system

Abstract

The system according to this embodiment aims to provide educational games tailored to the user's learning style and needs. [Solution] The system according to the embodiment comprises an input unit, a generation unit, a provision unit, a management unit, and a feedback unit. The input unit inputs the user's learning style and needs. The generation unit generates an educational game based on the information input by the input unit. The provision unit provides the educational game generated by the generation unit. The management unit manages the progress of the educational game provided by the provision unit. The feedback unit provides feedback based on the progress managed by the management unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide educational content tailored to the learning styles and needs of users, and there is room for improvement in maximizing the learning effect.

[0005] The system according to the embodiment aims to provide an educational game tailored to the learning styles and needs of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a generation unit, a provision unit, a management unit, and a feedback unit. The input unit receives input from the user regarding their learning style and needs. The generation unit generates an educational game based on the information input by the input unit. The provision unit provides the educational game generated by the generation unit. The management unit manages the progress of the educational game provided by the provision unit. The feedback unit provides feedback based on the progress managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide educational games tailored to the user's learning style and needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The educational game generation system according to an embodiment of the present invention is a system that uses AI technology to generate educational games tailored to each user's learning style. This educational game generation system allows users to learn at their own pace and maximize learning effectiveness. Specifically, the system takes the user's learning style and learning needs as input, and the generating AI analyzes this information to generate an optimal educational game. The generated educational game's content and difficulty level are adjusted considering the user's learning pace and interests. For example, quizzes and puzzles can be incorporated into the game to allow users to learn while having fun. Furthermore, it is possible to provide games specialized in specific subjects, helping users deepen their understanding of those subjects. For example, games specialized in subjects such as mathematics or English can be provided. Also, learning through games makes learning itself enjoyable and encourages continued learning. For example, by introducing a system where users can earn rewards or titles each time they complete a game, motivation to learn can be maintained. This system allows users who struggle to maintain motivation to study or who have difficulty tackling difficult problems to learn effectively while having fun. It also solves the problem of educational platforms failing to attract user interest. This allows the educational game generation system to maximize learning effectiveness by generating, providing, managing, and providing feedback on educational games tailored to the user's learning style and needs.

[0029] The educational game generation system according to this embodiment comprises an input unit, a generation unit, a provision unit, a management unit, and a feedback unit. The input unit receives input from the user regarding their learning style and needs. For example, if a user wants to learn a specific area of ​​mathematics, they can input that information into the system. The generation unit uses a generation AI to generate an educational game based on the information input by the input unit. The generation AI adjusts the game content and difficulty level, taking into account the user's learning pace and interests. For example, the generation AI can incorporate quizzes and puzzles into the game so that the user can learn while having fun. The provision unit provides the educational game generated by the generation unit to the user. For example, if the user wishes to focus on learning a specific subject, the provision unit can provide a game focused on that subject. The management unit manages the progress of the educational game provided by the provision unit. For example, the management unit can provide hints and explanations when the user is tackling difficult problems. The feedback unit provides feedback based on the progress managed by the management unit. For example, the feedback unit can provide rewards and titles each time the user completes a game. As a result, the educational game generation system according to this embodiment can maximize learning effectiveness by generating, providing, managing, and providing feedback on educational games tailored to the user's learning style and needs.

[0030] The input section allows users to input their learning style and needs. For example, if a user wants to learn a specific area of ​​mathematics, they can input that information into the system. Specifically, users can input details such as their learning goals, current level of understanding, and preferred learning methods. For instance, if a user wants to learn the basics of algebra, they can select fundamental algebraic concepts and types of specific problems. Information about the user's learning style can also be input. For example, a user who prefers visual learning might want materials that heavily utilize diagrams and graphs, while a user who prefers auditory learning might want audio explanations and interactive materials. Furthermore, the user can input their learning pace and time constraints. For example, information can be entered regarding whether a user can dedicate 30 minutes of study time each day or whether they prefer to study intensively on weekends. This allows the input section to accurately grasp the user's detailed learning style and needs, providing the information necessary for generating educational games in the subsequent generation section.

[0031] The generation unit uses a generation AI to generate educational games based on information input by the input unit. The generation AI adjusts the game content and difficulty level, taking into account the user's learning pace and interests. Specifically, the generation AI analyzes the user's input information and designs the optimal educational game scenario. For example, if a user wants to learn algebra, the generation AI will generate quizzes and puzzles that include basic algebraic concepts. The generation AI analyzes the user's learning history and progress in real time and provides problems of appropriate difficulty. For example, if a user can solve easy problems smoothly, it will then present slightly more difficult problems, and conversely, if the user gets stuck on a difficult problem, it will adjust the game back to basic problems. The generation AI can also incorporate stories and characters into the game to engage the user. For example, if a user prefers adventure-themed games, the generation AI can generate an adventure game that progresses by solving algebraic problems. Furthermore, the generation AI can continuously improve the game content based on user feedback, providing a more effective learning experience. In this way, the generation unit can generate educational games optimized for the user's learning style and needs, providing an environment where users can learn effectively while having fun.

[0032] The provider unit delivers educational games generated by the generator unit to the user. Specifically, if the user wishes to focus on learning a particular subject, the provider unit can provide a game tailored to that subject. For example, if the user wants to learn algebra, the provider unit will provide an educational game focused on algebra. The provider unit will either allow the user to download the game to their device or make it accessible online. Furthermore, the provider unit can provide new games and additional content at appropriate times according to the user's learning progress. For example, if the user completes a certain level of a game, the provider unit will automatically provide the next level of the game. The provider unit can also provide individually customized learning plans based on the user's learning history and progress. For example, if the user has difficulties in a particular area, the provider unit will provide a game designed to focus on that area. This allows the provider unit to ensure that the user always has access to the most suitable learning content and maximize learning effectiveness.

[0033] The administration department manages the progress of educational games provided by the service provider. Specifically, the administration department can provide hints and explanations when users are struggling with difficult problems. For example, if a user gets stuck on a particular problem, the administration department can display hints and explanations related to that problem to help the user deepen their understanding. The administration department can also monitor users' learning progress in real time and adjust the learning plan as needed. For example, if a user is progressing faster than planned, the administration department can provide the next level of content earlier, or conversely, if progress is behind schedule, it can provide games to review basic content. Furthermore, the administration department analyzes users' learning data and supports them in achieving long-term learning goals. For example, by setting goals that users should achieve within a specific period and regularly evaluating their progress, the administration department can help users learn effectively towards their goals. In this way, the administration department can maximize learning effectiveness by closely managing users' learning progress and providing appropriate support.

[0034] The Feedback Department provides feedback based on progress managed by the Management Department. Specifically, the Feedback Department can provide rewards and titles each time a user completes a game. For example, if a user completes a certain level, they can be awarded a badge or points to commemorate their achievement. The Feedback Department can also provide specific advice and suggest the next learning steps according to the user's learning progress. For example, if a user excels in a particular area, it can suggest more advanced learning content related to that area. Conversely, if a user is struggling with a particular problem, it can suggest a game to review the basic content related to that problem. Furthermore, based on the user's learning data, the Feedback Department provides feedback to maintain motivation toward achieving long-term learning goals. For example, by setting goals that the user should achieve within a certain period and regularly evaluating their progress, the Feedback Department can help users learn effectively toward their goals. In this way, the Feedback Department can provide appropriate feedback according to the user's learning progress and maximize learning effectiveness.

[0035] The generation unit can adjust the game content and difficulty based on the user's learning pace and interests. For example, the generation unit can adjust the game's progression speed according to the user's learning pace. For instance, if the user wants to learn slowly, the generation unit can slow down the game's progression speed. Conversely, if the user wants to learn quickly, the generation unit can speed up the game's progression speed. Furthermore, the generation unit can adjust the game content based on the user's interests. For example, the generation unit can incorporate quizzes and puzzles related to topics the user is interested in into the game. This makes it possible to adjust the game content and difficulty according to the user's learning pace and interests. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information about the user's learning pace and interests into a generation AI, which can then analyze that information to adjust the game content and difficulty.

[0036] The service provider can offer games related to a specific subject if the user desires specialized learning in that subject. For example, if the user wants to learn a specific area of ​​mathematics, the service provider can offer games specialized in that area. For example, if the user wants to learn algebra, the service provider can offer games that include quizzes and puzzles related to algebra. The service provider can also offer games specialized in a specific English skill if the user wants to improve that skill. For example, if the user wants to improve their vocabulary, the service provider can offer games that include quizzes and puzzles related to vocabulary. Furthermore, if the user wants to learn a specific topic in science, the service provider can offer games specialized in that topic. For example, if the user wants to understand a specific concept in physics, the service provider can offer games that include quizzes and puzzles related to that concept. This allows the service provider to offer appropriate educational games to users who desire specialized learning in a specific subject. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input information about the user's learning preferences into a generative AI, which can then analyze that information to generate and provide games related to the specific subject.

[0037] The management unit can provide hints and explanations when users are tackling complex problems. For example, when a user is working on a difficult mathematical problem, the management unit can provide step-by-step explanations. For instance, the management unit can explain the solution to the problem step by step, supporting the user in making it easier to understand. The management unit can also provide grammatical rules and example sentences when a user is working on a difficult English grammar problem. For example, the management unit can explain grammatical rules in detail and use example sentences to support the user in making it easier to understand. Furthermore, the management unit can provide visual hints and explanations when a user is working on a difficult scientific concept. For example, the management unit can visually explain the concept using diagrams and graphs to support the user in making it easier to understand. This allows the management unit to provide appropriate support when users are tackling difficult problems. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information about the user's problem into a generative AI, which can then analyze that information to generate and provide hints and explanations.

[0038] The feedback unit can provide rewards and titles each time a user completes a game. For example, the feedback unit can award points each time a user completes a math quiz. For example, the feedback unit can award titles each time a user earns a certain number of points. The feedback unit can also award badges each time a user completes an English puzzle. For example, the feedback unit can offer special rewards to users who collect specific badges. Furthermore, the feedback unit can award trophies each time a user completes a science game. For example, the feedback unit can award special titles to users who earn specific trophies. This provides rewards and titles to maintain the user's learning motivation. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the user's game completion information into a generative AI, which then analyzes that information to generate and provide rewards and titles.

[0039] The generation unit can incorporate quizzes and puzzles to generate educational games suited to the user's learning style. For example, if the user prefers visual learning, the generation unit can incorporate visual quizzes and puzzles into the game. For instance, the generation unit can enable the user to solve quizzes using images and diagrams. Furthermore, if the user prefers auditory learning, the generation unit can incorporate audio quizzes and puzzles into the game. For example, the generation unit can enable the user to solve quizzes while listening to audio guidance. Additionally, if the user prefers experiential learning, the generation unit can incorporate interactive quizzes and puzzles into the game. For example, the generation unit can provide puzzles that allow the user to learn by actually manipulating them. This enables the generation of educational games that include quizzes and puzzles tailored to the user's learning style. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information about the user's learning style into a generation AI, which can then analyze that information to generate optimal quizzes and puzzles and incorporate them into the game.

[0040] The input unit can analyze the user's past learning history and select an appropriate input method. For example, the input unit can automatically display learning styles that the user has frequently used in the past as candidates. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest learning styles to be used during specific time periods based on the user's past learning history. For example, the input unit can suggest the optimal input method based on the learning styles the user has used during specific time periods in the past. This allows the input unit to provide the optimal input method based on the user's past learning history. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's past learning history into a generative AI, which can then analyze that information and suggest the optimal input method.

[0041] The input unit can filter the input of learning styles and needs based on the user's current learning status and areas of interest. For example, the input unit can prioritize displaying styles and needs related to the area the user is currently studying. For example, the input unit can suggest relevant learning styles and needs based on the user's areas of interest. The input unit can also filter and display appropriate learning styles and needs according to the user's current learning progress. For example, the input unit can suggest the optimal learning style and needs based on the user's current learning progress. This enables filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's current learning status and areas of interest into a generative AI, which can then analyze that information to suggest the optimal learning style and needs.

[0042] The input unit can prioritize inputting highly relevant information based on the user's geographical location when inputting learning styles and needs. For example, if the user is in a specific region, the input unit can prioritize suggesting learning styles and needs related to that region. For example, the input unit can suggest region-specific learning resources based on the user's geographical location. Furthermore, if the user is traveling, the input unit can prioritize suggesting learning styles and needs related to their travel destination. For example, the input unit can suggest the optimal learning style and needs for the user at their travel destination. This enables the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's geographical location into a generative AI, which can then analyze that information to suggest the optimal learning style and needs.

[0043] The input unit can analyze the user's social media activity and input relevant information when inputting learning styles and needs. For example, the input unit can suggest learning styles and needs based on the user's interests and passions shared on social media. For example, the input unit can suggest learning styles and needs related to current trends based on the user's social media activity. The input unit can also suggest relevant learning styles and needs based on the education-related accounts the user follows. For example, the input unit can suggest the optimal learning style and needs based on information from the accounts the user follows. This makes it possible to input relevant information based on the user's social media activity. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's social media activity into a generative AI, which can then analyze that information and suggest the optimal learning style and needs.

[0044] The generation unit can select appropriate game content based on the user's learning history when generating educational games. For example, the generation unit can generate review games based on content the user has previously learned. For example, the generation unit can suggest what the user should learn next based on their learning history and generate a game based on that. The generation unit can also analyze the user's learning history and generate games specialized in areas where the user struggles. For example, the generation unit can generate games that include quizzes and puzzles related to topics the user has struggled with in the past. This makes it possible to select the optimal game content based on the user's learning history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information about the user's learning history into a generation AI, which can then analyze that information and suggest the optimal game content.

[0045] The generation unit can apply different game algorithms depending on the user's learning style when generating educational games. For example, if the user prefers visual learning, the generation unit can generate games that make extensive use of graphics and animations. For example, if the user prefers auditory learning, the generation unit can generate games that incorporate voice guidance and music. Furthermore, if the user prefers experiential learning, the generation unit can generate games that include many interactive elements. For example, the generation unit can provide games that allow the user to learn while actually interacting with the game. This makes it possible to apply game algorithms that match the user's learning style. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's learning style into a generation AI, which can then analyze that information and propose the optimal game algorithm.

[0046] The generation unit can determine the priority of educational games based on the user's learning progress. For example, the generation unit can prioritize generating games related to the content the user is currently learning. For example, the generation unit can generate games related to the next content the user should learn, depending on their learning progress. The generation unit can also analyze the user's learning progress and prioritize generating games related to content that needs review. For example, the generation unit can generate review games based on content the user has previously learned. This makes it possible to determine game priorities based on the user's learning progress. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input information about the user's learning progress into the generation AI, which can then analyze that information and propose the optimal game priority.

[0047] The generation unit can adjust the game content by referring to the user's relevant learning materials when generating educational games. For example, the generation unit can adjust the game content based on the textbooks and reference books the user is using. For example, the generation unit can refer to materials the user has studied in the past and generate a review game. The generation unit can also adjust the game content based on learning materials the user is accessing online. For example, the generation unit can suggest the most suitable game content based on the learning materials the user is accessing online. This makes it possible to adjust the game content based on the user's relevant learning materials. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information about the user's learning materials into a generation AI, which can then analyze that information and suggest the most suitable game content.

[0048] The service provider can select an appropriate service method when providing educational games by referring to the user's past gameplay history. For example, the service provider can propose the optimal service method based on the user's past gameplay history. For example, the service provider can predict and propose a preferred service method based on the user's gameplay history. The service provider can also analyze the user's past gameplay history and select the most effective service method. For example, the service provider can propose the optimal service method based on the type of game the user has enjoyed playing in the past. This makes it possible to select the optimal service method based on the user's past gameplay history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the user's gameplay history into a generative AI, which can then analyze that information and propose the optimal service method.

[0049] The service provider can apply different delivery methods depending on the user's learning style when providing educational games. For example, if the user prefers visual learning, the service provider can provide a delivery method that makes extensive use of graphics and animations. For example, if the user prefers auditory learning, the service provider can provide a delivery method that incorporates audio guides and music. Furthermore, if the user prefers experiential learning, the service provider can provide a delivery method that includes many interactive elements. For example, the service provider can provide a delivery method that allows the user to learn while actually operating the game. This makes it possible to apply delivery methods according to the user's learning style. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the user's learning style into a generative AI, which can then analyze that information and propose the optimal delivery method.

[0050] The service provider can select an appropriate delivery method based on the user's device information when providing educational games. For example, if the user is using a smartphone, the service provider can provide a delivery method that is adapted to the screen size. For example, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. For example, if the user is using a smartwatch, the service provider can provide information that can be viewed in a short amount of time. This makes it possible to select the optimal delivery method based on the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the user's device into a generative AI, which can then analyze that information and propose the optimal delivery method.

[0051] The service provider can analyze the user's learning environment and adjust the delivery method when providing educational games. For example, if the user is learning in a quiet environment, the service provider can provide a delivery method that includes audio guidance. For example, if the user is learning in a noisy environment, the service provider can provide a delivery method that emphasizes visual information. Furthermore, if the user is learning while on the go, the service provider can provide a delivery method that can be completed in a short time. For example, if the user is learning while on the go, the service provider can provide information that can be reviewed in a short time. This makes it possible to adjust the delivery method based on the user's learning environment. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input information about the user's learning environment into a generative AI, which can then analyze that information and propose the optimal delivery method.

[0052] The management unit can select the optimal management method by referring to the user's past learning data when managing learning progress. For example, the management unit can propose the optimal method based on the progress management methods the user has used in the past. For example, the management unit can predict and propose an effective progress management method from the user's past learning data. The management unit can also analyze the user's past learning data and select the most efficient progress management method. For example, the management unit can propose the optimal method based on the progress management methods the user has preferred to use in the past. This makes it possible to select the optimal management method based on the user's past learning data. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input information about the user's past learning data into a generative AI, which can then analyze that information and propose the optimal progress management method.

[0053] The management unit can apply different management methods to users according to their learning style when managing learning progress. For example, if a user prefers visual learning, the management unit can provide a progress management method that makes extensive use of graphics and charts. For example, if a user prefers auditory learning, the management unit can provide a progress management method that incorporates audio guidance. Furthermore, if a user prefers experiential learning, the management unit can provide a progress management method that includes interactive elements. For example, the management unit can provide a method that allows users to check their progress while actually operating the system. This makes it possible to apply management methods that are tailored to the user's learning style. Some or all of the above-described processes in the management unit may be performed using, for example, generative AI, or not using generative AI. For example, the management unit can input information about the user's learning style into a generative AI, which can then analyze that information and propose the optimal progress management method.

[0054] The management unit can determine management priorities based on the user's learning goals when managing learning progress. For example, if the user has short-term goals, the management unit can determine progress management priorities based on those goals. For example, if the user has long-term goals, the management unit can determine progress management priorities based on those goals. The management unit can also propose the optimal progress management method according to the user's learning goals. For example, the management unit can propose the optimal progress management method for the user to achieve short-term goals. This makes it possible to determine management priorities based on the user's learning goals. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information about the user's learning goals into a generative AI, which can then analyze that information and propose the optimal progress management method.

[0055] The management unit can analyze the user's learning environment and adjust the management method when managing learning progress. For example, if the user is learning in a quiet environment, the management unit can provide a detailed progress management method. For example, if the user is learning in a noisy environment, the management unit can provide a simple and highly visible progress management method. Furthermore, if the user is learning while on the go, the management unit can provide a progress management method that can be checked in a short amount of time. For example, if the user is learning while on the go, the management unit can provide information that can be checked in a short amount of time. This makes it possible to adjust the management method based on the user's learning environment. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information about the user's learning environment into a generative AI, which can then analyze that information and propose the optimal management method.

[0056] The feedback unit can select the most appropriate feedback by referring to the user's past learning achievements when providing feedback. For example, the feedback unit can provide positive feedback based on the user's past achievements. For example, the feedback unit can point out areas for improvement and provide specific advice based on the user's past learning achievements. The feedback unit can also analyze the user's past learning achievements and select the most effective feedback. For example, the feedback unit can propose the most appropriate feedback based on the user's past achievements. This makes it possible to select the most appropriate feedback based on the user's past learning achievements. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input information about the user's past learning achievements into a generative AI, which can then analyze that information and propose the most appropriate feedback.

[0057] The feedback unit can apply different feedback methods depending on the user's learning style when providing feedback. For example, if the user prefers visual learning, the feedback unit can provide feedback that makes extensive use of graphics and charts. For example, if the user prefers auditory learning, the feedback unit can provide feedback that incorporates audio guidance. Furthermore, if the user prefers experiential learning, the feedback unit can provide feedback that includes interactive elements. For example, the feedback unit can provide feedback that allows the user to learn while actually operating the system. This makes it possible to apply feedback methods that match the user's learning style. Some or all of the above processing in the feedback unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback unit can input information about the user's learning style into the generative AI, which can then analyze that information and propose the optimal feedback method.

[0058] The feedback unit can determine the priority of feedback based on the user's learning objectives when providing feedback. For example, if the user has short-term goals, the feedback unit can determine the priority of feedback based on those goals. For example, if the user has long-term goals, the feedback unit can determine the priority of feedback based on those goals. The feedback unit can also suggest the optimal feedback method according to the user's learning objectives. For example, the feedback unit can suggest the optimal feedback method for the user to achieve short-term goals. This makes it possible to determine the priority of feedback based on the user's learning objectives. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input information about the user's learning objectives into a generative AI, which can then analyze that information and suggest the optimal feedback method.

[0059] The feedback unit can analyze the user's learning environment and adjust the feedback method when providing feedback. For example, if the user is learning in a quiet environment, the feedback unit can provide detailed feedback. For example, if the user is learning in a noisy environment, the feedback unit can provide simple and highly visible feedback. Furthermore, if the user is learning while on the go, the feedback unit can provide feedback that can be checked in a short amount of time. For example, if the user is learning while on the go, the feedback unit can provide information that can be checked in a short amount of time. This makes it possible to adjust the feedback method based on the user's learning environment. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input information about the user's learning environment into a generative AI, which can then analyze that information and propose the optimal feedback method.

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

[0061] The educational game generation system can further provide a dashboard that visualizes learning progress based on the user's learning style. For example, users who prefer visual learning can be provided with progress displays using graphs and charts. Users who prefer auditory learning can be provided with progress reports using audio guidance. Furthermore, users who prefer experiential learning can be provided with interactive progress tracking tools. This allows users to intuitively grasp their learning progress and maintain their motivation to learn.

[0062] The generation unit not only adjusts the game content and difficulty based on the user's learning pace and interests, but can also analyze the user's learning history and automatically incorporate review questions related to past learning. For example, it can add quizzes and puzzles related to topics the user has previously studied. It can also provide questions tailored to areas the user struggles with. Furthermore, based on the user's learning history, it can suggest what to learn next and generate related games. This allows users to learn more efficiently.

[0063] The service provider can not only offer games related to specific subjects when users desire specialized learning in a particular field, but can also select different delivery methods according to the user's learning style. For example, users who prefer visual learning can be offered games that make extensive use of graphics and animations. Users who prefer auditory learning can be offered games that incorporate audio guides and music. Furthermore, users who prefer experiential learning can be offered games with many interactive elements. This allows the service provider to offer the most suitable educational games tailored to each user's learning style.

[0064] The management system can not only provide hints and explanations when users tackle complex problems, but also automatically suggest the next problems to tackle based on the user's learning progress. For example, if a user is struggling with a particular topic, it can provide additional practice problems related to that topic. Furthermore, if a user excels in a particular area, it can provide more advanced problems related to that area. In addition, it can automatically adjust learning priorities and suggest an optimal learning plan based on the user's learning progress. This allows users to learn efficiently.

[0065] The feedback system not only provides rewards and titles to users each time they complete a game, but can also select different feedback methods according to the user's learning style. For example, users who prefer visual learning can be provided with feedback using graphics and charts. Users who prefer auditory learning can be provided with feedback using audio guides. Furthermore, users who prefer experiential learning can be provided with interactive feedback tools. This allows users to intuitively grasp their learning progress and maintain their motivation to learn.

[0066] The input unit can estimate the user's emotions and adjust the input method based on those emotions to suit their learning style and needs. Furthermore, it can analyze the user's past learning history and select the appropriate input method. For example, it can automatically display learning styles frequently used by the user in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. Additionally, it can predict and suggest learning styles to be used during specific time periods based on the user's past learning history. This allows the system to provide the optimal input method based on the user's past learning history.

[0067] The management department can estimate user emotions and adjust learning progress management methods based on those emotions, as well as determine management priorities based on the user's learning goals. For example, if a user has short-term goals, the management department can determine progress management priorities based on those goals. Similarly, if a user has long-term goals, the management department can determine progress management priorities based on those goals. Furthermore, it can propose the optimal progress management method according to the user's learning goals. This allows the management department to provide the most suitable management method based on the user's learning goals.

[0068] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. Furthermore, it can select the most appropriate feedback by referencing the user's past learning achievements. For example, it can provide positive feedback based on the user's past accomplishments. It can also identify areas for improvement and provide specific advice based on the user's past learning achievements. Additionally, it can analyze the user's past learning achievements and select the most effective feedback. This allows for the provision of optimal feedback based on the user's past learning achievements.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The input section is where the user enters their learning style and needs. For example, if the user wants to learn a specific area of ​​mathematics, they can enter that information into the system. Step 2: The generation unit uses a generation AI to generate an educational game based on the information entered by the input unit. The generation AI adjusts the game content and difficulty level, taking into account the user's learning pace and interests. For example, the generation AI can incorporate quizzes and puzzles into the game so that the user can learn while having fun. Step 3: The provider unit provides the user with the educational game generated by the generator unit. For example, if the user wishes to study a specific subject, the provider unit can provide a game specialized in that subject. Step 4: The administration department manages the progress of the educational games provided by the delivery department. For example, the administration department can provide hints and explanations when users are working on difficult problems. Step 5: The Feedback Department provides feedback based on the progress managed by the Management Department. For example, the Feedback Department can provide rewards or titles each time a user completes a game.

[0071] (Example of form 2) The educational game generation system according to an embodiment of the present invention is a system that uses AI technology to generate educational games tailored to each user's learning style. This educational game generation system allows users to learn at their own pace and maximize learning effectiveness. Specifically, the system takes the user's learning style and learning needs as input, and the generating AI analyzes this information to generate an optimal educational game. The generated educational game's content and difficulty level are adjusted considering the user's learning pace and interests. For example, quizzes and puzzles can be incorporated into the game to allow users to learn while having fun. Furthermore, it is possible to provide games specialized in specific subjects, helping users deepen their understanding of those subjects. For example, games specialized in subjects such as mathematics or English can be provided. Also, learning through games makes learning itself enjoyable and encourages continued learning. For example, by introducing a system where users can earn rewards or titles each time they complete a game, motivation to learn can be maintained. This system allows users who struggle to maintain motivation to study or who have difficulty tackling difficult problems to learn effectively while having fun. It also solves the problem of educational platforms failing to attract user interest. This allows the educational game generation system to maximize learning effectiveness by generating, providing, managing, and providing feedback on educational games tailored to the user's learning style and needs.

[0072] The educational game generation system according to this embodiment comprises an input unit, a generation unit, a provision unit, a management unit, and a feedback unit. The input unit receives input from the user regarding their learning style and needs. For example, if a user wants to learn a specific area of ​​mathematics, they can input that information into the system. The generation unit uses a generation AI to generate an educational game based on the information input by the input unit. The generation AI adjusts the game content and difficulty level, taking into account the user's learning pace and interests. For example, the generation AI can incorporate quizzes and puzzles into the game so that the user can learn while having fun. The provision unit provides the educational game generated by the generation unit to the user. For example, if the user wishes to focus on learning a specific subject, the provision unit can provide a game focused on that subject. The management unit manages the progress of the educational game provided by the provision unit. For example, the management unit can provide hints and explanations when the user is tackling difficult problems. The feedback unit provides feedback based on the progress managed by the management unit. For example, the feedback unit can provide rewards and titles each time the user completes a game. As a result, the educational game generation system according to this embodiment can maximize learning effectiveness by generating, providing, managing, and providing feedback on educational games tailored to the user's learning style and needs.

[0073] The input section allows users to input their learning style and needs. For example, if a user wants to learn a specific area of ​​mathematics, they can input that information into the system. Specifically, users can input details such as their learning goals, current level of understanding, and preferred learning methods. For instance, if a user wants to learn the basics of algebra, they can select fundamental algebraic concepts and types of specific problems. Information about the user's learning style can also be input. For example, a user who prefers visual learning might want materials that heavily utilize diagrams and graphs, while a user who prefers auditory learning might want audio explanations and interactive materials. Furthermore, the user can input their learning pace and time constraints. For example, information can be entered regarding whether a user can dedicate 30 minutes of study time each day or whether they prefer to study intensively on weekends. This allows the input section to accurately grasp the user's detailed learning style and needs, providing the information necessary for generating educational games in the subsequent generation section.

[0074] The generation unit uses a generation AI to generate educational games based on information input by the input unit. The generation AI adjusts the game content and difficulty level, taking into account the user's learning pace and interests. Specifically, the generation AI analyzes the user's input information and designs the optimal educational game scenario. For example, if a user wants to learn algebra, the generation AI will generate quizzes and puzzles that include basic algebraic concepts. The generation AI analyzes the user's learning history and progress in real time and provides problems of appropriate difficulty. For example, if a user can solve easy problems smoothly, it will then present slightly more difficult problems, and conversely, if the user gets stuck on a difficult problem, it will adjust the game back to basic problems. The generation AI can also incorporate stories and characters into the game to engage the user. For example, if a user prefers adventure-themed games, the generation AI can generate an adventure game that progresses by solving algebraic problems. Furthermore, the generation AI can continuously improve the game content based on user feedback, providing a more effective learning experience. In this way, the generation unit can generate educational games optimized for the user's learning style and needs, providing an environment where users can learn effectively while having fun.

[0075] The provider unit delivers educational games generated by the generator unit to the user. Specifically, if the user wishes to focus on learning a particular subject, the provider unit can provide a game tailored to that subject. For example, if the user wants to learn algebra, the provider unit will provide an educational game focused on algebra. The provider unit will either allow the user to download the game to their device or make it accessible online. Furthermore, the provider unit can provide new games and additional content at appropriate times according to the user's learning progress. For example, if the user completes a certain level of a game, the provider unit will automatically provide the next level of the game. The provider unit can also provide individually customized learning plans based on the user's learning history and progress. For example, if the user has difficulties in a particular area, the provider unit will provide a game designed to focus on that area. This allows the provider unit to ensure that the user always has access to the most suitable learning content and maximize learning effectiveness.

[0076] The administration department manages the progress of educational games provided by the service provider. Specifically, the administration department can provide hints and explanations when users are struggling with difficult problems. For example, if a user gets stuck on a particular problem, the administration department can display hints and explanations related to that problem to help the user deepen their understanding. The administration department can also monitor users' learning progress in real time and adjust the learning plan as needed. For example, if a user is progressing faster than planned, the administration department can provide the next level of content earlier, or conversely, if progress is behind schedule, it can provide games to review basic content. Furthermore, the administration department analyzes users' learning data and supports them in achieving long-term learning goals. For example, by setting goals that users should achieve within a specific period and regularly evaluating their progress, the administration department can help users learn effectively towards their goals. In this way, the administration department can maximize learning effectiveness by closely managing users' learning progress and providing appropriate support.

[0077] The Feedback Department provides feedback based on progress managed by the Management Department. Specifically, the Feedback Department can provide rewards and titles each time a user completes a game. For example, if a user completes a certain level, they can be awarded a badge or points to commemorate their achievement. The Feedback Department can also provide specific advice and suggest the next learning steps according to the user's learning progress. For example, if a user excels in a particular area, it can suggest more advanced learning content related to that area. Conversely, if a user is struggling with a particular problem, it can suggest a game to review the basic content related to that problem. Furthermore, based on the user's learning data, the Feedback Department provides feedback to maintain motivation toward achieving long-term learning goals. For example, by setting goals that the user should achieve within a certain period and regularly evaluating their progress, the Feedback Department can help users learn effectively toward their goals. In this way, the Feedback Department can provide appropriate feedback according to the user's learning progress and maximize learning effectiveness.

[0078] The generation unit can adjust the game content and difficulty based on the user's learning pace and interests. For example, the generation unit can adjust the game's progression speed according to the user's learning pace. For instance, if the user wants to learn slowly, the generation unit can slow down the game's progression speed. Conversely, if the user wants to learn quickly, the generation unit can speed up the game's progression speed. Furthermore, the generation unit can adjust the game content based on the user's interests. For example, the generation unit can incorporate quizzes and puzzles related to topics the user is interested in into the game. This makes it possible to adjust the game content and difficulty according to the user's learning pace and interests. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information about the user's learning pace and interests into a generation AI, which can then analyze that information to adjust the game content and difficulty.

[0079] The service provider can offer games related to a specific subject if the user desires specialized learning in that subject. For example, if the user wants to learn a specific area of ​​mathematics, the service provider can offer games specialized in that area. For example, if the user wants to learn algebra, the service provider can offer games that include quizzes and puzzles related to algebra. The service provider can also offer games specialized in a specific English skill if the user wants to improve that skill. For example, if the user wants to improve their vocabulary, the service provider can offer games that include quizzes and puzzles related to vocabulary. Furthermore, if the user wants to learn a specific topic in science, the service provider can offer games specialized in that topic. For example, if the user wants to understand a specific concept in physics, the service provider can offer games that include quizzes and puzzles related to that concept. This allows the service provider to offer appropriate educational games to users who desire specialized learning in a specific subject. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input information about the user's learning preferences into a generative AI, which can then analyze that information to generate and provide games related to the specific subject.

[0080] The management unit can provide hints and explanations when users are tackling complex problems. For example, when a user is working on a difficult mathematical problem, the management unit can provide step-by-step explanations. For instance, the management unit can explain the solution to the problem step by step, supporting the user in making it easier to understand. The management unit can also provide grammatical rules and example sentences when a user is working on a difficult English grammar problem. For example, the management unit can explain grammatical rules in detail and use example sentences to support the user in making it easier to understand. Furthermore, the management unit can provide visual hints and explanations when a user is working on a difficult scientific concept. For example, the management unit can visually explain the concept using diagrams and graphs to support the user in making it easier to understand. This allows the management unit to provide appropriate support when users are tackling difficult problems. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information about the user's problem into a generative AI, which can then analyze that information to generate and provide hints and explanations.

[0081] The feedback unit can provide rewards and titles each time a user completes a game. For example, the feedback unit can award points each time a user completes a math quiz. For example, the feedback unit can award titles each time a user earns a certain number of points. The feedback unit can also award badges each time a user completes an English puzzle. For example, the feedback unit can offer special rewards to users who collect specific badges. Furthermore, the feedback unit can award trophies each time a user completes a science game. For example, the feedback unit can award special titles to users who earn specific trophies. This provides rewards and titles to maintain the user's learning motivation. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input the user's game completion information into a generative AI, which then analyzes that information to generate and provide rewards and titles.

[0082] The generation unit can incorporate quizzes and puzzles to generate educational games suited to the user's learning style. For example, if the user prefers visual learning, the generation unit can incorporate visual quizzes and puzzles into the game. For instance, the generation unit can enable the user to solve quizzes using images and diagrams. Furthermore, if the user prefers auditory learning, the generation unit can incorporate audio quizzes and puzzles into the game. For example, the generation unit can enable the user to solve quizzes while listening to audio guidance. Additionally, if the user prefers experiential learning, the generation unit can incorporate interactive quizzes and puzzles into the game. For example, the generation unit can provide puzzles that allow the user to learn by actually manipulating them. This enables the generation of educational games that include quizzes and puzzles tailored to the user's learning style. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input information about the user's learning style into a generation AI, which can then analyze that information to generate optimal quizzes and puzzles and incorporate them into the game.

[0083] The input unit can estimate the user's emotions and adjust the input method for learning style and needs based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Also, if the user is in a hurry, the input unit can prioritize voice input to allow for quick input of learning style and needs. For example, the input unit can allow the user to input learning style and needs using voice input. This allows for adjustment of the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using or without a generative AI. For example, the input unit can input information about the user's emotions into a generative AI, which can analyze that information and suggest the optimal input method.

[0084] The input unit can analyze the user's past learning history and select an appropriate input method. For example, the input unit can automatically display learning styles that the user has frequently used in the past as candidates. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest learning styles to be used during specific time periods based on the user's past learning history. For example, the input unit can suggest the optimal input method based on the learning styles the user has used during specific time periods in the past. This allows the input unit to provide the optimal input method based on the user's past learning history. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's past learning history into a generative AI, which can then analyze that information and suggest the optimal input method.

[0085] The input unit can filter the input of learning styles and needs based on the user's current learning status and areas of interest. For example, the input unit can prioritize displaying styles and needs related to the area the user is currently studying. For example, the input unit can suggest relevant learning styles and needs based on the user's areas of interest. The input unit can also filter and display appropriate learning styles and needs according to the user's current learning progress. For example, the input unit can suggest the optimal learning style and needs based on the user's current learning progress. This enables filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's current learning status and areas of interest into a generative AI, which can then analyze that information to suggest the optimal learning style and needs.

[0086] The input unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of learning styles and needs to be input. For example, if the user is stressed, the input unit can prioritize suggesting relaxing learning styles. For example, if the user is excited, the input unit can prioritize suggesting challenging learning needs. Also, if the user is tired, the input unit can prioritize suggesting easy and relaxing learning styles. For example, the input unit can suggest a learning style that allows the user to relax. This makes it possible to determine the priority of learning styles and needs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit can input information about the user's emotions into a generative AI, which can analyze that information and suggest the optimal learning style and priority of needs.

[0087] The input unit can prioritize inputting highly relevant information based on the user's geographical location when inputting learning styles and needs. For example, if the user is in a specific region, the input unit can prioritize suggesting learning styles and needs related to that region. For example, the input unit can suggest region-specific learning resources based on the user's geographical location. Furthermore, if the user is traveling, the input unit can prioritize suggesting learning styles and needs related to their travel destination. For example, the input unit can suggest the optimal learning style and needs for the user at their travel destination. This enables the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's geographical location into a generative AI, which can then analyze that information to suggest the optimal learning style and needs.

[0088] The input unit can analyze the user's social media activity and input relevant information when inputting learning styles and needs. For example, the input unit can suggest learning styles and needs based on the user's interests and passions shared on social media. For example, the input unit can suggest learning styles and needs related to current trends based on the user's social media activity. The input unit can also suggest relevant learning styles and needs based on the education-related accounts the user follows. For example, the input unit can suggest the optimal learning style and needs based on information from the accounts the user follows. This makes it possible to input relevant information based on the user's social media activity. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input information about the user's social media activity into a generative AI, which can then analyze that information and suggest the optimal learning style and needs.

[0089] The generation unit can estimate the user's emotions and adjust the content and difficulty of the educational game based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a game that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a game that can be completed in a short time. Also, if the user is excited, the generation unit can generate a game with a challenging difficulty level. For example, if the user is excited, the generation unit can generate a game that includes difficult quizzes and puzzles. This makes it possible to adjust the content and difficulty of the educational game according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input information about the user's emotions into the generation AI, which can analyze that information and suggest the optimal game content and difficulty level.

[0090] The generation unit can select appropriate game content based on the user's learning history when generating educational games. For example, the generation unit can generate review games based on content the user has previously learned. For example, the generation unit can suggest what the user should learn next based on their learning history and generate a game based on that. The generation unit can also analyze the user's learning history and generate games specialized in areas where the user struggles. For example, the generation unit can generate games that include quizzes and puzzles related to topics the user has struggled with in the past. This makes it possible to select the optimal game content based on the user's learning history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information about the user's learning history into a generation AI, which can then analyze that information and suggest the optimal game content.

[0091] The generation unit can apply different game algorithms depending on the user's learning style when generating educational games. For example, if the user prefers visual learning, the generation unit can generate games that make extensive use of graphics and animations. For example, if the user prefers auditory learning, the generation unit can generate games that incorporate voice guidance and music. Furthermore, if the user prefers experiential learning, the generation unit can generate games that include many interactive elements. For example, the generation unit can provide games that allow the user to learn while actually interacting with the game. This makes it possible to apply game algorithms that match the user's learning style. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's learning style into a generation AI, which can then analyze that information and propose the optimal game algorithm.

[0092] The generation unit can estimate the user's emotions and adjust the length of the educational game based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a game that can be completed in a short time. For example, if the user is relaxed, the generation unit can generate a game that can be enjoyed for a long time. The generation unit can also generate a game of appropriate length if the user is excited. For example, if the user is excited, the generation unit can generate a game that includes quizzes or puzzles of appropriate length. This makes it possible to adjust the length of the educational game according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input information about the user's emotions into a generation AI, which can analyze that information and suggest the optimal game length.

[0093] The generation unit can determine the priority of educational games based on the user's learning progress. For example, the generation unit can prioritize generating games related to the content the user is currently learning. For example, the generation unit can generate games related to the next content the user should learn, depending on their learning progress. The generation unit can also analyze the user's learning progress and prioritize generating games related to content that needs review. For example, the generation unit can generate review games based on content the user has previously learned. This makes it possible to determine game priorities based on the user's learning progress. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input information about the user's learning progress into the generation AI, which can then analyze that information and propose the optimal game priority.

[0094] The generation unit can adjust the game content by referring to the user's relevant learning materials when generating educational games. For example, the generation unit can adjust the game content based on the textbooks and reference books the user is using. For example, the generation unit can refer to materials the user has studied in the past and generate a review game. The generation unit can also adjust the game content based on learning materials the user is accessing online. For example, the generation unit can suggest the most suitable game content based on the learning materials the user is accessing online. This makes it possible to adjust the game content based on the user's relevant learning materials. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information about the user's learning materials into a generation AI, which can then analyze that information and suggest the most suitable game content.

[0095] The delivery unit can estimate the user's emotions and adjust the delivery method of the educational game based on the estimated user emotions. For example, if the user is nervous, the delivery unit can provide a simple and highly visual delivery method. For example, if the user is relaxed, the delivery unit can provide a delivery method that includes detailed information. Also, if the user is in a hurry, the delivery unit can provide a delivery method that gets straight to the point. For example, if the user is in a hurry, the delivery unit can provide concise and to-the-point information. This makes it possible to adjust the delivery method of the educational game according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not using a generative AI. For example, the delivery unit can input information about the user's emotions into a generative AI, which can analyze that information and propose the optimal delivery method.

[0096] The service provider can select an appropriate service method when providing educational games by referring to the user's past gameplay history. For example, the service provider can propose the optimal service method based on the user's past gameplay history. For example, the service provider can predict and propose a preferred service method based on the user's gameplay history. The service provider can also analyze the user's past gameplay history and select the most effective service method. For example, the service provider can propose the optimal service method based on the type of game the user has enjoyed playing in the past. This makes it possible to select the optimal service method based on the user's past gameplay history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the user's gameplay history into a generative AI, which can then analyze that information and propose the optimal service method.

[0097] The service provider can apply different delivery methods depending on the user's learning style when providing educational games. For example, if the user prefers visual learning, the service provider can provide a delivery method that makes extensive use of graphics and animations. For example, if the user prefers auditory learning, the service provider can provide a delivery method that incorporates audio guides and music. Furthermore, if the user prefers experiential learning, the service provider can provide a delivery method that includes many interactive elements. For example, the service provider can provide a delivery method that allows the user to learn while actually operating the game. This makes it possible to apply delivery methods according to the user's learning style. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the user's learning style into a generative AI, which can then analyze that information and propose the optimal delivery method.

[0098] The service provider can estimate the user's emotions and adjust the order in which educational games are provided based on the estimated emotions. For example, if the user is tense, the service provider can prioritize providing relaxing games. For example, if the user is excited, the service provider can prioritize providing challenging games. Also, if the user is tired, the service provider can prioritize providing easy and relaxing games. For example, if the user is tired, the service provider can provide games that can be completed in a short time. This makes it possible to adjust the order in which educational games are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input information about the user's emotions into a generative AI, which can analyze that information and propose the optimal order of provision.

[0099] The service provider can select an appropriate delivery method based on the user's device information when providing educational games. For example, if the user is using a smartphone, the service provider can provide a delivery method that is adapted to the screen size. For example, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible delivery method. For example, if the user is using a smartwatch, the service provider can provide information that can be viewed in a short amount of time. This makes it possible to select the optimal delivery method based on the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input information about the user's device into a generative AI, which can then analyze that information and propose the optimal delivery method.

[0100] The service provider can analyze the user's learning environment and adjust the delivery method when providing educational games. For example, if the user is learning in a quiet environment, the service provider can provide a delivery method that includes audio guidance. For example, if the user is learning in a noisy environment, the service provider can provide a delivery method that emphasizes visual information. Furthermore, if the user is learning while on the go, the service provider can provide a delivery method that can be completed in a short time. For example, if the user is learning while on the go, the service provider can provide information that can be reviewed in a short time. This makes it possible to adjust the delivery method based on the user's learning environment. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input information about the user's learning environment into a generative AI, which can then analyze that information and propose the optimal delivery method.

[0101] The management unit can estimate the user's emotions and adjust the learning progress management method based on the estimated user emotions. For example, if the user is stressed, the management unit can simplify progress management and reduce the burden. For example, if the user is relaxed, the management unit can provide detailed progress management to enhance the sense of accomplishment in learning. The management unit can also provide a way for the user to quickly check their progress if they are in a hurry. For example, if the user is in a hurry, the management unit can provide a concise and to-the-point progress management method. This makes it possible to adjust the learning progress management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using, for example, generative AI, or not using generative AI. For example, the management unit can input information about the user's emotions into the generative AI, which can analyze that information and propose the optimal progress management method.

[0102] The management unit can select the optimal management method by referring to the user's past learning data when managing learning progress. For example, the management unit can propose the optimal method based on the progress management methods the user has used in the past. For example, the management unit can predict and propose an effective progress management method from the user's past learning data. The management unit can also analyze the user's past learning data and select the most efficient progress management method. For example, the management unit can propose the optimal method based on the progress management methods the user has preferred to use in the past. This makes it possible to select the optimal management method based on the user's past learning data. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input information about the user's past learning data into a generative AI, which can then analyze that information and propose the optimal progress management method.

[0103] The management unit can apply different management methods to users according to their learning style when managing learning progress. For example, if a user prefers visual learning, the management unit can provide a progress management method that makes extensive use of graphics and charts. For example, if a user prefers auditory learning, the management unit can provide a progress management method that incorporates audio guidance. Furthermore, if a user prefers experiential learning, the management unit can provide a progress management method that includes interactive elements. For example, the management unit can provide a method that allows users to check their progress while actually operating the system. This makes it possible to apply management methods that are tailored to the user's learning style. Some or all of the above-described processes in the management unit may be performed using, for example, generative AI, or not using generative AI. For example, the management unit can input information about the user's learning style into a generative AI, which can then analyze that information and propose the optimal progress management method.

[0104] The management unit can estimate the user's emotions and adjust the display method of learning progress based on the estimated user emotions. For example, if the user is nervous, the management unit can provide a simple and highly visible display method. For example, if the user is relaxed, the management unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a concise display method. For example, if the user is in a hurry, the management unit can provide concise and to-the-point information. This makes it possible to adjust the display method of learning progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using a generative AI, or not using a generative AI. For example, the management unit can input information about the user's emotions into a generative AI, which can analyze that information and propose the optimal display method.

[0105] The management unit can determine management priorities based on the user's learning goals when managing learning progress. For example, if the user has short-term goals, the management unit can determine progress management priorities based on those goals. For example, if the user has long-term goals, the management unit can determine progress management priorities based on those goals. The management unit can also propose the optimal progress management method according to the user's learning goals. For example, the management unit can propose the optimal progress management method for the user to achieve short-term goals. This makes it possible to determine management priorities based on the user's learning goals. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information about the user's learning goals into a generative AI, which can then analyze that information and propose the optimal progress management method.

[0106] The management unit can analyze the user's learning environment and adjust the management method when managing learning progress. For example, if the user is learning in a quiet environment, the management unit can provide a detailed progress management method. For example, if the user is learning in a noisy environment, the management unit can provide a simple and highly visible progress management method. Furthermore, if the user is learning while on the go, the management unit can provide a progress management method that can be checked in a short amount of time. For example, if the user is learning while on the go, the management unit can provide information that can be checked in a short amount of time. This makes it possible to adjust the management method based on the user's learning environment. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input information about the user's learning environment into a generative AI, which can then analyze that information and propose the optimal management method.

[0107] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide positive feedback to increase motivation. For example, if the user is relaxed, the feedback unit can provide detailed feedback to enhance the user's sense of accomplishment in learning. The feedback unit can also provide quick and concise feedback if the user is in a hurry. For example, if the user is in a hurry, the feedback unit can provide concise and to-the-point feedback. This makes it possible to adjust the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the feedback unit may be performed using a generative AI, or not using a generative AI. For example, the feedback unit can input information about the user's emotions into a generative AI, which can analyze that information and propose the most appropriate feedback content.

[0108] The feedback unit can select the most appropriate feedback by referring to the user's past learning achievements when providing feedback. For example, the feedback unit can provide positive feedback based on the user's past achievements. For example, the feedback unit can point out areas for improvement and provide specific advice based on the user's past learning achievements. The feedback unit can also analyze the user's past learning achievements and select the most effective feedback. For example, the feedback unit can propose the most appropriate feedback based on the user's past achievements. This makes it possible to select the most appropriate feedback based on the user's past learning achievements. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input information about the user's past learning achievements into a generative AI, which can then analyze that information and propose the most appropriate feedback.

[0109] The feedback unit can apply different feedback methods depending on the user's learning style when providing feedback. For example, if the user prefers visual learning, the feedback unit can provide feedback that makes extensive use of graphics and charts. For example, if the user prefers auditory learning, the feedback unit can provide feedback that incorporates audio guidance. Furthermore, if the user prefers experiential learning, the feedback unit can provide feedback that includes interactive elements. For example, the feedback unit can provide feedback that allows the user to learn while actually operating the system. This makes it possible to apply feedback methods that match the user's learning style. Some or all of the above processing in the feedback unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback unit can input information about the user's learning style into the generative AI, which can then analyze that information and propose the optimal feedback method.

[0110] The feedback unit can estimate the user's emotions and adjust the order in which feedback is provided based on the estimated emotions. For example, if the user is tense, the feedback unit can prioritize providing relaxing feedback. For example, if the user is excited, the feedback unit can prioritize providing challenging feedback. Also, if the user is tired, the feedback unit can prioritize providing simple and relaxing feedback. For example, if the user is tired, the feedback unit can provide feedback that can be reviewed in a short time. This makes it possible to adjust the order in which feedback is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using a generative AI, or not using a generative AI. For example, the feedback unit can input information about the user's emotions into a generative AI, which can analyze that information and propose the optimal order in which feedback is provided.

[0111] The feedback unit can determine the priority of feedback based on the user's learning objectives when providing feedback. For example, if the user has short-term goals, the feedback unit can determine the priority of feedback based on those goals. For example, if the user has long-term goals, the feedback unit can determine the priority of feedback based on those goals. The feedback unit can also suggest the optimal feedback method according to the user's learning objectives. For example, the feedback unit can suggest the optimal feedback method for the user to achieve short-term goals. This makes it possible to determine the priority of feedback based on the user's learning objectives. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input information about the user's learning objectives into a generative AI, which can then analyze that information and suggest the optimal feedback method.

[0112] The feedback unit can analyze the user's learning environment and adjust the feedback method when providing feedback. For example, if the user is learning in a quiet environment, the feedback unit can provide detailed feedback. For example, if the user is learning in a noisy environment, the feedback unit can provide simple and highly visible feedback. Furthermore, if the user is learning while on the go, the feedback unit can provide feedback that can be checked in a short amount of time. For example, if the user is learning while on the go, the feedback unit can provide information that can be checked in a short amount of time. This makes it possible to adjust the feedback method based on the user's learning environment. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback unit can input information about the user's learning environment into a generative AI, which can then analyze that information and propose the optimal feedback method.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The educational game generation system can further provide a dashboard that visualizes learning progress based on the user's learning style. For example, users who prefer visual learning can be provided with progress displays using graphs and charts. Users who prefer auditory learning can be provided with progress reports using audio guidance. Furthermore, users who prefer experiential learning can be provided with interactive progress tracking tools. This allows users to intuitively grasp their learning progress and maintain their motivation to learn.

[0115] The generation unit not only adjusts the game content and difficulty based on the user's learning pace and interests, but can also analyze the user's learning history and automatically incorporate review questions related to past learning. For example, it can add quizzes and puzzles related to topics the user has previously studied. It can also provide questions tailored to areas the user struggles with. Furthermore, based on the user's learning history, it can suggest what to learn next and generate related games. This allows users to learn more efficiently.

[0116] The service provider can not only offer games related to specific subjects when users desire specialized learning in a particular field, but can also select different delivery methods according to the user's learning style. For example, users who prefer visual learning can be offered games that make extensive use of graphics and animations. Users who prefer auditory learning can be offered games that incorporate audio guides and music. Furthermore, users who prefer experiential learning can be offered games with many interactive elements. This allows the service provider to offer the most suitable educational games tailored to each user's learning style.

[0117] The management system can not only provide hints and explanations when users tackle complex problems, but also automatically suggest the next problems to tackle based on the user's learning progress. For example, if a user is struggling with a particular topic, it can provide additional practice problems related to that topic. Furthermore, if a user excels in a particular area, it can provide more advanced problems related to that area. In addition, it can automatically adjust learning priorities and suggest an optimal learning plan based on the user's learning progress. This allows users to learn efficiently.

[0118] The feedback system not only provides rewards and titles to users each time they complete a game, but can also select different feedback methods according to the user's learning style. For example, users who prefer visual learning can be provided with feedback using graphics and charts. Users who prefer auditory learning can be provided with feedback using audio guides. Furthermore, users who prefer experiential learning can be provided with interactive feedback tools. This allows users to intuitively grasp their learning progress and maintain their motivation to learn.

[0119] The generation unit can not only incorporate quizzes and puzzles to generate educational games suited to the user's learning style, but can also estimate the user's emotions and adjust the game content and difficulty based on those emotions. For example, if the user is stressed, it can provide a relaxing game. If the user is excited, it can provide a challenging game. Furthermore, if the user is tired, it can provide an easy and relaxing game. This allows for the provision of optimal educational games tailored to the user's emotions.

[0120] The input unit can estimate the user's emotions and adjust the input method based on those emotions to suit their learning style and needs. Furthermore, it can analyze the user's past learning history and select the appropriate input method. For example, it can automatically display learning styles frequently used by the user in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. Additionally, it can predict and suggest learning styles to be used during specific time periods based on the user's past learning history. This allows the system to provide the optimal input method based on the user's past learning history.

[0121] The delivery unit can not only estimate the user's emotions and adjust the delivery method of educational games based on those estimated emotions, but can also analyze the user's learning environment and adjust the delivery method accordingly. For example, if the user is learning in a quiet environment, a delivery method including audio guidance can be provided. If the user is learning in a noisy environment, a delivery method emphasizing visual information can be provided. Furthermore, if the user is learning while on the go, a delivery method that can be completed in a short time can be provided. This allows for the provision of the optimal delivery method based on the user's learning environment.

[0122] The management department can estimate user emotions and adjust learning progress management methods based on those emotions, as well as determine management priorities based on the user's learning goals. For example, if a user has short-term goals, the management department can determine progress management priorities based on those goals. Similarly, if a user has long-term goals, the management department can determine progress management priorities based on those goals. Furthermore, it can propose the optimal progress management method according to the user's learning goals. This allows the management department to provide the most suitable management method based on the user's learning goals.

[0123] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. Furthermore, it can select the most appropriate feedback by referencing the user's past learning achievements. For example, it can provide positive feedback based on the user's past accomplishments. It can also identify areas for improvement and provide specific advice based on the user's past learning achievements. Additionally, it can analyze the user's past learning achievements and select the most effective feedback. This allows for the provision of optimal feedback based on the user's past learning achievements.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The input section is where the user enters their learning style and needs. For example, if the user wants to learn a specific area of ​​mathematics, they can enter that information into the system. Step 2: The generation unit uses a generation AI to generate an educational game based on the information entered by the input unit. The generation AI adjusts the game content and difficulty level, taking into account the user's learning pace and interests. For example, the generation AI can incorporate quizzes and puzzles into the game so that the user can learn while having fun. Step 3: The provider unit provides the user with the educational game generated by the generator unit. For example, if the user wishes to study a specific subject, the provider unit can provide a game specialized in that subject. Step 4: The administration department manages the progress of the educational games provided by the delivery department. For example, the administration department can provide hints and explanations when users are working on difficult problems. Step 5: The Feedback Department provides feedback based on the progress managed by the Management Department. For example, the Feedback Department can provide rewards or titles each time a user completes a game.

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0129] For example, the input unit can input the user's learning style and needs using the reception device 38 and camera 42 of the smart device 14. The generation unit can generate educational games using the generation AI via the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated educational games to the user using the output device 40 of the smart device 14. The management unit can manage the progress of the educational games via the specific processing unit 290 of the data processing device 12. The feedback unit can provide feedback based on the progress using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the input unit can input the user's learning style and needs using the microphone 238 and camera 42 of the smart glasses 214. The generation unit can generate educational games using the generation AI via the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated educational games to the user using the speaker 240 of the smart glasses 214. The management unit can manage the progress of the educational games via the specific processing unit 290 of the data processing device 12. The feedback unit can provide feedback based on progress using the speaker 240 of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] For example, the input unit can input the user's learning style and needs using the microphone 238 and camera 42 of the headset terminal 314. The generation unit can generate educational games using the generation AI via the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated educational games to the user using the display 343 of the headset terminal 314. The management unit can manage the progress of the educational games via the specific processing unit 290 of the data processing device 12. The feedback unit can provide feedback based on progress using the display 343 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] For example, the input unit can input the user's learning style and needs using the microphone 238 and camera 42 of the robot 414. The generation unit can generate educational games using the generation AI via the specific processing unit 290 of the data processing device 12. The provision unit can provide the generated educational games to the user using the speaker 240 of the robot 414. The management unit can manage the progress of the educational games via the specific processing unit 290 of the data processing device 12. The feedback unit can provide feedback based on the progress using the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0179] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) An input section for entering the user's learning style and needs, A generation unit that generates an educational game based on the information input by the input unit, A providing unit that provides the educational game generated by the generation unit, A management unit that manages the progress of the educational games provided by the aforementioned provisioning unit, The system includes a feedback unit that provides feedback based on the progress managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The generating unit is The game content and difficulty are adjusted based on the user's learning pace and interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, If a user wants to study a subject that is specialized in a particular field, provide them with games related to that subject. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Provides hints and explanations when users are tackling complex problems. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is The game provides rewards and titles to users each time they complete a game. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is To create educational games that suit the user's learning style, incorporate quizzes and puzzles. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is It estimates the user's emotions and adjusts the learning style and how needs are input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Analyze the user's past learning history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is When users input their learning style and needs, the system filters the results based on their current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is It estimates the user's emotions and determines the learning style and priority of needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is When users input their learning style and needs, the system prioritizes inputting information that is highly relevant based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When users input their learning style and needs, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the content and difficulty level of the educational game based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating educational games, appropriate game content is selected based on the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating educational games, different game algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the user's emotions and adjusts the length of the educational game based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating educational games, prioritize games based on the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating educational games, the game content is adjusted by referencing the user's relevant learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how educational games are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing educational games, the appropriate delivery method is selected by referring to the user's past gameplay history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing educational games, different delivery methods should be applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which educational games are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing educational games, the appropriate delivery method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing educational games, we analyze the user's learning environment and adjust the delivery method accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates the user's emotions and adjusts the learning progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing learning progress, the system selects the optimal management method by referring to the user's past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing learning progress, apply different management methods depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates the user's emotions and adjusts how learning progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing learning progress, prioritize management based on the user's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing learning progress, analyze the user's learning environment and adjust the management method accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, the system selects the most appropriate feedback by referring to the user's past learning achievements. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, different feedback methods are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is It estimates the user's emotions and adjusts the order in which feedback is provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, prioritize the feedback based on the user's learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, we analyze the user's learning environment and adjust the feedback method accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering the user's learning style and needs, A generation unit that generates an educational game based on the information input by the input unit, A providing unit that provides the educational game generated by the generation unit, A management unit that manages the progress of the educational games provided by the aforementioned provisioning unit, The system includes a feedback unit that provides feedback based on the progress managed by the aforementioned management unit. A system characterized by the following features.

2. The generating unit is The game content and difficulty are adjusted based on the user's learning pace and interests. The system according to feature 1.

3. The aforementioned supply unit is, If a user wants to study a subject that is specialized in a particular field, provide them with games related to that subject. The system according to feature 1.

4. The aforementioned management department, Provides hints and explanations when users are tackling complex problems. The system according to feature 1.

5. The aforementioned feedback unit is The game provides rewards and titles to users each time they complete a game. The system according to feature 1.

6. The generating unit is To create educational games that suit the user's learning style, incorporate quizzes and puzzles. The system according to feature 1.

7. The aforementioned input unit is It estimates the user's emotions and adjusts the learning style and how needs are input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned input unit is Analyze the user's past learning history and select the appropriate input method. The system according to feature 1.

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