system

Abstract

The system according to this embodiment aims to provide a curriculum that is individually optimized based on the user's talents and learning style, and to support the creative process in real time. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit to identify the user's talents and learning style. The provision unit provides an individually optimized curriculum based on the talents and learning style identified by the analysis unit. The support unit provides real-time support for the creative process based on the curriculum provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 conventional technology, an educational curriculum individually optimized based on a user's talent and learning style has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a curriculum individually optimized based on a user's talent and learning style and support the creation process in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a support unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit to identify the user's talents and learning style. The data provision unit provides an individually optimized curriculum based on the talents and learning style identified by the analysis unit. The support unit provides real-time support for the creative process based on the curriculum provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a curriculum that is individually optimized based on the user's talents and learning style, and can support the creative process in real time. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The VirtualMuse system, according to an embodiment of the present invention, is a next-generation creative education platform that fuses AI technology with VR / AR. This VirtualMuse system allows users to learn and practice creative activities such as art, music, literature, and programming while interacting with world-class AI creators in a VR space. The VirtualMuse system uses AI to analyze each user's talents and learning style, providing a personalized curriculum. Furthermore, the VirtualMuse system provides real-time AI support throughout the creative process, maximizing the user's imagination. For example, a user accesses the VR space and begins interacting with an AI creator. If the user says, "I want to draw," the AI ​​creator provides optimal advice and techniques based on the user's past works and learning history. As the user begins to draw, the AI ​​provides real-time feedback, pointing out areas for improvement in color usage and composition. Next, the AI ​​analyzes the user's talents and learning style. For example, if a user wants to learn music, the AI ​​analyzes the user's past performance data and learning history to provide a personalized curriculum. This allows users to learn at their own pace and efficiently improve their skills. Furthermore, the AI ​​provides real-time support throughout the creative process. For example, if a user is learning programming, the AI ​​can detect code errors in real time and suggest corrections. Similarly, if a user is writing a literary work, the AI ​​can point out areas for grammatical and stylistic improvement, helping them write better. In this way, the VirtualMuse system maximizes user creativity and provides high-quality creative education. Users can learn directly from top creators and receive personalized instruction without geographical or economic constraints. Furthermore, the visualization of the creative process allows users to effectively learn the process of bringing ideas to life. Moreover, by leveraging the latest AI technology, new possibilities in creative activities can be explored. Ultimately, the VirtualMuse system maximizes user creativity and provides high-quality creative education.

[0029] The VirtualMuse system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects user data. User data includes, but is not limited to, behavioral data, learning history, and sentiment data. The collection unit collects, for example, the user's past works and learning history. The collection unit can collect the user's past works in the form of project reports or artwork. The collection unit can also collect the user's learning history in the form of learning logs or test results. For example, the collection unit collects project reports created by the user in the past and analyzes learning logs. The analysis unit analyzes the data collected by the collection unit to identify the user's talents and learning style. The analysis is performed by, for example, machine learning algorithms or statistical analysis, but is not limited to these methods. For example, the analysis unit uses machine learning algorithms to identify the user's talents. The analysis unit can also use statistical analysis to identify the user's learning style. For example, the analysis unit inputs the user's behavioral data into a machine learning algorithm to identify talents. The service provider provides a curriculum that is individually optimized based on the talents and learning styles identified by the analysis provider. Individual optimization is achieved through methods such as personalized curricula and adaptive learning systems, but is not limited to these examples. For example, the service provider provides a personalized curriculum. The service provider can also provide a curriculum using an adaptive learning system. For example, the service provider personalizes the curriculum based on the user's learning history. The support provider provides real-time support for the creative process based on the curriculum provided by the service provider. Support for the creative process is achieved through methods such as idea generation and prototype creation, but is not limited to these examples. For example, if the user is learning programming, the support provider can detect code errors in real time and suggest how to correct them. The support provider can also point out areas for improvement in grammar and style if the user is writing a literary work. For example, the support provider analyzes the code written by the user and detects errors.As a result, the VirtualMuse system according to this embodiment can provide individually optimized creative education by collecting and analyzing user data, providing curriculum, and offering support.

[0030] The data collection unit collects user data. User data includes, but is not limited to, behavioral data, learning history, and emotional data. For example, the data collection unit collects users' past works and learning history. Specifically, it collects project reports and artwork created by users in the past and stores this data in digital format. Project reports are stored as text files or PDFs, and artwork is stored as image files or video files. The data collection unit can also collect users' learning history in the form of learning logs and test results. Learning logs record what materials users used and how much time they spent learning, while test results include the scores and feedback on tests taken by the user. For example, the data collection unit collects project reports created by users in the past and analyzes learning logs. This allows the data collection unit to gain a detailed understanding of users' past activities and learning history and provide the necessary data to subsequent analysis and provision units. Furthermore, the data collection unit can also collect users' emotional data. Emotional data records the emotions users felt during learning and creative activities and is used, for example, to analyze how users felt about specific tasks. Emotional data can be collected through user self-reporting or biosensors. This allows the data collection unit to gather multifaceted user data, improving the overall accuracy and effectiveness of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to identify user talents and learning styles. Analysis is performed using methods such as machine learning algorithms and statistical analysis, but is not limited to these examples. Specifically, machine learning algorithms are used to identify user talents. For example, user behavioral data is input into a machine learning algorithm to identify talents. Behavioral data includes what activities the user performed, what results they achieved, and what patterns were observed. The machine learning algorithm analyzes this data to identify the user's strengths and weaknesses. The analysis unit can also use statistical analysis to identify user learning styles. For example, statistical analysis of user learning logs and test results can identify what learning methods the user prefers and at what pace. This allows the analysis unit to gain a detailed understanding of individual user characteristics and provide the necessary information to the service provider. Furthermore, the analysis unit can analyze emotional data to understand fluctuations in user emotions. Emotional data shows how users felt about specific tasks, and by analyzing this, it is possible to identify situations in which users feel stressed and situations in which they are highly motivated. This allows the analysis unit to perform analyses that take into account not only the user's talents and learning style, but also emotional fluctuations, enabling it to provide more accurate information.

[0032] The service provider offers a curriculum that is individually optimized based on the talents and learning styles identified by the analysis unit. Individual optimization is achieved through methods such as personalized curricula and adaptive learning systems, but is not limited to these examples. Specifically, the service provider offers personalized curricula. For example, it personalizes the curriculum based on the user's learning history. It considers what materials the user has used in the past and what results they have achieved, and proposes the most suitable materials and learning methods. The service provider can also provide a curriculum using an adaptive learning system. An adaptive learning system dynamically adjusts the curriculum according to the user's learning progress and understanding, supporting the user in learning efficiently. For example, the service provider personalizes the curriculum based on the user's learning history. This allows the service provider to provide an optimal curriculum tailored to the user's individual characteristics and maximize learning effectiveness. Furthermore, the service provider can adjust the curriculum considering the user's emotional data. For example, if a user is feeling stressed by a particular task, the service provider will suggest a more relaxing task. It can also provide tasks that give the user a sense of accomplishment to increase their motivation. This allows the service provider to provide a curriculum that takes into account the user's emotional fluctuations and further improve learning effectiveness.

[0033] The support department provides real-time support for the creative process based on the curriculum provided by the service provider. This support is not limited to methods such as idea generation and prototype creation. Specifically, if a user is learning programming, the support department can detect code errors in real time and suggest correction methods. For example, it can analyze the code written by the user and detect errors. If an error is detected, the support department will present the user with the cause and how to correct it. Furthermore, if a user is writing a literary work, the support department can point out areas for grammatical and stylistic improvement. For example, it can analyze the text written by the user and point out grammatical errors and areas for stylistic improvement. This allows the support department to support the user's creative process in real time and improve the quality of their work. In addition, the support department can adjust its support based on the user's emotional data. For example, if a user is feeling stressed about a particular task, the support department can suggest a more relaxing support method. It can also provide support that helps the user feel a sense of accomplishment to increase their motivation. This allows the support department to provide support that takes into account the user's emotional fluctuations, further supporting the creative process more effectively.

[0034] The data collection unit can collect the user's past works and learning history. For example, the data collection unit can collect the user's past works in the form of project reports or artwork. The data collection unit can also collect the user's learning history in the form of learning logs or test results. For example, the data collection unit can collect project reports created by the user in the past and analyze the learning logs. This allows for more accurate analysis by collecting the user's past works and learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past works and learning history into a generating AI and have the generating AI perform the data collection.

[0035] The support unit can detect errors in code in real time and suggest correction methods when a user is learning programming. For example, the support unit can analyze the code written by the user and detect errors. Depending on the type of error, the support unit can also suggest appropriate correction methods. For example, the support unit can detect syntax errors and suggest correction methods. The support unit can also detect logic errors and suggest correction methods. For example, the support unit can detect syntax errors in the code written by the user and suggest correction methods. This makes it possible to detect errors and suggest correction methods in real time during programming learning. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the code written by the user into a generating AI and have the generating AI perform error detection and suggest correction methods.

[0036] The support unit can point out areas for improvement in grammar and style when a user is writing a literary work. For example, the support unit can analyze the text written by the user and point out grammatical errors. The support unit can also suggest appropriate correction methods based on grammatical rules. For example, the support unit can use a grammar analysis tool to detect grammatical errors and suggest correction methods. The support unit can also perform stylistic analysis and point out areas for improvement in style. For example, the support unit can analyze the style of the text written by the user and point out areas for improvement based on a style guide. This makes it possible to create better writing by pointing out areas for improvement in grammar and style when writing a literary work. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the text written by the user into a generation AI and have the generation AI point out areas for improvement in grammar and style.

[0037] The data collection unit can analyze the user's past works and learning history to select the optimal data collection method. For example, the data collection unit can analyze the style and themes of works the user has created in the past and collect similar works. The data collection unit can also prioritize the collection of data related to specific skills and knowledge based on the user's learning history. For example, the data collection unit can analyze topics and fields the user has shown interest in in the past and collect data related to them. This allows the optimal data collection method to be selected by analyzing the user's past works and learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past works and learning history into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, if the user is interested in a particular theme, the data collection unit will collect data related to that theme. This allows for the collection of highly relevant data by filtering the data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. If the user is traveling, the data collection unit can also collect data related to the travel destination. For example, if the user is participating in a specific event, the data collection unit will collect data related to that event. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze a user's social media activity and collect relevant data when collecting data. For example, the data collection unit can analyze works and comments shared by a user on social media and collect relevant data. The data collection unit can also analyze accounts followed by a user and groups they participate in and collect relevant data. For example, the data collection unit can analyze topics and hashtags that a user has shown interest in on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and provide it to the user. For example, the analysis unit can perform a simplified analysis on low-importance data and provide it to the user. For example, the analysis unit can determine the priority of the analysis according to the importance of the data and perform the analysis efficiently. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply color and composition analysis algorithms to works of art. For musical works, it can also apply scale and rhythm analysis algorithms. For example, the analysis unit can apply error detection and optimization analysis algorithms to programming code. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the latest data and provide results quickly. The analysis unit can also postpone the analysis of older data. For example, the analysis unit can adjust the analysis schedule according to the submission date to perform the analysis efficiently. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and provide results quickly. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data to perform analysis efficiently. This makes efficient analysis possible by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The service provider can adjust the level of detail in the curriculum based on the user's learning progress when providing the curriculum. For example, the service provider can provide a detailed curriculum to users who are learning quickly, deepening their learning. For users who are learning slowly, the service provider can also provide a concise curriculum to solidify their foundation. For example, the service provider can adjust the level of detail in the curriculum according to the learning progress, enabling efficient learning. This makes efficient learning possible by adjusting the level of detail in the curriculum based on the user's learning progress. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail in the curriculum.

[0046] The service provider can apply different curricula to users according to their learning style when providing the curriculum. For example, the service provider can provide a visually-oriented curriculum to users with a visual learning style. The service provider can also provide an audio-oriented curriculum to users with an auditory learning style. For example, the service provider can provide a practice-oriented curriculum to users with an experiential learning style. By applying different curricula according to the user's learning style, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user learning style data into a generating AI and have the generating AI execute the application of different curricula.

[0047] The service provider can determine curriculum priorities based on the user's learning history when providing the curriculum. For example, the service provider can prioritize incorporating important topics into the curriculum based on the user's past learning history. The service provider can also prioritize incorporating areas where the user struggles, based on the user's learning history. For example, the service provider can suggest an efficient learning order based on the user's learning history. This enables efficient learning by determining curriculum priorities based on the user's learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning history data into a generating AI and have the generating AI perform the determination of curriculum priorities.

[0048] The service provider can adjust the order of the curriculum based on the user's areas of interest when providing the curriculum. For example, the service provider can prioritize incorporating topics related to the user's areas of interest into the curriculum. The service provider can also adjust the order of learning based on the user's areas of interest to facilitate efficient learning. For example, the service provider can customize the curriculum content according to the user's areas of interest to keep them engaged. This allows for more effective learning by adjusting the order of the curriculum based on the user's areas of interest. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the adjustment of the curriculum order.

[0049] The support unit can analyze the user's past creative activities to select the most appropriate support method during the support process. For example, the support unit can analyze the style and themes of works the user has created in the past and provide similar support. Based on the user's past creative activities, the support unit can also prioritize support related to specific skills and knowledge. For example, the support unit can analyze topics and fields the user has shown interest in in the past and provide support related to those. This allows the support unit to select the most appropriate support method by analyzing the user's past creative activities. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input the user's past creative activity data into a generating AI and have the generating AI select the most appropriate support method.

[0050] The support unit can customize the means of support based on the user's current creative status. For example, the support unit can prioritize providing support related to the project the user is currently working on. The support unit can also provide highly relevant support based on the user's current creative status. For example, if the support unit is interested in a particular theme, it will provide support related to that theme. This allows for more effective support by customizing the means of support based on the user's current creative status. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current creative status data into a generating AI and have the generating AI perform the customization of the means of support.

[0051] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit will prioritize providing support related to that region. If the user is traveling, the support unit can also provide support related to the travel destination. For example, if the support unit is participating in a specific event, the support unit will provide support related to that event. In this way, the optimal support method can be selected by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0052] The support unit can analyze a user's social media activity and suggest support methods when providing assistance. For example, the support unit can analyze works and comments shared by the user on social media and provide relevant support. The support unit can also analyze accounts followed and groups joined by the user and provide relevant support. For example, the support unit can analyze topics and hashtags that the user has shown interest in on social media and provide relevant support. In this way, by analyzing the user's social media activity, it is possible to suggest relevant support methods. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media activity data into a generating AI and have the generating AI suggest support methods.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The VirtualMuse system may also include an inspiration-providing unit. This unit analyzes the user's past creative activities and learning history and provides content to give the user new inspiration. For example, it can analyze the style of paintings the user has created in the past and introduce works by famous artists with similar styles. It can also analyze the genre of music the user has created and suggest new songs and artists related to that genre. Furthermore, it can provide related literary works and interview articles with authors based on the themes of literary works written by the user. This allows the user to gain new perspectives and ideas and broaden the scope of their creative activities. Some or all of the above processing in the inspiration-providing unit may be performed using AI, for example, or not. For example, the inspiration-providing unit can input the user's past creative activity data into a generating AI and have the generating AI perform the inspiration provision.

[0055] The VirtualMuse system may also include a feedback unit. This feedback unit provides detailed feedback on the user's creative activities. For example, it can offer specific advice on color selection and compositional balance for a user's painting. It can also point out areas for improvement in melody structure and rhythm for music created by the user. Furthermore, it can provide detailed explanations of grammatical and stylistic improvements for literary works written by the user. This allows the user to objectively evaluate their own work and engage in higher-level creative activities. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For instance, the feedback unit could input the user's creative activity data into a generating AI and have the generating AI provide the feedback.

[0056] The VirtualMuse system can also include a collaboration section. This collaboration section provides a platform for users to engage in collaborative creative activities. For example, users can launch a collaborative painting project and complete the artwork in real time, working together with other users. They can also launch a collaborative music production project, dividing up different parts for performance and recording. Furthermore, users can launch a collaborative literary writing project, dividing up chapters and ultimately compiling them into a single work. This allows users to collaborate with other creators and incorporate a wider range of perspectives and ideas. Some or all of the processes described above in the collaboration section may be performed using AI, or not. For example, the collaboration section can input user project data into a generating AI and have the generating AI perform collaboration support.

[0057] The VirtualMuse system may also include a motivation management unit. The motivation management unit provides functions to maintain and improve the user's motivation for creative activities. For example, it can award badges or points to users when they achieve certain goals, giving them a sense of accomplishment. It can also send reminders to users to encourage them to continue their creative activities regularly. Furthermore, it can provide a ranking function that allows users to compete with other creators, stimulating a competitive spirit. This will increase the user's motivation for creative activities and enable them to continue their work. Some or all of the above processes in the motivation management unit may be performed using AI, for example, or not using AI. For example, the motivation management unit can input user activity data into a generating AI and have the generating AI execute the motivation management functions.

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

[0059] Step 1: The data collection unit collects user data. User data includes behavioral data, learning history, and sentiment data. For example, the data collection unit collects the user's past works and learning history, and gathers them in the form of project reports, artwork, learning logs, and test results. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify the user's talents and learning style. The analysis is performed using methods such as machine learning algorithms and statistical analysis. For example, the analysis unit uses machine learning algorithms to identify the user's talents and statistical analysis to identify their learning style. Step 3: The service provider provides an individually optimized curriculum based on the talents and learning styles identified by the analysis unit. Individual optimization is achieved through methods such as personalized curricula and adaptive learning systems. For example, the service provider provides a personalized curriculum and delivers the curriculum using an adaptive learning system. Step 4: The support team provides real-time support for the creative process based on the curriculum provided by the provider team. This support includes methods such as idea generation and prototype creation. For example, if the user is learning programming, the support team will detect code errors in real time and suggest ways to correct them. If the user is writing a literary work, the support team will point out areas for improvement in grammar and style.

[0060] (Example of form 2) The VirtualMuse system, according to an embodiment of the present invention, is a next-generation creative education platform that fuses AI technology with VR / AR. This VirtualMuse system allows users to learn and practice creative activities such as art, music, literature, and programming while interacting with world-class AI creators in a VR space. The VirtualMuse system uses AI to analyze each user's talents and learning style, providing a personalized curriculum. Furthermore, the VirtualMuse system provides real-time AI support throughout the creative process, maximizing the user's imagination. For example, a user accesses the VR space and begins interacting with an AI creator. If the user says, "I want to draw," the AI ​​creator provides optimal advice and techniques based on the user's past works and learning history. As the user begins to draw, the AI ​​provides real-time feedback, pointing out areas for improvement in color usage and composition. Next, the AI ​​analyzes the user's talents and learning style. For example, if a user wants to learn music, the AI ​​analyzes the user's past performance data and learning history to provide a personalized curriculum. This allows users to learn at their own pace and efficiently improve their skills. Furthermore, the AI ​​provides real-time support throughout the creative process. For example, if a user is learning programming, the AI ​​can detect code errors in real time and suggest corrections. Similarly, if a user is writing a literary work, the AI ​​can point out areas for grammatical and stylistic improvement, helping them write better. In this way, the VirtualMuse system maximizes user creativity and provides high-quality creative education. Users can learn directly from top creators and receive personalized instruction without geographical or economic constraints. Furthermore, the visualization of the creative process allows users to effectively learn the process of bringing ideas to life. Moreover, by leveraging the latest AI technology, new possibilities in creative activities can be explored. Ultimately, the VirtualMuse system maximizes user creativity and provides high-quality creative education.

[0061] The VirtualMuse system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects user data. User data includes, but is not limited to, behavioral data, learning history, and sentiment data. The collection unit collects, for example, the user's past works and learning history. The collection unit can collect the user's past works in the form of project reports or artwork. The collection unit can also collect the user's learning history in the form of learning logs or test results. For example, the collection unit collects project reports created by the user in the past and analyzes learning logs. The analysis unit analyzes the data collected by the collection unit to identify the user's talents and learning style. The analysis is performed by, for example, machine learning algorithms or statistical analysis, but is not limited to these methods. For example, the analysis unit uses machine learning algorithms to identify the user's talents. The analysis unit can also use statistical analysis to identify the user's learning style. For example, the analysis unit inputs the user's behavioral data into a machine learning algorithm to identify talents. The service provider provides a curriculum that is individually optimized based on the talents and learning styles identified by the analysis provider. Individual optimization is achieved through methods such as personalized curricula and adaptive learning systems, but is not limited to these examples. For example, the service provider provides a personalized curriculum. The service provider can also provide a curriculum using an adaptive learning system. For example, the service provider personalizes the curriculum based on the user's learning history. The support provider provides real-time support for the creative process based on the curriculum provided by the service provider. Support for the creative process is achieved through methods such as idea generation and prototype creation, but is not limited to these examples. For example, if the user is learning programming, the support provider can detect code errors in real time and suggest how to correct them. The support provider can also point out areas for improvement in grammar and style if the user is writing a literary work. For example, the support provider analyzes the code written by the user and detects errors.As a result, the VirtualMuse system according to this embodiment can provide individually optimized creative education by collecting and analyzing user data, providing curriculum, and offering support.

[0062] The data collection unit collects user data. User data includes, but is not limited to, behavioral data, learning history, and emotional data. For example, the data collection unit collects users' past works and learning history. Specifically, it collects project reports and artwork created by users in the past and stores this data in digital format. Project reports are stored as text files or PDFs, and artwork is stored as image files or video files. The data collection unit can also collect users' learning history in the form of learning logs and test results. Learning logs record what materials users used and how much time they spent learning, while test results include the scores and feedback on tests taken by the user. For example, the data collection unit collects project reports created by users in the past and analyzes learning logs. This allows the data collection unit to gain a detailed understanding of users' past activities and learning history and provide the necessary data to subsequent analysis and provision units. Furthermore, the data collection unit can also collect users' emotional data. Emotional data records the emotions users felt during learning and creative activities and is used, for example, to analyze how users felt about specific tasks. Emotional data can be collected through user self-reporting or biosensors. This allows the data collection unit to gather multifaceted user data, improving the overall accuracy and effectiveness of the system.

[0063] The analysis unit analyzes data collected by the data collection unit to identify user talents and learning styles. Analysis is performed using methods such as machine learning algorithms and statistical analysis, but is not limited to these examples. Specifically, machine learning algorithms are used to identify user talents. For example, user behavioral data is input into a machine learning algorithm to identify talents. Behavioral data includes what activities the user performed, what results they achieved, and what patterns were observed. The machine learning algorithm analyzes this data to identify the user's strengths and weaknesses. The analysis unit can also use statistical analysis to identify user learning styles. For example, statistical analysis of user learning logs and test results can identify what learning methods the user prefers and at what pace. This allows the analysis unit to gain a detailed understanding of individual user characteristics and provide the necessary information to the service provider. Furthermore, the analysis unit can analyze emotional data to understand fluctuations in user emotions. Emotional data shows how users felt about specific tasks, and by analyzing this, it is possible to identify situations in which users feel stressed and situations in which they are highly motivated. This allows the analysis unit to perform analyses that take into account not only the user's talents and learning style, but also emotional fluctuations, enabling it to provide more accurate information.

[0064] The service provider offers a curriculum that is individually optimized based on the talents and learning styles identified by the analysis unit. Individual optimization is achieved through methods such as personalized curricula and adaptive learning systems, but is not limited to these examples. Specifically, the service provider offers personalized curricula. For example, it personalizes the curriculum based on the user's learning history. It considers what materials the user has used in the past and what results they have achieved, and proposes the most suitable materials and learning methods. The service provider can also provide a curriculum using an adaptive learning system. An adaptive learning system dynamically adjusts the curriculum according to the user's learning progress and understanding, supporting the user in learning efficiently. For example, the service provider personalizes the curriculum based on the user's learning history. This allows the service provider to provide an optimal curriculum tailored to the user's individual characteristics and maximize learning effectiveness. Furthermore, the service provider can adjust the curriculum considering the user's emotional data. For example, if a user is feeling stressed by a particular task, the service provider will suggest a more relaxing task. It can also provide tasks that give the user a sense of accomplishment to increase their motivation. This allows the service provider to provide a curriculum that takes into account the user's emotional fluctuations and further improve learning effectiveness.

[0065] The support department provides real-time support for the creative process based on the curriculum provided by the service provider. This support is not limited to methods such as idea generation and prototype creation. Specifically, if a user is learning programming, the support department can detect code errors in real time and suggest correction methods. For example, it can analyze the code written by the user and detect errors. If an error is detected, the support department will present the user with the cause and how to correct it. Furthermore, if a user is writing a literary work, the support department can point out areas for grammatical and stylistic improvement. For example, it can analyze the text written by the user and point out grammatical errors and areas for stylistic improvement. This allows the support department to support the user's creative process in real time and improve the quality of their work. In addition, the support department can adjust its support based on the user's emotional data. For example, if a user is feeling stressed about a particular task, the support department can suggest a more relaxing support method. It can also provide support that helps the user feel a sense of accomplishment to increase their motivation. This allows the support department to provide support that takes into account the user's emotional fluctuations, further supporting the creative process more effectively.

[0066] The data collection unit can collect the user's past works and learning history. For example, the data collection unit can collect the user's past works in the form of project reports or artwork. The data collection unit can also collect the user's learning history in the form of learning logs or test results. For example, the data collection unit can collect project reports created by the user in the past and analyze the learning logs. This allows for more accurate analysis by collecting the user's past works and learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past works and learning history into a generating AI and have the generating AI perform the data collection.

[0067] The support unit can detect errors in code in real time and suggest correction methods when a user is learning programming. For example, the support unit can analyze the code written by the user and detect errors. Depending on the type of error, the support unit can also suggest appropriate correction methods. For example, the support unit can detect syntax errors and suggest correction methods. The support unit can also detect logic errors and suggest correction methods. For example, the support unit can detect syntax errors in the code written by the user and suggest correction methods. This makes it possible to detect errors and suggest correction methods in real time during programming learning. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the code written by the user into a generating AI and have the generating AI perform error detection and suggest correction methods.

[0068] The support unit can point out areas for improvement in grammar and style when a user is writing a literary work. For example, the support unit can analyze the text written by the user and point out grammatical errors. The support unit can also suggest appropriate correction methods based on grammatical rules. For example, the support unit can use a grammar analysis tool to detect grammatical errors and suggest correction methods. The support unit can also perform stylistic analysis and point out areas for improvement in style. For example, the support unit can analyze the style of the text written by the user and point out areas for improvement based on a style guide. This makes it possible to create better writing by pointing out areas for improvement in grammar and style when writing a literary work. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the text written by the user into a generation AI and have the generation AI point out areas for improvement in grammar and style.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the AI ​​can collect data during the user's creative activities, acquiring information in a natural flow. If the user is focused, the AI ​​can also collect data during breaks in the creative activities so as not to interrupt the user's concentration. For example, the data collection unit refrains from collecting data while the user is focused and collects data during breaks. Furthermore, if the user is stressed, the data collection unit can refrain from collecting data until the user is relaxed, in order to alleviate the user's stress. For example, if the user is stressed, the data collection unit refrains from collecting data until the user is relaxed. This allows for more natural data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the timing of data collection.

[0070] The data collection unit can analyze the user's past works and learning history to select the optimal data collection method. For example, the data collection unit can analyze the style and themes of works the user has created in the past and collect similar works. The data collection unit can also prioritize the collection of data related to specific skills and knowledge based on the user's learning history. For example, the data collection unit can analyze topics and fields the user has shown interest in in the past and collect data related to them. This allows the optimal data collection method to be selected by analyzing the user's past works and learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past works and learning history into a generating AI and have the generating AI select the optimal data collection method.

[0071] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, if the user is interested in a particular theme, the data collection unit will collect data related to that theme. This allows for the collection of highly relevant data by filtering the data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting inspiring data to enhance creativity. If the user is relaxed, the data collection unit may also prioritize collecting data that is helpful for learning. For example, if the user is stressed, the data collection unit may prioritize collecting data that has a relaxing effect. This allows for more effective data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and data prioritization.

[0073] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. If the user is traveling, the data collection unit can also collect data related to the travel destination. For example, if the user is participating in a specific event, the data collection unit will collect data related to that event. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0074] The data collection unit can analyze a user's social media activity and collect relevant data when collecting data. For example, the data collection unit can analyze works and comments shared by a user on social media and collect relevant data. The data collection unit can also analyze accounts followed by a user and groups they participate in and collect relevant data. For example, the data collection unit can analyze topics and hashtags that a user has shown interest in on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen learning. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows for more effective analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and provide it to the user. For example, the analysis unit can perform a simplified analysis on low-importance data and provide it to the user. For example, the analysis unit can determine the priority of the analysis according to the importance of the data and perform the analysis efficiently. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply color and composition analysis algorithms to works of art. For musical works, it can also apply scale and rhythm analysis algorithms. For example, the analysis unit can apply error detection and optimization analysis algorithms to programming code. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide a longer report. If the user is in a hurry, the analysis unit can also perform a concise analysis and provide a shorter report. For example, if the user is excited, the analysis unit can perform a visually engaging analysis and provide a report of appropriate length. By adjusting the length of the analysis based on the user's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the analysis length.

[0079] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the latest data and provide results quickly. The analysis unit can also postpone the analysis of older data. For example, the analysis unit can adjust the analysis schedule according to the submission date to perform the analysis efficiently. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and provide results quickly. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data to perform analysis efficiently. This makes efficient analysis possible by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The service provider can estimate the user's emotions and adjust the curriculum presentation based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed curriculum to deepen learning. If the user is in a hurry, the service provider can also provide a concise curriculum that gets straight to the point. For example, if the user is excited, the service provider can provide a visually appealing curriculum. This allows for more effective learning by adjusting the curriculum presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the curriculum presentation.

[0082] The service provider can adjust the level of detail in the curriculum based on the user's learning progress when providing the curriculum. For example, the service provider can provide a detailed curriculum to users who are learning quickly, deepening their learning. For users who are learning slowly, the service provider can also provide a concise curriculum to solidify their foundation. For example, the service provider can adjust the level of detail in the curriculum according to the learning progress, enabling efficient learning. This makes efficient learning possible by adjusting the level of detail in the curriculum based on the user's learning progress. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input user learning progress data into a generating AI and have the generating AI perform the adjustment of the level of detail in the curriculum.

[0083] The service provider can apply different curricula to users according to their learning style when providing the curriculum. For example, the service provider can provide a visually-oriented curriculum to users with a visual learning style. The service provider can also provide an audio-oriented curriculum to users with an auditory learning style. For example, the service provider can provide a practice-oriented curriculum to users with an experiential learning style. By applying different curricula according to the user's learning style, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user learning style data into a generating AI and have the generating AI execute the application of different curricula.

[0084] The service provider can estimate the user's emotions and adjust the curriculum length based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed curriculum and conduct longer learning sessions. If the user is in a hurry, the service provider can also provide a concise curriculum and conduct shorter learning sessions. For example, if the user is excited, the service provider can provide a visually appealing curriculum and conduct learning sessions of an appropriate length. This allows for more effective learning by adjusting the curriculum length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and curriculum length adjustment.

[0085] The service provider can determine curriculum priorities based on the user's learning history when providing the curriculum. For example, the service provider can prioritize incorporating important topics into the curriculum based on the user's past learning history. The service provider can also prioritize incorporating areas where the user struggles, based on the user's learning history. For example, the service provider can suggest an efficient learning order based on the user's learning history. This enables efficient learning by determining curriculum priorities based on the user's learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning history data into a generating AI and have the generating AI perform the determination of curriculum priorities.

[0086] The service provider can adjust the order of the curriculum based on the user's areas of interest when providing the curriculum. For example, the service provider can prioritize incorporating topics related to the user's areas of interest into the curriculum. The service provider can also adjust the order of learning based on the user's areas of interest to facilitate efficient learning. For example, the service provider can customize the curriculum content according to the user's areas of interest to keep them engaged. This allows for more effective learning by adjusting the order of the curriculum based on the user's areas of interest. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the adjustment of the curriculum order.

[0087] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is relaxed, the support unit can provide detailed support to deepen their learning. If the user is in a hurry, the support unit can also provide concise support that gets straight to the point. For example, if the user is excited, the support unit can provide visually appealing support. This allows for more effective support by adjusting the support methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the support methods.

[0088] The support unit can analyze the user's past creative activities to select the most appropriate support method during the support process. For example, the support unit can analyze the style and themes of works the user has created in the past and provide similar support. Based on the user's past creative activities, the support unit can also prioritize support related to specific skills and knowledge. For example, the support unit can analyze topics and fields the user has shown interest in in the past and provide support related to those. This allows the support unit to select the most appropriate support method by analyzing the user's past creative activities. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input the user's past creative activity data into a generating AI and have the generating AI select the most appropriate support method.

[0089] The support unit can customize the means of support based on the user's current creative status. For example, the support unit can prioritize providing support related to the project the user is currently working on. The support unit can also provide highly relevant support based on the user's current creative status. For example, if the support unit is interested in a particular theme, it will provide support related to that theme. This allows for more effective support by customizing the means of support based on the user's current creative status. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current creative status data into a generating AI and have the generating AI perform the customization of the means of support.

[0090] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is excited, the support unit can prioritize providing inspirational support to enhance their creative drive. If the user is relaxed, the support unit can also prioritize providing support that helps with learning. For example, if the user is stressed, the support unit can prioritize providing support that has a relaxing effect. This allows for more effective support by prioritizing support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of support.

[0091] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit will prioritize providing support related to that region. If the user is traveling, the support unit can also provide support related to the travel destination. For example, if the support unit is participating in a specific event, the support unit will provide support related to that event. In this way, the optimal support method can be selected by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0092] The support unit can analyze a user's social media activity and suggest support methods when providing assistance. For example, the support unit can analyze works and comments shared by the user on social media and provide relevant support. The support unit can also analyze accounts followed and groups joined by the user and provide relevant support. For example, the support unit can analyze topics and hashtags that the user has shown interest in on social media and provide relevant support. In this way, by analyzing the user's social media activity, it is possible to suggest relevant support methods. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media activity data into a generating AI and have the generating AI suggest support methods.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The VirtualMuse system may also include an inspiration-providing unit. This unit analyzes the user's past creative activities and learning history and provides content to give the user new inspiration. For example, it can analyze the style of paintings the user has created in the past and introduce works by famous artists with similar styles. It can also analyze the genre of music the user has created and suggest new songs and artists related to that genre. Furthermore, it can provide related literary works and interview articles with authors based on the themes of literary works written by the user. This allows the user to gain new perspectives and ideas and broaden the scope of their creative activities. Some or all of the above processing in the inspiration-providing unit may be performed using AI, for example, or not. For example, the inspiration-providing unit can input the user's past creative activity data into a generating AI and have the generating AI perform the inspiration provision.

[0095] The VirtualMuse system may also include a feedback unit. This feedback unit provides detailed feedback on the user's creative activities. For example, it can offer specific advice on color selection and compositional balance for a user's painting. It can also point out areas for improvement in melody structure and rhythm for music created by the user. Furthermore, it can provide detailed explanations of grammatical and stylistic improvements for literary works written by the user. This allows the user to objectively evaluate their own work and engage in higher-level creative activities. Some or all of the above-described processes in the feedback unit may be performed using AI, for example, or without AI. For instance, the feedback unit could input the user's creative activity data into a generating AI and have the generating AI provide the feedback.

[0096] The VirtualMuse system can also include a collaboration section. This collaboration section provides a platform for users to engage in collaborative creative activities. For example, users can launch a collaborative painting project and complete the artwork in real time, working together with other users. They can also launch a collaborative music production project, dividing up different parts for performance and recording. Furthermore, users can launch a collaborative literary writing project, dividing up chapters and ultimately compiling them into a single work. This allows users to collaborate with other creators and incorporate a wider range of perspectives and ideas. Some or all of the processes described above in the collaboration section may be performed using AI, or not. For example, the collaboration section can input user project data into a generating AI and have the generating AI perform collaboration support.

[0097] The VirtualMuse system may also include a motivation management unit. The motivation management unit provides functions to maintain and improve the user's motivation for creative activities. For example, it can award badges or points to users when they achieve certain goals, giving them a sense of accomplishment. It can also send reminders to users to encourage them to continue their creative activities regularly. Furthermore, it can provide a ranking function that allows users to compete with other creators, stimulating a competitive spirit. This will increase the user's motivation for creative activities and enable them to continue their work. Some or all of the above processes in the motivation management unit may be performed using AI, for example, or not using AI. For example, the motivation management unit can input user activity data into a generating AI and have the generating AI execute the motivation management functions.

[0098] The VirtualMuse system may also include an emotion estimation unit. This unit estimates the user's emotions in real time and uses this information to support creative activities. For example, if the user is relaxed, the emotion estimation unit can provide relaxing music or scenery to maintain that state. If the user is focused, the emotion estimation unit can also adjust the environment to enhance concentration. Furthermore, if the user is stressed, the emotion estimation unit can suggest relaxation techniques to reduce stress. This allows the user to receive optimal support tailored to their emotional state and concentrate on their creative activities. Some or all of the above-described processes in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the user's emotional data into a generating AI, which can then perform emotion estimation and provide support.

[0099] The VirtualMuse system may also include an emotional feedback unit. This unit estimates the user's emotions and provides feedback based on those emotions. For example, if the user is feeling joy, the emotional feedback unit can provide positive feedback to reinforce that emotion. If the user is feeling anxious, the emotional feedback unit can provide encouraging messages to alleviate that anxiety. Furthermore, if the user is excited, the emotional feedback unit can suggest challenging tasks to maintain that excitement. This allows the user to receive appropriate feedback according to their emotional state and maintain their motivation for creative activities. Some or all of the above processing in the emotional feedback unit may be performed using AI, for example, or without AI. For example, the emotional feedback unit can input the user's emotional data into a generating AI, which can then perform emotion estimation and provide feedback.

[0100] The VirtualMuse system may also include an emotion monitoring unit. This unit continuously monitors the user's emotions and provides appropriate support according to the progress of the creative activity. For example, if the user is feeling stressed during the creative activity, the emotion monitoring unit can suggest relaxation techniques to alleviate that stress. If the user is excited about the creative activity, the emotion monitoring unit can also provide challenging tasks to maintain that excitement. Furthermore, if the user is feeling anxious about the creative activity, the emotion monitoring unit can provide encouraging messages to reduce that anxiety. This allows the user to receive appropriate support according to their emotional state and concentrate on their creative activity. Some or all of the above processing in the emotion monitoring unit may be performed using AI, for example, or without AI. For example, the emotion monitoring unit can input the user's emotional data into a generating AI, which can then perform the emotional monitoring and support provision.

[0101] The VirtualMuse system may also include an emotion adjustment unit. This unit estimates the user's emotions and adjusts the creative environment based on those emotions. For example, if the user is relaxed, the emotion adjustment unit can provide relaxing music or scenery to maintain that state. If the user is focused, the emotion adjustment unit can also adjust the environment to enhance concentration. Furthermore, if the user is stressed, the emotion adjustment unit can suggest relaxation techniques to reduce stress. This allows the user to engage in creative activities in an optimal environment tailored to their emotional state. Some or all of the above-described processes in the emotion adjustment unit may be performed using AI, for example, or without AI. For example, the emotion adjustment unit can input the user's emotional data into a generating AI, which can then perform emotion estimation and environment adjustment.

[0102] The VirtualMuse system may also include an emotion analysis unit. This unit analyzes the user's emotions in detail and provides support for creative activities based on the results. For example, it can analyze patterns of joy and excitement the user experiences during creative activities and adjust the progress of those activities based on those patterns. It can also identify the causes of anxiety and stress the user experiences during creative activities and suggest measures to eliminate those causes. Furthermore, it can analyze fluctuations in the user's motivation for creative activities and provide support to maintain that motivation. This allows the user to receive optimal support tailored to their emotional state and concentrate on their creative activities. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, or not. For example, the emotion analysis unit can input the user's emotional data into a generating AI, which can then perform the emotional analysis and provide support.

[0103] The VirtualMuse system can also be equipped with an emotion prediction unit. This unit predicts the user's future emotional state based on their past emotional data and provides support for creative activities based on that prediction. For example, it can analyze the emotions a user felt when performing a specific creative activity in the past and provide predicted emotional states for similar activities. It can also provide predicted emotional states when a user attempts a new creative activity and prepare support accordingly in advance. Furthermore, it can predict emotional fluctuations as a user continues creative activities over a long period and provide support tailored to those fluctuations. This allows the user to receive optimal support based on their emotional state and concentrate on their creative activities. Some or all of the above-described processes in the emotion prediction unit may be performed using AI, or not. For example, the emotion prediction unit can input the user's emotional data into a generating AI and have the generating AI perform emotion prediction and provide support.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The data collection unit collects user data. User data includes behavioral data, learning history, and sentiment data. For example, the data collection unit collects the user's past works and learning history, and gathers them in the form of project reports, artwork, learning logs, and test results. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify the user's talents and learning style. The analysis is performed using methods such as machine learning algorithms and statistical analysis. For example, the analysis unit uses machine learning algorithms to identify the user's talents and statistical analysis to identify their learning style. Step 3: The service provider provides an individually optimized curriculum based on the talents and learning styles identified by the analysis unit. Individual optimization is achieved through methods such as personalized curricula and adaptive learning systems. For example, the service provider provides a personalized curriculum and delivers the curriculum using an adaptive learning system. Step 4: The support team provides real-time support for the creative process based on the curriculum provided by the provider team. This support includes methods such as idea generation and prototype creation. For example, if the user is learning programming, the support team will detect code errors in real time and suggest ways to correct them. If the user is writing a literary work, the support team will point out areas for improvement in grammar and style.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user behavior data and learning history using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's talents and learning style. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides an individually optimized curriculum. The support unit is implemented in the control unit 46A of the smart device 14, for example, and supports the creative process in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user behavior data and learning history using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's talents and learning style. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides an individually optimized curriculum. The support unit is implemented in the control unit 46A of the smart glasses 214, for example, and supports the creative process in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user behavior data and learning history using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's talents and learning style. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides an individually optimized curriculum. The support unit is implemented in the control unit 46A of the headset terminal 314, for example, and supports the creative process in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data and learning history using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's talents and learning style. The provision unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and provides an individually optimized curriculum. The support unit is implemented in the control unit 46A of the robot 414, for example, and supports the creative process in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's talents and learning style, A provisioning unit provides an individually optimized curriculum based on the talents and learning styles identified by the aforementioned analysis unit. The system includes a support unit that provides real-time support for the creative process based on the curriculum provided by the aforementioned supply unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the user's past works and learning history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is If the user is learning to program, it will detect code errors in real time and suggest ways to fix them. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is If the user is writing a literary work, it will point out areas for improvement in grammar and style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past works and learning history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates user emotions and adjusts the curriculum's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the curriculum, adjust the level of detail based on the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing a curriculum, different curricula are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the curriculum length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing a curriculum, prioritize the curriculum based on the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the curriculum, the order of the curriculum is adjusted based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is During support, we analyze the user's past creative activities to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is During support, customize the support methods based on the user's current creative stage. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 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. A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's talents and learning style, A provisioning unit provides an individually optimized curriculum based on the talents and learning styles identified by the aforementioned analysis unit. The system includes a support unit that provides real-time support for the creative process based on the curriculum provided by the aforementioned supply unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect the user's past works and learning history. The system according to feature 1.

3. The aforementioned support unit is If the user is learning to program, it will detect code errors in real time and suggest ways to fix them. The system according to feature 1.

4. The aforementioned support unit is If the user is writing a literary work, it will point out areas for improvement in grammar and style. The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past works and learning history to select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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