those of their friends or siblings. This makes it possible to provide content that promotes collaborative learning and competition.

[0066] The customization unit can use the emotion estimation function to identify the learning style that the child enjoys most and provide new learning content based on that style. The customization unit, for example, uses the emotion estimation function to identify the learning style that the child enjoys most. For example, it analyzes facial expressions and voice tone to determine whether the child is enjoying the learning. The customization unit also provides new learning content based on the identified learning style. For example, it provides game-style content that matches the learning style that the child enjoys. The customization unit can also provide story-style content based on the learning style that the child enjoys. The customization unit can also provide quiz-style content based on the learning style that the child enjoys. In this way, it is possible to provide content based on the learning style that the child enjoys most.

[0067] The learning game can estimate a child's emotional state in real time and adjust the difficulty level and content of the game according to the emotion. The learning game, for example, analyzes the child's facial expressions and vocal tone to estimate the emotional state in real time. For example, a camera and a microphone are used to detect changes in the child's facial expressions and voice and calculate an emotion score. The learning game also adjusts the difficulty level and content of the game based on the emotional state estimated by the emotion estimation unit. For example, if the child is having fun, the difficulty level of the game is increased. If the child is tired, the difficulty level of the game is decreased. The learning game also adjusts the content of the game according to the emotional state. For example, if the child is excited, the game with more action elements is provided. If the child is relaxed, the game with more puzzle elements is provided. This makes it possible to provide the difficulty level and content of the game according to the child's emotional state.

[0068] An educational game can analyze a child's learning style and interests based on choices and actions in the game and customize the next game content. For example, an educational game collects choices and actions in the game as data and analyzes a child's learning style and interests. For example, it records which choices were chosen and which actions were taken. The educational game also customizes the next game content based on the analysis results. For example, it changes the content of the next stage based on the choice chosen by the child. The educational game also adjusts the game progress based on the child's actions. For example, if a child takes a specific action, it provides a reward according to that action. In this way, it is possible to provide game content based on a child's learning style and interests.

[0069] The learning game can analyze the progress of the game and, if a particular skill is lacking, insert a mini-game to strengthen that skill. For example, the learning game collects data on the progress of the game and analyzes whether a particular skill is lacking. For example, if a child repeatedly makes mistakes on a particular problem, it can be determined that that skill is lacking. Furthermore, if a particular skill is lacking, the learning game can insert a mini-game to strengthen that skill. For example, if a child is lacking in calculation skills, a mini-game for solving calculation problems can be provided. Furthermore, the learning game can provide practice problems to strengthen that skill. For example, if a child is lacking in grammar skills, a practice problem for solving grammar problems can be provided. In this way, if a particular skill is lacking, a mini-game for strengthening that skill can be provided.

[0070] The learning game can be a multidisciplinary learning game that incorporates different learning fields. For example, the learning game can be a learning game that combines different learning fields. For example, a rhythm game that combines music and mathematics is provided, and calculation problems are solved to the rhythm. The learning game can also be a learning game that combines art and science. For example, basic principles of science are learned through artwork. The learning game can also be a learning game that combines history and geography. For example, geographical knowledge is learned through historical events. In this way, it is possible to provide a multidisciplinary learning game that incorporates different learning fields.

[0071] Learning games can add a feature that allows parents or teachers to monitor game progress and adjust game content based on the feedback. For example, learning games add a feature that allows parents or teachers to monitor game progress in real time. For example, a dashboard is provided that allows parents or teachers to see which problems a child is struggling with. The learning game also collects feedback provided by parents or teachers and reflects it in the game content. For example, the child may enter feedback about areas in which they are weak. The learning game also adjusts the game content based on the feedback. For example, the learning game provides content that focuses on specific areas based on the feedback. The learning game also adjusts the difficulty of the game based on the feedback. For example, the learning game determines whether to increase or decrease the difficulty of the game based on the feedback. This allows parents or teachers to monitor game progress and adjust the game content based on the feedback.

[0072] The learning game can use the emotion estimation function to identify the game elements that a child enjoys most and provide a new game that enhances those elements. The learning game, for example, uses the emotion estimation function to identify the game elements that a child enjoys most. For example, it analyzes facial expressions and tone of voice to determine whether the child is enjoying the game. The learning game then provides a new game that enhances the identified game elements. For example, it provides an enhanced version of a mini-game that the child enjoys. The learning game also adds a new stage based on the game elements that the child enjoys. For example, it provides a stage that enhances the action elements that the child enjoys. This makes it possible to provide a new game that enhances the game elements that the child enjoys most.

[0073] The story can estimate a child's emotional state in real time and adjust the story development according to the emotion. The story, for example, analyzes the child's facial expressions and vocal tone to estimate the emotional state in real time. For example, a camera and a microphone are used to detect changes in the child's facial expressions and voice and calculate an emotion score. The story also adjusts the story development based on the emotional state estimated by the emotion estimation unit. For example, if the child is having fun, the story is made more thrilling. If the child is tired, the story is made more relaxing. The story also adjusts the behavior of characters according to the emotional state. For example, if the child is excited, a scene is added in which the character takes action. If the child is relaxed, a scene is added in which the character behaves calmly. This makes it possible to provide a story development that suits the child's emotional state.

[0074] The story can analyze a child's learning style and interests based on choices and actions within the story and customize the next story content. For example, the story collects choices and actions within the story as data and analyzes a child's learning style and interests. For example, it records which choices were chosen and which actions were taken. The story also customizes the next story content based on the analysis results. For example, it changes the content of the next scene based on the choice chosen by the child. The story also adjusts the progress of the story based on the child's actions. For example, if a child takes a specific action, it provides a development that corresponds to that action. This makes it possible to provide story content based on a child's learning style and interests.

[0075] The story can analyze the progress of the story, and if a particular skill is lacking, insert a scene to strengthen that skill. For example, the story collects the progress of the story as data and analyzes whether a particular skill is lacking. For example, if a child makes repeated mistakes on a particular problem, it determines that that skill is lacking. Furthermore, if a particular skill is lacking, the story inserts a scene to strengthen that skill. For example, if calculation skills are lacking, a scene to solve a calculation problem is provided. Furthermore, the story provides a practice scene to strengthen a particular skill. For example, if grammar skills are lacking, a practice scene to solve a grammar problem is provided. In this way, if a particular skill is lacking, a scene to strengthen that skill can be provided.

[0076] The stories can provide multicultural learning stories that incorporate different cultures and historical backgrounds. For example, stories that incorporate different cultures and historical backgrounds can be provided. For example, stories that teach African history and culture can be provided to help children understand multiple cultures. Furthermore, stories that incorporate cultures from different eras and regions can be provided. For example, stories that teach ancient Egyptian culture can be provided. Furthermore, stories that incorporate different historical events can be provided. For example, stories that teach the history of World War II can be provided. In this way, multicultural learning stories that incorporate different cultures and historical backgrounds can be provided.

[0077] Stories can add a function that allows parents and teachers to monitor the progress of a story and adjust the story content based on the feedback. For example, Stories can add a function that allows parents and teachers to monitor the progress of a story in real time. For example, it can provide a dashboard that allows them to see which scenes a child is struggling with. Stories can also collect feedback provided by parents and teachers and reflect it in the story content. For example, a child can enter feedback about areas in which they are struggling. Stories can also adjust the story content based on the feedback. For example, it can provide scenes that focus on specific areas based on the feedback. Stories can also adjust the difficulty of the story based on the feedback. For example, it can decide whether to increase or decrease the difficulty of the story based on the feedback. This allows parents and teachers to monitor the progress of a story and adjust the story content based on the feedback.

[0078] The story can use the emotion estimation function to identify the story elements that a child is most interested in and provide a new story that enhances those elements. The story, for example, uses the emotion estimation function to identify the story elements that a child is most interested in. For example, it analyzes facial expressions and voice tone to determine whether the child is interested. The story then provides a new story that enhances the identified story elements. For example, it provides a story that enhances the adventure elements that the child is interested in. The story also adds new scenes based on the story elements that the child is interested in. For example, it provides a scene that enhances the mystery elements that the child is interested in. This makes it possible to provide a new story that enhances the story elements that the child is most interested in.

[0079] The quiz can estimate a child's emotional state in real time and adjust the difficulty and content of the quiz according to the emotion. The quiz, for example, analyzes the child's facial expression and vocal tone to estimate the emotional state in real time. For example, a camera and a microphone are used to detect changes in the child's facial expression and voice and calculate an emotion score. The quiz also adjusts the difficulty and content of the quiz based on the emotional state estimated by the emotion estimation unit. For example, if the child is having fun, the difficulty of the quiz is increased. If the child is tired, the difficulty of the quiz is decreased. The quiz also adjusts the content of the quiz according to the emotional state. For example, if the child is excited, a quiz with many action elements is provided. If the child is relaxed, a quiz with many puzzle elements is provided. This makes it possible to provide the difficulty and content of the quiz according to the child's emotional state.

[0080] The quiz analyzes the quiz answer history, and if understanding in a particular field is lacking, additional quizzes related to that field can be provided. For example, the quiz collects the quiz answer history as data and analyzes whether understanding in a particular field is lacking. For example, if a child repeatedly makes mistakes on a particular question, it can be determined that the child does not understand that field. Furthermore, if understanding in a particular field is lacking, the quiz provides additional quizzes related to that field. For example, if understanding of history is lacking, additional quizzes related to history can be provided. Furthermore, the quiz provides practice questions to deepen understanding in a particular field. For example, if understanding of geography is lacking, practice questions related to geography can be provided. In this way, if understanding in a particular field is lacking, additional quizzes related to that field can be provided.

[0081] The quiz can analyze the progress of the quiz, and if a particular skill is lacking, insert a quiz to strengthen that skill. For example, the quiz collects data on the progress of the quiz and analyzes whether a particular skill is lacking. For example, if a child repeatedly makes mistakes on a particular question, it is determined that that skill is lacking. Furthermore, if a particular skill is lacking, the quiz inserts a quiz to strengthen that skill. For example, if a child is lacking in calculation skills, a quiz to solve calculation problems is provided. Furthermore, the quiz provides practice questions to strengthen a particular skill. For example, if a child is lacking in grammar skills, practice questions to solve grammar problems are provided. In this way, if a particular skill is lacking, a quiz to strengthen that skill can be provided.

[0082] The quiz can provide a multidisciplinary quiz that incorporates different learning fields. For example, the quiz can provide a quiz that combines different learning fields. For example, a quiz can be provided that combines music and mathematics, in which calculation problems are solved to the rhythm. Alternatively, the quiz can provide a quiz that combines art and science. For example, a quiz can be provided that teaches basic scientific principles through artwork. Alternatively, the quiz can be provided that combines history and geography. For example, a quiz can be provided that teaches geographical knowledge through historical events. In this way, it is possible to provide a multidisciplinary quiz that incorporates different learning fields.

[0083] The quiz may add a function that allows parents or teachers to monitor the progress of the quiz and adjust the quiz content based on the feedback. For example, the quiz may add a function that allows parents or teachers to monitor the progress of the quiz in real time. For example, it may provide a dashboard that allows them to see which questions a child is struggling with. The quiz may also collect feedback provided by parents or teachers and reflect it in the quiz content. For example, the quiz may input as feedback information about areas in which the child is weak. The quiz may also adjust the quiz content based on the feedback. For example, it may provide questions that focus on specific areas based on the feedback. The quiz may also adjust the difficulty of the quiz based on the feedback. For example, it may decide whether to increase or decrease the difficulty of the quiz based on the feedback. This allows parents and teachers to monitor the progress of the quiz and adjust the quiz content based on the feedback.

[0084] The quiz can use the emotion estimation function to identify the quiz elements that a child enjoys most and provide a new quiz that enhances those elements. For example, the quiz can use the emotion estimation function to identify the quiz elements that a child enjoys most. For example, the quiz can analyze facial expressions and tone of voice to determine whether the child is enjoying the quiz. The quiz can then provide a new quiz that enhances the identified quiz elements. For example, the quiz can provide an enhanced version of a mini-game that the child enjoys. The quiz can also add new questions based on the quiz elements that the child enjoys. For example, the quiz can provide questions that enhance the action elements that the child enjoys. This makes it possible to provide a new quiz that enhances the quiz elements that the child enjoys most.

[0085] The learning progress can estimate a child's emotional state in real time and provide feedback according to the emotion. For example, the learning progress analyzes the child's facial expressions and vocal tone to estimate the emotional state in real time. For example, a camera and a microphone are used to detect changes in the child's facial expressions and voice and calculate an emotion score. The learning progress also provides feedback based on the emotional state estimated by the emotion estimation unit. For example, if the child is having fun, positive feedback is provided. If the child is tired, relaxing feedback is provided. The learning progress also adjusts the content of the feedback according to the emotional state. For example, if the child is excited, encouraging feedback is provided. If the child is relaxed, calm feedback is provided. This makes it possible to provide feedback according to the child's emotional state.

[0086] Learning progress can analyze learning progress data and, if understanding in a particular area is lacking, provide additional learning content related to that area. Learning progress, for example, collects learning progress data as data and analyzes whether understanding in a particular area is lacking. For example, if a child repeatedly makes mistakes on a particular problem, it can be determined that the child does not understand that area. Furthermore, if understanding in a particular area is lacking, learning progress can provide additional learning content related to that area. For example, if understanding of history is lacking, additional learning content related to history can be provided. Furthermore, learning progress can provide practice questions to deepen understanding of a particular area. For example, if understanding of geography is lacking, practice questions related to geography can be provided. In this way, if understanding in a particular area is lacking, additional learning content related to that area can be provided.

[0087] Learning progress can analyze a child's learning style and interests based on learning progress data and customize the next learning step. Learning progress, for example, collects learning progress data and analyzes a child's learning style and interests. For example, it stores in a database the level of understanding in each area. Learning progress also customizes the next learning step based on the analysis results. For example, it changes the content of the next step based on the child's learning style. Learning progress also adjusts the content of the next step based on the child's interests. For example, it provides content related to areas that the child is interested in. This makes it possible to provide the next learning step based on the child's learning style and interests.

[0088] Learning progress can add a function that allows parents and teachers to monitor learning progress and adjust learning content based on the feedback. Learning progress, for example, adds a function that allows parents and teachers to monitor learning progress in real time. For example, it provides a dashboard that allows them to see in which areas a child is struggling. Learning progress also collects feedback provided by parents and teachers and reflects it in the learning content. For example, it enters as feedback the content that a child is weak in in a particular area. Learning progress also adjusts the learning content based on the feedback. For example, it provides content that focuses on a particular area based on the feedback. Learning progress also adjusts the difficulty of learning based on the feedback. For example, it decides whether to increase or decrease the difficulty of learning based on the feedback. This allows parents and teachers to monitor learning progress and adjust learning content based on the feedback.

[0089] Learning progress can provide a ranking function that allows learning progress data to be compared with other children and brings out a competitive spirit. Learning progress, for example, provides a function that allows learning progress data to be compared with other children and displays a ranking. For example, it can display the ranking among children in the same grade. Learning progress also provides a reward system that brings out a competitive spirit. For example, it can provide special content to children who rank highly. Learning progress also adjusts the frequency of updating the ranking. For example, it can update the ranking every week to maintain children's competitive spirit. This makes it possible to provide a ranking function that allows learning progress data to be compared with other children and bring out a competitive spirit.

[0090] The learning progress can use the emotion estimation function to identify the learning element that a child enjoys most and provide new learning content that reinforces that element. The learning progress, for example, uses the emotion estimation function to identify the learning element that a child enjoys most. For example, it analyzes facial expressions and tone of voice to determine whether the child is enjoying it. The learning progress then provides new learning content that reinforces the identified learning element. For example, it provides an enhanced version of a mini-game that the child enjoys. The learning progress also adds new steps based on the learning element that the child enjoys. For example, it provides steps that reinforce action elements that the child enjoys. This makes it possible to provide new learning content that reinforces the learning element that the child enjoys most.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The learning experience system can also monitor a child's health condition and provide learning content appropriate to the child's health. For example, if a child has a cold, light learning content that does not drain the child's energy can be provided. On the other hand, if a child is in good health, more active learning content can be provided. Furthermore, the system can suggest break times based on the child's health condition. For example, encouraging a child to take an appropriate break after a long period of study can help maintain the child's concentration. This makes it possible to provide a learning experience appropriate to the child's health condition.

[0093] The learning experience system can further use the child's emotion estimation function to identify the environment in which the child finds the most relaxing and provide learning content based on that environment. For example, the system can analyze the background sounds and lighting settings when the child is relaxing and recreate that environment. Also, by providing relaxing music and videos, the effectiveness of learning can be enhanced. Furthermore, the learning progress can be adjusted based on the relaxing environment. For example, extending the study time in a relaxing environment can help maintain the child's concentration. This allows the system to provide a learning experience based on the child's most relaxing environment.

[0094] The learning experience system can also provide a dashboard that visualizes learning progress based on a child's learning history. For example, it can display the child's level of understanding in each subject using graphs and charts. It can also set goals according to learning progress and display the degree of achievement in real time. It can also introduce a reward system based on learning progress. For example, by awarding badges or points when a specific goal is achieved, it can increase a child's motivation to learn. This makes it possible to provide a progress visualization and reward system based on a child's learning history.

[0095] The learning experience system can also detect changes in a child's interests and, if their interest wanes, provide interactive content to stimulate new interests. For example, if a child loses interest in a particular topic, it can suggest new related topics. It can also re-stimulate interest by providing quizzes or games based on topics that interest children. It can also provide storytelling-style content to stimulate interest. For example, it can stimulate a child's interest by progressing learning through adventure or mystery-themed stories. This makes it possible to provide an interactive learning experience that responds to changes in a child's interests.

[0096] The learning experience system can further customize learning content based on feedback from parents and teachers. For example, it can collect feedback provided by parents and teachers and provide content to improve understanding in a particular area. It can also adjust learning progress based on the feedback. For example, if understanding in a particular area is lacking, it can provide additional learning content related to that area. It can also adjust the difficulty of learning based on the feedback. For example, it can decide whether to make the content more or less difficult based on the feedback. This allows learning content to be customized based on feedback from parents and teachers.

[0097] The learning experience system can further refer to the learning data of a child's friends and siblings to provide content that promotes collaborative learning and competition. For example, it can provide a quiz that allows a child to compete with friends in the same grade. It can also provide game-style content to promote collaborative learning. For example, it can provide interactive content that allows a child to study together with friends or siblings. It can also provide a ranking function to promote competition. For example, it can display a ranking that compares the learning results of friends or siblings. This makes it possible to provide content that promotes collaborative learning and competition.

[0098] The learning experience system can further use the emotion estimation function to identify the learning style that a child enjoys most and provide new learning content based on that style. For example, it can analyze facial expressions and tone of voice to determine whether the child is enjoying the learning. It can also provide new learning content based on the identified learning style. For example, it can provide game-style content that matches the learning style that the child enjoys. It can also provide story-style content based on the learning style that the child enjoys. This makes it possible to provide content based on the learning style that the child enjoys most.

[0099] The learning experience system can also estimate a child's emotional state in real time and adjust the learning progress according to the child's emotions. For example, if the child is having fun, the learning progress can be smoothly continued. On the other hand, if the child is tired, the learning progress can be temporarily slowed down. Furthermore, the learning content can be adjusted according to the child's emotional state. For example, if the child is excited, active learning content can be provided. On the other hand, if the child is relaxed, calm learning content can be provided. In this way, the learning progress and content can be provided according to the child's emotional state.

[0100] The learning experience system can also analyze a child's learning style and interests to customize the next learning step. For example, it can collect a child's learning data and analyze the child's level of understanding in each area. It can also suggest the next learning step based on the analysis results. For example, if a child's understanding in a particular area is lacking, it can provide additional learning content related to that area. It can also adjust the learning progress based on the child's learning style. For example, if a child prefers a visual learning style, it can provide a learning step that includes a lot of visual content. This makes it possible to provide the child with the next learning step based on their learning style and interests.

[0101] The learning experience system can further use its emotion estimation function to identify the learning elements in which a child is most interested and provide new learning content that reinforces those elements. For example, it can analyze facial expressions and voice tone to determine whether a child is interested. It can also provide new learning content that reinforces the identified learning elements. For example, it can provide quizzes or games based on themes that the child is interested in. It can also add new steps based on the learning elements that the child is interested in. This makes it possible to provide new learning content that reinforces the learning elements that the child is most interested in.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: Generative AI generates learning content based on the child's age, interests, and learning style. For example, the generative AI selects appropriate learning content based on the child's age and interests. Step 2: The customization unit provides learning content generated by the generative AI. For example, the customization unit adjusts the content to suit the child's learning style. Step 3: The learning content generator actually displays the learning content provided by the customization unit. For example, the learning content generator can display learning content in the form of a game, a story, or a quiz.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. Equipped with generative AI, The generated AI is A customization unit that generates learning content based on the child's age, interests, and learning style; a study content generation unit that provides the study content generated by the customization unit. A system characterized by:

2. Learning games are Estimates a child's emotional state in real time and adjusts the difficulty and content of the game according to their emotions 2. The system of claim 1.

3. The story is Estimates a child's emotional state in real time and adjusts the storyline accordingly 2. The system of claim 1.

4. The quiz is Estimates a child's emotional state in real time and adjusts the difficulty and content of the quiz according to their emotions 2. The system of claim 1.

5. The learning progress is Estimate a child's emotional state in real time and provide emotional feedback 2. The system of claim 1.

Images