system

Abstract

The system according to this embodiment aims to provide optimal learning content based on the learner's level of understanding and interests. [Solution] The system according to this embodiment comprises an analysis unit, a provision unit, a generation unit, and a feedback unit. The analysis unit analyzes the learner's level of understanding and interests. The provision unit provides optimal learning content based on the analysis results obtained by the analysis unit. The generation unit generates virtual teachers and characters based on the content provided by the provision unit. The feedback unit analyzes the learner's progress in real time through the virtual teachers and characters generated by the generation unit and provides feedback.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully achieved to provide optimal learning content based on the understanding level and interests of learners, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal learning content based on the understanding level and interests of learners.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a generation unit, and a feedback unit. The analysis unit analyzes the learner's level of understanding and interests. The provision unit provides optimal learning content based on the analysis results obtained by the analysis unit. The generation unit generates virtual teachers and characters based on the content provided by the provision unit. The feedback unit analyzes the learner's progress in real time through the virtual teachers and characters generated by the generation unit and provides feedback. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal learning content based on the learner's level of understanding and interests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An educational platform according to an embodiment of the present invention is a system that allows learners to experience learning content individually optimized by a generating AI in an immersive VR environment. The educational platform consists of elements such as an analysis unit that analyzes the learner's level of understanding and interests, a provision unit that provides optimal learning content, a feedback unit that analyzes the learner's progress in real time and provides feedback, and a generation unit that generates virtual teachers and characters. This enables experiential learning that was difficult with conventional teaching methods, such as reliving historical events or visualizing complex scientific concepts in 3D space. For example, the educational platform analyzes the learner's level of understanding and interests. For example, the educational platform evaluates the learner's level of understanding based on test results and quiz correct answer rates. The educational platform also evaluates interests based on the learner's survey results and browsing history. Next, the educational platform provides optimal learning content. For example, the educational platform provides teaching materials and individualized learning plans according to the learner's level. The educational platform also generates virtual teachers and characters. For example, the educational platform generates animated characters and voice assistants. Furthermore, the educational platform analyzes the learner's progress in real time and provides feedback. For example, the educational platform performs real-time analysis based on the frequency of data updates and analytical algorithms. This allows the platform to provide optimal learning content based on learners' understanding and interests, and to analyze progress in real time and provide feedback through virtual teachers and characters, thereby enabling effective learning.

[0029] The educational platform according to this embodiment comprises an analysis unit, a provision unit, a generation unit, and a feedback unit. The analysis unit analyzes the learner's level of understanding and interests. For example, the analysis unit evaluates the level of understanding based on the learner's test results or quiz accuracy rate. The analysis unit can also evaluate interests based on the learner's survey results or browsing history. Furthermore, the analysis unit can analyze the learner's past learning history and select the optimal method for evaluating understanding. For example, the analysis unit can identify areas where the learner has struggled in the past and select an evaluation method that focuses on those areas. The analysis unit can also select an evaluation method that strengthens areas where the learner has excelled based on areas where the learner has scored highly in the past. Furthermore, the analysis unit can analyze the learner's past learning pace and select an appropriate evaluation timing. The provision unit provides optimal learning content based on the analysis results obtained by the analysis unit. For example, the provision unit provides learning materials and individualized learning plans according to the learner's level. The provision unit can also update learning content according to the learner's progress. For example, the provision unit adds or deletes content based on progress data. Furthermore, the provider can customize the themes and formats of learning content according to the learner's interests. For example, if the learner is interested in history, the provider can provide history-related content. Similarly, if the learner is interested in science, the provider can provide science-related content. The generator generates virtual teachers and characters based on the content provided by the provider. For example, the generator can generate animated characters or voice assistants. The generator can also dynamically generate scenarios based on the learner's progress. For example, it can generate scenarios using a scenario generation algorithm based on the learner's progress data. Additionally, the generator can estimate the learner's emotions and adjust the virtual teacher's and character's expressions based on the estimated emotions. For example, if the learner is stressed, the virtual teacher can explain in a gentle tone. Conversely, if the learner is excited, the virtual teacher can explain in an energetic tone.The feedback unit analyzes the learner's progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. The feedback unit performs real-time analysis based, for example, on the data update frequency and analysis algorithm. The feedback unit can also estimate the learner's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the learner is feeling stressed, the feedback unit will provide feedback in gentle words. Conversely, if the learner is excited, the feedback unit can provide feedback in energetic words. As a result, the educational platform according to this embodiment can provide optimal learning content based on the learner's level of understanding and interests, and analyze progress in real time and provide feedback through virtual teachers and characters, thereby enabling effective learning.

[0030] The analytics department analyzes learners' understanding and interests. Specifically, it evaluates understanding based on learners' test results and quiz accuracy rates. For example, it analyzes the accuracy rate and error patterns of test results to identify areas where learners lack understanding. It can also evaluate learners' interests based on survey results and browsing history. Surveys can ask learners about topics they are interested in and what type of learning materials they prefer. Furthermore, it can analyze learners' past learning history to select the optimal method for evaluating understanding. For example, it can identify areas where learners have difficulty based on their past learning history and select an evaluation method that focuses on those areas. It can also select an evaluation method that strengthens areas where learners have scored highly in the past. In addition, it can analyze learners' past learning pace to select appropriate evaluation timing. For example, if a learner is the type who learns intensively in a short period, evaluations should be conducted frequently to closely monitor their progress. On the other hand, if a learner is the type who learns slowly over a long period, the evaluation intervals should be widened to reduce the learner's burden. This allows the analysis department to conduct flexible assessments tailored to the individual characteristics of each learner, thereby maximizing the effectiveness of their learning.

[0031] The content delivery department provides optimal learning content based on the analysis results obtained by the analysis department. Specifically, it provides learning materials and individualized learning plans tailored to the learner's level. For example, it selects appropriate materials according to the learner's level of understanding, from basic materials for beginners to advanced materials for advanced learners. The content delivery department can also update learning content according to the learner's progress. For example, when a learner completes a particular unit, it automatically provides the next unit based on that progress data. Furthermore, the content delivery department can customize the themes and formats of learning content according to the learner's interests. For example, if a learner is interested in history, it will provide history-related content. Similarly, if a learner is interested in science, it can provide science-related content. In addition, the content delivery department continuously improves the quality of content based on learner feedback. For example, it collects evaluations and comments from learners and reviews the content and format of the materials based on them. This allows the content delivery department to provide high-quality learning content that meets the learner's needs and maximizes the effectiveness of learning.

[0032] The generation unit generates virtual teachers and characters based on the content provided by the provider unit. Specifically, it generates animated characters and voice assistants. For example, if a learner prefers visual learning, an animated character will explain the learning materials. If a learner prefers auditory learning, a voice assistant will read the materials aloud. Furthermore, the generation unit can dynamically generate scenarios according to the learner's progress. For example, when a learner completes a particular unit, it uses a scenario generation algorithm based on that progress data to generate the next scenario. In addition, the generation unit can estimate the learner's emotions and adjust the way the virtual teachers and characters are portrayed based on the estimated emotions. For example, if a learner is stressed, the virtual teacher will explain in a gentle tone. If a learner is excited, the virtual teacher can explain in an energetic tone. This allows the generation unit to respond flexibly to the learner's emotions and progress, maximizing the effectiveness of learning.

[0033] The feedback unit analyzes learners' progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. Specifically, it performs real-time analysis based on data update frequency and analysis algorithms. For example, it analyzes learners' test results and quiz accuracy rates in real time to understand changes in their comprehension. The feedback unit can also estimate learners' emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if a learner is stressed, it provides feedback in gentle language. If a learner is excited, it can provide feedback in energetic language. Furthermore, the feedback unit continuously improves the quality of feedback based on learner feedback. For example, it collects evaluations and comments from learners and reviews the content and format of the feedback based on them. This allows the feedback unit to provide high-quality feedback tailored to learners' needs and maximize the effectiveness of learning.

[0034] The generation unit can dynamically generate scenarios according to the learner's progress. For example, the generation unit generates scenarios using a scenario generation algorithm based on the learner's progress data. For example, the generation unit can generate scenarios using a generation AI model that takes the learner's progress data as input and outputs scenarios. This allows for the dynamic generation of scenarios according to the learner's progress, thereby providing an individually optimized learning experience.

[0035] The service provider can update learning content according to the learner's progress. For example, the service provider can add or delete content based on progress data. For instance, the service provider can update learning content using an AI model that takes learner progress data as input and outputs updated learning content. This allows the service provider to always provide optimal learning content by updating learning content according to the learner's progress.

[0036] The analysis unit can analyze a learner's past learning history and select the optimal method for assessing comprehension. For example, the analysis unit can identify areas where the learner has struggled in the past and select an assessment method that focuses on those areas. It can also select an assessment method that strengthens areas where the learner has excelled, based on areas where the learner has scored highly in the past. Furthermore, the analysis unit can analyze the learner's past learning pace and select the appropriate timing for assessment. By selecting the optimal method for assessing comprehension based on past learning history, assessments tailored to the learner become possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI.

[0037] The analysis unit can analyze the learner's level of comprehension based on their current learning environment and circumstances. For example, if a learner is studying in a quiet environment, the analysis unit can select an assessment method to enhance their concentration. If a learner is studying in a noisy environment, the analysis unit can also select an assessment method that can be completed in a short time. Furthermore, if a learner is studying while on the go, the analysis unit can select an assessment method optimized for mobile devices. This allows for more appropriate assessment by analyzing comprehension based on the current learning environment and circumstances. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0038] The analysis unit can analyze region-specific interests by considering the learner's geographical location. For example, if a learner lives in a particular region, the analysis unit can provide content related to the history and culture of that region. If a learner is traveling, the analysis unit can also provide content related to tourist attractions and landmarks in their destination. Furthermore, if a learner lives in a different region, the analysis unit can provide content related to the language and dialect of that region. This allows for the provision of learning content that reflects region-specific interests by considering geographical location. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI.

[0039] The analytics unit can analyze learners' social media activity and extract relevant interests. For example, the analytics unit can identify topics that learners frequently share on social media and provide content related to those topics. The analytics unit can also analyze the posts of influencers that learners follow and extract relevant interests. Furthermore, the analytics unit can analyze the activities of online communities that learners participate in and extract relevant interests. In this way, by analyzing social media activity, it is possible to provide content based on learners' interests. Some or all of the above processing in the analytics unit may be performed using AI, for example, or not using AI.

[0040] The content provider can dynamically adjust the difficulty level of learning content based on the learner's level of understanding. For example, if the learner demonstrates a high level of understanding, the provider can provide content of a higher difficulty level. Conversely, if the learner demonstrates a low level of understanding, the provider can also provide content of a lower difficulty level. Furthermore, the provider can adjust the difficulty level of the content in real time according to the learner's level of understanding. This allows for learning at an appropriate difficulty level by adjusting the difficulty level of the content based on the learner's level of understanding. Some or all of the above processing in the content provider may be performed using AI, for example, or without using AI.

[0041] The content provider can customize the themes and formats of learning content according to the learner's interests. For example, if a learner is interested in history, the provider can provide history-related content. Similarly, if a learner is interested in science, the provider can provide science-related content. Furthermore, the provider can customize the themes and formats of the content according to the learner's interests. This allows for more engaging learning by customizing content according to the learner's interests. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI.

[0042] The content provider can provide region-specific learning content, taking into account the learner's geographical location. For example, if a learner lives in a specific region, the provider can provide content related to the history and culture of that region. If a learner is traveling, the provider can also provide content related to tourist attractions and landmarks in their destination. Furthermore, if a learner lives in a different region, the provider can provide content related to the language and dialect of that region. This allows for the provision of region-specific learning content by considering geographical location. Some or all of the processing described above in the content provider may be performed using AI, for example, or without AI.

[0043] The service provider can analyze learners' social media activity and provide relevant learning content. For example, it can provide content related to topics that learners frequently share on social media. It can also provide content related to posts by influencers that learners follow. Furthermore, it can provide content related to the activities of online communities that learners participate in. In this way, by analyzing social media activity, the service provider can provide content relevant to learners. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0044] The generation unit can dynamically adjust the explanation methods of the virtual teacher or character based on the learner's level of understanding. For example, if the learner shows a high level of understanding, the generation unit may omit detailed explanations from the virtual teacher. Conversely, if the learner shows a low level of understanding, the generation unit may also have the virtual teacher add detailed explanations. Furthermore, the generation unit can adjust the explanation methods of the virtual teacher in real time according to the learner's level of understanding. This allows for more appropriate learning by adjusting the explanation method based on the learner's level of understanding. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0045] The generation unit can customize the virtual teacher and character scenarios according to the learner's interests. For example, if the learner is interested in history, the generation unit can provide a history-related scenario with the virtual teacher. Similarly, if the learner is interested in science, the generation unit can provide a science-related scenario with the virtual teacher. Furthermore, the generation unit can customize the virtual teacher's scenario according to the learner's interests. This allows for more engaging learning by customizing scenarios according to the learner's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0046] The generation unit can generate region-specific virtual teachers and characters, taking into account the learner's geographical location. For example, if a learner lives in a specific region, the generation unit can generate a virtual teacher who speaks the local dialect. Furthermore, if a learner is traveling, the generation unit can generate a virtual character related to the culture of the place they are visiting. Additionally, if a learner lives in a different region, the generation unit can generate a virtual character related to the history and culture of that region. This allows for the provision of region-specific virtual teachers and characters by considering geographical location. Some or all of the processing described above in the generation unit may be performed using AI, for example, or without AI.

[0047] The generation unit can analyze learners' social media activity and generate relevant virtual teachers and characters. For example, it can generate virtual teachers related to topics that learners frequently share on social media. It can also generate virtual characters related to the content of posts by influencers that learners follow. Furthermore, it can generate virtual characters related to the activities of online communities that learners participate in. In this way, by analyzing social media activity, it is possible to provide learners with relevant virtual teachers and characters. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0048] The feedback unit can dynamically adjust the level of detail of the feedback based on the learner's level of understanding. For example, if the learner shows a high level of understanding, the feedback unit will provide concise feedback. Conversely, if the learner shows a low level of understanding, the feedback unit can also provide detailed feedback. Furthermore, the feedback unit can adjust the level of detail of the feedback in real time according to the learner's level of understanding. This allows for appropriate feedback by adjusting the level of detail of the feedback based on the learner's level of understanding. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0049] The feedback unit can customize the content of the feedback according to the learner's interests. For example, if the learner is interested in history, the feedback unit will provide history-related feedback. Similarly, if the learner is interested in science, the feedback unit can provide science-related feedback. Furthermore, the feedback unit can customize the content of the feedback according to the learner's interests. This allows for more engaging feedback by customizing the content according to the learner's interests. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI.

[0050] The feedback unit can provide region-specific feedback by taking into account the learner's geographical location. For example, if the learner lives in a specific region, the feedback unit can provide feedback related to the culture and customs of that region. Furthermore, if the learner is traveling, the feedback unit can provide feedback related to the culture and sights of the destination. Additionally, if the learner lives in a different region, the feedback unit can provide feedback related to the language and dialect of that region. This allows for the provision of region-specific feedback by considering geographical location. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI.

[0051] The feedback unit can analyze learners' social media activity and provide relevant feedback. For example, the feedback unit can provide feedback related to topics that learners frequently share on social media. It can also provide feedback related to the content of posts by influencers that learners follow. Furthermore, the feedback unit can provide feedback related to the activities of online communities that learners participate in. In this way, by analyzing social media activity, it is possible to provide learners with relevant feedback. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or not using AI.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] An educational platform can analyze learners' learning styles and provide learning content based on those styles. For example, if a learner is a visual learner, the analytics department can provide materials that heavily utilize visual content. If the learner is an auditory learner, it can provide materials that incorporate audio and music. Furthermore, if the learner is an experiential learner, it can provide interactive simulations and experiments. By providing content based on the learner's learning style, more effective learning becomes possible.

[0054] Educational platforms can provide learning content while considering learners' social relationships. For example, if a learner prefers to study with friends, the platform can offer group learning opportunities. If a learner prefers to study alone, it can provide individualized learning content. Furthermore, if a learner prefers interaction within an online community, it can provide opportunities to interact with other learners through forums and chat functions. By providing learning content based on learners' social relationships, more effective learning becomes possible.

[0055] Educational platforms can analyze learners' past learning history and provide learning content based on that history. For example, the analytics department can identify areas where learners have struggled in the past and provide materials that focus on those areas. It can also provide materials that reinforce areas where learners have excelled, based on areas where they have scored highly in the past. Furthermore, it can analyze learners' past learning pace and provide appropriate learning schedules. In this way, by providing optimal learning content based on past learning history, it becomes possible to enable learning that is tailored to each learner.

[0056] Educational platforms can provide region-specific learning content by considering learners' geographical location. For example, if a learner lives in a particular region, the platform can provide content related to the history and culture of that region. If the learner is traveling, it can provide content related to tourist attractions and landmarks in their destination. Furthermore, if the learner lives in a different region, it can provide content related to the language and dialect of that region. In this way, by considering geographical location, region-specific learning content can be provided.

[0057] Educational platforms can analyze learners' social media activity and provide learning content based on that activity. For example, the analytics department can identify topics that learners frequently share on social media and provide content related to those topics. It can also analyze the posts of influencers that learners follow and provide relevant content. Furthermore, it can analyze the activities of online communities that learners participate in and provide relevant content. In this way, by analyzing social media activity, it is possible to provide content based on learners' interests.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The analysis department analyzes the learner's level of understanding and interests. Specifically, it evaluates understanding based on the learner's test results and quiz accuracy rates, and evaluates interests based on survey results and browsing history. It also analyzes past learning history to select the optimal method for evaluating understanding. For example, it selects an evaluation method that focuses on areas of weakness or an evaluation method that strengthens areas of strength, and selects the appropriate timing for evaluation. Step 2: The provision department provides optimal learning content based on the analysis results obtained by the analysis department. Specifically, it provides learning materials and personalized learning plans tailored to the learner's level, and updates the content as they progress. Furthermore, it customizes themes and formats according to the learner's interests. For example, it provides history-related content to learners interested in history, and science-related content to learners interested in science. Step 3: The generation unit generates virtual teachers and characters based on the content provided by the delivery unit. Specifically, it generates animated characters and voice assistants, and dynamically generates scenarios according to the learner's progress. Furthermore, it estimates the learner's emotions and adjusts the way the virtual teachers and characters express themselves based on the estimated emotions. For example, if the learner is stressed, the teacher will explain in a gentle tone, and if they are excited, they will explain in an energetic tone. Step 4: The feedback unit analyzes the learner's progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. Specifically, it performs real-time analysis based on the data update frequency and analysis algorithm, estimates the learner's emotions, and adjusts the way the feedback is expressed. For example, if the learner is stressed, it provides feedback in gentle words, and if they are excited, it provides feedback in energetic words.

[0060] (Example of form 2) An educational platform according to an embodiment of the present invention is a system that allows learners to experience learning content individually optimized by a generating AI in an immersive VR environment. The educational platform consists of elements such as an analysis unit that analyzes the learner's level of understanding and interests, a provision unit that provides optimal learning content, a feedback unit that analyzes the learner's progress in real time and provides feedback, and a generation unit that generates virtual teachers and characters. This enables experiential learning that was difficult with conventional teaching methods, such as reliving historical events or visualizing complex scientific concepts in 3D space. For example, the educational platform analyzes the learner's level of understanding and interests. For example, the educational platform evaluates the learner's level of understanding based on test results and quiz correct answer rates. The educational platform also evaluates interests based on the learner's survey results and browsing history. Next, the educational platform provides optimal learning content. For example, the educational platform provides teaching materials and individualized learning plans according to the learner's level. The educational platform also generates virtual teachers and characters. For example, the educational platform generates animated characters and voice assistants. Furthermore, the educational platform analyzes the learner's progress in real time and provides feedback. For example, the educational platform performs real-time analysis based on the frequency of data updates and analytical algorithms. This allows the platform to provide optimal learning content based on learners' understanding and interests, and to analyze progress in real time and provide feedback through virtual teachers and characters, thereby enabling effective learning.

[0061] The educational platform according to this embodiment comprises an analysis unit, a provision unit, a generation unit, and a feedback unit. The analysis unit analyzes the learner's level of understanding and interests. For example, the analysis unit evaluates the level of understanding based on the learner's test results or quiz accuracy rate. The analysis unit can also evaluate interests based on the learner's survey results or browsing history. Furthermore, the analysis unit can analyze the learner's past learning history and select the optimal method for evaluating understanding. For example, the analysis unit can identify areas where the learner has struggled in the past and select an evaluation method that focuses on those areas. The analysis unit can also select an evaluation method that strengthens areas where the learner has excelled based on areas where the learner has scored highly in the past. Furthermore, the analysis unit can analyze the learner's past learning pace and select an appropriate evaluation timing. The provision unit provides optimal learning content based on the analysis results obtained by the analysis unit. For example, the provision unit provides learning materials and individualized learning plans according to the learner's level. The provision unit can also update learning content according to the learner's progress. For example, the provision unit adds or deletes content based on progress data. Furthermore, the provider can customize the themes and formats of learning content according to the learner's interests. For example, if the learner is interested in history, the provider can provide history-related content. Similarly, if the learner is interested in science, the provider can provide science-related content. The generator generates virtual teachers and characters based on the content provided by the provider. For example, the generator can generate animated characters or voice assistants. The generator can also dynamically generate scenarios based on the learner's progress. For example, it can generate scenarios using a scenario generation algorithm based on the learner's progress data. Additionally, the generator can estimate the learner's emotions and adjust the virtual teacher's and character's expressions based on the estimated emotions. For example, if the learner is stressed, the virtual teacher can explain in a gentle tone. Conversely, if the learner is excited, the virtual teacher can explain in an energetic tone.The feedback unit analyzes the learner's progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. The feedback unit performs real-time analysis based, for example, on the data update frequency and analysis algorithm. The feedback unit can also estimate the learner's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the learner is feeling stressed, the feedback unit will provide feedback in gentle words. Conversely, if the learner is excited, the feedback unit can provide feedback in energetic words. As a result, the educational platform according to this embodiment can provide optimal learning content based on the learner's level of understanding and interests, and analyze progress in real time and provide feedback through virtual teachers and characters, thereby enabling effective learning.

[0062] The analytics department analyzes learners' understanding and interests. Specifically, it evaluates understanding based on learners' test results and quiz accuracy rates. For example, it analyzes the accuracy rate and error patterns of test results to identify areas where learners lack understanding. It can also evaluate learners' interests based on survey results and browsing history. Surveys can ask learners about topics they are interested in and what type of learning materials they prefer. Furthermore, it can analyze learners' past learning history to select the optimal method for evaluating understanding. For example, it can identify areas where learners have difficulty based on their past learning history and select an evaluation method that focuses on those areas. It can also select an evaluation method that strengthens areas where learners have scored highly in the past. In addition, it can analyze learners' past learning pace to select appropriate evaluation timing. For example, if a learner is the type who learns intensively in a short period, evaluations should be conducted frequently to closely monitor their progress. On the other hand, if a learner is the type who learns slowly over a long period, the evaluation intervals should be widened to reduce the learner's burden. This allows the analysis department to conduct flexible assessments tailored to the individual characteristics of each learner, thereby maximizing the effectiveness of their learning.

[0063] The content delivery department provides optimal learning content based on the analysis results obtained by the analysis department. Specifically, it provides learning materials and individualized learning plans tailored to the learner's level. For example, it selects appropriate materials according to the learner's level of understanding, from basic materials for beginners to advanced materials for advanced learners. The content delivery department can also update learning content according to the learner's progress. For example, when a learner completes a particular unit, it automatically provides the next unit based on that progress data. Furthermore, the content delivery department can customize the themes and formats of learning content according to the learner's interests. For example, if a learner is interested in history, it will provide history-related content. Similarly, if a learner is interested in science, it can provide science-related content. In addition, the content delivery department continuously improves the quality of content based on learner feedback. For example, it collects evaluations and comments from learners and reviews the content and format of the materials based on them. This allows the content delivery department to provide high-quality learning content that meets the learner's needs and maximizes the effectiveness of learning.

[0064] The generation unit generates virtual teachers and characters based on the content provided by the provider unit. Specifically, it generates animated characters and voice assistants. For example, if a learner prefers visual learning, an animated character will explain the learning materials. If a learner prefers auditory learning, a voice assistant will read the materials aloud. Furthermore, the generation unit can dynamically generate scenarios according to the learner's progress. For example, when a learner completes a particular unit, it uses a scenario generation algorithm based on that progress data to generate the next scenario. In addition, the generation unit can estimate the learner's emotions and adjust the way the virtual teachers and characters are portrayed based on the estimated emotions. For example, if a learner is stressed, the virtual teacher will explain in a gentle tone. If a learner is excited, the virtual teacher can explain in an energetic tone. This allows the generation unit to respond flexibly to the learner's emotions and progress, maximizing the effectiveness of learning.

[0065] The feedback unit analyzes learners' progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. Specifically, it performs real-time analysis based on data update frequency and analysis algorithms. For example, it analyzes learners' test results and quiz accuracy rates in real time to understand changes in their comprehension. The feedback unit can also estimate learners' emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if a learner is stressed, it provides feedback in gentle language. If a learner is excited, it can provide feedback in energetic language. Furthermore, the feedback unit continuously improves the quality of feedback based on learner feedback. For example, it collects evaluations and comments from learners and reviews the content and format of the feedback based on them. This allows the feedback unit to provide high-quality feedback tailored to learners' needs and maximize the effectiveness of learning.

[0066] The generation unit can dynamically generate scenarios according to the learner's progress. For example, the generation unit generates scenarios using a scenario generation algorithm based on the learner's progress data. For example, the generation unit can generate scenarios using a generation AI model that takes the learner's progress data as input and outputs scenarios. This allows for the dynamic generation of scenarios according to the learner's progress, thereby providing an individually optimized learning experience.

[0067] The service provider can update learning content according to the learner's progress. For example, the service provider can add or delete content based on progress data. For instance, the service provider can update learning content using an AI model that takes learner progress data as input and outputs updated learning content. This allows the service provider to always provide optimal learning content by updating learning content according to the learner's progress.

[0068] The analysis unit can estimate the learner's emotions and adjust the comprehension analysis method based on the estimated emotions. For example, if the learner is stressed, the analysis unit can have the AI ​​present easy questions and provide content to help them relax. If the learner is agitated, the analysis unit can have the AI ​​present more difficult questions and provide challenging content. Furthermore, if the learner is tired, the analysis unit can have the AI ​​provide short tasks to encourage them to take a break. This allows for a more accurate assessment of comprehension by adjusting the comprehension analysis method based on the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The analysis unit can analyze a learner's past learning history and select the optimal method for assessing comprehension. For example, the analysis unit can identify areas where the learner has struggled in the past and select an assessment method that focuses on those areas. It can also select an assessment method that strengthens areas where the learner has excelled, based on areas where the learner has scored highly in the past. Furthermore, the analysis unit can analyze the learner's past learning pace and select the appropriate timing for assessment. By selecting the optimal method for assessing comprehension based on past learning history, assessments tailored to the learner become possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI.

[0070] The analysis unit can analyze the learner's level of comprehension based on their current learning environment and circumstances. For example, if a learner is studying in a quiet environment, the analysis unit can select an assessment method to enhance their concentration. If a learner is studying in a noisy environment, the analysis unit can also select an assessment method that can be completed in a short time. Furthermore, if a learner is studying while on the go, the analysis unit can select an assessment method optimized for mobile devices. This allows for more appropriate assessment by analyzing comprehension based on the current learning environment and circumstances. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0071] The analysis unit can estimate the learner's emotions and adjust the analysis method of interests based on the estimated learner's emotions. For example, if the learner is excited, the analysis unit can introduce new topics to keep them interested. If the learner is bored, the analysis unit can provide interactive content to rekindle their interest. Furthermore, if the learner is relaxed, the analysis unit can provide more in-depth content to deepen their interest. This allows for a more accurate assessment of interests by adjusting the analysis method of interests based on the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The analysis unit can analyze region-specific interests by considering the learner's geographical location. For example, if a learner lives in a particular region, the analysis unit can provide content related to the history and culture of that region. If a learner is traveling, the analysis unit can also provide content related to tourist attractions and landmarks in their destination. Furthermore, if a learner lives in a different region, the analysis unit can provide content related to the language and dialect of that region. This allows for the provision of learning content that reflects region-specific interests by considering geographical location. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI.

[0073] The analytics unit can analyze learners' social media activity and extract relevant interests. For example, the analytics unit can identify topics that learners frequently share on social media and provide content related to those topics. The analytics unit can also analyze the posts of influencers that learners follow and extract relevant interests. Furthermore, the analytics unit can analyze the activities of online communities that learners participate in and extract relevant interests. In this way, by analyzing social media activity, it is possible to provide content based on learners' interests. Some or all of the above processing in the analytics unit may be performed using AI, for example, or not using AI.

[0074] The delivery unit can estimate the learner's emotions and adjust the way learning content is delivered based on the estimated emotions. For example, if the learner is stressed, the delivery unit can provide relaxing content. It can also provide challenging content if the learner is excited. Furthermore, if the learner is tired, it can provide content that can be completed in a short time. This allows for more effective learning by adjusting the delivery method of learning content based on the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The content provider can dynamically adjust the difficulty level of learning content based on the learner's level of understanding. For example, if the learner demonstrates a high level of understanding, the provider can provide content of a higher difficulty level. Conversely, if the learner demonstrates a low level of understanding, the provider can also provide content of a lower difficulty level. Furthermore, the provider can adjust the difficulty level of the content in real time according to the learner's level of understanding. This allows for learning at an appropriate difficulty level by adjusting the difficulty level of the content based on the learner's level of understanding. Some or all of the above processing in the content provider may be performed using AI, for example, or without using AI.

[0076] The content provider can customize the themes and formats of learning content according to the learner's interests. For example, if a learner is interested in history, the provider can provide history-related content. Similarly, if a learner is interested in science, the provider can provide science-related content. Furthermore, the provider can customize the themes and formats of the content according to the learner's interests. This allows for more engaging learning by customizing content according to the learner's interests. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI.

[0077] The content provider can estimate the learner's emotions and adjust the display order of learning content based on the estimated emotions. For example, if the learner is stressed, the provider can display relaxing content first. It can also display challenging content first if the learner is excited. Furthermore, if the learner is tired, it can display content that can be completed quickly first. This allows for more effective learning by adjusting the content display order based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The content provider can provide region-specific learning content, taking into account the learner's geographical location. For example, if a learner lives in a specific region, the provider can provide content related to the history and culture of that region. If a learner is traveling, the provider can also provide content related to tourist attractions and landmarks in their destination. Furthermore, if a learner lives in a different region, the provider can provide content related to the language and dialect of that region. This allows for the provision of region-specific learning content by considering geographical location. Some or all of the processing described above in the content provider may be performed using AI, for example, or without AI.

[0079] The service provider can analyze learners' social media activity and provide relevant learning content. For example, it can provide content related to topics that learners frequently share on social media. It can also provide content related to posts by influencers that learners follow. Furthermore, it can provide content related to the activities of online communities that learners participate in. In this way, by analyzing social media activity, the service provider can provide content relevant to learners. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0080] The generation unit can estimate the learner's emotions and adjust the way the virtual teacher or character is portrayed based on the estimated emotions. For example, if the learner is stressed, the virtual teacher will explain in a gentle tone. If the learner is excited, the virtual teacher can explain in an energetic tone. Furthermore, if the learner is tired, the virtual teacher can explain in a calm tone. This allows for more effective learning by adjusting the way the virtual teacher or character is portrayed based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The generation unit can dynamically adjust the explanation methods of the virtual teacher or character based on the learner's level of understanding. For example, if the learner shows a high level of understanding, the generation unit may omit detailed explanations from the virtual teacher. Conversely, if the learner shows a low level of understanding, the generation unit may also have the virtual teacher add detailed explanations. Furthermore, the generation unit can adjust the explanation methods of the virtual teacher in real time according to the learner's level of understanding. This allows for more appropriate learning by adjusting the explanation method based on the learner's level of understanding. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0082] The generation unit can customize the virtual teacher and character scenarios according to the learner's interests. For example, if the learner is interested in history, the generation unit can provide a history-related scenario with the virtual teacher. Similarly, if the learner is interested in science, the generation unit can provide a science-related scenario with the virtual teacher. Furthermore, the generation unit can customize the virtual teacher's scenario according to the learner's interests. This allows for more engaging learning by customizing scenarios according to the learner's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0083] The generation unit can estimate the learner's emotions and adjust the actions of the virtual teacher or character based on the estimated emotions. For example, if the learner is stressed, the generation unit can have the virtual teacher explain with slow actions. Similarly, if the learner is excited, the generation unit can have the virtual teacher explain with energetic actions. Furthermore, if the learner is tired, the generation unit can have the virtual teacher explain with calm actions. This allows for more effective learning by adjusting actions based on the learner's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can generate region-specific virtual teachers and characters, taking into account the learner's geographical location. For example, if a learner lives in a specific region, the generation unit can generate a virtual teacher who speaks the local dialect. Furthermore, if a learner is traveling, the generation unit can generate a virtual character related to the culture of the place they are visiting. Additionally, if a learner lives in a different region, the generation unit can generate a virtual character related to the history and culture of that region. This allows for the provision of region-specific virtual teachers and characters by considering geographical location. Some or all of the processing described above in the generation unit may be performed using AI, for example, or without AI.

[0085] The generation unit can analyze learners' social media activity and generate relevant virtual teachers and characters. For example, it can generate virtual teachers related to topics that learners frequently share on social media. It can also generate virtual characters related to the content of posts by influencers that learners follow. Furthermore, it can generate virtual characters related to the activities of online communities that learners participate in. In this way, by analyzing social media activity, it is possible to provide learners with relevant virtual teachers and characters. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0086] The feedback unit can estimate the learner's emotions and adjust the way feedback is expressed based on those estimated emotions. For example, if the learner is stressed, the feedback unit will provide feedback in gentle language. If the learner is excited, the feedback unit can provide feedback in energetic language. Furthermore, if the learner is tired, the feedback unit can provide feedback in calm language. This allows for more effective feedback by adjusting the expression of feedback based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The feedback unit can dynamically adjust the level of detail of the feedback based on the learner's level of understanding. For example, if the learner shows a high level of understanding, the feedback unit will provide concise feedback. Conversely, if the learner shows a low level of understanding, the feedback unit can also provide detailed feedback. Furthermore, the feedback unit can adjust the level of detail of the feedback in real time according to the learner's level of understanding. This allows for appropriate feedback by adjusting the level of detail of the feedback based on the learner's level of understanding. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI.

[0088] The feedback unit can customize the content of the feedback according to the learner's interests. For example, if the learner is interested in history, the feedback unit will provide history-related feedback. Similarly, if the learner is interested in science, the feedback unit can provide science-related feedback. Furthermore, the feedback unit can customize the content of the feedback according to the learner's interests. This allows for more engaging feedback by customizing the content according to the learner's interests. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI.

[0089] The feedback unit can estimate the learner's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the learner is stressed, the feedback unit can provide feedback at a time when the learner can relax. It can also provide immediate feedback if the learner is agitated. Furthermore, if the learner is tired, the feedback unit can provide feedback after a break. This allows for more effective feedback by adjusting the timing of feedback based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The feedback unit can provide region-specific feedback by taking into account the learner's geographical location. For example, if the learner lives in a specific region, the feedback unit can provide feedback related to the culture and customs of that region. Furthermore, if the learner is traveling, the feedback unit can provide feedback related to the culture and sights of the destination. Additionally, if the learner lives in a different region, the feedback unit can provide feedback related to the language and dialect of that region. This allows for the provision of region-specific feedback by considering geographical location. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI.

[0091] The feedback unit can analyze learners' social media activity and provide relevant feedback. For example, the feedback unit can provide feedback related to topics that learners frequently share on social media. It can also provide feedback related to the content of posts by influencers that learners follow. Furthermore, the feedback unit can provide feedback related to the activities of online communities that learners participate in. In this way, by analyzing social media activity, it is possible to provide learners with relevant feedback. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or not using AI.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The educational platform can monitor the learner's physical state and adjust learning content based on that state. For example, the platform can use sensors to acquire the learner's heart rate and body temperature and detect signs of stress or fatigue. If the learner is stressed, it can provide relaxing content. If the learner is tired, it can provide tasks that can be completed in a short time. Furthermore, if the learner is focused, it can present challenging problems and provide challenging content. In this way, by adjusting learning content based on the learner's physical state, more effective learning becomes possible.

[0094] An educational platform can analyze learners' learning styles and provide learning content based on those styles. For example, if a learner is a visual learner, the analytics department can provide materials that heavily utilize visual content. If the learner is an auditory learner, it can provide materials that incorporate audio and music. Furthermore, if the learner is an experiential learner, it can provide interactive simulations and experiments. By providing content based on the learner's learning style, more effective learning becomes possible.

[0095] Educational platforms can provide learning content while considering learners' social relationships. For example, if a learner prefers to study with friends, the platform can offer group learning opportunities. If a learner prefers to study alone, it can provide individualized learning content. Furthermore, if a learner prefers interaction within an online community, it can provide opportunities to interact with other learners through forums and chat functions. By providing learning content based on learners' social relationships, more effective learning becomes possible.

[0096] Educational platforms can estimate learners' emotions and adjust the difficulty level of learning content based on those emotions. For example, if a learner is feeling stressed, the platform can provide easier problems to help them relax. Conversely, if a learner is excited, it can provide more challenging problems and content. Furthermore, if a learner is tired, it can provide short tasks to encourage them to take a break. By adjusting the difficulty level of learning content based on learners' emotions, more effective learning becomes possible.

[0097] Educational platforms can analyze learners' past learning history and provide learning content based on that history. For example, the analytics department can identify areas where learners have struggled in the past and provide materials that focus on those areas. It can also provide materials that reinforce areas where learners have excelled, based on areas where they have scored highly in the past. Furthermore, it can analyze learners' past learning pace and provide appropriate learning schedules. In this way, by providing optimal learning content based on past learning history, it becomes possible to enable learning that is tailored to each learner.

[0098] The educational platform can estimate the learner's emotions and adjust the content of the feedback based on those emotions. For example, if the learner is feeling stressed, the feedback system can provide feedback in gentle language. If the learner is excited, it can provide feedback in energetic language. Furthermore, if the learner is tired, it can provide feedback in calm language. By adjusting the content of the feedback based on the learner's emotions, more effective feedback becomes possible.

[0099] Educational platforms can provide region-specific learning content by considering learners' geographical location. For example, if a learner lives in a particular region, the platform can provide content related to the history and culture of that region. If the learner is traveling, it can provide content related to tourist attractions and landmarks in their destination. Furthermore, if the learner lives in a different region, it can provide content related to the language and dialect of that region. In this way, by considering geographical location, region-specific learning content can be provided.

[0100] The educational platform can estimate learners' emotions and adjust the way virtual teachers and characters express themselves based on those estimated emotions. For example, if a learner is stressed, the virtual teacher can explain in a gentle tone. If the learner is excited, the virtual teacher can explain in an energetic tone. Furthermore, if the learner is tired, the virtual teacher can explain in a calm tone. By adjusting the way virtual teachers and characters express themselves based on learners' emotions, more effective learning becomes possible.

[0101] Educational platforms can analyze learners' social media activity and provide learning content based on that activity. For example, the analytics department can identify topics that learners frequently share on social media and provide content related to those topics. It can also analyze the posts of influencers that learners follow and provide relevant content. Furthermore, it can analyze the activities of online communities that learners participate in and provide relevant content. In this way, by analyzing social media activity, it is possible to provide content based on learners' interests.

[0102] The educational platform can estimate the learner's emotions and adjust the display order of learning content based on those emotions. For example, if a learner is feeling stressed, the platform can display relaxing content first. If the learner is excited, it can display challenging content first. Furthermore, if the learner is tired, it can display content that can be completed in a short amount of time first. By adjusting the display order of content based on the learner's emotions, more effective learning becomes possible.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The analysis department analyzes the learner's level of understanding and interests. Specifically, it evaluates understanding based on the learner's test results and quiz accuracy rates, and evaluates interests based on survey results and browsing history. It also analyzes past learning history to select the optimal method for evaluating understanding. For example, it selects an evaluation method that focuses on areas of weakness or an evaluation method that strengthens areas of strength, and selects the appropriate timing for evaluation. Step 2: The provision department provides optimal learning content based on the analysis results obtained by the analysis department. Specifically, it provides learning materials and personalized learning plans tailored to the learner's level, and updates the content as they progress. Furthermore, it customizes themes and formats according to the learner's interests. For example, it provides history-related content to learners interested in history, and science-related content to learners interested in science. Step 3: The generation unit generates virtual teachers and characters based on the content provided by the delivery unit. Specifically, it generates animated characters and voice assistants, and dynamically generates scenarios according to the learner's progress. Furthermore, it estimates the learner's emotions and adjusts the way the virtual teachers and characters express themselves based on the estimated emotions. For example, if the learner is stressed, the teacher will explain in a gentle tone, and if they are excited, they will explain in an energetic tone. Step 4: The feedback unit analyzes the learner's progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. Specifically, it performs real-time analysis based on the data update frequency and analysis algorithm, estimates the learner's emotions, and adjusts the way the feedback is expressed. For example, if the learner is stressed, it provides feedback in gentle words, and if they are excited, it provides feedback in energetic words.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the analysis unit, provision unit, generation unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 38B of the smart device 14 to analyze the learner's level of understanding and interests, and processes the data with the control unit 46A. The provision unit provides optimal learning content with the specific processing unit 290 of the data processing unit 12. The generation unit generates virtual teachers and characters with the control unit 46A of the smart device 14, and the feedback unit analyzes the learner's progress in real time with the specific processing unit 290 of the data processing unit 12 and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the analysis unit, provision unit, generation unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to analyze the learner's level of understanding and interests, and processes the data with the control unit 46A. The provision unit provides optimal learning content with the specific processing unit 290 of the data processing unit 12. The generation unit generates virtual teachers and characters with the control unit 46A of the smart glasses 214, and the feedback unit analyzes the learner's progress in real time with the specific processing unit 290 of the data processing unit 12 and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the analysis unit, provision unit, generation unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the headset terminal 314 to analyze the learner's level of understanding and interests, and processes the data with the control unit 46A. The provision unit provides optimal learning content with the specific processing unit 290 of the data processing unit 12. The generation unit generates virtual teachers and characters with the control unit 46A of the headset terminal 314, and the feedback unit analyzes the learner's progress in real time with the specific processing unit 290 of the data processing unit 12 and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the analysis unit, provision unit, generation unit, and feedback unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the robot 414 to analyze the learner's level of understanding and interests, and processes the data with the control unit 46A. The provision unit provides optimal learning content with the specific processing unit 290 of the data processing unit 12. The generation unit generates virtual teachers and characters with the control unit 46A of the robot 414, and the feedback unit analyzes the learner's progress in real time with the specific processing unit 290 of the data processing unit 12 and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) The analysis department analyzes learners' level of understanding and interest, A provisioning unit provides optimal learning content based on the analysis results obtained by the aforementioned analysis unit, A generation unit that generates virtual teachers and characters based on the content provided by the aforementioned provisioning unit, The system includes a feedback unit that analyzes the learner's progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Dynamically generate scenarios based on the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The learning content is updated according to the learner's progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We estimate learners' emotions and adjust the analysis method of comprehension based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze the learner's past learning history and select the optimal method for assessing their level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is The level of comprehension is analyzed based on the learner's current learning environment and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate learners' emotions and adjust the analysis method of their interests based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Analyze region-specific interests and concerns, taking into account learners' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Analyze learners' social media activity and extract relevant interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, The system estimates learners' emotions and adjusts how learning content is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, Dynamically adjust the difficulty level of learning content based on the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, Customize the themes and formats of learning content according to the learner's interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, The system estimates the learner's emotions and adjusts the display order of learning content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, Provide region-specific learning content, taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, Analyze learners' social media activity and provide relevant learning content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the learner's emotions and adjusts the way virtual teachers and characters are portrayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Dynamically adjust the explanation methods of virtual teachers and characters based on the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Customize virtual teacher and character scenarios according to the learner's interests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the learner's emotions and adjusts the behavior of the virtual teacher or character based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is Considering the learner's geographical location, region-specific virtual teachers and characters are generated. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is Analyze learners' social media activity and generate relevant virtual teachers and characters. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is Dynamically adjust the level of detail in feedback based on the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is Customize the feedback content according to the learner's interests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is Provide region-specific feedback, taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is Analyze learners' social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes learners' level of understanding and interest, A provisioning unit provides optimal learning content based on the analysis results obtained by the aforementioned analysis unit, A generation unit that generates virtual teachers and characters based on the content provided by the aforementioned provisioning unit, The system includes a feedback unit that analyzes the learner's progress in real time and provides feedback through virtual teachers and characters generated by the generation unit. A system characterized by the following features.

2. The generating unit is Dynamically generate scenarios based on the learner's progress. The system according to feature 1.

3. The aforementioned supply unit is, The learning content is updated according to the learner's progress. The system according to feature 1.

4. The aforementioned analysis unit is We estimate learners' emotions and adjust the analysis method of comprehension based on the estimated learners' emotions. The system according to feature 1.

5. The aforementioned analysis unit is Analyze the learner's past learning history and select the optimal method for assessing their level of understanding. The system according to feature 1.

6. The aforementioned analysis unit is The level of comprehension is analyzed based on the learner's current learning environment and circumstances. The system according to feature 1.

7. The aforementioned analysis unit is We estimate learners' emotions and adjust the analysis method of their interests based on the estimated learners' emotions. The system according to feature 1.

8. The aforementioned analysis unit is Analyze region-specific interests and concerns, taking into account learners' geographical location. The system according to feature 1.

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