system

Abstract

The system according to this embodiment aims to record in detail the techniques of traditional crafts and Living National Treasures and to effectively pass them on to future generations. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a learning unit, a feedback unit, and a community unit. The analysis unit analyzes and records the craftsman's skills in detail. The generation unit generates interactive 3D educational content based on the data collected by the analysis unit. The learning unit provides an immersive learning experience using the content generated by the generation unit. The feedback unit analyzes the learner's actions and provides real-time feedback. The community unit builds an online craftsman community.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, there is a problem that it is difficult to record traditional crafts and the techniques of national living treasures in detail and effectively inherit them.

[0005] The system according to the embodiment aims to record traditional crafts and the techniques of national living treasures in detail and effectively inherit them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a learning unit, a feedback unit, and a community unit. The analysis unit analyzes and records the skills of the craftsmen in detail. The generation unit generates interactive 3D educational content based on the data collected by the analysis unit. The learning unit provides an immersive learning experience using the content generated by the generation unit. The feedback unit analyzes the learner's actions and provides real-time feedback. The community unit builds an online community of craftsmen. [Effects of the Invention]

[0007] The system according to this embodiment can record in detail the techniques of traditional crafts and Living National Treasures, and effectively pass them on to future generations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017]

[0018] 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.

[0019] ​The smart device 14 comprises a computer 36, a reception 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 reception 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 traditional technology preservation system according to an embodiment of the present invention is a system that utilizes generative AI to automatically generate interactive educational content for preserving and passing on to the next generation the knowledge and skills of Japanese traditional crafts, traditional skills, and Living National Treasures. The traditional technology preservation system analyzes and records the skills of master craftsmen in detail using high-precision sensors and AI, and the generative AI automatically generates interactive 3D educational content based on the collected data. This content provides an immersive learning experience utilizing VR / AR technology and proposes a personalized learning program according to the learner's progress and characteristics. In addition, a real-time feedback system allows the AI ​​to analyze the learner's actions and provide immediate advice. Furthermore, it builds an online community of master craftsmen to promote interaction between master craftsmen and learners, and among learners themselves. This promotes the preservation and transmission of valuable traditional technologies and contributes to the creation of new value and industrial promotion of traditional crafts. For example, the traditional technology preservation system analyzes and records the actions and judgments of master craftsmen in detail using high-precision sensors. Next, the generative AI generates interactive 3D educational content based on the collected data. This content provides an immersive learning experience utilizing VR / AR technology and proposes a personalized learning program according to the learner's progress and characteristics. Furthermore, a real-time feedback system allows AI to analyze learners' actions and provide immediate advice. In addition, an online community of master craftsmen is built to promote interaction between craftsmen and learners, and among learners themselves. This promotes the preservation and transmission of valuable traditional techniques, contributing to the creation of new value and industrial development in traditional crafts. Thus, the traditional technique preservation system can promote the preservation and transmission of valuable traditional techniques, contributing to the creation of new value and industrial development in traditional crafts.

[0029] The traditional technology preservation system according to this embodiment comprises an analysis unit, a generation unit, a learning unit, a feedback unit, and a community unit. The analysis unit analyzes and records the skills of artisans in detail. The analysis unit, for example, uses high-precision sensors to analyze and record the movements and judgments of artisans in detail. The analysis unit can record in detail the minute details of the artisans' movements and the basis for their judgments. The generation unit generates interactive 3D educational content based on the collected data. The generation unit, for example, uses a generation AI to generate interactive 3D educational content utilizing VR / AR technology based on the collected data. The generation unit uses a generation AI to analyze the collected data and automatically generates interactive 3D educational content. The learning unit provides an immersive learning experience using the generated content. The learning unit provides an immersive learning experience to learners using a VR headset, for example. The learning unit can provide personalized learning programs according to the learner's progress and characteristics. The feedback unit analyzes the learner's movements and provides feedback in real time. The feedback unit, for example, uses AI to analyze the learner's movements and provides immediate advice. The feedback unit can monitor learners' actions in real time and provide appropriate feedback. The community unit builds an online craftsman community. The community unit builds an online craftsman community that promotes interaction between craftsmen and learners, and among learners themselves. The community unit can promote interaction between craftsmen and learners, and among learners themselves, through forums, chat functions, and event hosting. As a result, the traditional technology preservation system according to this embodiment can provide an immersive learning experience by analyzing and recording craftsmanship in detail and generating interactive 3D educational content, thereby promoting the preservation and transmission of traditional technology.

[0030] The analysis department meticulously analyzes and records the skills of master craftsmen. Specifically, it uses high-precision sensors to analyze and record the movements and decisions of the craftsmen in detail. For example, a motion capture system can be used to record the movements of the craftsmen's hands and body posture in milliseconds. This allows for the accurate capture of the subtle details and flow of the craftsmen's movements. In addition, to record the basis of the craftsmen's decisions, speech recognition technology can be used to transcribe the craftsmen's explanations and instructions into text and record them in sync with the movements. Furthermore, detailed data on the craftsmen's working environment is also collected. For example, environmental data such as temperature, humidity, and light intensity are measured with sensors to meticulously record the conditions under which the craftsmen's skills are performed. This allows for the understanding of the environmental conditions necessary to reproduce the craftsmen's skills. The analysis department centrally manages this data and makes it available for subsequent analysis and content generation. The data is stored on a cloud server and can be accessed as needed. In this way, the analysis department can meticulously and accurately record the skills of master craftsmen and provide a foundation for passing them on to future generations.

[0031] The generation unit generates interactive 3D educational content based on collected data. Specifically, it uses a generation AI to analyze the collected data and generate interactive 3D educational content utilizing VR / AR technology. The generation AI analyzes the movement data and environmental data of the craftsman to generate realistic 3D models. For example, it generates a 3D avatar that reproduces the hand movements and body posture of the craftsman, and makes its movements interactively controllable. It also generates a virtual space that reproduces the craftsman's work environment, allowing learners to move around freely and learn within it. Furthermore, the generation AI generates scenarios based on the rationale behind the craftsman's decisions, enabling learners to learn while actually experiencing the craftsman's techniques. For example, it simulates what kind of decisions the craftsman would make in what situations, allowing learners to experience those decisions. The generation unit automatically generates this content and provides it to learners. In this way, the generation unit can provide educational content that allows learners to learn the craftsman's techniques interactively and provide an environment where learners can learn while actually experiencing them.

[0032] The learning department provides an immersive learning experience using generated content. Specifically, it provides learners with an immersive learning experience using VR headsets. By wearing a VR headset, learners can immerse themselves in a virtual space where they can realistically experience the skills of master craftsmen. The learning department provides personalized learning programs according to the learner's progress and characteristics. For example, it analyzes the learner's movement data and learning history to understand their strengths and weaknesses. Based on this, it provides learners with the most suitable learning content and practice tasks. Furthermore, the learning department provides learning programs that incorporate game elements to maintain learners' motivation. For example, it introduces a system where learners can earn points each time they complete a specific task, and rewards are provided according to their learning progress. This allows learners to learn while having fun. Through these functions, the learning department can provide learners with an effective and enjoyable learning experience and support them in acquiring the skills of master craftsmen.

[0033] The feedback unit analyzes the learner's actions and provides real-time feedback. Specifically, the AI ​​analyzes the learner's actions and provides immediate advice. For example, when a learner is practicing a master's technique in a VR space, the AI ​​monitors the learner's actions in real time and evaluates the accuracy and speed of the movements. If the learner is performing the movements correctly, positive feedback is provided; if the movements are inaccurate, specific areas for improvement are pointed out. Feedback is provided in various formats, including voice, text, and visual guidelines. For example, if a learner makes a mistake in hand movements, visual guidelines are displayed to show the correct movement. Specific advice is also provided via voice to make it easier for the learner to understand. Furthermore, the feedback unit records the learner's progress and visualizes their growth by comparing it with past feedback. This allows learners to feel their own progress and maintain their motivation. Through these functions, the feedback unit can provide effective feedback to learners and support them in acquiring master's techniques.

[0034] The Community Department will build an online community for master craftsmen. Specifically, it will build an online community for master craftsmen that promotes interaction between master craftsmen and learners, and among learners themselves. The Community Department will promote interaction between master craftsmen and learners, and among learners themselves, through forums, chat functions, and the hosting of events. For example, in the forum, master craftsmen can answer questions about their techniques, and learners can exchange information with each other. Real-time communication is also possible using the chat function. Furthermore, the Community Department will host online events and workshops to provide opportunities for master craftsmen and learners to interact directly. This will allow learners to receive direct instruction from master craftsmen and learn together with other learners. In addition, the Community Department will provide a platform for learners to share their achievements, allowing learners to share their growth with other members. This will allow learners to inspire each other and increase their motivation to learn. Through these functions, the Community Department can support the preservation and transmission of master craftsmanship and build a community where learners can learn from one another.

[0035] The analysis unit can analyze and record in detail the movements and decisions of the craftsman using high-precision sensors. For example, the analysis unit can record the movements of the craftsman in detail using high-precision sensors. The analysis unit can capture and record even the minute details of the craftsman's movements. The analysis unit can also record in detail the basis for the craftsman's decisions. For example, the analysis unit can record in detail what decisions the craftsman made and the reasoning behind them. In this way, by using high-precision sensors, the movements and decisions of the craftsman can be analyzed and recorded in detail. High-precision sensors include, but are not limited to, accelerometers and gyroscopes. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data acquired by high-precision sensors into a generating AI and have the generating AI perform a detailed analysis of the craftsman's movements and decisions.

[0036] The generation unit can generate interactive 3D educational content using VR / AR technology based on the collected data. For example, the generation unit generates interactive 3D educational content using a generation AI based on the collected data. The generation unit's generation AI analyzes the collected data and automatically generates interactive 3D educational content using VR / AR technology. For example, the generation unit's generation AI generates interactive 3D educational content based on the collected data. This makes it possible to generate interactive 3D educational content by utilizing VR / AR technology. VR / AR technology includes, but is not limited to, VR headsets and AR glasses. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected data into a generation AI and have the generation AI perform the generation of interactive 3D educational content.

[0037] The learning unit can provide personalized learning programs according to the learner's progress and characteristics. For example, the learning unit can provide personalized learning programs based on the learner's progress data. The learning unit can analyze the learner's characteristics and provide appropriate learning programs. For example, the learning unit can monitor the learner's progress in real time and provide learning programs according to that progress. The learning unit can also analyze the learner's characteristics and provide learning programs according to those characteristics. For example, the learning unit can analyze the learner's characteristics and provide learning programs according to those characteristics. This enables effective learning by providing personalized learning programs according to the learner's progress and characteristics. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input learner progress data into a generating AI and have the generating AI perform the task of providing personalized learning programs.

[0038] The feedback unit can analyze the learner's actions and provide immediate advice. For example, the feedback unit can monitor the learner's actions in real time and provide immediate advice. The feedback unit can analyze the learner's actions and provide appropriate advice. For example, the feedback unit can monitor the learner's actions in real time and point out areas for improvement. The feedback unit can also analyze the learner's actions and provide immediate advice. For example, the feedback unit can analyze the learner's actions and provide immediate advice. This allows for improved learning effectiveness by analyzing the learner's actions and providing immediate advice. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learner action data into a generating AI and have the generating AI perform action analysis and provide advice.

[0039] The Community Department can build an online community of master craftsmen that promotes interaction between master craftsmen and learners, and among learners themselves. For example, the Community Department can build an online community of master craftsmen to promote interaction between master craftsmen and learners, and among learners themselves. The Community Department can promote interaction between master craftsmen and learners, and among learners themselves, through forums, chat functions, and event hosting. For example, the Community Department can build an online forum to provide a space for master craftsmen and learners, and among learners themselves, to exchange opinions and share information. The Community Department can also provide a chat function to promote real-time communication. Furthermore, the Community Department can host online events to deepen interaction between master craftsmen and learners, and among learners themselves. For example, the Community Department can host online workshops and seminars to provide opportunities to learn the skills and knowledge of master craftsmen. In this way, by building an online community of master craftsmen, interaction between master craftsmen and learners, and among learners themselves, can be promoted. Some or all of the above processes in the Community Department may be performed using AI, for example, or not. For example, the Community Department can have a generative AI run the online forum and plan events.

[0040] The analysis unit can analyze the craftsman's past movement history and select the optimal recording method. For example, the analysis unit can analyze patterns of movements the craftsman has performed in the past and select an efficient recording method. The analysis unit can select a method to prioritize recording important movements from the craftsman's past movement history. For example, the analysis unit prioritizes recording important movements based on the craftsman's past movement history. The analysis unit can also select a recording method to avoid repeating movements based on the craftsman's movement history. For example, the analysis unit analyzes the craftsman's movement history and selects a recording method to avoid repeating movements. In this way, the optimal recording method can be selected by analyzing the craftsman's past movement history. The optimal recording method includes, but is not limited to, the type of movement and the accuracy of the recording. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the craftsman's past movement history data into a generating AI and have the generating AI select the optimal recording method.

[0041] The analysis unit can filter the recording of actions based on the craftsman's current work environment and the tools they use. For example, the analysis unit can adjust the level of detail of the actions to be recorded depending on the type of tools the craftsman uses. The analysis unit can optimize the recording method based on the craftsman's work environment (indoors / outdoors, brightness, etc.). For example, the analysis unit optimizes the recording method based on the craftsman's work environment. The analysis unit can also highlight and record important parts of actions based on the characteristics of the tools the craftsman uses. For example, the analysis unit highlights and records important parts of actions based on the characteristics of the tools the craftsman uses. This allows for more appropriate recording by filtering based on the craftsman's work environment and the tools they use. The work environment and tools used include, but are not limited to, the conditions of the work location and the types of tools used. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data on the craftsman's work environment and tools used into a generating AI and have the generating AI perform the filtering.

[0042] The analysis unit can prioritize recording highly relevant actions by considering the geographical location information of the craftsman when recording actions. For example, the analysis unit can prioritize recording actions performed by the craftsman in a specific region. The analysis unit can record region-specific actions based on the geographical location information of the craftsman. For example, the analysis unit can record region-specific actions based on the geographical location information of the craftsman. The analysis unit can also record actions performed by the craftsman in real time while they are moving. For example, the analysis unit can record actions performed by the craftsman in real time while they are moving. This allows for the priority recording of highly relevant actions by considering the geographical location information of the craftsman. Geographical location information includes, but is not limited to, the method of acquiring location information and the criteria for highly relevant actions. 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 geographical location information data of the craftsman into a generating AI and have the generating AI record highly relevant actions.

[0043] The analysis unit can analyze the craftsman's social media activities when recording actions and record relevant actions. For example, the analysis unit can prioritize recording actions shared by the craftsman on social media. The analysis unit can record actions that are attracting attention from the craftsman's social media activities. For example, the analysis unit can analyze the craftsman's social media activities and record actions that are attracting attention. The analysis unit can also record in detail the actions that the craftsman has introduced on social media. For example, the analysis unit can record in detail the actions that the craftsman has introduced on social media. This allows for the recording of relevant actions by analyzing the craftsman's social media activities. Social media activities include, but are not limited to, the analysis of posts and methods for extracting highly relevant actions. 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 craftsman's social media activity data into a generating AI and have the generating AI record relevant actions.

[0044] The generation unit can adjust the level of detail of content based on the importance of the actions during content generation. For example, the generation unit can generate content with detailed explanations and videos for important actions. For general actions, the generation unit can generate content with concise explanations and videos. For example, the generation unit can adjust the level of detail in stages according to the importance of the actions. The generation unit can also generate content with detailed explanations and videos based on the importance of the actions. For example, the generation unit can generate content with detailed explanations and videos for important actions. This allows important actions to be represented in detail by adjusting the level of detail of the content based on the importance of the actions. The importance of an action includes, but is not limited to, the degree of impact and frequency of the action. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input action importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0045] The generation unit can apply different generation algorithms depending on the category of action when generating content. For example, in the case of woodworking, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the wood. In the case of pottery, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the clay. For example, in the case of dyeing, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the dye. The generation unit can also apply different generation algorithms depending on the category of action. For example, in the case of woodworking, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the wood. By applying different generation algorithms depending on the category of action, more appropriate content can be generated. The category of action includes, but is not limited to, the type and purpose of the action. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input action category data into a generation AI and cause the generation AI to apply different generation algorithms.

[0046] The generation unit can determine content priorities based on the recording date of actions when generating content. For example, the generation unit may prioritize recently recorded actions as content. The generation unit may also prioritize actions related to seasons or events as content. For example, the generation unit may prioritize important actions as content based on the craftsman's schedule. The generation unit can also determine content priorities based on the recording date of actions. For example, the generation unit may prioritize recently recorded actions as content. This allows for prioritizing important actions as content by determining content priorities based on the recording date of actions. The recording date of actions includes, but is not limited to, the timing and importance of the recording. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input action recording date data into a generation AI and have the generation AI perform the content prioritization.

[0047] The generation unit can adjust the order of content based on the relevance of actions when generating content. For example, the generation unit can generate content that displays related actions in sequence. The generation unit can generate content in an order that follows the flow of actions. For example, the generation unit can generate content in a visually easy-to-understand order based on the relevance of actions. The generation unit can also adjust the order of content based on the relevance of actions. For example, the generation unit can generate content that displays related actions in sequence. By adjusting the order of content based on the relevance of actions, it is possible to generate visually easy-to-understand content. Relevance of actions includes, but is not limited to, the continuity of actions or the criteria for related actions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input action relevance data into a generation AI and have the generation AI perform the adjustment of the order of content.

[0048] The generation unit can adjust the order of content based on the relevance of actions when generating content. For example, the generation unit can generate content that displays related actions in sequence. The generation unit can generate content in an order that follows the flow of actions. For example, the generation unit can generate content in a visually easy-to-understand order based on the relevance of actions. The generation unit can also adjust the order of content based on the relevance of actions. For example, the generation unit can generate content that displays related actions in sequence. By adjusting the order of content based on the relevance of actions, it is possible to generate visually easy-to-understand content. Relevance of actions includes, but is not limited to, the continuity of actions or the criteria for related actions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input action relevance data into a generation AI and have the generation AI perform the adjustment of the order of content.

[0049] The learning unit can select the optimal program by referring to the learner's past learning history when providing a learning program. For example, the learning unit can select an effective learning program from the learner's past learning history. The learning unit can provide a learning program that suggests the next step based on what the learner has learned in the past. For example, the learning unit can analyze the learner's learning history and provide a program that matches their learning progress. The learning unit can also provide the optimal learning program by referring to the learner's past learning history. For example, the learning unit can select the optimal learning program based on the learner's past learning history. In this way, the optimal learning program can be provided by referring to the learner's past learning history. Past learning history includes, but is not limited to, the method of acquiring historical data and the criteria for selecting the optimal program. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the learner's past learning history data into a generating AI and have the generating AI select the optimal program.

[0050] The learning unit can customize learning programs based on the learner's current skill level when providing them. For example, the learning unit can provide programs ranging from basic to advanced levels, depending on the learner's skill level. The learning unit can also assess the learner's skill level and provide programs of appropriate difficulty. For example, the learning unit can assess the learner's skill level and provide programs of appropriate difficulty. The learning unit can also provide individually customized programs based on the learner's skill level. For example, the learning unit can provide individually customized programs based on the learner's skill level. This allows for more appropriate learning by customizing programs based on the learner's skill level. Current skill level includes, but is not limited to, skill assessment tests and past learning outcomes. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input learner skill level data into a generating AI and have the generating AI perform program customization.

[0051] The learning unit can select the most suitable program when providing learning programs, taking into account the learner's geographical location information. For example, the learning unit can provide a program that teaches region-specific technologies based on the learner's geographical location information. The learning unit can select a program that provides the most suitable learning environment, taking into account the learner's geographical location information. For example, the learning unit can provide a program that promotes interaction with local artisans based on the learner's geographical location information. The learning unit can also provide the most suitable learning program, taking into account the learner's geographical location information. For example, the learning unit can provide a program that teaches region-specific technologies based on the learner's geographical location information. This makes it possible to provide the most suitable program for learning region-specific technologies by taking into account the learner's geographical location information. Geographical location information includes, but is not limited to, the method of acquiring location information and the standards for region-specific technologies. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the learner's geographical location information data into a generating AI and have the generating AI select the most suitable program.

[0052] The learning department can analyze learners' social media activity and propose programs when providing learning programs. For example, the learning department can propose programs that teach technologies of interest based on the learner's social media activity. The learning department can analyze learners' social media activity and provide programs that teach relevant technologies. For example, the learning department can propose programs tailored to the learner's interests based on the learner's social media activity. The learning department can also analyze learners' social media activity and propose programs. For example, the learning department can propose programs that teach technologies of interest based on the learner's social media activity. In this way, by analyzing learners' social media activity, it is possible to provide programs tailored to the learner's interests. Social media activity includes, but is not limited to, the analysis of posted content and methods for extracting highly relevant programs. Some or all of the above processing in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input learner's social media activity data into a generating AI and have the generating AI execute program proposals.

[0053] The feedback unit can provide optimal feedback by referring to the learner's past action history when providing feedback. For example, the feedback unit can provide feedback that points out areas for improvement based on the learner's past action history. The feedback unit can provide positive feedback based on successful examples of actions the learner has performed in the past. For example, the feedback unit can analyze the learner's action history and provide feedback to help them move to the next step. The feedback unit can also provide optimal feedback by referring to the learner's past action history. For example, the feedback unit can provide feedback that points out areas for improvement based on the learner's past action history. In this way, the feedback unit can provide optimal feedback by referring to the learner's past action history. Past action history includes, but is not limited to, the method of acquiring history data and the criteria for selecting optimal feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the learner's past action history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0054] The feedback unit can customize the feedback provided based on the learner's current learning status. For example, the feedback unit can provide feedback that points out specific areas for improvement according to the learner's current learning status. The feedback unit can provide feedback to help the learner move on to the next step based on their progress. For example, the feedback unit can evaluate the learner's learning status and provide individually customized feedback. The feedback unit can also customize the feedback based on the learner's current learning status. For example, the feedback unit can provide feedback that points out specific areas for improvement according to the learner's current learning status. By customizing the feedback based on the learner's current learning status, more appropriate feedback can be provided. The current learning status includes, but is not limited to, methods for evaluating learning progress and methods for acquiring learning status data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the learner's current learning status data into a generating AI and have the generating AI perform the customization of the feedback.

[0055] The feedback unit can provide optimal feedback by considering the learner's geographical location information when providing feedback. For example, the feedback unit can provide feedback on region-specific technologies based on the learner's geographical location information. The feedback unit can also provide feedback on the optimal learning environment by considering the learner's geographical location information. For example, the feedback unit can provide feedback that promotes interaction with local artisans based on the learner's geographical location information. Furthermore, the feedback unit can also provide optimal feedback by considering the learner's geographical location information. For example, the feedback unit can provide feedback on region-specific technologies based on the learner's geographical location information. This allows for the provision of optimal feedback on region-specific technologies by considering the learner's geographical location information. Geographical location information includes, but is not limited to, the method of acquiring location information and the standards for region-specific technologies. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the learner's geographical location information data into a generating AI and have the generating AI perform the provision of optimal feedback.

[0056] The feedback unit can analyze the learner's social media activity and propose feedback when providing feedback. For example, the feedback unit can provide feedback on technologies of interest based on the learner's social media activity. The feedback unit can analyze the learner's social media activity and provide feedback on relevant technologies. For example, the feedback unit can provide feedback tailored to the learner's interests based on the learner's social media activity. The feedback unit can also analyze the learner's social media activity and propose feedback. For example, the feedback unit can provide feedback on technologies of interest based on the learner's social media activity. This allows the feedback unit to provide feedback tailored to the learner's interests by analyzing the learner's social media activity. Social media activity includes, but is not limited to, the analysis of posted content and methods for extracting highly relevant feedback. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the learner's social media activity data into a generating AI and have the generating AI execute the feedback proposal.

[0057] The Community Department can select the optimal interaction method by referring to the past interaction history of artisans and learners when building a community. For example, the Community Department can select an effective interaction method from the past interaction history of artisans and learners. The Community Department can provide the optimal interaction method based on successful past interactions of artisans and learners. For example, the Community Department can analyze the interaction history of artisans and learners and provide an interaction method to move to the next step. The Community Department can also provide the optimal interaction method by referring to the past interaction history of artisans and learners. For example, the Community Department can select an effective interaction method from the past interaction history of artisans and learners. This allows the Community Department to provide the optimal interaction method by referring to the past interaction history of artisans and learners. Past interaction history includes, but is not limited to, the method of acquiring historical data and the criteria for selecting the optimal interaction method. Some or all of the above processing in the Community Department may be performed using AI, for example, or not using AI. For example, the Community Department can input the past interaction history data of artisans and learners into a generating AI and have the generating AI select the optimal interaction method.

[0058] The Community Department can customize the means of interaction based on the current interests of artisans and learners when building a community. For example, the Community Department can provide interaction on relevant themes according to the current interests of artisans and learners. The Community Department can evaluate the interests of artisans and learners and provide appropriate means of interaction. For example, the Community Department can provide individually customized means of interaction based on the interests of artisans and learners. The Community Department can also customize the means of interaction based on the current interests of artisans and learners. For example, the Community Department can provide interaction on relevant themes according to the current interests of artisans and learners. This makes it possible to have more appropriate interactions by customizing the means of interaction based on the current interests of artisans and learners. Current interests include, but are not limited to, survey results and social media posts. Some or all of the above processing in the Community Department may be performed using, for example, AI, or not using AI. For example, the Community Department can input data on the interests of artisans and learners into a generating AI and have the generating AI perform the customization of the means of interaction.

[0059] The Community Department can select the optimal method of interaction when building a community, taking into account the geographical location information of artisans and learners. For example, the Community Department can provide exchanges on region-specific technologies based on the geographical location information of artisans and learners. The Community Department can provide an optimal environment for interaction, taking into account the geographical location information of artisans and learners. For example, the Community Department can promote interaction with local artisans based on the geographical location information of artisans and learners. The Community Department can also provide the optimal method of interaction, taking into account the geographical location information of artisans and learners. For example, the Community Department can provide exchanges on region-specific technologies based on the geographical location information of artisans and learners. This allows for the provision of the optimal method of interaction on region-specific technologies by considering the geographical location information of artisans and learners. Geographical location information includes, but is not limited to, methods for acquiring location information and standards for region-specific technologies. Some or all of the above processing in the Community Department may be performed using, for example, AI, or not using AI. For example, the Community Department can input geographical location data of artisans and learners into a generating AI and have the generating AI select the optimal method of interaction.

[0060] The Community Department can analyze the social media activities of artisans and learners during community building and propose means of interaction. For example, the Community Department can provide interaction related to technologies of interest based on the social media activities of artisans and learners. The Community Department can analyze the social media activities of artisans and learners and provide interaction related to relevant technologies. For example, the Community Department can provide interaction tailored to learners' interests based on the social media activities of artisans and learners. The Community Department can also analyze the social media activities of artisans and learners and propose means of interaction. For example, the Community Department can provide interaction related to technologies of interest based on the social media activities of artisans and learners. This allows for the provision of interaction tailored to learners' interests by analyzing the social media activities of artisans and learners. Social media activities include, but are not limited to, analysis of posted content and methods for extracting highly relevant means of interaction. Some or all of the above processing in the Community Department may be performed using, for example, AI, or not using AI. For example, the Community Department can input social media activity data of artisans and learners into a generating AI and have the generating AI propose means of interaction.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The traditional skills preservation system can also include an evaluation unit to assess the skills of artisans. This unit can quantitatively evaluate the artisans' skills and adjust the content of the learning program based on the evaluation results. For example, the evaluation unit can score the artisans' skills and provide learners with content at an appropriate level based on those scores. The evaluation unit can also monitor the progress of the artisans' skills and evaluate the degree of improvement. Furthermore, the evaluation unit can compare the artisans' skills with those of other artisans and conduct relative evaluations. This allows for an objective evaluation of the artisans' skills and improves the quality of the learning program.

[0063] The analysis department can compare traditional craftsmanship with techniques from other fields, identifying similarities and differences. For example, it can compare traditional craftsmanship with modern manufacturing techniques to highlight the advantages and areas for improvement of traditional techniques. It can also compare traditional craftsmanship with techniques from other countries, analyzing cultural differences and similarities. Furthermore, it can compare traditional craftsmanship with other artistic fields (such as music and dance) to explore potential applications of these techniques. This allows for a multifaceted analysis of traditional craftsmanship, uncovering new value.

[0064] The production unit can propose new designs and products based on the skills of master craftsmen. For example, the production unit can create handcrafted items with modern designs using the skills of master craftsmen. It can also propose new products (such as furniture and accessories) by applying the skills of master craftsmen. Furthermore, the production unit can generate digital art and interactive exhibits based on the skills of master craftsmen. This allows for the application of master craftsmanship to modern designs and products, creating new value.

[0065] The Analysis Department can propose methods for integrating traditional craftsmanship with technologies from other fields. For example, it can propose methods for integrating traditional craftsmanship with modern robotics technology. It can also propose methods for integrating traditional craftsmanship with biotechnology. Furthermore, it can propose methods for integrating traditional craftsmanship with digital fabrication technology. This allows for the integration of traditional craftsmanship with technologies from other fields, creating new value.

[0066] The production unit can develop new educational programs based on the techniques of master craftsmen. For example, the production unit can develop online courses using these techniques. It can also develop workshop programs based on these techniques. Furthermore, it can develop internship programs that apply these techniques. This allows for the provision of diverse educational programs based on these techniques, offering learning opportunities tailored to the needs of learners.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis unit analyzes and records the craftsman's skills in detail. For example, high-precision sensors can be used to analyze and record the craftsman's movements and decisions in detail, capturing even the minute details of their actions and the basis for their decisions. Step 2: The generation unit generates interactive 3D educational content based on the data collected by the analysis unit. For example, it generates interactive 3D educational content utilizing VR / AR technology based on the data collected using the generation AI, and the generation AI analyzes the collected data and generates the content automatically. Step 3: The learning unit provides an immersive learning experience using the content generated by the generation unit. For example, it can provide learners with an immersive learning experience using a VR headset and offer personalized learning programs tailored to the learners' progress and characteristics. Step 4: The feedback unit analyzes the learner's actions and provides feedback in real time. For example, the AI ​​can analyze the learner's actions and provide immediate advice, or it can monitor the learner's actions in real time to provide appropriate feedback. Step 5: The Community Department will build an online craftsman community. For example, they can build an online craftsman community that promotes interaction between craftsmen and learners, and among learners themselves, by facilitating interaction through forums, chat functions, and event hosting.

[0069] (Example of form 2) The traditional technology preservation system according to an embodiment of the present invention is a system that utilizes generative AI to automatically generate interactive educational content for preserving and passing on to the next generation the knowledge and skills of Japanese traditional crafts, traditional skills, and Living National Treasures. The traditional technology preservation system analyzes and records the skills of master craftsmen in detail using high-precision sensors and AI, and the generative AI automatically generates interactive 3D educational content based on the collected data. This content provides an immersive learning experience utilizing VR / AR technology and proposes a personalized learning program according to the learner's progress and characteristics. In addition, a real-time feedback system allows the AI ​​to analyze the learner's actions and provide immediate advice. Furthermore, it builds an online community of master craftsmen to promote interaction between master craftsmen and learners, and among learners themselves. This promotes the preservation and transmission of valuable traditional technologies and contributes to the creation of new value and industrial promotion of traditional crafts. For example, the traditional technology preservation system analyzes and records the actions and judgments of master craftsmen in detail using high-precision sensors. Next, the generative AI generates interactive 3D educational content based on the collected data. This content provides an immersive learning experience utilizing VR / AR technology and proposes a personalized learning program according to the learner's progress and characteristics. Furthermore, a real-time feedback system allows AI to analyze learners' actions and provide immediate advice. In addition, an online community of master craftsmen is built to promote interaction between craftsmen and learners, and among learners themselves. This promotes the preservation and transmission of valuable traditional techniques, contributing to the creation of new value and industrial development in traditional crafts. Thus, the traditional technique preservation system can promote the preservation and transmission of valuable traditional techniques, contributing to the creation of new value and industrial development in traditional crafts.

[0070] The traditional technology preservation system according to this embodiment comprises an analysis unit, a generation unit, a learning unit, a feedback unit, and a community unit. The analysis unit analyzes and records the skills of artisans in detail. The analysis unit, for example, uses high-precision sensors to analyze and record the movements and judgments of artisans in detail. The analysis unit can record in detail the minute details of the artisans' movements and the basis for their judgments. The generation unit generates interactive 3D educational content based on the collected data. The generation unit, for example, uses a generation AI to generate interactive 3D educational content utilizing VR / AR technology based on the collected data. The generation unit uses a generation AI to analyze the collected data and automatically generates interactive 3D educational content. The learning unit provides an immersive learning experience using the generated content. The learning unit provides an immersive learning experience to learners using a VR headset, for example. The learning unit can provide personalized learning programs according to the learner's progress and characteristics. The feedback unit analyzes the learner's movements and provides feedback in real time. The feedback unit, for example, uses AI to analyze the learner's movements and provides immediate advice. The feedback unit can monitor learners' actions in real time and provide appropriate feedback. The community unit builds an online craftsman community. The community unit builds an online craftsman community that promotes interaction between craftsmen and learners, and among learners themselves. The community unit can promote interaction between craftsmen and learners, and among learners themselves, through forums, chat functions, and event hosting. As a result, the traditional technology preservation system according to this embodiment can provide an immersive learning experience by analyzing and recording craftsmanship in detail and generating interactive 3D educational content, thereby promoting the preservation and transmission of traditional technology.

[0071] The analysis department meticulously analyzes and records the skills of master craftsmen. Specifically, it uses high-precision sensors to analyze and record the movements and decisions of the craftsmen in detail. For example, a motion capture system can be used to record the movements of the craftsmen's hands and body posture in milliseconds. This allows for the accurate capture of the subtle details and flow of the craftsmen's movements. In addition, to record the basis of the craftsmen's decisions, speech recognition technology can be used to transcribe the craftsmen's explanations and instructions into text and record them in sync with the movements. Furthermore, detailed data on the craftsmen's working environment is also collected. For example, environmental data such as temperature, humidity, and light intensity are measured with sensors to meticulously record the conditions under which the craftsmen's skills are performed. This allows for the understanding of the environmental conditions necessary to reproduce the craftsmen's skills. The analysis department centrally manages this data and makes it available for subsequent analysis and content generation. The data is stored on a cloud server and can be accessed as needed. In this way, the analysis department can meticulously and accurately record the skills of master craftsmen and provide a foundation for passing them on to future generations.

[0072] The generation unit generates interactive 3D educational content based on collected data. Specifically, it uses a generation AI to analyze the collected data and generate interactive 3D educational content utilizing VR / AR technology. The generation AI analyzes the movement data and environmental data of the craftsman to generate realistic 3D models. For example, it generates a 3D avatar that reproduces the hand movements and body posture of the craftsman, and makes its movements interactively controllable. It also generates a virtual space that reproduces the craftsman's work environment, allowing learners to move around freely and learn within it. Furthermore, the generation AI generates scenarios based on the rationale behind the craftsman's decisions, enabling learners to learn while actually experiencing the craftsman's techniques. For example, it simulates what kind of decisions the craftsman would make in what situations, allowing learners to experience those decisions. The generation unit automatically generates this content and provides it to learners. In this way, the generation unit can provide educational content that allows learners to learn the craftsman's techniques interactively and provide an environment where learners can learn while actually experiencing them.

[0073] The learning department provides an immersive learning experience using generated content. Specifically, it provides learners with an immersive learning experience using VR headsets. By wearing a VR headset, learners can immerse themselves in a virtual space where they can realistically experience the skills of master craftsmen. The learning department provides personalized learning programs according to the learner's progress and characteristics. For example, it analyzes the learner's movement data and learning history to understand their strengths and weaknesses. Based on this, it provides learners with the most suitable learning content and practice tasks. Furthermore, the learning department provides learning programs that incorporate game elements to maintain learners' motivation. For example, it introduces a system where learners can earn points each time they complete a specific task, and rewards are provided according to their learning progress. This allows learners to learn while having fun. Through these functions, the learning department can provide learners with an effective and enjoyable learning experience and support them in acquiring the skills of master craftsmen.

[0074] The feedback unit analyzes the learner's actions and provides real-time feedback. Specifically, the AI ​​analyzes the learner's actions and provides immediate advice. For example, when a learner is practicing a master's technique in a VR space, the AI ​​monitors the learner's actions in real time and evaluates the accuracy and speed of the movements. If the learner is performing the movements correctly, positive feedback is provided; if the movements are inaccurate, specific areas for improvement are pointed out. Feedback is provided in various formats, including voice, text, and visual guidelines. For example, if a learner makes a mistake in hand movements, visual guidelines are displayed to show the correct movement. Specific advice is also provided via voice to make it easier for the learner to understand. Furthermore, the feedback unit records the learner's progress and visualizes their growth by comparing it with past feedback. This allows learners to feel their own progress and maintain their motivation. Through these functions, the feedback unit can provide effective feedback to learners and support them in acquiring master's techniques.

[0075] The Community Department will build an online community for master craftsmen. Specifically, it will build an online community for master craftsmen that promotes interaction between master craftsmen and learners, and among learners themselves. The Community Department will promote interaction between master craftsmen and learners, and among learners themselves, through forums, chat functions, and the hosting of events. For example, in the forum, master craftsmen can answer questions about their techniques, and learners can exchange information with each other. Real-time communication is also possible using the chat function. Furthermore, the Community Department will host online events and workshops to provide opportunities for master craftsmen and learners to interact directly. This will allow learners to receive direct instruction from master craftsmen and learn together with other learners. In addition, the Community Department will provide a platform for learners to share their achievements, allowing learners to share their growth with other members. This will allow learners to inspire each other and increase their motivation to learn. Through these functions, the Community Department can support the preservation and transmission of master craftsmanship and build a community where learners can learn from one another.

[0076] The analysis unit can analyze and record in detail the movements and decisions of the craftsman using high-precision sensors. For example, the analysis unit can record the movements of the craftsman in detail using high-precision sensors. The analysis unit can capture and record even the minute details of the craftsman's movements. The analysis unit can also record in detail the basis for the craftsman's decisions. For example, the analysis unit can record in detail what decisions the craftsman made and the reasoning behind them. In this way, by using high-precision sensors, the movements and decisions of the craftsman can be analyzed and recorded in detail. High-precision sensors include, but are not limited to, accelerometers and gyroscopes. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data acquired by high-precision sensors into a generating AI and have the generating AI perform a detailed analysis of the craftsman's movements and decisions.

[0077] The generation unit can generate interactive 3D educational content using VR / AR technology based on the collected data. For example, the generation unit generates interactive 3D educational content using a generation AI based on the collected data. The generation unit's generation AI analyzes the collected data and automatically generates interactive 3D educational content using VR / AR technology. For example, the generation unit's generation AI generates interactive 3D educational content based on the collected data. This makes it possible to generate interactive 3D educational content by utilizing VR / AR technology. VR / AR technology includes, but is not limited to, VR headsets and AR glasses. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input collected data into a generation AI and have the generation AI perform the generation of interactive 3D educational content.

[0078] The learning unit can provide personalized learning programs according to the learner's progress and characteristics. For example, the learning unit can provide personalized learning programs based on the learner's progress data. The learning unit can analyze the learner's characteristics and provide appropriate learning programs. For example, the learning unit can monitor the learner's progress in real time and provide learning programs according to that progress. The learning unit can also analyze the learner's characteristics and provide learning programs according to those characteristics. For example, the learning unit can analyze the learner's characteristics and provide learning programs according to those characteristics. This enables effective learning by providing personalized learning programs according to the learner's progress and characteristics. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input learner progress data into a generating AI and have the generating AI perform the task of providing personalized learning programs.

[0079] The feedback unit can analyze the learner's actions and provide immediate advice. For example, the feedback unit can monitor the learner's actions in real time and provide immediate advice. The feedback unit can analyze the learner's actions and provide appropriate advice. For example, the feedback unit can monitor the learner's actions in real time and point out areas for improvement. The feedback unit can also analyze the learner's actions and provide immediate advice. For example, the feedback unit can analyze the learner's actions and provide immediate advice. This allows for improved learning effectiveness by analyzing the learner's actions and providing immediate advice. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learner action data into a generating AI and have the generating AI perform action analysis and provide advice.

[0080] The Community Department can build an online community of master craftsmen that promotes interaction between master craftsmen and learners, and among learners themselves. For example, the Community Department can build an online community of master craftsmen to promote interaction between master craftsmen and learners, and among learners themselves. The Community Department can promote interaction between master craftsmen and learners, and among learners themselves, through forums, chat functions, and event hosting. For example, the Community Department can build an online forum to provide a space for master craftsmen and learners, and among learners themselves, to exchange opinions and share information. The Community Department can also provide a chat function to promote real-time communication. Furthermore, the Community Department can host online events to deepen interaction between master craftsmen and learners, and among learners themselves. For example, the Community Department can host online workshops and seminars to provide opportunities to learn the skills and knowledge of master craftsmen. In this way, by building an online community of master craftsmen, interaction between master craftsmen and learners, and among learners themselves, can be promoted. Some or all of the above processes in the Community Department may be performed using AI, for example, or not. For example, the Community Department can have a generative AI run the online forum and plan events.

[0081] The analysis unit can estimate the emotions of the craftsman and adjust the recording method of their actions based on the estimated emotions. For example, the analysis unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the craftsman's facial expressions. The analysis unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the craftsman's voice and calculate an emotion score. The analysis unit can also collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in the craftsman's heart rate. This allows for more appropriate recording by adjusting the recording method of actions based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 without AI. For example, the analysis unit can input the craftsman's emotional data into a generating AI and have the generating AI adjust the method of recording the actions.

[0082] The analysis unit can analyze the craftsman's past movement history and select the optimal recording method. For example, the analysis unit can analyze patterns of movements the craftsman has performed in the past and select an efficient recording method. The analysis unit can select a method to prioritize recording important movements from the craftsman's past movement history. For example, the analysis unit prioritizes recording important movements based on the craftsman's past movement history. The analysis unit can also select a recording method to avoid repeating movements based on the craftsman's movement history. For example, the analysis unit analyzes the craftsman's movement history and selects a recording method to avoid repeating movements. In this way, the optimal recording method can be selected by analyzing the craftsman's past movement history. The optimal recording method includes, but is not limited to, the type of movement and the accuracy of the recording. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the craftsman's past movement history data into a generating AI and have the generating AI select the optimal recording method.

[0083] The analysis unit can filter the recording of actions based on the craftsman's current work environment and the tools they use. For example, the analysis unit can adjust the level of detail of the actions to be recorded depending on the type of tools the craftsman uses. The analysis unit can optimize the recording method based on the craftsman's work environment (indoors / outdoors, brightness, etc.). For example, the analysis unit optimizes the recording method based on the craftsman's work environment. The analysis unit can also highlight and record important parts of actions based on the characteristics of the tools the craftsman uses. For example, the analysis unit highlights and records important parts of actions based on the characteristics of the tools the craftsman uses. This allows for more appropriate recording by filtering based on the craftsman's work environment and the tools they use. The work environment and tools used include, but are not limited to, the conditions of the work location and the types of tools used. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data on the craftsman's work environment and tools used into a generating AI and have the generating AI perform the filtering.

[0084] The analysis unit can estimate the emotions of the craftsman and determine the priority of actions to record based on the estimated emotions. For example, the analysis unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in the craftsman's facial expressions. The analysis unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the craftsman's voice and calculate an emotion score. The analysis unit can also collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in the craftsman's heart rate. This allows important actions to be recorded preferentially by determining the priority of actions to record based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 without AI. For example, the analysis unit can input the craftsman's emotional data into a generating AI and have the generating AI determine the priority of actions.

[0085] The analysis unit can prioritize recording highly relevant actions by considering the geographical location information of the craftsman when recording actions. For example, the analysis unit can prioritize recording actions performed by the craftsman in a specific region. The analysis unit can record region-specific actions based on the geographical location information of the craftsman. For example, the analysis unit can record region-specific actions based on the geographical location information of the craftsman. The analysis unit can also record actions performed by the craftsman in real time while they are moving. For example, the analysis unit can record actions performed by the craftsman in real time while they are moving. This allows for the priority recording of highly relevant actions by considering the geographical location information of the craftsman. Geographical location information includes, but is not limited to, the method of acquiring location information and the criteria for highly relevant actions. 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 geographical location information data of the craftsman into a generating AI and have the generating AI record highly relevant actions.

[0086] The analysis unit can analyze the craftsman's social media activities when recording actions and record relevant actions. For example, the analysis unit can prioritize recording actions shared by the craftsman on social media. The analysis unit can record actions that are attracting attention from the craftsman's social media activities. For example, the analysis unit can analyze the craftsman's social media activities and record actions that are attracting attention. The analysis unit can also record in detail the actions that the craftsman has introduced on social media. For example, the analysis unit can record in detail the actions that the craftsman has introduced on social media. This allows for the recording of relevant actions by analyzing the craftsman's social media activities. Social media activities include, but are not limited to, the analysis of posts and methods for extracting highly relevant actions. 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 craftsman's social media activity data into a generating AI and have the generating AI record relevant actions.

[0087] The generation unit can estimate the emotions of the craftsman and adjust the way the content is presented based on the estimated emotions. For example, the generation unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in the craftsman's facial expressions. The generation unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the craftsman's voice and calculate an emotion score. The generation unit can also collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in the craftsman's heart rate. By adjusting the way the content is presented based on the craftsman's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the craftsman's emotional data into a generation AI and have the generation AI adjust the way the content is expressed.

[0088] The generation unit can adjust the level of detail of content based on the importance of the actions during content generation. For example, the generation unit can generate content with detailed explanations and videos for important actions. For general actions, the generation unit can generate content with concise explanations and videos. For example, the generation unit can adjust the level of detail in stages according to the importance of the actions. The generation unit can also generate content with detailed explanations and videos based on the importance of the actions. For example, the generation unit can generate content with detailed explanations and videos for important actions. This allows important actions to be represented in detail by adjusting the level of detail of the content based on the importance of the actions. The importance of an action includes, but is not limited to, the degree of impact and frequency of the action. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input action importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0089] The generation unit can apply different generation algorithms depending on the category of action when generating content. For example, in the case of woodworking, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the wood. In the case of pottery, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the clay. For example, in the case of dyeing, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the dye. The generation unit can also apply different generation algorithms depending on the category of action. For example, in the case of woodworking, the generation unit can apply a generation algorithm that is appropriate to the characteristics of the wood. By applying different generation algorithms depending on the category of action, more appropriate content can be generated. The category of action includes, but is not limited to, the type and purpose of the action. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input action category data into a generation AI and cause the generation AI to apply different generation algorithms.

[0090] The generation unit can estimate the emotions of the craftsman and adjust the length of the content based on the estimated emotions. For example, the generation unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in the craftsman's facial expressions. The generation unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the craftsman's voice and calculate an emotion score. The generation unit can also collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in the craftsman's heart rate. This allows for the generation of more appropriate content by adjusting the length of the content based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the craftsman's emotional data into a generation AI and have the generation AI adjust the length of the content.

[0091] The generation unit can determine content priorities based on the recording date of actions when generating content. For example, the generation unit may prioritize recently recorded actions as content. The generation unit may also prioritize actions related to seasons or events as content. For example, the generation unit may prioritize important actions as content based on the craftsman's schedule. The generation unit can also determine content priorities based on the recording date of actions. For example, the generation unit may prioritize recently recorded actions as content. This allows for prioritizing important actions as content by determining content priorities based on the recording date of actions. The recording date of actions includes, but is not limited to, the timing and importance of the recording. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input action recording date data into a generation AI and have the generation AI perform the content prioritization.

[0092] The generation unit can adjust the order of content based on the relevance of actions when generating content. For example, the generation unit can generate content that displays related actions in sequence. The generation unit can generate content in an order that follows the flow of actions. For example, the generation unit can generate content in a visually easy-to-understand order based on the relevance of actions. The generation unit can also adjust the order of content based on the relevance of actions. For example, the generation unit can generate content that displays related actions in sequence. By adjusting the order of content based on the relevance of actions, it is possible to generate visually easy-to-understand content. Relevance of actions includes, but is not limited to, the continuity of actions or the criteria for related actions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input action relevance data into a generation AI and have the generation AI perform the adjustment of the order of content.

[0093] The generation unit can adjust the order of content based on the relevance of actions when generating content. For example, the generation unit can generate content that displays related actions in sequence. The generation unit can generate content in an order that follows the flow of actions. For example, the generation unit can generate content in a visually easy-to-understand order based on the relevance of actions. The generation unit can also adjust the order of content based on the relevance of actions. For example, the generation unit can generate content that displays related actions in sequence. By adjusting the order of content based on the relevance of actions, it is possible to generate visually easy-to-understand content. Relevance of actions includes, but is not limited to, the continuity of actions or the criteria for related actions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input action relevance data into a generation AI and have the generation AI perform the adjustment of the order of content.

[0094] The learning unit can estimate the learner's emotions and adjust the content of the learning program based on the estimated emotions. For example, the learning unit can capture the learner's facial expressions with a camera and estimate the learner's emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on changes in the learner's facial expressions. The learning unit can also record the learner's voice and estimate the learner's emotions using voice analysis technology. For example, the learning unit can analyze the tone and speed of the learner's voice and calculate an emotion score. The learning unit can also collect the learner's biometric data (heart rate and skin electrical activity) with sensors and estimate the learner's emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on fluctuations in the learner's heart rate. This allows for more effective learning by adjusting the content of the learning program based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input learner emotion data into a generating AI and have the generating AI adjust the content of the learning program.

[0095] The learning unit can select the optimal program by referring to the learner's past learning history when providing a learning program. For example, the learning unit can select an effective learning program from the learner's past learning history. The learning unit can provide a learning program that suggests the next step based on what the learner has learned in the past. For example, the learning unit can analyze the learner's learning history and provide a program that matches their learning progress. The learning unit can also provide the optimal learning program by referring to the learner's past learning history. For example, the learning unit can select the optimal learning program based on the learner's past learning history. In this way, the optimal learning program can be provided by referring to the learner's past learning history. Past learning history includes, but is not limited to, the method of acquiring historical data and the criteria for selecting the optimal program. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the learner's past learning history data into a generating AI and have the generating AI select the optimal program.

[0096] The learning unit can customize learning programs based on the learner's current skill level when providing them. For example, the learning unit can provide programs ranging from basic to advanced levels, depending on the learner's skill level. The learning unit can also assess the learner's skill level and provide programs of appropriate difficulty. For example, the learning unit can assess the learner's skill level and provide programs of appropriate difficulty. The learning unit can also provide individually customized programs based on the learner's skill level. For example, the learning unit can provide individually customized programs based on the learner's skill level. This allows for more appropriate learning by customizing programs based on the learner's skill level. Current skill level includes, but is not limited to, skill assessment tests and past learning outcomes. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input learner skill level data into a generating AI and have the generating AI perform program customization.

[0097] The learning unit can estimate the learner's emotions and prioritize learning programs based on the estimated emotions. For example, the learning unit can capture the learner's facial expressions with a camera and estimate the learner's emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on changes in the learner's facial expressions. The learning unit can also record the learner's voice and estimate the learner's emotions using voice analysis technology. For example, the learning unit can analyze the tone and speed of the learner's voice and calculate an emotion score. The learning unit can also collect the learner's biometric data (heart rate and skin electrical activity) with sensors and estimate the learner's emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on fluctuations in the learner's heart rate. This allows the learning unit to prioritize learning programs based on the learner's emotions, thereby providing important learning content preferentially. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input learner emotion data into a generating AI and have the generating AI determine the priorities of the learning program.

[0098] The learning unit can select the most suitable program when providing learning programs, taking into account the learner's geographical location information. For example, the learning unit can provide a program that teaches region-specific technologies based on the learner's geographical location information. The learning unit can select a program that provides the most suitable learning environment, taking into account the learner's geographical location information. For example, the learning unit can provide a program that promotes interaction with local artisans based on the learner's geographical location information. The learning unit can also provide the most suitable learning program, taking into account the learner's geographical location information. For example, the learning unit can provide a program that teaches region-specific technologies based on the learner's geographical location information. This makes it possible to provide the most suitable program for learning region-specific technologies by taking into account the learner's geographical location information. Geographical location information includes, but is not limited to, the method of acquiring location information and the standards for region-specific technologies. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the learner's geographical location information data into a generating AI and have the generating AI select the most suitable program.

[0099] The learning department can analyze learners' social media activity and propose programs when providing learning programs. For example, the learning department can propose programs that teach technologies of interest based on the learner's social media activity. The learning department can analyze learners' social media activity and provide programs that teach relevant technologies. For example, the learning department can propose programs tailored to the learner's interests based on the learner's social media activity. The learning department can also analyze learners' social media activity and propose programs. For example, the learning department can propose programs that teach technologies of interest based on the learner's social media activity. In this way, by analyzing learners' social media activity, it is possible to provide programs tailored to the learner's interests. Social media activity includes, but is not limited to, the analysis of posted content and methods for extracting highly relevant programs. Some or all of the above processing in the learning department may be performed using AI, for example, or not using AI. For example, the learning department can input learner's social media activity data into a generating AI and have the generating AI execute program proposals.

[0100] The feedback unit can estimate the learner's emotions and adjust the content of the feedback based on the estimated emotions. For example, the feedback unit can capture the learner's facial expressions with a camera and estimate the learner's emotions using an emotion estimation algorithm. For example, the feedback unit can calculate an emotion score based on changes in the learner's facial expressions. The feedback unit can also record the learner's voice and estimate the learner's emotions using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the learner's voice and calculate an emotion score. The feedback unit can also collect the learner's biometric data (heart rate and skin electrical activity) with sensors and estimate the learner's emotions using an emotion estimation algorithm. For example, the feedback unit can calculate an emotion score based on fluctuations in the learner's heart rate. This allows for the provision of more appropriate feedback by adjusting the content of the feedback based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learner's emotional data into a generating AI and have the generating AI adjust the content of the feedback.

[0101] The feedback unit can provide optimal feedback by referring to the learner's past action history when providing feedback. For example, the feedback unit can provide feedback that points out areas for improvement based on the learner's past action history. The feedback unit can provide positive feedback based on successful examples of actions the learner has performed in the past. For example, the feedback unit can analyze the learner's action history and provide feedback to help them move to the next step. The feedback unit can also provide optimal feedback by referring to the learner's past action history. For example, the feedback unit can provide feedback that points out areas for improvement based on the learner's past action history. In this way, the feedback unit can provide optimal feedback by referring to the learner's past action history. Past action history includes, but is not limited to, the method of acquiring history data and the criteria for selecting optimal feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the learner's past action history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0102] The feedback unit can customize the feedback provided based on the learner's current learning status. For example, the feedback unit can provide feedback that points out specific areas for improvement according to the learner's current learning status. The feedback unit can provide feedback to help the learner move on to the next step based on their progress. For example, the feedback unit can evaluate the learner's learning status and provide individually customized feedback. The feedback unit can also customize the feedback based on the learner's current learning status. For example, the feedback unit can provide feedback that points out specific areas for improvement according to the learner's current learning status. By customizing the feedback based on the learner's current learning status, more appropriate feedback can be provided. The current learning status includes, but is not limited to, methods for evaluating learning progress and methods for acquiring learning status data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the learner's current learning status data into a generating AI and have the generating AI perform the customization of the feedback.

[0103] The feedback unit can estimate the learner's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit can capture the learner's facial expressions with a camera and estimate the learner's emotions using an emotion estimation algorithm. For example, the feedback unit can calculate an emotion score based on changes in the learner's facial expressions. The feedback unit can also record the learner's voice and estimate the learner's emotions using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the learner's voice and calculate an emotion score. The feedback unit can also collect the learner's biometric data (heart rate and skin electrical activity) with sensors and estimate the learner's emotions using an emotion estimation algorithm. For example, the feedback unit can calculate an emotion score based on fluctuations in the learner's heart rate. This allows for prioritizing feedback based on the learner's emotions, thereby providing important feedback preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input learner emotion data into a generating AI and have the generating AI determine the priority of the feedback.

[0104] The feedback unit can provide optimal feedback by considering the learner's geographical location information when providing feedback. For example, the feedback unit can provide feedback on region-specific technologies based on the learner's geographical location information. The feedback unit can also provide feedback on the optimal learning environment by considering the learner's geographical location information. For example, the feedback unit can provide feedback that promotes interaction with local artisans based on the learner's geographical location information. Furthermore, the feedback unit can also provide optimal feedback by considering the learner's geographical location information. For example, the feedback unit can provide feedback on region-specific technologies based on the learner's geographical location information. This allows for the provision of optimal feedback on region-specific technologies by considering the learner's geographical location information. Geographical location information includes, but is not limited to, the method of acquiring location information and the standards for region-specific technologies. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the learner's geographical location information data into a generating AI and have the generating AI perform the provision of optimal feedback.

[0105] The feedback unit can analyze the learner's social media activity and propose feedback when providing feedback. For example, the feedback unit can provide feedback on technologies of interest based on the learner's social media activity. The feedback unit can analyze the learner's social media activity and provide feedback on relevant technologies. For example, the feedback unit can provide feedback tailored to the learner's interests based on the learner's social media activity. The feedback unit can also analyze the learner's social media activity and propose feedback. For example, the feedback unit can provide feedback on technologies of interest based on the learner's social media activity. This allows the feedback unit to provide feedback tailored to the learner's interests by analyzing the learner's social media activity. Social media activity includes, but is not limited to, the analysis of posted content and methods for extracting highly relevant feedback. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not using AI. For example, the feedback unit can input the learner's social media activity data into a generating AI and have the generating AI execute the feedback proposal.

[0106] The community unit can estimate the emotions of artisans and learners and adjust the community's interaction methods based on the estimated emotions. For example, the community unit can capture the facial expressions of artisans and learners with a camera and estimate their emotions using an emotion estimation algorithm. For example, the community unit can calculate an emotion score based on changes in the facial expressions of artisans and learners. The community unit can also record the voices of artisans and learners and estimate their emotions using voice analysis technology. For example, the community unit can analyze the tone and speed of the voices of artisans and learners and calculate an emotion score. The community unit can also collect biometric data (heart rate and skin electrical activity) of artisans and learners with sensors and estimate their emotions using an emotion estimation algorithm. For example, the community unit can calculate an emotion score based on fluctuations in the heart rate of artisans and learners. This allows for more appropriate interaction by adjusting the community's interaction methods based on the emotions of artisans and learners. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the community department may be performed using AI, for example, or without AI. For example, the community department can input emotional data of artisans and learners into a generating AI and have the generating AI adjust the interaction methods.

[0107] The Community Department can select the optimal interaction method by referring to the past interaction history of artisans and learners when building a community. For example, the Community Department can select an effective interaction method from the past interaction history of artisans and learners. The Community Department can provide the optimal interaction method based on successful past interactions of artisans and learners. For example, the Community Department can analyze the interaction history of artisans and learners and provide an interaction method to move to the next step. The Community Department can also provide the optimal interaction method by referring to the past interaction history of artisans and learners. For example, the Community Department can select an effective interaction method from the past interaction history of artisans and learners. This allows the Community Department to provide the optimal interaction method by referring to the past interaction history of artisans and learners. Past interaction history includes, but is not limited to, the method of acquiring historical data and the criteria for selecting the optimal interaction method. Some or all of the above processing in the Community Department may be performed using AI, for example, or not using AI. For example, the Community Department can input the past interaction history data of artisans and learners into a generating AI and have the generating AI select the optimal interaction method.

[0108] The Community Department can customize the means of interaction based on the current interests of artisans and learners when building a community. For example, the Community Department can provide interaction on relevant themes according to the current interests of artisans and learners. The Community Department can evaluate the interests of artisans and learners and provide appropriate means of interaction. For example, the Community Department can provide individually customized means of interaction based on the interests of artisans and learners. The Community Department can also customize the means of interaction based on the current interests of artisans and learners. For example, the Community Department can provide interaction on relevant themes according to the current interests of artisans and learners. This makes it possible to have more appropriate interactions by customizing the means of interaction based on the current interests of artisans and learners. Current interests include, but are not limited to, survey results and social media posts. Some or all of the above processing in the Community Department may be performed using, for example, AI, or not using AI. For example, the Community Department can input data on the interests of artisans and learners into a generating AI and have the generating AI perform the customization of the means of interaction.

[0109] The community unit can estimate the emotions of artisans and learners and determine community priorities based on the estimated emotions. For example, the community unit can capture the facial expressions of artisans and learners with a camera and estimate their emotions using an emotion estimation algorithm. For example, the community unit can calculate an emotion score based on changes in the facial expressions of artisans and learners. The community unit can also record the voices of artisans and learners and estimate their emotions using voice analysis technology. For example, the community unit can analyze the tone and speed of the voices of artisans and learners and calculate an emotion score. The community unit can also collect biometric data (heart rate and skin electrical activity) of artisans and learners with sensors and estimate their emotions using an emotion estimation algorithm. For example, the community unit can calculate an emotion score based on fluctuations in the heart rate of artisans and learners. This allows for prioritizing important interactions by determining community priorities based on the emotions of artisans and learners. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the community department may be performed using AI, for example, or without AI. For example, the community department can input emotional data of artisans and learners into a generating AI and have the generating AI determine the priorities of the community.

[0110] The Community Department can select the optimal method of interaction when building a community, taking into account the geographical location information of artisans and learners. For example, the Community Department can provide exchanges on region-specific technologies based on the geographical location information of artisans and learners. The Community Department can provide an optimal environment for interaction, taking into account the geographical location information of artisans and learners. For example, the Community Department can promote interaction with local artisans based on the geographical location information of artisans and learners. The Community Department can also provide the optimal method of interaction, taking into account the geographical location information of artisans and learners. For example, the Community Department can provide exchanges on region-specific technologies based on the geographical location information of artisans and learners. This allows for the provision of the optimal method of interaction on region-specific technologies by considering the geographical location information of artisans and learners. Geographical location information includes, but is not limited to, methods for acquiring location information and standards for region-specific technologies. Some or all of the above processing in the Community Department may be performed using, for example, AI, or not using AI. For example, the Community Department can input geographical location data of artisans and learners into a generating AI and have the generating AI select the optimal method of interaction.

[0111] The Community Department can analyze the social media activities of artisans and learners during community building and propose means of interaction. For example, the Community Department can provide interaction related to technologies of interest based on the social media activities of artisans and learners. The Community Department can analyze the social media activities of artisans and learners and provide interaction related to relevant technologies. For example, the Community Department can provide interaction tailored to learners' interests based on the social media activities of artisans and learners. The Community Department can also analyze the social media activities of artisans and learners and propose means of interaction. For example, the Community Department can provide interaction related to technologies of interest based on the social media activities of artisans and learners. This allows for the provision of interaction tailored to learners' interests by analyzing the social media activities of artisans and learners. Social media activities include, but are not limited to, analysis of posted content and methods for extracting highly relevant means of interaction. Some or all of the above processing in the Community Department may be performed using, for example, AI, or not using AI. For example, the Community Department can input social media activity data of artisans and learners into a generating AI and have the generating AI propose means of interaction.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The traditional skills preservation system can also include an evaluation unit to assess the skills of artisans. This unit can quantitatively evaluate the artisans' skills and adjust the content of the learning program based on the evaluation results. For example, the evaluation unit can score the artisans' skills and provide learners with content at an appropriate level based on those scores. The evaluation unit can also monitor the progress of the artisans' skills and evaluate the degree of improvement. Furthermore, the evaluation unit can compare the artisans' skills with those of other artisans and conduct relative evaluations. This allows for an objective evaluation of the artisans' skills and improves the quality of the learning program.

[0114] The analysis department can compare traditional craftsmanship with techniques from other fields, identifying similarities and differences. For example, it can compare traditional craftsmanship with modern manufacturing techniques to highlight the advantages and areas for improvement of traditional techniques. It can also compare traditional craftsmanship with techniques from other countries, analyzing cultural differences and similarities. Furthermore, it can compare traditional craftsmanship with other artistic fields (such as music and dance) to explore potential applications of these techniques. This allows for a multifaceted analysis of traditional craftsmanship, uncovering new value.

[0115] The production unit can propose new designs and products based on the skills of master craftsmen. For example, the production unit can create handcrafted items with modern designs using the skills of master craftsmen. It can also propose new products (such as furniture and accessories) by applying the skills of master craftsmen. Furthermore, the production unit can generate digital art and interactive exhibits based on the skills of master craftsmen. This allows for the application of master craftsmanship to modern designs and products, creating new value.

[0116] The learning unit can estimate the learner's emotions and adjust the pace of the learning program based on those estimates. For example, if the learner is excited, the pace of the learning program can be increased. Conversely, if the learner is tired, the pace of the learning program can be slowed down. Furthermore, if the learner is focused, the pace of the learning program can be maintained. This allows the learning program's pace to be adjusted according to the learner's emotions, thereby promoting effective learning.

[0117] The feedback unit can estimate the learner's emotions and adjust the format of the feedback based on those estimates. For example, if the learner is feeling down, it can provide feedback that includes words of encouragement. If the learner is confident, it can provide feedback that points out specific areas for improvement. Furthermore, if the learner is anxious, it can provide feedback that includes advice on how to relax. This allows the feedback format to be adjusted according to the learner's emotions, resulting in more effective feedback.

[0118] The Community Department can estimate the emotions of artisans and learners and plan community events based on those estimates. For example, if artisans and learners are excited, they can plan active events such as workshops and seminars. If they are tired, they can plan relaxing social events. Furthermore, if they are focused, they can plan discussion events that delve deeper into the techniques. This allows for the planning of community events in accordance with the emotions of artisans and learners, promoting more effective interaction.

[0119] The Analysis Department can propose methods for integrating traditional craftsmanship with technologies from other fields. For example, it can propose methods for integrating traditional craftsmanship with modern robotics technology. It can also propose methods for integrating traditional craftsmanship with biotechnology. Furthermore, it can propose methods for integrating traditional craftsmanship with digital fabrication technology. This allows for the integration of traditional craftsmanship with technologies from other fields, creating new value.

[0120] The production unit can develop new educational programs based on the techniques of master craftsmen. For example, the production unit can develop online courses using these techniques. It can also develop workshop programs based on these techniques. Furthermore, it can develop internship programs that apply these techniques. This allows for the provision of diverse educational programs based on these techniques, offering learning opportunities tailored to the needs of learners.

[0121] The learning unit can estimate the learner's emotions and adjust the content of the learning program based on those estimates. For example, if the learner is excited, it can provide challenging tasks. If the learner is depressed, it can provide easier tasks. Furthermore, if the learner is focused, it can provide more in-depth content. This allows the learning program to be adjusted according to the learner's emotions, promoting effective learning.

[0122] The feedback unit can estimate the learner's emotions and adjust the timing of feedback based on those emotions. For example, if the learner is concentrating, feedback can be delayed. Conversely, if the learner is anxious, feedback can be provided immediately. Furthermore, if the learner is relaxed, feedback can be provided at an appropriate time. This allows for the timing of feedback to be adjusted according to the learner's emotions, enabling the provision of effective feedback.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The analysis unit analyzes and records the craftsman's skills in detail. For example, high-precision sensors can be used to analyze and record the craftsman's movements and decisions in detail, capturing even the minute details of their actions and the basis for their decisions. Step 2: The generation unit generates interactive 3D educational content based on the data collected by the analysis unit. For example, it generates interactive 3D educational content utilizing VR / AR technology based on the data collected using the generation AI, and the generation AI analyzes the collected data and generates the content automatically. Step 3: The learning unit provides an immersive learning experience using the content generated by the generation unit. For example, it can provide learners with an immersive learning experience using a VR headset and offer personalized learning programs tailored to the learners' progress and characteristics. Step 4: The feedback unit analyzes the learner's actions and provides feedback in real time. For example, the AI ​​can analyze the learner's actions and provide immediate advice, or it can monitor the learner's actions in real time to provide appropriate feedback. Step 5: The Community Department will build an online craftsman community. For example, they can build an online craftsman community that promotes interaction between craftsmen and learners, and among learners themselves, by facilitating interaction through forums, chat functions, and event hosting.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, generation unit, learning unit, feedback unit, and community unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the high-precision sensors of the smart device 14 to analyze and record in detail the actions and decisions of the craftsman. The generation unit generates interactive 3D educational content based on data collected by the specific processing unit 290 of the data processing unit 12. The learning unit provides an immersive learning experience using content generated by the control unit 46A of the smart device 14. The feedback unit analyzes the learner's actions using the specific processing unit 290 of the data processing unit 12 and provides real-time feedback. The community unit builds an online craftsman community using the control unit 46A of the smart device 14 and promotes interaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, generation unit, learning unit, feedback unit, and community unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the high-precision sensors of the smart glasses 214 to analyze and record in detail the actions and decisions of the craftsman. The generation unit generates interactive 3D educational content based on data collected by the specific processing unit 290 of the data processing unit 12. The learning unit provides an immersive learning experience using content generated by the control unit 46A of the smart glasses 214. The feedback unit analyzes the learner's actions using the specific processing unit 290 of the data processing unit 12 and provides real-time feedback. The community unit builds an online craftsman community using the control unit 46A of the smart glasses 214 and promotes interaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the analysis unit, generation unit, learning unit, feedback unit, and community unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the high-precision sensors of the headset terminal 314 to analyze and record the actions and decisions of the craftsman in detail. The generation unit generates interactive 3D educational content based on data collected by the specific processing unit 290 of the data processing unit 12. The learning unit provides an immersive learning experience using content generated by the control unit 46A of the headset terminal 314. The feedback unit analyzes the learner's actions using the specific processing unit 290 of the data processing unit 12 and provides real-time feedback. The community unit builds an online craftsman community using the control unit 46A of the headset terminal 314 and promotes interaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the analysis unit, generation unit, learning unit, feedback unit, and community unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit uses the high-precision sensors of the robot 414 to analyze and record in detail the movements and decisions of the craftsman. The generation unit generates interactive 3D educational content based on data collected by the specific processing unit 290 of the data processing unit 12. The learning unit provides an immersive learning experience using content generated by the control unit 46A of the robot 414. The feedback unit analyzes the learner's movements using the specific processing unit 290 of the data processing unit 12 and provides real-time feedback. The community unit builds an online craftsman community using the control unit 46A of the robot 414 and promotes interaction. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The analysis department meticulously analyzes and records the craftsmanship, A generation unit generates interactive 3D educational content based on the data collected by the aforementioned analysis unit, A learning unit that provides an immersive learning experience using content generated by the generation unit, A feedback unit that analyzes the learner's actions and provides real-time feedback, It includes a community department that builds an online community of skilled craftsmen. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Using high-precision sensors, the movements and decisions of skilled craftsmen are analyzed and recorded in detail. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the collected data, we generate interactive 3D educational content using VR / AR technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, We provide personalized learning programs tailored to the learner's progress and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Analyze learners' actions and provide immediate advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned community department, We will build an online community of master craftsmen to promote interaction between master craftsmen and learners, and among learners themselves. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate the emotions of the craftsman and adjust the method of recording their actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Analyze the craftsman's past work history and select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is When recording the movements, filtering is performed based on the craftsman's current work environment and the tools they use. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates the emotions of the craftsman and determines the priority of actions to record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When recording actions, the system prioritizes recording highly relevant actions by considering the craftsman's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When recording actions, the system analyzes the craftsman's social media activity and records relevant actions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the emotions of the craftsman and adjust the way the content is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating content, adjust the level of detail of the content based on the importance of the action. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating content, different generation algorithms are applied depending on the category of the action. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the emotions of the craftsman and adjusts the length of the content based on the estimated emotions of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating content, prioritize content based on when the actions were recorded. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating content, adjust the order of content based on the relevance of its actions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating content, adjust the order of content based on the relevance of its actions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, The system estimates learners' emotions and adjusts the content of the learning program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, When providing learning programs, the system selects the most suitable program by referring to the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, When providing a learning program, customize the program based on the learner's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, The system estimates learners' emotions and prioritizes learning programs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, When providing learning programs, the most suitable program is selected considering the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, When providing learning programs, we analyze learners' social media activity and propose programs accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is The system estimates the learner's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, refer to the learner's past behavior history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, customize the feedback based on the learner's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is The system estimates the learner's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we take the learner's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, analyze the learner's social media activity and suggest appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned community department, It estimates the emotions of artisans and learners, and adjusts community interaction methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned community department, When building a community, the optimal method of interaction is selected by referring to the past interaction history of the artisans and learners. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned community department, When building a community, customize the means of interaction based on the current interests of the artisans and learners. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned community department, It estimates the emotions of craftsmen and learners, and determines community priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned community department, When building a community, the optimal method of interaction is selected by considering the geographical location information of the artisans and learners. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned community department, When building a community, we analyze the social media activities of artisans and learners and propose ways for them to interact. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department meticulously analyzes and records the techniques of the master craftsmen, A generation unit generates interactive 3D educational content based on the data collected by the aforementioned analysis unit, A learning unit that provides an immersive learning experience using content generated by the generation unit, A feedback unit that analyzes the learner's actions and provides real-time feedback, It includes a community department that builds an online community of skilled craftsmen. A system characterized by the following features.

2. The aforementioned analysis unit is Using high-precision sensors, the movements and decisions of skilled craftsmen are analyzed and recorded in detail. The system according to feature 1.

3. The generating unit is Based on the collected data, we generate interactive 3D educational content using VR / AR technology. The system according to feature 1.

4. The aforementioned learning unit, We provide personalized learning programs tailored to the learner's progress and characteristics. The system according to feature 1.

5. The aforementioned feedback unit is Analyze learners' actions and provide immediate advice. The system according to feature 1.

6. The aforementioned community section, We will build an online community of master craftsmen to promote interaction between master craftsmen and learners, and among learners themselves. The system according to feature 1.

7. The aforementioned analysis unit is The system estimates the emotions of the craftsman and adjusts the method of recording their actions based on those estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is Analyze the craftsman's past work history and select the optimal recording method. The system according to feature 1.

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