System

Abstract

An object of a system according to an embodiment is to provide a customized educational game based on a learning style or interest of a user.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a learning style and an interest of the user. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an educational game based on the analysis result obtained by the analysis unit. The providing unit provides the educational game generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques fall short in providing customized educational content based on a user's learning style and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to provide educational games that are customized based on the user's learning style and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects information about a user's learning style and interests. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an educational game based on the analysis results obtained by the analysis unit. The provision unit provides the educational game generated by the generation unit to the user. [Effects of the Invention]

[0007] An embodiment of the system can provide educational games that are customized based on a user's learning style and interests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational game generation system according to an embodiment of the present invention analyzes a user's learning style and interests to automatically generate fun and effective educational games. This educational game generation system collects information about the user's learning style and interests, and a generation AI analyzes the collected data to generate an educational game optimized for the user. The generated game is customized to match the user's learning pace and interests, promoting knowledge retention and skill improvement. Because users can learn while having fun, they are more motivated to learn and can absorb knowledge more effectively. Furthermore, this system is an innovative educational platform that supports autonomous learning and maximizes learners' potential. For example, by collecting and analyzing data such as a user's past learning history, topics of interest, and learning progress, the generation AI can identify the user's learning style and interests. The generation AI then analyzes the collected data to generate an educational game optimized for the user. For example, if a user is interested in mathematics, the generation AI can generate a mathematics-related educational game. This provides a personalized learning experience tailored to the user's interests. Furthermore, the generated educational game is customized to match the user's learning pace and interests. For example, the difficulty and content of the game can change according to the user's progress. This allows users to study at their own pace, promoting the solidification of knowledge and the improvement of skills. This allows users to learn while having fun, increasing their motivation for education and allowing them to absorb knowledge more effectively. For example, when users learn through games, learning becomes fun and naturally increases their motivation to learn. This system is also an innovative educational platform that supports autonomous learning and maximizes learners' potential. For example, by allowing users to study at their own pace, an autonomous learning attitude is fostered and learners' potential is brought out. As a result, the educational game generation system can improve learning effectiveness by providing educational games customized based on the user's learning style and interests.

[0029] An educational game generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's learning style and interests. The collection unit collects data such as the user's past learning history, topics of interest, and learning progress. The collection unit, for example, collects the user's past learning content and topics of interest, and a generation AI analyzes the data to understand the user's learning style and interests. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand the user's learning style and interests. For example, the analysis unit analyzes the collected data and provides information for the generation AI to generate an educational game optimal for the user. The generation unit generates an educational game based on the analysis results obtained by the analysis unit. For example, if the user is interested in mathematics, the generation AI can generate an educational game related to mathematics. The generation unit generates the educational game to provide a personalized learning experience tailored to the user's interests. The provision unit provides the educational game generated by the generation unit to the user. The providing unit, for example, provides the generated educational game to the user, allowing the user to learn while having fun. The providing unit, for example, provides an educational game customized to the user's learning pace and interests. In this way, the educational game generation system according to the embodiment can enhance learning effectiveness by providing an educational game customized based on the user's learning style and interests.

[0030] The collection unit can collect the user's past learning history, topics of interest, learning progress, and other related data. The collection unit collects, for example, data such as the user's past learning history, topics of interest, and learning progress. The collection unit can, for example, collect content the user has previously studied and topics of interest, and have the generation AI analyze the data to understand the user's learning style and interests. The collection unit, for example, collects data to understand the user's learning progress based on the user's learning history and provide the user with an optimal learning experience. In this way, by collecting data such as the user's past learning history, topics of interest, and learning progress, a more personalized learning experience can be provided. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's learning history data into the generation AI and have the generation AI understand the user's learning style and interests.

[0031] The analysis unit can analyze the collected data to understand the user's learning style and interests. The analysis unit, for example, analyzes the collected data to understand the user's learning style and interests. The analysis unit, for example, allows a generation AI to analyze the collected data and provide information for generating an optimal educational game for the user. The analysis unit, for example, analyzes the collected data in detail to understand the user's learning style and interests. In this way, by analyzing the collected data, the user's learning style and interests can be accurately understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI understand the user's learning style and interests.

[0032] The generation unit can generate an educational game tailored to the user's interests. For example, if the user is interested in mathematics, the generation AI can generate an educational game related to mathematics. The generation unit generates the educational game to provide a personalized learning experience tailored to the user's interests. For example, the generation unit customizes the content and format of the educational game based on the user's interests. This can increase the user's motivation to learn by generating an educational game tailored to the user's interests. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to generate an educational game.

[0033] The generation unit can customize the difficulty and content of the game to match the user's learning pace. The generation unit, for example, customizes the difficulty and content of the game to match the user's learning pace. The generation unit, for example, sets the difficulty and content of the game to change according to the user's progress. The generation unit, for example, adjusts the difficulty and content of the game based on the user's learning pace. This allows the learning effect to be maximized by customizing the difficulty and content of the game to match the user's learning pace. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's learning pace data into the generation AI and cause the generation AI to customize the difficulty and content of the game.

[0034] The providing unit can provide the generated educational game to the user, allowing the user to learn while having fun. The providing unit, for example, provides the generated educational game to the user, allowing the user to learn while having fun. The providing unit, for example, provides an educational game customized to the user's learning pace and interests. The providing unit, for example, provides the generated educational game to the user, increasing motivation for learning. In this way, by providing the generated educational game, the user can learn while having fun. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the generated educational game into a generating AI and causes the generating AI to execute an optimal method for providing the game to the user.

[0035] The collection unit can analyze the user's past learning history and select the optimal data collection method. The collection unit, for example, analyzes the user's past learning history and selects the optimal data collection method. The collection unit, for example, collects data by prioritizing learning methods that the user has used favorably in the past. The collection unit can also collect data based on learning methods that the user has used in the past with high results. The collection unit can also collect data by excluding learning methods that the user has avoided in the past. In this way, the optimal data collection method can be selected by analyzing the user's past learning history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI. For example, the collection unit can input the user's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

[0036] When collecting learning data, the collection unit can filter the learning data based on the user's current learning environment and areas of interest. For example, if the user is studying in a quiet environment, the collection unit can collect data appropriate for that environment. For example, if the user is interested in a particular subject, the collection unit can also prioritize collecting data related to that subject. For example, if the user prefers online learning, the collection unit can also collect data from online resources. This allows for more relevant data to be collected by filtering the data based on the user's current learning environment and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's learning environment data into a generation AI and have the generation AI filter the data.

[0037] When collecting learning data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. For example, if the user prefers text input, the collection unit can also prioritize collecting text data. For example, if the user prefers image input, the collection unit can also prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting learning data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. For example, if the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. For example, if the user is at home, the collection unit can also prioritize collecting data suitable for home learning. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting learning data. For example, the collection unit collects data related to topics in which the user has shown interest on social media. For example, the collection unit can also collect data useful for learning from the user's social media activities. For example, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0040] When collecting learning data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially uses collection methods for which the user has given favorable feedback in the past. For example, the collection unit can also exclude collection methods for which the user has given negative feedback in the past. For example, the collection unit can also optimize the collection method based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the training data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit can apply a numerical analysis algorithm to mathematical data. For example, the analysis unit can also apply a natural language processing algorithm to English data. For example, the analysis unit can also apply a scientific data analysis algorithm to scientific data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the training data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also improve accuracy by correcting errors from the user's past analysis results. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the learning data. For example, the analysis unit prioritizes analysis of recently submitted data. For example, the analysis unit can also analyze data that was submitted earlier later. For example, the analysis unit can also analyze data that was submitted a medium time ago appropriately. This enables efficient analysis by determining the priority of analysis based on the submission date of the learning data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the learning data to the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the training data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit can also postpone analysis of data with low relevance. For example, the analysis unit can also moderately analyze data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the training data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for a user with high expertise. For example, the analysis unit can avoid technical terminology for a user with low expertise. For example, the analysis unit can use technical terminology moderately for a user with medium expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] When generating an educational game, the generation unit can customize the difficulty and content of the game based on the user's learning pace. For example, if the user is learning at a fast pace, the generation unit can generate a game with a high level of difficulty. For example, if the user is learning at a slow pace, the generation unit can also generate a game with a low level of difficulty. For example, the generation unit can also adjust the content of the game according to the user's learning pace. This allows the learning effect to be maximized by customizing the difficulty and content of the game based on the user's learning pace. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning pace data into the generation AI and cause the generation AI to customize the difficulty and content of the game.

[0048] When generating an educational game, the generation unit can apply different game algorithms depending on the user's interests. For example, if the user is interested in mathematics, the generation unit can apply a game algorithm related to mathematics. For example, if the user is interested in English, the generation unit can also apply a game algorithm related to English. For example, if the user is interested in science, the generation unit can also apply a game algorithm related to science. This can increase the user's motivation to learn by applying different game algorithms depending on the user's interests. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to apply different game algorithms.

[0049] When generating an educational game, the generation unit can improve the accuracy of the game by referring to the user's past game play results. The generation unit, for example, improves the accuracy of the current game based on the user's past game play results. The generation unit can also improve accuracy by correcting errors based on the user's past game play results. The generation unit can also select an optimal game algorithm by referring to the user's past game play results. This allows the accuracy of the game to be improved by referring to the user's past game play results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past game play result data into the generation AI and cause the generation AI to improve the accuracy of the game.

[0050] When generating educational games, the generation unit can adjust the order of games based on the user's relevance. For example, the generation unit prioritizes generating games related to topics in which the user is interested. For example, the generation unit can also prioritize generating highly relevant games according to the user's learning progress. For example, the generation unit can also prioritize generating highly relevant games based on the user's past learning history. In this way, by adjusting the order of games based on the user's relevance, a more effective learning experience can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI and cause the generation AI to adjust the order of games.

[0051] When generating an educational game, the generation unit can adjust the use of technical terminology in the game according to the user's level of expertise. For example, the generation unit generates a game that uses a lot of technical terminology for a user with high level of expertise. For example, the generation unit can also generate a game that avoids technical terminology for a user with low level of expertise. For example, the generation unit can also generate a game that uses technical terminology moderately for a user with intermediate level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, a game that is easier to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] When providing an educational game, the providing unit can select the optimal providing method by referring to the user's past play history. For example, the providing unit prioritizes the providing method of a game that the user has played with preference in the past. For example, the providing unit can also provide the game based on the providing method of a game in which the user has achieved high results in the past. For example, the providing unit can also select the optimal providing method based on the user's past play history. In this way, the optimal providing method can be selected by referring to the user's past play history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past play history data into the generating AI and cause the generating AI to select the optimal providing method.

[0053] When providing an educational game, the providing unit can customize the content to be provided based on the user's current learning situation. The providing unit, for example, provides the user with an optimal game depending on their current learning situation. The providing unit can also customize the content of the game based on the user's learning progress, for example. The providing unit can also select optimal content to be provided based on the user's current learning situation, for example. This allows the user to customize the content to be provided based on the user's current learning situation, thereby providing a more effective learning experience. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current learning situation data into a generating AI and cause the generating AI to customize the content to be provided.

[0054] The providing unit can improve the providing method by reflecting user feedback when providing an educational game. For example, the providing unit prioritizes a providing method for which a user has previously given favorable feedback. For example, the providing unit can also eliminate a providing method for which a user has previously given negative feedback. For example, the providing unit can also optimize the providing method based on user feedback. In this way, the providing method can be optimized by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0055] When providing an educational game, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a game related to that area. For example, if the user is traveling, the providing unit can also provide a game related to the travel destination. For example, if the user is at home, the providing unit can also provide a game suitable for home learning. In this way, the optimal providing method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to select the optimal providing method.

[0056] When providing an educational game, the providing unit can customize the content to be provided by analyzing the user's social media activity. For example, the providing unit provides a game related to a topic in which the user has shown interest on social media. For example, the providing unit can also provide a game that is useful for learning based on the user's social media activity. For example, the providing unit can also provide a related game by referring to the activity of the user's friends on social media. In this way, the content to be provided can be customized by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the content to be provided.

[0057] When providing an educational game, the providing unit can customize the providing method by reflecting the user's past feedback. For example, the providing unit can prioritize the use of a providing method for which the user has given favorable feedback in the past. For example, the providing unit can also eliminate a providing method for which the user has given negative feedback in the past. For example, the providing unit can also optimize the providing method based on the user's past feedback. In this way, the providing method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the providing method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The educational game generation system may further include a feedback unit that monitors the user's learning progress in real time and provides appropriate feedback based on the user's learning style and interests. For example, if the user is struggling with a particular task, the feedback unit may provide hints or additional resources for that task. If the user demonstrates a high level of understanding of a particular topic, the feedback unit may also provide more advanced tasks related to that topic. Furthermore, the feedback unit may evaluate the user's learning progress and provide appropriate feedback in real time according to the user's learning pace. This allows the user to constantly understand their learning situation and progress with their studies while receiving the necessary support.

[0060] The educational game generation system may further include an environment adjustment unit for optimizing the user's learning environment. For example, the environment adjustment unit may provide a noise canceling function if the user prefers to study in a quiet environment. If the user prefers to study while listening to specific music, the environment adjustment unit may also play that music. Furthermore, the environment adjustment unit may adjust lighting and temperature to suit the user's learning environment. This allows the user to study in an optimal learning environment, maximizing the learning effect.

[0061] The educational game generation system may further include a visualization unit that visualizes the user's learning progress based on the user's learning style and interests. The visualization unit may, for example, display the user's learning progress in graphs or charts. The visualization unit may also visually indicate the user's level of understanding of a particular topic. Furthermore, the visualization unit may display the user's learning history in a timeline format, allowing the user to review past learning results. This allows the user to grasp their learning progress at a glance and maintain their motivation to study.

[0062] The educational game generation system may further include a sharing unit for sharing learning progress based on the user's learning style and interests. The sharing unit may provide, for example, a platform for users to share what they have learned with other users. The sharing unit may also allow users to share what they have learned on social media. Furthermore, the sharing unit may allow users to share what they have learned with family and friends and report their learning progress. This allows users to share their learning results with others and increase their motivation to learn.

[0063] The educational game generation system may further include an evaluation unit that evaluates the user's learning progress based on the user's learning style and interests. For example, when a user completes a specific task, the evaluation unit evaluates the user's achievement and awards points or badges. When a user demonstrates a high level of understanding of a particular topic, the evaluation unit may also issue a certificate related to that topic. Furthermore, the evaluation unit may periodically evaluate the user's learning progress and provide feedback. This allows the user to objectively evaluate their learning achievements and maintain their motivation to learn.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects information about the user's learning style and interests. For example, it collects data such as the user's past learning history, topics of interest, and learning progress. This allows the system to understand the user's learning style and interests. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand the user's learning style and interests. This provides information to generate an educational game that is optimal for the user. Step 3: The generator generates educational games based on the analysis results obtained by the analyzer. For example, if the user is interested in mathematics, the generator AI can generate educational games related to mathematics, thereby providing a personalized learning experience tailored to the user's interests. Step 4: The providing unit provides the user with the educational game generated by the generating unit. For example, the providing unit provides the user with the generated educational game so that the user can learn while having fun. In this way, the educational game is provided that is customized to the user's learning pace and interests.

[0066] (Example 2) An educational game generation system according to an embodiment of the present invention analyzes a user's learning style and interests to automatically generate fun and effective educational games. This educational game generation system collects information about the user's learning style and interests, and a generation AI analyzes the collected data to generate an educational game optimized for the user. The generated game is customized to match the user's learning pace and interests, promoting knowledge retention and skill improvement. Because users can learn while having fun, they are more motivated to learn and can absorb knowledge more effectively. Furthermore, this system is an innovative educational platform that supports autonomous learning and maximizes learners' potential. For example, by collecting and analyzing data such as a user's past learning history, topics of interest, and learning progress, the generation AI can identify the user's learning style and interests. The generation AI then analyzes the collected data to generate an educational game optimized for the user. For example, if a user is interested in mathematics, the generation AI can generate a mathematics-related educational game. This provides a personalized learning experience tailored to the user's interests. Furthermore, the generated educational game is customized to match the user's learning pace and interests. For example, the difficulty and content of the game can change according to the user's progress. This allows users to study at their own pace, promoting the solidification of knowledge and the improvement of skills. This allows users to learn while having fun, increasing their motivation for education and allowing them to absorb knowledge more effectively. For example, when users learn through games, learning becomes fun and naturally increases their motivation to learn. This system is also an innovative educational platform that supports autonomous learning and maximizes learners' potential. For example, by allowing users to study at their own pace, an autonomous learning attitude is fostered and learners' potential is brought out. As a result, the educational game generation system can improve learning effectiveness by providing educational games customized based on the user's learning style and interests.

[0067] An educational game generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's learning style and interests. The collection unit collects data such as the user's past learning history, topics of interest, and learning progress. The collection unit, for example, collects the user's past learning content and topics of interest, and a generation AI analyzes the data to understand the user's learning style and interests. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand the user's learning style and interests. For example, the analysis unit analyzes the collected data and provides information for the generation AI to generate an educational game optimal for the user. The generation unit generates an educational game based on the analysis results obtained by the analysis unit. For example, if the user is interested in mathematics, the generation AI can generate an educational game related to mathematics. The generation unit generates the educational game to provide a personalized learning experience tailored to the user's interests. The provision unit provides the educational game generated by the generation unit to the user. The providing unit, for example, provides the generated educational game to the user, allowing the user to learn while having fun. The providing unit, for example, provides an educational game customized to the user's learning pace and interests. In this way, the educational game generation system according to the embodiment can enhance learning effectiveness by providing an educational game customized based on the user's learning style and interests.

[0068] The collection unit can collect the user's past learning history, topics of interest, learning progress, and other related data. The collection unit collects, for example, data such as the user's past learning history, topics of interest, and learning progress. The collection unit can, for example, collect content the user has previously studied and topics of interest, and have the generation AI analyze the data to understand the user's learning style and interests. The collection unit, for example, collects data to understand the user's learning progress based on the user's learning history and provide the user with an optimal learning experience. In this way, by collecting data such as the user's past learning history, topics of interest, and learning progress, a more personalized learning experience can be provided. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's learning history data into the generation AI and have the generation AI understand the user's learning style and interests.

[0069] The analysis unit can analyze the collected data to understand the user's learning style and interests. The analysis unit, for example, analyzes the collected data to understand the user's learning style and interests. The analysis unit, for example, allows a generation AI to analyze the collected data and provide information for generating an optimal educational game for the user. The analysis unit, for example, analyzes the collected data in detail to understand the user's learning style and interests. In this way, by analyzing the collected data, the user's learning style and interests can be accurately understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI understand the user's learning style and interests.

[0070] The generation unit can generate an educational game tailored to the user's interests. For example, if the user is interested in mathematics, the generation AI can generate an educational game related to mathematics. The generation unit generates the educational game to provide a personalized learning experience tailored to the user's interests. For example, the generation unit customizes the content and format of the educational game based on the user's interests. This can increase the user's motivation to learn by generating an educational game tailored to the user's interests. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to generate an educational game.

[0071] The generation unit can customize the difficulty and content of the game to match the user's learning pace. The generation unit, for example, customizes the difficulty and content of the game to match the user's learning pace. The generation unit, for example, sets the difficulty and content of the game to change according to the user's progress. The generation unit, for example, adjusts the difficulty and content of the game based on the user's learning pace. This allows the learning effect to be maximized by customizing the difficulty and content of the game to match the user's learning pace. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's learning pace data into the generation AI and cause the generation AI to customize the difficulty and content of the game.

[0072] The providing unit can provide the generated educational game to the user, allowing the user to learn while having fun. The providing unit, for example, provides the generated educational game to the user, allowing the user to learn while having fun. The providing unit, for example, provides an educational game customized to the user's learning pace and interests. The providing unit, for example, provides the generated educational game to the user, increasing motivation for learning. In this way, by providing the generated educational game, the user can learn while having fun. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the generated educational game into a generating AI and causes the generating AI to execute an optimal method for providing the game to the user.

[0073] The educational game generation system further includes a collection unit that estimates a user's emotions and adjusts the timing of collecting learning data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects learning data during a time when the user is able to relax. For example, if the user is concentrating, the collection unit can also collect learning data at that time. For example, if the user is tired, the collection unit can also collect learning data after a break. This enables more effective data collection by adjusting the timing of collecting learning data based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of collecting learning data.

[0074] The collection unit can analyze the user's past learning history and select the optimal data collection method. The collection unit, for example, analyzes the user's past learning history and selects the optimal data collection method. The collection unit, for example, collects data by prioritizing learning methods that the user has used favorably in the past. The collection unit can also collect data based on learning methods that the user has used in the past with high results. The collection unit can also collect data by excluding learning methods that the user has avoided in the past. In this way, the optimal data collection method can be selected by analyzing the user's past learning history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI. For example, the collection unit can input the user's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

[0075] When collecting learning data, the collection unit can filter the learning data based on the user's current learning environment and areas of interest. For example, if the user is studying in a quiet environment, the collection unit can collect data appropriate for that environment. For example, if the user is interested in a particular subject, the collection unit can also prioritize collecting data related to that subject. For example, if the user prefers online learning, the collection unit can also collect data from online resources. This allows for more relevant data to be collected by filtering the data based on the user's current learning environment and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's learning environment data into a generation AI and have the generation AI filter the data.

[0076] When collecting learning data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. For example, if the user prefers text input, the collection unit can also prioritize collecting text data. For example, if the user prefers image input, the collection unit can also prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into the generation AI and cause the generation AI to select the optimal collection means.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting data with a high level of difficulty. For example, if the user is relaxed, the collection unit can prioritize collecting data with relaxing content. For example, if the user is tired, the collection unit can prioritize collecting data with easy content. This enables more effective data collection by determining the priority of data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0078] When collecting learning data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. For example, if the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. For example, if the user is at home, the collection unit can also prioritize collecting data suitable for home learning. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0079] The collection unit can analyze the user's social media activities and collect related data when collecting learning data. For example, the collection unit collects data related to topics in which the user has shown interest on social media. For example, the collection unit can also collect data useful for learning from the user's social media activities. For example, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0080] When collecting learning data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially uses collection methods for which the user has given favorable feedback in the past. For example, the collection unit can also exclude collection methods for which the user has given negative feedback in the past. For example, the collection unit can also optimize the collection method based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0081] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. For example, the analysis unit can perform a simplified analysis when the user is in a hurry. For example, the analysis unit can provide visually stimulating analysis results when the user is excited. This enables more effective analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the data analysis method.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the training data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the training data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the training data. For example, the analysis unit can apply a numerical analysis algorithm to mathematical data. For example, the analysis unit can also apply a natural language processing algorithm to English data. For example, the analysis unit can also apply a scientific data analysis algorithm to scientific data. This enables more appropriate analysis by applying different analysis algorithms depending on the category of the training data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the training data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also improve accuracy by correcting errors from the user's past analysis results. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit prioritizes the analysis of important data. For example, if the user is relaxed, the analysis unit prioritizes the analysis of detailed data. The analysis unit can also prioritize analysis of simplified data, for example, when the user is in a hurry. This enables more effective analysis by determining analysis priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the learning data. For example, the analysis unit prioritizes analysis of recently submitted data. For example, the analysis unit can also analyze data that was submitted earlier later. For example, the analysis unit can also analyze data that was submitted a medium time ago appropriately. This enables efficient analysis by determining the priority of analysis based on the submission date of the learning data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the learning data to the generation AI and have the generation AI determine the priority of analysis.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the training data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit can also postpone analysis of data with low relevance. For example, the analysis unit can also moderately analyze data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the training data to the generation AI and have the generation AI adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for a user with high expertise. For example, the analysis unit can avoid technical terminology for a user with low expertise. For example, the analysis unit can use technical terminology moderately for a user with medium expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0089] The generation unit can estimate the user's emotions and adjust the generation method of the educational game based on the estimated user's 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. For example, if the user is excited, the generation unit can generate a visually stimulating game. This allows for a more effective learning experience by adjusting the generation method of the educational game based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the generation method of the educational game.

[0090] When generating an educational game, the generation unit can customize the difficulty and content of the game based on the user's learning pace. For example, if the user is learning at a fast pace, the generation unit can generate a game with a high level of difficulty. For example, if the user is learning at a slow pace, the generation unit can also generate a game with a low level of difficulty. For example, the generation unit can also adjust the content of the game according to the user's learning pace. This allows the learning effect to be maximized by customizing the difficulty and content of the game based on the user's learning pace. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's learning pace data into the generation AI and cause the generation AI to customize the difficulty and content of the game.

[0091] When generating an educational game, the generation unit can apply different game algorithms depending on the user's interests. For example, if the user is interested in mathematics, the generation unit can apply a game algorithm related to mathematics. For example, if the user is interested in English, the generation unit can also apply a game algorithm related to English. For example, if the user is interested in science, the generation unit can also apply a game algorithm related to science. This can increase the user's motivation to learn by applying different game algorithms depending on the user's interests. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input user interest data into the generation AI and cause the generation AI to apply different game algorithms.

[0092] When generating an educational game, the generation unit can improve the accuracy of the game by referring to the user's past game play results. The generation unit, for example, improves the accuracy of the current game based on the user's past game play results. The generation unit can also improve accuracy by correcting errors based on the user's past game play results. The generation unit can also select an optimal game algorithm by referring to the user's past game play results. This allows the accuracy of the game to be improved by referring to the user's past game play results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past game play result data into the generation AI and cause the generation AI to improve the accuracy of the game.

[0093] The generation unit can estimate the user's emotions and adjust the length of the game based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a longer game. For example, if the user is in a hurry, the generation unit can also generate a shorter game. For example, if the user is excited, the generation unit can also generate a visually stimulating game. This allows for adjusting the game length based on the user's emotions to provide a more effective learning experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the game length.

[0094] When generating educational games, the generation unit can adjust the order of games based on the user's relevance. For example, the generation unit prioritizes generating games related to topics in which the user is interested. For example, the generation unit can also prioritize generating highly relevant games according to the user's learning progress. For example, the generation unit can also prioritize generating highly relevant games based on the user's past learning history. In this way, by adjusting the order of games based on the user's relevance, a more effective learning experience can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI and cause the generation AI to adjust the order of games.

[0095] When generating an educational game, the generation unit can adjust the use of technical terminology in the game according to the user's level of expertise. For example, the generation unit generates a game that uses a lot of technical terminology for a user with high level of expertise. For example, the generation unit can also generate a game that avoids technical terminology for a user with low level of expertise. For example, the generation unit can also generate a game that uses technical terminology moderately for a user with intermediate level of expertise. In this way, by adjusting the use of technical terminology according to the user's level of expertise, a game that is easier to understand can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The providing unit estimates the user's emotion, The method of providing the educational game can be adjusted based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the game at a leisurely pace. For example, if the user is in a hurry, the providing unit can provide a game that can be completed in a short time. For example, if the user is excited, the providing unit can provide a visually stimulating game. This allows for adjusting the method of providing the educational game based on the user's emotions to provide a more effective learning experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing the educational game.

[0097] When providing an educational game, the providing unit can select the optimal providing method by referring to the user's past play history. For example, the providing unit prioritizes the providing method of a game that the user has played with preference in the past. For example, the providing unit can also provide the game based on the providing method of a game in which the user has achieved high results in the past. For example, the providing unit can also select the optimal providing method based on the user's past play history. In this way, the optimal providing method can be selected by referring to the user's past play history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past play history data into the generating AI and cause the generating AI to select the optimal providing method.

[0098] When providing an educational game, the providing unit can customize the content to be provided based on the user's current learning situation. The providing unit, for example, provides the user with an optimal game depending on their current learning situation. The providing unit can also customize the content of the game based on the user's learning progress, for example. The providing unit can also select optimal content to be provided based on the user's current learning situation, for example. This allows the user to customize the content to be provided based on the user's current learning situation, thereby providing a more effective learning experience. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current learning situation data into a generating AI and cause the generating AI to customize the content to be provided.

[0099] The providing unit can improve the providing method by reflecting user feedback when providing an educational game. For example, the providing unit prioritizes a providing method for which a user has previously given favorable feedback. For example, the providing unit can also eliminate a providing method for which a user has previously given negative feedback. For example, the providing unit can also optimize the providing method based on user feedback. In this way, the providing method can be optimized by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0100] The providing unit can estimate the user's emotions and determine the order in which educational games should be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can prioritize providing visually stimulating games. For example, if the user is relaxed, the providing unit can also prioritize providing leisurely games. For example, if the user is in a hurry, the providing unit can also prioritize providing games that can be completed in a short time. This allows for a more effective learning experience by determining the order in which educational games should be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the order in which educational games should be provided.

[0101] When providing an educational game, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a game related to that area. For example, if the user is traveling, the providing unit can also provide a game related to the travel destination. For example, if the user is at home, the providing unit can also provide a game suitable for home learning. In this way, the optimal providing method can be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generating AI and cause the generating AI to select the optimal providing method.

[0102] When providing an educational game, the providing unit can customize the content to be provided by analyzing the user's social media activity. For example, the providing unit provides a game related to a topic in which the user has shown interest on social media. For example, the providing unit can also provide a game that is useful for learning based on the user's social media activity. For example, the providing unit can also provide a related game by referring to the activity of the user's friends on social media. In this way, the content to be provided can be customized by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the content to be provided.

[0103] When providing an educational game, the providing unit can customize the providing method by reflecting the user's past feedback. For example, the providing unit can prioritize the use of a providing method for which the user has given favorable feedback in the past. For example, the providing unit can also eliminate a providing method for which the user has given negative feedback in the past. For example, the providing unit can also optimize the providing method based on the user's past feedback. In this way, the providing method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's learning style and interests using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates an educational game based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the smart device 14, provides the generated educational game to the user. Furthermore, the emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting learning data based on the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's learning style and interests using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates an educational game based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the smart glasses 214, provides the generated educational game to the user. Furthermore, the emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting learning data based on the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's learning style and interests using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates an educational game based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the headset-type terminal 314, provides the generated educational game to the user. Furthermore, the emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, adjusts the timing of collecting learning data based on the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's learning style and interests using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an educational game based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated educational game to the user. Furthermore, the emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the timing of collecting learning data based on the user's emotions.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The educational game generation system may further include a feedback unit that monitors the user's learning progress in real time and provides appropriate feedback based on the user's learning style and interests. For example, if the user is struggling with a particular task, the feedback unit may provide hints or additional resources for that task. If the user demonstrates a high level of understanding of a particular topic, the feedback unit may also provide more advanced tasks related to that topic. Furthermore, the feedback unit may evaluate the user's learning progress and provide appropriate feedback in real time according to the user's learning pace. This allows the user to constantly understand their learning situation and progress with their studies while receiving the necessary support.

[0106] The educational game generation system may further include an environment adjustment unit for optimizing the user's learning environment. For example, the environment adjustment unit may provide a noise canceling function if the user prefers to study in a quiet environment. If the user prefers to study while listening to specific music, the environment adjustment unit may also play that music. Furthermore, the environment adjustment unit may adjust lighting and temperature to suit the user's learning environment. This allows the user to study in an optimal learning environment, maximizing the learning effect.

[0107] The educational game generation system may further include a visualization unit that visualizes the user's learning progress based on the user's learning style and interests. The visualization unit may, for example, display the user's learning progress in graphs or charts. The visualization unit may also visually indicate the user's level of understanding of a particular topic. Furthermore, the visualization unit may display the user's learning history in a timeline format, allowing the user to review past learning results. This allows the user to grasp their learning progress at a glance and maintain their motivation to study.

[0108] The educational game generation system may further include a sharing unit for sharing learning progress based on the user's learning style and interests. The sharing unit may provide, for example, a platform for users to share what they have learned with other users. The sharing unit may also allow users to share what they have learned on social media. Furthermore, the sharing unit may allow users to share what they have learned with family and friends and report their learning progress. This allows users to share their learning results with others and increase their motivation to learn.

[0109] The educational game generation system may further include an evaluation unit that evaluates the user's learning progress based on the user's learning style and interests. For example, when a user completes a specific task, the evaluation unit evaluates the user's achievement and awards points or badges. When a user demonstrates a high level of understanding of a particular topic, the evaluation unit may also issue a certificate related to that topic. Furthermore, the evaluation unit may periodically evaluate the user's learning progress and provide feedback. This allows the user to objectively evaluate their learning achievements and maintain their motivation to learn.

[0110] The educational game generation system may further include an adjustment unit that estimates the user's emotions and adjusts the learning progress based on the estimated user emotions. For example, the adjustment unit may slow down the learning progress when the user is stressed. The adjustment unit may also accelerate the learning progress when the user is relaxed. Furthermore, the adjustment unit may display the learning progress in a visually stimulating manner when the user is excited. This allows for a more effective learning experience by adjusting the learning progress based on the user's emotions.

[0111] The educational game generation system may further include a support unit that estimates the user's emotions and supports the progress of learning based on the estimated user emotions. For example, if the user is tired, the support unit displays a message encouraging the user to take a break. If the user is concentrating, the support unit may also provide advice on how to maintain that concentration. Furthermore, if the user is excited, the support unit may suggest ways to utilize that excitement in learning. In this way, a more effective learning experience can be provided by supporting the progress of learning based on the user's emotions.

[0112] The educational game generation system may further include an optimization unit that estimates the user's emotions and optimizes learning progress based on the estimated user emotions. For example, if the user is relaxed, the optimization unit provides resources for optimizing learning progress. If the user is feeling stressed, the optimization unit may also suggest a method for reducing stress. Furthermore, if the user is excited, the optimization unit may also provide a method for utilizing the excitement in learning. In this way, a more effective learning experience can be provided by optimizing learning progress based on the user's emotions.

[0113] The educational game generation system may further include an adjustment unit that estimates the user's emotions and adjusts the learning progress based on the estimated user emotions. For example, the adjustment unit may slow down the learning progress when the user is stressed. The adjustment unit may also accelerate the learning progress when the user is relaxed. Furthermore, the adjustment unit may display the learning progress in a visually stimulating manner when the user is excited. This allows for a more effective learning experience by adjusting the learning progress based on the user's emotions.

[0114] The educational game generation system may further include a support unit that estimates the user's emotions and supports the progress of learning based on the estimated user emotions. For example, if the user is tired, the support unit displays a message encouraging the user to take a break. If the user is concentrating, the support unit may also provide advice on how to maintain that concentration. Furthermore, if the user is excited, the support unit may suggest ways to utilize that excitement in learning. In this way, a more effective learning experience can be provided by supporting the progress of learning based on the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects information about the user's learning style and interests. For example, it collects data such as the user's past learning history, topics of interest, and learning progress. This allows the system to understand the user's learning style and interests. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand the user's learning style and interests. This provides information to generate an educational game that is optimal for the user. Step 3: The generator generates educational games based on the analysis results obtained by the analyzer. For example, if the user is interested in mathematics, the generator AI can generate educational games related to mathematics, thereby providing a personalized learning experience tailored to the user's interests. Step 4: The providing unit provides the user with the educational game generated by the generating unit. For example, the providing unit provides the user with the generated educational game so that the user can learn while having fun. In this way, the educational game is provided that is customized to the user's learning pace and interests.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about a user's learning style and interests; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates an educational game based on the analysis results obtained by the analysis unit; a providing unit that provides the educational game generated by the generating unit to a user; Equipped with A system characterized by:

2. The collecting unit Collecting your learning history, topics of interest, learning progress, and other relevant data 2. The system of claim 1.

3. The analysis unit Analyze the collected data to understand users' learning styles and interests 2. The system of claim 1.

4. The generation unit Generate educational games tailored to user interests 2. The system of claim 1.

5. The generation unit Customize the difficulty and content of the game to suit the user's learning pace 2. The system of claim 1.

6. The providing unit Providing the generated educational game to users, allowing them to learn while having fun 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of learning data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze users' past learning history and select the appropriate data collection method 2. The system of claim 1.

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