System

Abstract

An object of the system according to the embodiment is to provide an optimal curriculum for each learner and to perform individual guidance and feedback.SOLUTION: A system includes a curriculum generation part, an individual instruction part, and a feedback provision part. A curriculum generation part generates a curriculum on the basis of the profile and learning target of the learner by using the generation AI. The individual instruction part gives individual instruction to each learner based on the curriculum generated by the curriculum generation part. The feedback providing unit provides feedback according to the progress of the learner based on the instruction given by the individual instructing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to provide an optimal curriculum for each learner and provide individualized instruction and feedback.

[0005] The system according to the embodiment aims to provide an optimal curriculum for each learner and provide individualized instruction and feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a curriculum generation unit, an individual instruction unit, and a feedback provision unit. The curriculum generation unit uses a generation AI to generate a curriculum based on the learner's profile and learning goals. The individual instruction unit provides individual instruction to each learner based on the curriculum generated by the curriculum generation unit. The feedback provision unit provides feedback according to the learner's progress based on the instruction provided by the individual instruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal curriculum for each learner and provide individualized instruction and feedback. [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) The e-sports learning system according to an embodiment of the present invention provides a curriculum covering a wide range of e-sports topics and uses generative AI to provide individualized instruction and feedback. This allows the e-sports learning system to provide an optimal curriculum for each learner and maximize learning outcomes by providing individualized instruction and feedback.

[0029] An e-sports learning system according to an embodiment includes a curriculum generation unit, an individualized instruction unit, and a feedback provision unit. The curriculum generation unit uses a generation AI to generate a curriculum based on a learner's profile and learning goals. For example, the generation AI receives profile information, such as the learner's age, learning history, and areas of interest, as input and generates an optimal curriculum based on the information. The generation AI also sets achievement standards and customizes the curriculum, taking into account the learner's short-term and long-term goals. The individualized instruction unit provides individualized instruction to each learner based on the curriculum generated by the curriculum generation unit. For example, if a learner wants to learn how to operate a specific game, the generation AI provides detailed instructions and practice problems on how to operate that game. The generation AI also suggests the next topic to learn and practice methods based on the learner's progress data and feedback. The feedback provision unit provides feedback according to the learner's progress based on the instruction provided by the individualized instruction unit. For example, the generation AI evaluates whether the learner understands a specific topic and provides additional explanations and practice problems if understanding is insufficient. The generation AI also analyzes the learner's performance data and specifically identifies areas for improvement and strengthening. As a result, the e-sports learning system according to the embodiment can maximize learning effectiveness by providing an optimal curriculum for each learner and providing individualized instruction and feedback.

[0030] The curriculum generation unit can analyze the learner's gameplay data in real time and dynamically generate a curriculum based on their play style. For example, the curriculum generation unit uses a generation AI to collect the learner's gameplay data in real time and analyze their play style and performance. For example, it analyzes the movements and strategies within a specific game and dynamically generates a curriculum based on that. Furthermore, the curriculum generation unit uses the generation AI to propose practice menus and training plans optimal for the learner's play style based on the learner's real-time gameplay data. For example, it provides practice methods to strengthen specific skills. Furthermore, the curriculum generation unit uses the generation AI to analyze the learner's gameplay data in real time and provide feedback according to their play style. For example, it suggests areas for improvement in strategy and efficient operation methods. This allows for the provision of a curriculum optimal for the learner's play style, thereby improving learning effectiveness.

[0031] The curriculum generation unit can analyze a learner's past learning history, identify the most effective learning pattern, and reflect it in the curriculum. In the curriculum generation unit, for example, the generation AI collects the learner's past learning history and analyzes their learning patterns and progress. For example, it identifies the optimal learning pattern based on topics learned in the past and goals achieved. In addition, the curriculum generation unit identifies effective learning patterns based on the learner's past learning history and customizes the curriculum based on them. For example, it evaluates the learner's level of understanding of a specific topic and provides necessary supplementary learning. In addition, the curriculum generation unit analyzes a learner's past learning history and identifies the most effective learning pattern. For example, it suggests the optimal learning method for the learner based on past successes and failures. This makes it possible to improve learning effectiveness by providing the optimal curriculum based on the learner's past learning history.

[0032] The curriculum generation department can add topics from related fields other than esports to aim for overall skill improvement. For example, the curriculum generation department can add psychology topics to the esports curriculum to provide learning content on mental management and stress management. For example, students can learn how to improve concentration and mental rehearsal techniques. The curriculum generation department can also incorporate nutrition topics into the curriculum to provide knowledge on healthy eating and nutritional balance. For example, students can learn about pre-game meals and how to replenish their energy. The curriculum generation department can also add physical fitness topics to the esports curriculum to provide training plans to improve physical strength and flexibility. For example, students can learn about stretching and strength training methods. This makes it possible to incorporate knowledge from related fields other than esports to aim for overall skill improvement.

[0033] The curriculum generation unit can generate curricula specialized for different game genres, allowing learners to select based on their interests. For example, the curriculum generation unit uses a generation AI to generate curricula specialized for different game genres, allowing learners to select based on their interests. For example, the curriculum generation unit provides a curriculum related to strategies and operation methods for FPS games. The curriculum generation unit also generates a curriculum specialized for MOBA games based on the learner's interests and play style. For example, the curriculum generation unit provides learning content related to character selection and team play strategies. The curriculum generation unit also generates a curriculum specialized for RTS games, providing content for learners to learn strategies in real time. For example, the curriculum generation unit provides learning methods related to resource management and unit placement. This allows learners to improve their learning effectiveness by providing curricula that match their interests.

[0034] The curriculum generation unit can analyze a learner's social media activities and comments in online communities to generate a curriculum that reflects their interests and concerns. In the curriculum generation unit, for example, a generation AI analyzes a learner's social media activities to generate a curriculum that reflects their interests and concerns. For example, the curriculum is customized based on topics that the learner frequently posts to and accounts that the learner follows. The curriculum generation unit also analyzes comments in online communities to identify the learner's interests and concerns. For example, the curriculum is generated based on the content of forums and discussions in which the learner participates. The curriculum generation unit also provides a curriculum that reflects the learner's interests and concerns through a generation AI based on the learner's social media activities and comments in online communities. For example, learning content related to topics that interest the learner is provided preferentially. This makes it possible to improve learning effectiveness by providing a curriculum based on the learner's interests and concerns.

[0035] The curriculum generation unit collects physiological data from learners and can suggest optimal learning timing and content. For example, the generation AI in the curriculum generation unit collects physiological data such as the learner's heart rate and sleep patterns and suggests optimal learning timing and content. For example, it recommends studying during times when the heart rate is stable. The curriculum generation unit also customizes the optimal learning timing and content based on the learner's physiological data. For example, it analyzes sleep patterns and suggests studying during times when the learner can concentrate best. The curriculum generation unit also collects physiological data and the generation AI provides a curriculum tailored to the learner's physical condition and rhythm. For example, it suggests optimal learning content and timing for the learner based on heart rate and sleep patterns. This makes it possible to improve learning effectiveness by providing optimal learning timing and content based on the learner's physiological data.

[0036] The curriculum generation unit can incorporate local esports events and community activities into the curriculum based on the learner's geographical location information. For example, the generation AI in the curriculum generation unit collects the learner's geographical location information and incorporates local esports events and community activities into the curriculum. For example, it introduces esports tournaments and workshops held nearby. The curriculum generation unit also suggests local esports community activities based on the learner's geographical location information. For example, it provides opportunities to participate in local esports clubs and practice sessions. The curriculum generation unit also utilizes the geographical location information to incorporate local esports events and community activities into the curriculum. For example, it provides information on local esports-related facilities and events. In this way, by incorporating local esports events and community activities into the curriculum, it is possible to pique the learner's interest and improve learning effectiveness.

[0037] The curriculum generation unit generates a curriculum according to the learner's learning style, thereby meeting individual learning needs. For example, the generation AI in the curriculum generation unit evaluates the learner's learning style and generates a curriculum according to whether the learner is visual, auditory, experiential, or the like. For example, visual content is provided to a visual learner. The curriculum generation unit also provides a curriculum that meets individual learning needs based on the learner's learning style. For example, audio commentary or podcasts is provided to an auditory learner. The curriculum generation unit also takes learning style into consideration, and the generation AI provides practical training and simulations to experiential learners. For example, practical practice in a game or interactive learning content is provided. This makes it possible to improve learning effectiveness by providing a curriculum that meets the learner's learning style.

[0038] The individual tutoring unit can observe the learner's real-time gameplay and provide immediate advice and correction instructions. In the individual tutoring unit, for example, the generation AI observes the learner's real-time gameplay and provides immediate advice and correction instructions. For example, it points out operational errors and areas for strategic improvement in real time. The individual tutoring unit also analyzes the learner's gameplay in real time and the generation AI provides immediate advice. For example, it suggests the optimal actions and tactics for specific situations. In the individual tutoring unit, the generation AI observes the learner's real-time gameplay and provides immediate correction instructions. For example, it instructs the learner to improve their operational methods or change their strategy in real time. This makes it possible to improve learning effectiveness by providing advice and correction instructions in real time.

[0039] The individual instruction unit can analyze a learner's past mistakes and patterns of success, and provide detailed instruction on individual points for improvement. For example, the generation AI in the individual instruction unit analyzes a learner's past mistakes and patterns of success, and provides detailed instruction on individual points for improvement. For example, it analyzes the causes of specific mistakes and factors behind success, and suggests specific methods for improvement. The generation AI in the individual instruction unit also identifies individual points for improvement based on the learner's past data, and provides detailed instruction. For example, it offers methods for improving specific skills and strategies. The generation AI in the individual instruction unit also analyzes a learner's past mistakes and patterns of success, and provides detailed instruction on individual points for improvement. For example, it suggests the best practice methods and tactics for the learner based on past data. In this way, by analyzing past mistakes and patterns of success and providing instruction on individual points for improvement, it is possible to improve learning effectiveness.

[0040] The individual instruction department can analyze instructional videos by professional players of different games and provide the most appropriate instructional content to the learner. For example, the generation AI analyzes instructional videos by professional players of different games and provides the most appropriate instructional content to the learner. For example, the tactics and operation methods of the professional players are customized to suit the learner. The individual instruction department also uses the generation AI to provide the most appropriate instructional content to the learner based on the instructional videos of the professional players. For example, detailed explanations of specific skills and strategies are provided. The individual instruction department also uses the generation AI to analyze instructional videos of professional players and provide the most appropriate instructional content to the learner. For example, the generation AI tailors instruction to the learner's playing style. This allows the learning effect to be improved by analyzing instructional videos of professional players and providing the most appropriate instructional content to the learner.

[0041] The individual tutoring unit can provide a curriculum that promotes paired learning between learners and supports their progress. In the individual tutoring unit, for example, a generating AI promotes paired learning between learners and provides a curriculum that supports their progress. For example, the progress of paired learning is monitored in real time and necessary support is provided. In addition, in order to promote paired learning between learners, the generating AI customizes the curriculum. For example, it provides assignments and exercises for pairs to work on. In addition, in the individual tutoring unit, the generating AI supports paired learning between learners and monitors their progress. For example, it evaluates the progress of paired learning and provides necessary feedback. In this way, it is possible to promote paired learning between learners and support their progress, thereby improving learning effectiveness.

[0042] The feedback providing unit can analyze the learner's gameplay data in detail and provide feedback on specific areas for improvement and factors for success. For example, the generation AI in the feedback providing unit analyzes the learner's gameplay data in detail and provides feedback on specific areas for improvement. For example, it points out operational errors and areas for improvement in strategy. The feedback providing unit also identifies success factors based on the learner's gameplay data and provides feedback. For example, it explains why a particular tactic or operating method was successful. The feedback providing unit also analyzes the learner's gameplay data in detail and provides feedback on specific areas for improvement and factors for success. For example, it suggests the optimal improvement method for the learner based on past data. In this way, by analyzing the learner's gameplay data in detail and providing feedback on specific areas for improvement and factors for success, it is possible to improve learning effectiveness.

[0043] The feedback providing unit can periodically evaluate the learner's progress and provide feedback on long-term growth trends. In the feedback providing unit, for example, the generation AI periodically evaluates the learner's progress and provides feedback on long-term growth trends. For example, the feedback providing unit evaluates the learner's skill improvement and strategy improvement over the long term. Furthermore, the feedback providing unit identifies long-term growth trends based on the learner's progress data and provides feedback. For example, the feedback providing unit visualizes the learner's growth curve and evaluates progress. Furthermore, the feedback providing unit periodically evaluates the learner's progress and provides feedback on long-term growth trends. For example, the feedback providing unit predicts the learner's growth based on past data and provides feedback. In this way, by periodically evaluating the learner's progress and providing feedback on long-term growth trends, it is possible to improve learning effectiveness.

[0044] The feedback providing unit can extract and share common points for improvement that are also beneficial to other learners based on the learner's feedback. In the feedback providing unit, for example, the generation AI analyzes the learner's feedback and extracts common points for improvement that are also beneficial to other learners. For example, it identifies points that multiple learners commonly struggle with and shares improvement methods. In addition, the feedback providing unit extracts common points for improvement based on the learner's feedback and provides them to other learners. For example, it provides a summary of common points for improvement related to specific skills or strategies. In addition, the feedback providing unit analyzes the learner's feedback and extracts and shares common points for improvement that are also beneficial to other learners. For example, it identifies and shares common challenges and improvement methods based on the learner's feedback. In this way, by extracting common points for improvement based on the learner's feedback and sharing them with other learners, learning effectiveness can be improved.

[0045] The feedback providing unit can visualize the feedback so that the learner can intuitively understand it. For example, the feedback providing unit causes the generation AI to visualize the feedback so that the learner can intuitively understand it. For example, the feedback providing unit visually displays the feedback using graphs or charts. The feedback providing unit also visualizes the learner's progress and areas for improvement so that the generation AI can provide feedback that is intuitively understandable. For example, the progress status is displayed on a timeline. The feedback providing unit also causes the generation AI to visualize the feedback so that the learner can intuitively understand it. For example, success factors and areas for improvement are displayed using icons or diagrams. In this way, visualizing the feedback makes it easier for the learner to intuitively understand, thereby improving learning effectiveness.

[0046] The curriculum generation unit can analyze learner feedback in real time and instantly update the curriculum. In the curriculum generation unit, for example, the generation AI analyzes learner feedback in real time and instantly updates the curriculum. For example, if a learner has an insufficient understanding of a particular topic, additional learning content related to that topic is provided. In addition, in the curriculum generation unit, the generation AI updates the curriculum in real time based on learner feedback. For example, new content related to a topic in which the learner has shown interest is added. In addition, in the curriculum generation unit, the generation AI analyzes learner feedback in real time and instantly updates the curriculum. For example, the difficulty level of the curriculum is adjusted according to the learner's progress. In this way, by analyzing learner feedback in real time and instantly updating the curriculum, it is possible to improve learning effectiveness.

[0047] The curriculum generation unit can track learner progress data over the long term, identify optimal learning patterns, and reflect them in the curriculum. In the curriculum generation unit, for example, the generation AI tracks learner progress data over the long term and identifies optimal learning patterns. For example, it identifies effective learning methods based on the learner's past data and reflects them in the curriculum. In addition, the curriculum generation unit identifies optimal learning patterns based on the learner's progress data and customizes the curriculum. For example, it identifies the time of day and method in which the learner can study most effectively. In addition, the curriculum generation unit tracks learner progress data over the long term, identifies optimal learning patterns, and reflects them in the curriculum. For example, it analyzes the learner's growth curve and provides optimal learning content. In this way, learning effectiveness can be improved by tracking learner progress data over the long term, identifying optimal learning patterns, and reflecting them in the curriculum.

[0048] The curriculum generation unit can integrate data from different learner groups, extract common optimization points, and reflect them in the curriculum. In the curriculum generation unit, for example, the generation AI integrates data from different learner groups and extracts common optimization points. For example, it identifies topics that multiple learners commonly struggle with and reflects them in the curriculum. In addition, the curriculum generation unit identifies common optimization points based on the data from the learner groups and customizes the curriculum. For example, it provides common improvement points for specific skills or strategies. In addition, the curriculum generation unit integrates data from different learner groups using the generation AI, extracts common optimization points, and reflects them in the curriculum. For example, it identifies common challenges and improvement methods based on learner feedback and reflects them in the curriculum. In this way, by integrating data from different learner groups, extracting common optimization points, and reflecting them in the curriculum, it is possible to improve learning effectiveness.

[0049] The curriculum generation unit can visualize the curriculum updates so that learners can intuitively understand the changes. In the curriculum generation unit, for example, the generation AI visualizes the curriculum updates so that learners can intuitively understand them. For example, the curriculum changes are visually displayed using graphs and charts. The curriculum generation unit also visualizes the learner's progress and the curriculum updates so that the generation AI can provide intuitively understandable feedback. For example, the progress is displayed on a timeline. In addition, the curriculum generation unit visualizes the curriculum updates so that learners can intuitively understand them. For example, changes and new content are displayed with icons and diagrams. In this way, visualizing the curriculum updates makes it easier for learners to intuitively understand the changes, thereby improving learning effectiveness.

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

[0051] The curriculum generation unit can collect physiological data from learners and suggest optimal learning timing and content. For example, the generation AI collects physiological data such as the learner's heart rate and sleep patterns and suggests optimal learning timing and content. For example, it recommends studying during times when the heart rate is stable. The curriculum generation unit also customizes the optimal learning timing and content based on the learner's physiological data. For example, it analyzes sleep patterns and suggests studying during times when the learner can concentrate best. The curriculum generation unit also collects physiological data and the generation AI provides a curriculum tailored to the learner's physical condition and rhythm. For example, it suggests the optimal learning content and timing for the learner based on heart rate and sleep patterns. This makes it possible to improve learning effectiveness by providing optimal learning timing and content based on the learner's physiological data.

[0052] The curriculum generation unit can generate curricula specialized for different game genres, allowing learners to select based on their interests. For example, the generation AI generates curricula specialized for different game genres, allowing learners to select based on their interests. For example, a curriculum on strategies and operation methods for FPS games is provided. The curriculum generation unit also generates a curriculum specialized for MOBA games based on the learner's interests and play style. For example, learning content on character selection and team play strategies is provided. The curriculum generation unit also generates a curriculum specialized for RTS games, providing content for learners to learn strategies in real time. For example, learning methods on resource management and unit placement are provided. This allows for the provision of curricula tailored to the learner's interests, thereby improving learning effectiveness.

[0053] The curriculum creation department can add topics from related fields outside of esports to aim for overall skill improvement. For example, a psychology topic could be added to the esports curriculum to provide learning content on mental management and stress management. For example, students could learn how to improve concentration and mental rehearsal techniques. The curriculum creation department could also incorporate nutrition topics into the curriculum to provide knowledge on healthy eating and nutritional balance. For example, students could learn about pre-game meals and how to replenish their energy. The curriculum creation department could also add physical fitness topics to the esports curriculum to provide training plans to improve physical strength and flexibility. For example, students could learn about stretching and strength training methods. In this way, it is possible to improve overall skills by incorporating knowledge from related fields outside of esports.

[0054] The curriculum generation unit can analyze a learner's social media activities and comments in online communities to generate a curriculum that reflects their interests and concerns. For example, the generation AI analyzes a learner's social media activities to generate a curriculum that reflects their interests and concerns. For example, the curriculum is customized based on topics the learner frequently posts to and accounts the learner follows. The curriculum generation unit also analyzes comments in online communities to identify the learner's interests and concerns. For example, the curriculum is generated based on the content of forums and discussions in which the learner participates. The curriculum generation unit also provides a curriculum that reflects the learner's interests and concerns based on the learner's social media activities and comments in online communities. For example, learning content related to topics that interest the learner is provided preferentially. This makes it possible to improve learning effectiveness by providing a curriculum based on the learner's interests and concerns.

[0055] The curriculum generation unit can incorporate local esports events and community activities into the curriculum based on the learner's geographical location information. For example, the generation AI collects the learner's geographical location information and incorporates local esports events and community activities into the curriculum. For example, it may introduce esports tournaments and workshops held nearby. The curriculum generation unit also suggests local esports community activities based on the learner's geographical location information. For example, it may provide opportunities to participate in local esports clubs and practice sessions. The curriculum generation unit also utilizes the geographical location information to incorporate local esports events and community activities into the curriculum. For example, it may provide information on local esports-related facilities and events. In this way, by incorporating local esports events and community activities into the curriculum, it is possible to pique the learner's interest and improve learning outcomes.

[0056] The curriculum generation unit generates a curriculum that matches the learner's learning style, enabling it to meet individual learning needs. For example, the generation AI evaluates the learner's learning style and generates a curriculum that matches visual, auditory, experiential, etc. For example, visual content is provided to visual learners. The curriculum generation unit also provides a curriculum that meets individual learning needs based on the learner's learning style, with the generation AI providing audio commentary and podcasts to auditory learners. The curriculum generation unit also takes learning style into consideration, with the generation AI providing practical training and simulations to experiential learners. For example, practical practice within games and interactive learning content are provided. This allows for the provision of a curriculum that matches the learner's learning style, thereby improving learning effectiveness.

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

[0058] Step 1: The curriculum generation unit uses generation AI to generate a curriculum based on the learner's profile and learning goals. For example, the generation AI receives profile information such as the learner's age, learning history, and areas of interest as input, and generates an optimal curriculum based on that information. The generation AI also sets achievement standards and customizes the curriculum, taking into account the learner's short-term and long-term goals. Step 2: The individualized instruction unit provides individualized instruction to each learner based on the curriculum generated by the curriculum generation unit. For example, if a learner wants to learn how to play a specific game, the generation AI will provide detailed instructions and practice questions on how to play that game. The generation AI will also suggest the next topic to learn and the practice method based on the learner's progress data and feedback. Step 3: The feedback provider provides feedback based on the learner's progress based on the instruction provided by the individual tutor. For example, the generator evaluates whether the learner has understood a specific topic and provides additional explanations or practice questions if understanding is insufficient. The generator also analyzes the learner's performance data and specifically points out areas for improvement and strengthening.

[0059] (Example 2) The e-sports learning system according to an embodiment of the present invention provides a curriculum covering a wide range of e-sports topics and uses generative AI to provide individualized instruction and feedback. This allows the e-sports learning system to provide an optimal curriculum for each learner and maximize learning outcomes by providing individualized instruction and feedback.

[0060] An e-sports learning system according to an embodiment includes a curriculum generation unit, an individualized instruction unit, and a feedback provision unit. The curriculum generation unit uses a generation AI to generate a curriculum based on a learner's profile and learning goals. For example, the generation AI receives profile information, such as the learner's age, learning history, and areas of interest, as input and generates an optimal curriculum based on the information. The generation AI also sets achievement standards and customizes the curriculum, taking into account the learner's short-term and long-term goals. The individualized instruction unit provides individualized instruction to each learner based on the curriculum generated by the curriculum generation unit. For example, if a learner wants to learn how to operate a specific game, the generation AI provides detailed instructions and practice problems on how to operate that game. The generation AI also suggests the next topic to learn and practice methods based on the learner's progress data and feedback. The feedback provision unit provides feedback according to the learner's progress based on the instruction provided by the individualized instruction unit. For example, the generation AI evaluates whether the learner understands a specific topic and provides additional explanations and practice problems if understanding is insufficient. The generation AI also analyzes the learner's performance data and specifically identifies areas for improvement and strengthening. As a result, the e-sports learning system according to the embodiment can maximize learning effectiveness by providing an optimal curriculum for each learner and providing individualized instruction and feedback.

[0061] The curriculum generation unit can analyze the learner's gameplay data in real time and dynamically generate a curriculum based on their play style. For example, the curriculum generation unit uses a generation AI to collect the learner's gameplay data in real time and analyze their play style and performance. For example, it analyzes the movements and strategies within a specific game and dynamically generates a curriculum based on that. Furthermore, the curriculum generation unit uses the generation AI to propose practice menus and training plans optimal for the learner's play style based on the learner's real-time gameplay data. For example, it provides practice methods to strengthen specific skills. Furthermore, the curriculum generation unit uses the generation AI to analyze the learner's gameplay data in real time and provide feedback according to their play style. For example, it suggests areas for improvement in strategy and efficient operation methods. This allows for the provision of a curriculum optimal for the learner's play style, thereby improving learning effectiveness.

[0062] The curriculum generation unit can analyze a learner's past learning history, identify the most effective learning pattern, and reflect it in the curriculum. In the curriculum generation unit, for example, the generation AI collects the learner's past learning history and analyzes their learning patterns and progress. For example, it identifies the optimal learning pattern based on topics learned in the past and goals achieved. In addition, the curriculum generation unit identifies effective learning patterns based on the learner's past learning history and customizes the curriculum based on them. For example, it evaluates the learner's level of understanding of a specific topic and provides necessary supplementary learning. In addition, the curriculum generation unit analyzes a learner's past learning history and identifies the most effective learning pattern. For example, it suggests the optimal learning method for the learner based on past successes and failures. This makes it possible to improve learning effectiveness by providing the optimal curriculum based on the learner's past learning history.

[0063] The curriculum generation unit can use the emotion estimation function to evaluate the learner's emotional state and incorporate break or refreshment topics to reduce stress or fatigue into the curriculum. The curriculum generation unit, for example, uses the emotion estimation function to evaluate the learner's emotional state in real time and detect stress or fatigue. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. The curriculum generation unit also incorporates break or refreshment topics to reduce stress and fatigue into the curriculum using a generation AI based on the learner's emotional state. For example, it provides content related to relaxation exercises or mental health. The curriculum generation unit also uses the emotion estimation function to evaluate the learner's emotional state and suggest timing for breaks to reduce stress and fatigue. For example, it displays an alert encouraging the learner to take a break after a certain amount of study time. This allows the learning effect to be improved by providing a curriculum that corresponds to the learner's emotional state.

[0064] The curriculum generation department can add topics from related fields other than esports to aim for overall skill improvement. For example, the curriculum generation department can add psychology topics to the esports curriculum to provide learning content on mental management and stress management. For example, students can learn how to improve concentration and mental rehearsal techniques. The curriculum generation department can also incorporate nutrition topics into the curriculum to provide knowledge on healthy eating and nutritional balance. For example, students can learn about pre-game meals and how to replenish their energy. The curriculum generation department can also add physical fitness topics to the esports curriculum to provide training plans to improve physical strength and flexibility. For example, students can learn about stretching and strength training methods. This makes it possible to incorporate knowledge from related fields other than esports to aim for overall skill improvement.

[0065] The curriculum generation unit can generate curricula specialized for different game genres, allowing learners to select based on their interests. For example, the curriculum generation unit uses a generation AI to generate curricula specialized for different game genres, allowing learners to select based on their interests. For example, the curriculum generation unit provides a curriculum related to strategies and operation methods for FPS games. The curriculum generation unit also generates a curriculum specialized for MOBA games based on the learner's interests and play style. For example, the curriculum generation unit provides learning content related to character selection and team play strategies. The curriculum generation unit also generates a curriculum specialized for RTS games, providing content for learners to learn strategies in real time. For example, the curriculum generation unit provides learning methods related to resource management and unit placement. This allows learners to improve their learning effectiveness by providing curricula that match their interests.

[0066] The curriculum generation unit can use the emotion estimation function to identify the topic in which the learner is most interested and provide curriculum related to that topic preferentially. The curriculum generation unit, for example, uses the emotion estimation function to identify the topic in which the learner is most interested. For example, it analyzes facial expressions and vocal tone to evaluate the level of interest. The curriculum generation unit also preferentially provides curriculum related to the topic in which the generation AI is most interested based on the learner's emotional state. For example, it provides detailed learning content related to a topic of high interest. The curriculum generation unit also uses the emotion estimation function to identify the topic in which the learner is most interested and customizes a curriculum related to that topic. For example, it provides practice questions and assignments related to the topic of high interest. This makes it possible to improve learning effectiveness by providing a curriculum that matches the learner's interests.

[0067] The curriculum generation unit can analyze a learner's social media activities and comments in online communities to generate a curriculum that reflects their interests and concerns. In the curriculum generation unit, for example, a generation AI analyzes a learner's social media activities to generate a curriculum that reflects their interests and concerns. For example, the curriculum is customized based on topics that the learner frequently posts to and accounts that the learner follows. The curriculum generation unit also analyzes comments in online communities to identify the learner's interests and concerns. For example, the curriculum is generated based on the content of forums and discussions in which the learner participates. The curriculum generation unit also provides a curriculum that reflects the learner's interests and concerns through a generation AI based on the learner's social media activities and comments in online communities. For example, learning content related to topics that interest the learner is provided preferentially. This makes it possible to improve learning effectiveness by providing a curriculum based on the learner's interests and concerns.

[0068] The curriculum generation unit collects physiological data from learners and can suggest optimal learning timing and content. For example, the generation AI in the curriculum generation unit collects physiological data such as the learner's heart rate and sleep patterns and suggests optimal learning timing and content. For example, it recommends studying during times when the heart rate is stable. The curriculum generation unit also customizes the optimal learning timing and content based on the learner's physiological data. For example, it analyzes sleep patterns and suggests studying during times when the learner can concentrate best. The curriculum generation unit also collects physiological data and the generation AI provides a curriculum tailored to the learner's physical condition and rhythm. For example, it suggests optimal learning content and timing for the learner based on heart rate and sleep patterns. This makes it possible to improve learning effectiveness by providing optimal learning timing and content based on the learner's physiological data.

[0069] The curriculum generation unit can use the emotion estimation function to evaluate the learner's motivation level and generate a curriculum for maintaining motivation. The curriculum generation unit, for example, uses the emotion estimation function to evaluate the learner's motivation level in real time. For example, it analyzes facial expressions and vocal tone to calculate a motivation score. The curriculum generation unit also generates a curriculum for maintaining motivation using a generation AI based on the learner's motivation level. For example, it provides encouraging messages and success stories when motivation drops. The curriculum generation unit also uses the emotion estimation function to evaluate the learner's motivation level and customize a curriculum for maintaining motivation. For example, it provides topics and practice methods that increase motivation. This makes it possible to improve learning effectiveness by providing a curriculum for maintaining learners' motivation.

[0070] The curriculum generation unit can incorporate local esports events and community activities into the curriculum based on the learner's geographical location information. For example, the generation AI in the curriculum generation unit collects the learner's geographical location information and incorporates local esports events and community activities into the curriculum. For example, it introduces esports tournaments and workshops held nearby. The curriculum generation unit also suggests local esports community activities based on the learner's geographical location information. For example, it provides opportunities to participate in local esports clubs and practice sessions. The curriculum generation unit also utilizes the geographical location information to incorporate local esports events and community activities into the curriculum. For example, it provides information on local esports-related facilities and events. In this way, by incorporating local esports events and community activities into the curriculum, it is possible to pique the learner's interest and improve learning effectiveness.

[0071] The curriculum generation unit generates a curriculum according to the learner's learning style, thereby meeting individual learning needs. For example, the generation AI in the curriculum generation unit evaluates the learner's learning style and generates a curriculum according to whether the learner is visual, auditory, experiential, or the like. For example, visual content is provided to a visual learner. The curriculum generation unit also provides a curriculum that meets individual learning needs based on the learner's learning style. For example, audio commentary or podcasts is provided to an auditory learner. The curriculum generation unit also takes learning style into consideration, and the generation AI provides practical training and simulations to experiential learners. For example, practical practice in a game or interactive learning content is provided. This makes it possible to improve learning effectiveness by providing a curriculum that meets the learner's learning style.

[0072] The curriculum generation unit can use the emotion estimation function to identify the environment in which the learner finds the most relaxing, and generate a curriculum that recommends studying in that environment. The curriculum generation unit, for example, uses the emotion estimation function to identify the environment in which the learner finds the most relaxing. For example, it analyzes the learner's heart rate and facial expressions to evaluate the learner's level of relaxation. The curriculum generation unit also generates a curriculum in which the generation AI recommends studying in that environment based on the learner's relaxing environment. For example, it suggests studying in a quiet place or in nature. The curriculum generation unit also uses the emotion estimation function to identify the environment in which the learner finds the most relaxing, and customizes a curriculum that recommends studying in that environment. For example, it provides a learning method that uses relaxing music or aromas. This allows the learner to study in the most relaxing environment, thereby improving learning effectiveness.

[0073] The individual tutoring unit can observe the learner's real-time gameplay and provide immediate advice and correction instructions. In the individual tutoring unit, for example, the generation AI observes the learner's real-time gameplay and provides immediate advice and correction instructions. For example, it points out operational errors and areas for strategic improvement in real time. The individual tutoring unit also analyzes the learner's gameplay in real time and the generation AI provides immediate advice. For example, it suggests the optimal actions and tactics for specific situations. In the individual tutoring unit, the generation AI observes the learner's real-time gameplay and provides immediate correction instructions. For example, it instructs the learner to improve their operational methods or change their strategy in real time. This makes it possible to improve learning effectiveness by providing advice and correction instructions in real time.

[0074] The individual instruction unit can analyze a learner's past mistakes and patterns of success, and provide detailed instruction on individual points for improvement. For example, the generation AI in the individual instruction unit analyzes a learner's past mistakes and patterns of success, and provides detailed instruction on individual points for improvement. For example, it analyzes the causes of specific mistakes and factors behind success, and suggests specific methods for improvement. The generation AI in the individual instruction unit also identifies individual points for improvement based on the learner's past data, and provides detailed instruction. For example, it offers methods for improving specific skills and strategies. The generation AI in the individual instruction unit also analyzes a learner's past mistakes and patterns of success, and provides detailed instruction on individual points for improvement. For example, it suggests the best practice methods and tactics for the learner based on past data. In this way, by analyzing past mistakes and patterns of success and providing instruction on individual points for improvement, it is possible to improve learning effectiveness.

[0075] The individual tutoring unit can use the emotion estimation function to suggest encouragement and relaxation methods according to the learner's emotional state, thereby improving learning effectiveness. For example, the individual tutoring unit can use the emotion estimation function to evaluate the learner's emotional state in real time and suggest encouragement and relaxation methods. For example, if the learner is feeling stressed, it can provide relaxation methods. Furthermore, the individual tutoring unit uses a generative AI to suggest encouragement and relaxation methods based on the learner's emotional state. For example, if the learner is losing motivation, it can provide an encouraging message. Furthermore, the individual tutoring unit can use the emotion estimation function to suggest encouragement and relaxation methods according to the learner's emotional state, thereby improving learning effectiveness. For example, if the learner is tired, it can suggest a break to refresh themselves. In this way, learning effectiveness can be improved by suggesting encouragement and relaxation methods according to the learner's emotional state.

[0076] The individual instruction department can analyze instructional videos by professional players of different games and provide the most appropriate instructional content to the learner. For example, the generation AI analyzes instructional videos by professional players of different games and provides the most appropriate instructional content to the learner. For example, the tactics and operation methods of the professional players are customized to suit the learner. The individual instruction department also uses the generation AI to provide the most appropriate instructional content to the learner based on the instructional videos of the professional players. For example, detailed explanations of specific skills and strategies are provided. The individual instruction department also uses the generation AI to analyze instructional videos of professional players and provide the most appropriate instructional content to the learner. For example, the generation AI tailors instruction to the learner's playing style. This allows the learning effect to be improved by analyzing instructional videos of professional players and providing the most appropriate instructional content to the learner.

[0077] The individual tutoring unit can provide a curriculum that promotes paired learning between learners and supports their progress. In the individual tutoring unit, for example, a generating AI promotes paired learning between learners and provides a curriculum that supports their progress. For example, the progress of paired learning is monitored in real time and necessary support is provided. In addition, in order to promote paired learning between learners, the generating AI customizes the curriculum. For example, it provides assignments and exercises for pairs to work on. In addition, in the individual tutoring unit, the generating AI supports paired learning between learners and monitors their progress. For example, it evaluates the progress of paired learning and provides necessary feedback. In this way, it is possible to promote paired learning between learners and support their progress, thereby improving learning effectiveness.

[0078] The individual tutoring unit can use the emotion estimation function to identify the time periods when the learner can concentrate best and provide individual instruction during those times. The individual tutoring unit, for example, uses the emotion estimation function to identify the time periods when the learner can concentrate best. For example, it analyzes the learner's heart rate and facial expressions to evaluate the level of concentration. The individual tutoring unit also uses the generation AI to provide individual instruction during those time periods based on the time periods when the learner can concentrate best. For example, it suggests that important topics be studied during times when concentration is high. The individual tutoring unit also uses the emotion estimation function to identify the time periods when the learner can concentrate best and provide individual instruction during those time periods. For example, it provides practice problems and assignments during times when concentration is high. This allows individual instruction to be provided during times when the learner can concentrate best, thereby improving learning effectiveness.

[0079] The feedback providing unit can analyze the learner's gameplay data in detail and provide feedback on specific areas for improvement and factors for success. For example, the generation AI in the feedback providing unit analyzes the learner's gameplay data in detail and provides feedback on specific areas for improvement. For example, it points out operational errors and areas for improvement in strategy. The feedback providing unit also identifies success factors based on the learner's gameplay data and provides feedback. For example, it explains why a particular tactic or operating method was successful. The feedback providing unit also analyzes the learner's gameplay data in detail and provides feedback on specific areas for improvement and factors for success. For example, it suggests the optimal improvement method for the learner based on past data. In this way, by analyzing the learner's gameplay data in detail and providing feedback on specific areas for improvement and factors for success, it is possible to improve learning effectiveness.

[0080] The feedback providing unit can periodically evaluate the learner's progress and provide feedback on long-term growth trends. In the feedback providing unit, for example, the generation AI periodically evaluates the learner's progress and provides feedback on long-term growth trends. For example, the feedback providing unit evaluates the learner's skill improvement and strategy improvement over the long term. Furthermore, the feedback providing unit identifies long-term growth trends based on the learner's progress data and provides feedback. For example, the feedback providing unit visualizes the learner's growth curve and evaluates progress. Furthermore, the feedback providing unit periodically evaluates the learner's progress and provides feedback on long-term growth trends. For example, the feedback providing unit predicts the learner's growth based on past data and provides feedback. In this way, by periodically evaluating the learner's progress and providing feedback on long-term growth trends, it is possible to improve learning effectiveness.

[0081] The feedback providing unit can maintain motivation by using the emotion estimation function to provide feedback according to the learner's emotional state. For example, the feedback providing unit can use the emotion estimation function to evaluate the learner's emotional state in real time and provide feedback according to the emotion. For example, if the learner is feeling stressed, it can provide an encouraging message. The feedback providing unit also provides feedback to the generation AI based on the learner's emotional state to maintain motivation. For example, if the learner is losing motivation, it can introduce success stories. The feedback providing unit also uses the emotion estimation function to provide feedback according to the learner's emotional state to maintain motivation. For example, if the learner is tired, it can suggest a break to refresh themselves. In this way, by providing feedback according to the learner's emotional state, motivation can be maintained and learning effectiveness can be improved.

[0082] The feedback providing unit can extract and share common points for improvement that are also beneficial to other learners based on the learner's feedback. In the feedback providing unit, for example, the generation AI analyzes the learner's feedback and extracts common points for improvement that are also beneficial to other learners. For example, it identifies points that multiple learners commonly struggle with and shares improvement methods. In addition, the feedback providing unit extracts common points for improvement based on the learner's feedback and provides them to other learners. For example, it provides a summary of common points for improvement related to specific skills or strategies. In addition, the feedback providing unit analyzes the learner's feedback and extracts and shares common points for improvement that are also beneficial to other learners. For example, it identifies and shares common challenges and improvement methods based on the learner's feedback. In this way, by extracting common points for improvement based on the learner's feedback and sharing them with other learners, learning effectiveness can be improved.

[0083] The feedback providing unit can visualize the feedback so that the learner can intuitively understand it. For example, the feedback providing unit causes the generation AI to visualize the feedback so that the learner can intuitively understand it. For example, the feedback providing unit visually displays the feedback using graphs or charts. The feedback providing unit also visualizes the learner's progress and areas for improvement so that the generation AI can provide feedback that is intuitively understandable. For example, the progress status is displayed on a timeline. The feedback providing unit also causes the generation AI to visualize the feedback so that the learner can intuitively understand it. For example, success factors and areas for improvement are displayed using icons or diagrams. In this way, visualizing the feedback makes it easier for the learner to intuitively understand, thereby improving learning effectiveness.

[0084] The curriculum generation unit can analyze learner feedback in real time and instantly update the curriculum. In the curriculum generation unit, for example, the generation AI analyzes learner feedback in real time and instantly updates the curriculum. For example, if a learner has an insufficient understanding of a particular topic, additional learning content related to that topic is provided. In addition, in the curriculum generation unit, the generation AI updates the curriculum in real time based on learner feedback. For example, new content related to a topic in which the learner has shown interest is added. In addition, in the curriculum generation unit, the generation AI analyzes learner feedback in real time and instantly updates the curriculum. For example, the difficulty level of the curriculum is adjusted according to the learner's progress. In this way, by analyzing learner feedback in real time and instantly updating the curriculum, it is possible to improve learning effectiveness.

[0085] The curriculum generation unit can track learner progress data over the long term, identify optimal learning patterns, and reflect them in the curriculum. In the curriculum generation unit, for example, the generation AI tracks learner progress data over the long term and identifies optimal learning patterns. For example, it identifies effective learning methods based on the learner's past data and reflects them in the curriculum. In addition, the curriculum generation unit identifies optimal learning patterns based on the learner's progress data and customizes the curriculum. For example, it identifies the time of day and method in which the learner can study most effectively. In addition, the curriculum generation unit tracks learner progress data over the long term, identifies optimal learning patterns, and reflects them in the curriculum. For example, it analyzes the learner's growth curve and provides optimal learning content. In this way, learning effectiveness can be improved by tracking learner progress data over the long term, identifying optimal learning patterns, and reflecting them in the curriculum.

[0086] The curriculum generation unit can use the emotion estimation function to update the curriculum according to the learner's emotional state and maximize learning effectiveness. The curriculum generation unit, for example, uses the emotion estimation function to evaluate the learner's emotional state in real time and update the curriculum. For example, if the learner is feeling stressed, a topic that helps the learner relax can be added. The curriculum generation unit also uses the generation AI to update the curriculum based on the learner's emotional state and maximize learning effectiveness. For example, content related to topics that the learner has shown interest in can be preferentially provided. The curriculum generation unit also uses the emotion estimation function to update the curriculum according to the learner's emotional state and maximize learning effectiveness. For example, if the learner is tired, a curriculum that encourages the learner to take a break can be provided. In this way, learning effectiveness can be maximized by updating the curriculum according to the learner's emotional state.

[0087] The curriculum generation unit can integrate data from different learner groups, extract common optimization points, and reflect them in the curriculum. In the curriculum generation unit, for example, the generation AI integrates data from different learner groups and extracts common optimization points. For example, it identifies topics that multiple learners commonly struggle with and reflects them in the curriculum. In addition, the curriculum generation unit identifies common optimization points based on the data from the learner groups and customizes the curriculum. For example, it provides common improvement points for specific skills or strategies. In addition, the curriculum generation unit integrates data from different learner groups using the generation AI, extracts common optimization points, and reflects them in the curriculum. For example, it identifies common challenges and improvement methods based on learner feedback and reflects them in the curriculum. In this way, by integrating data from different learner groups, extracting common optimization points, and reflecting them in the curriculum, it is possible to improve learning effectiveness.

[0088] The curriculum generation unit can visualize the curriculum updates so that learners can intuitively understand the changes. In the curriculum generation unit, for example, the generation AI visualizes the curriculum updates so that learners can intuitively understand them. For example, the curriculum changes are visually displayed using graphs and charts. The curriculum generation unit also visualizes the learner's progress and the curriculum updates so that the generation AI can provide intuitively understandable feedback. For example, the progress is displayed on a timeline. In addition, the curriculum generation unit visualizes the curriculum updates so that learners can intuitively understand them. For example, changes and new content are displayed with icons and diagrams. In this way, visualizing the curriculum updates makes it easier for learners to intuitively understand the changes, thereby improving learning effectiveness.

[0089] The curriculum generation unit can use the emotion estimation function to identify the curriculum update method that will evoke the most positive emotions in the learner and perform the update using that method. The curriculum generation unit, for example, uses the emotion estimation function to identify the curriculum update method that will evoke the most positive emotions in the learner. For example, the curriculum generation unit analyzes the learner's facial expressions and tone of voice to calculate an emotion score. The curriculum generation unit also provides a curriculum update method that will evoke the most positive emotions in the generation AI based on the learner's emotional state. For example, it uses messages or visuals that elicit positive emotions. The curriculum generation unit also uses the emotion estimation function to identify the curriculum update method that will evoke the most positive emotions in the learner and perform the update using that method. For example, it updates the curriculum in a format that generates a high emotion score. In this way, by identifying the curriculum update method that will evoke the most positive emotions in the learner and performing the update using that method, the learning effect can be improved.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The curriculum generation unit can collect physiological data from learners and suggest optimal learning timing and content. For example, the generation AI collects physiological data such as the learner's heart rate and sleep patterns and suggests optimal learning timing and content. For example, it recommends studying during times when the heart rate is stable. The curriculum generation unit also customizes the optimal learning timing and content based on the learner's physiological data. For example, it analyzes sleep patterns and suggests studying during times when the learner can concentrate best. The curriculum generation unit also collects physiological data and the generation AI provides a curriculum tailored to the learner's physical condition and rhythm. For example, it suggests the optimal learning content and timing for the learner based on heart rate and sleep patterns. This makes it possible to improve learning effectiveness by providing optimal learning timing and content based on the learner's physiological data.

[0092] The curriculum generation unit can generate curricula specialized for different game genres, allowing learners to select based on their interests. For example, the generation AI generates curricula specialized for different game genres, allowing learners to select based on their interests. For example, a curriculum on strategies and operation methods for FPS games is provided. The curriculum generation unit also generates a curriculum specialized for MOBA games based on the learner's interests and play style. For example, learning content on character selection and team play strategies is provided. The curriculum generation unit also generates a curriculum specialized for RTS games, providing content for learners to learn strategies in real time. For example, learning methods on resource management and unit placement are provided. This allows for the provision of curricula tailored to the learner's interests, thereby improving learning effectiveness.

[0093] The curriculum generation unit can use the emotion estimation function to evaluate the learner's emotional state and incorporate break or refreshment topics to reduce stress or fatigue into the curriculum. For example, the emotion estimation function can be used to evaluate the learner's emotional state in real time and detect stress or fatigue. For example, facial expressions and vocal tone can be analyzed to calculate an emotion score. The curriculum generation unit also uses the generation AI to incorporate break or refreshment topics to reduce stress and fatigue into the curriculum based on the learner's emotional state. For example, it can provide content related to relaxation exercises or mental health. The curriculum generation unit also uses the emotion estimation function to evaluate the learner's emotional state and suggest break times to reduce stress and fatigue. For example, it can display an alert encouraging the learner to take a break after a certain amount of study time. This can improve learning effectiveness by providing a curriculum that corresponds to the learner's emotional state.

[0094] The curriculum creation department can add topics from related fields outside of esports to aim for overall skill improvement. For example, a psychology topic could be added to the esports curriculum to provide learning content on mental management and stress management. For example, students could learn how to improve concentration and mental rehearsal techniques. The curriculum creation department could also incorporate nutrition topics into the curriculum to provide knowledge on healthy eating and nutritional balance. For example, students could learn about pre-game meals and how to replenish their energy. The curriculum creation department could also add physical fitness topics to the esports curriculum to provide training plans to improve physical strength and flexibility. For example, students could learn about stretching and strength training methods. In this way, it is possible to improve overall skills by incorporating knowledge from related fields outside of esports.

[0095] The curriculum generation unit can use the emotion estimation function to identify the topic in which the learner is most interested and provide curriculum related to that topic preferentially. For example, the emotion estimation function can be used to identify the topic in which the learner is most interested. For example, facial expressions and tone of voice can be analyzed to evaluate the level of interest. The curriculum generation unit can also preferentially provide curriculum related to the topic in which the generation AI is most interested based on the learner's emotional state. For example, detailed learning content related to a topic of high interest can be provided. The curriculum generation unit can also use the emotion estimation function to identify the topic in which the learner is most interested and customize a curriculum related to that topic. For example, practice questions and assignments related to the topic of high interest can be provided. This can improve learning effectiveness by providing a curriculum that matches the learner's interests.

[0096] The curriculum generation unit can analyze a learner's social media activities and comments in online communities to generate a curriculum that reflects their interests and concerns. For example, the generation AI analyzes a learner's social media activities to generate a curriculum that reflects their interests and concerns. For example, the curriculum is customized based on topics the learner frequently posts to and accounts the learner follows. The curriculum generation unit also analyzes comments in online communities to identify the learner's interests and concerns. For example, the curriculum is generated based on the content of forums and discussions in which the learner participates. The curriculum generation unit also provides a curriculum that reflects the learner's interests and concerns based on the learner's social media activities and comments in online communities. For example, learning content related to topics that interest the learner is provided preferentially. This makes it possible to improve learning effectiveness by providing a curriculum based on the learner's interests and concerns.

[0097] The curriculum generation unit can incorporate local esports events and community activities into the curriculum based on the learner's geographical location information. For example, the generation AI collects the learner's geographical location information and incorporates local esports events and community activities into the curriculum. For example, it may introduce esports tournaments and workshops held nearby. The curriculum generation unit also suggests local esports community activities based on the learner's geographical location information. For example, it may provide opportunities to participate in local esports clubs and practice sessions. The curriculum generation unit also utilizes the geographical location information to incorporate local esports events and community activities into the curriculum. For example, it may provide information on local esports-related facilities and events. In this way, by incorporating local esports events and community activities into the curriculum, it is possible to pique the learner's interest and improve learning outcomes.

[0098] The curriculum generation unit can use the emotion estimation function to evaluate the learner's motivation level and generate a curriculum to maintain motivation. For example, the emotion estimation function is used to evaluate the learner's motivation level in real time. For example, facial expressions and vocal tone are analyzed to calculate a motivation score. The curriculum generation unit also generates a curriculum for maintaining motivation using a generation AI based on the learner's motivation level. For example, it provides encouraging messages and success stories when motivation drops. The curriculum generation unit also uses the emotion estimation function to evaluate the learner's motivation level and customize a curriculum for maintaining motivation. For example, it provides topics and practice methods that increase motivation. This makes it possible to improve learning effectiveness by providing a curriculum to maintain learners' motivation.

[0099] The curriculum generation unit generates a curriculum that matches the learner's learning style, enabling it to meet individual learning needs. For example, the generation AI evaluates the learner's learning style and generates a curriculum that matches visual, auditory, experiential, etc. For example, visual content is provided to visual learners. The curriculum generation unit also provides a curriculum that meets individual learning needs based on the learner's learning style, with the generation AI providing audio commentary and podcasts to auditory learners. The curriculum generation unit also takes learning style into consideration, with the generation AI providing practical training and simulations to experiential learners. For example, practical practice within games and interactive learning content are provided. This allows for the provision of a curriculum that matches the learner's learning style, thereby improving learning effectiveness.

[0100] The curriculum generation unit can use the emotion estimation function to identify the environment in which the learner finds the most relaxing, and generate a curriculum that recommends studying in that environment. For example, the emotion estimation function can be used to identify the environment in which the learner finds the most relaxing. For example, the learner's heart rate and facial expressions can be analyzed to evaluate the level of relaxation. The curriculum generation unit also generates a curriculum that uses the generation AI to recommend studying in that environment based on the learner's relaxing environment. For example, it can suggest studying in a quiet place or in nature. The curriculum generation unit also uses the emotion estimation function to identify the environment in which the learner finds the most relaxing, and customizes a curriculum that recommends studying in that environment. For example, it can provide a learning method that uses relaxing music or aromas. This allows the learner to study in the most relaxing environment, thereby improving learning effectiveness.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The curriculum generation unit uses generation AI to generate a curriculum based on the learner's profile and learning goals. For example, the generation AI receives profile information such as the learner's age, learning history, and areas of interest as input, and generates an optimal curriculum based on that information. The generation AI also sets achievement standards and customizes the curriculum, taking into account the learner's short-term and long-term goals. Step 2: The individualized instruction unit provides individualized instruction to each learner based on the curriculum generated by the curriculum generation unit. For example, if a learner wants to learn how to play a specific game, the generation AI will provide detailed instructions and practice questions on how to play that game. The generation AI will also suggest the next topic to learn and the practice method based on the learner's progress data and feedback. Step 3: The feedback provider provides feedback based on the learner's progress based on the instruction provided by the individual tutor. For example, the generator evaluates whether the learner has understood a specific topic and provides additional explanations or practice questions if understanding is insufficient. The generator also analyzes the learner's performance data and specifically points out areas for improvement and strengthening.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 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 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.

[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 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[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 robot 414, 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 robot 414 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.

[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 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.

[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 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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. Using generative AI, a curriculum generation unit that generates a curriculum based on a learner's profile and learning goals; an individual instruction unit that provides individual instruction to each learner based on the curriculum generated by the curriculum generation unit; a feedback providing unit that provides feedback according to the progress of the learner based on the instruction provided by the individual instruction unit. A system characterized by:

2. The curriculum generation unit The learner's gameplay data is analyzed in real time, and a curriculum based on the learner's playing style is dynamically generated.

2. The system of claim 1.

3. The curriculum generation unit Analyzing the learner's past learning history, identifying the most effective learning patterns, and reflecting them in the curriculum 2. The system of claim 1.

4. The curriculum generation unit Assessing the learner's emotional state and incorporating breaks or refreshment topics into the curriculum to reduce stress or fatigue 2. The system of claim 1.

5. The curriculum generation unit Add topics related to fields other than esports to improve overall skills 2. The system of claim 1.

6. The curriculum generation unit Generate curricula specialized in different game genres and allow learners to select according to their interests.

2. The system of claim 1.

Images