system

Abstract

The system according to this embodiment aims to efficiently learn the techniques and knowledge of traditional crafts. [Solution] The system according to the embodiment comprises a learning unit, an instruction unit, and a learning support unit. The learning unit learns training data of craftsmanship. The instruction unit provides production instruction based on the data learned by the learning unit. The learning support unit learns the content instructed by the instruction unit experientially using VR / AR technology.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

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 efficiently learn the techniques and knowledge of traditional crafts, and it takes time to train young craftsmen.

[0005] The system according to the embodiment aims to efficiently learn the techniques and knowledge of traditional crafts.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning unit, an instruction unit, and a learning support unit. The learning unit learns training data of craftsman skills. The instruction unit gives production instructions based on the data learned by the learning unit. The learning support unit experientially learns the content instructed by the instruction unit using VR / AR technology.

Effects of the Invention

[0007] The system according to this embodiment allows for the efficient learning of traditional craft techniques and knowledge. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​agent system according to an embodiment of the present invention is a system that reproduces the techniques and knowledge of traditional crafts using AI and provides 24 / 7 support to young craftspeople, consumers, and local governments. This AI agent system learns from training data of craftspeople's skills and provides production guidance using image recognition and natural language processing. Furthermore, it supports experiential learning by utilizing VR and AR technologies. For example, the AI ​​agent system learns from training data of craftspeople's skills. For example, the AI ​​agent system learns from training data of craftspeople's skills using video data, sensor data, text data, etc. Next, the AI ​​agent system provides production guidance based on the learned data. For example, the AI ​​agent system analyzes the movements of craftspeople's skills using image recognition technology and generates instruction content using natural language processing technology. Furthermore, the AI ​​agent system supports experiential learning by utilizing VR and AR technologies. For example, the AI ​​agent system simulates craftspeople's skills using VR technology so that users can actually experience them. In addition, the AI ​​agent system provides interactive learning content of craftspeople's skills using AR technology. As a result, the AI ​​agent system simultaneously realizes the transmission of skills and the preservation of culture, and supports the training of young craftspeople, the improvement of consumer satisfaction, and the cultural preservation activities of local governments. This allows the AI ​​agent system to replicate the techniques and knowledge of traditional crafts and provide 24 / 7 support to young artisans, consumers, and local governments.

[0029] The AI ​​agent system according to this embodiment comprises a learning unit, an instruction unit, and a learning support unit. The learning unit learns training data of craftsmanship. The training data of craftsmanship includes, but is not limited to, video data, sensor data, and text data. The learning unit learns the training data of craftsmanship using, for example, machine learning or deep learning. The learning unit can also perform motion analysis of craftsmanship and generate learning data. The instruction unit provides production instruction based on the data learned by the learning unit. The instruction unit analyzes the motion of craftsmanship using, for example, image recognition technology and generates instruction content using natural language processing technology. The instruction unit performs image recognition using, for example, CNN (convolutional neural network) or object detection algorithms. The instruction unit can also generate instruction content using natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. The learning support unit learns the content instructed by the instruction unit experientially using VR / AR technology. The learning support unit simulates craftsmanship using, for example, VR technology, allowing the user to actually experience it. Furthermore, the Learning Support Department can also provide interactive learning content on craftsmanship using AR technology. For example, the Learning Support Department can enable users to experience and learn craftsmanship using VR headsets or AR glasses. This allows the AI ​​agent system according to the embodiment to learn from training data on craftsmanship, provide production guidance, and learn experientially.

[0030] The learning unit learns from training data of craftsmanship. This training data includes, but is not limited to, video data, sensor data, and text data. The learning unit learns from this training data using, for example, machine learning or deep learning. Specifically, video data records in detail the movements of the craftsman's hands and how they use tools, while sensor data quantifies the amount of force applied and the speed of movement. Text data verbalizes the craftsman's techniques and procedures. By integrating this data, the learning unit grasps the overall picture of the craftsmanship. The learning unit can also use this data to perform motion analysis of the craftsmanship and generate training data. For example, it can use a recurrent neural network (RNN), a type of deep learning, to learn patterns of consecutive movements and build a model that reproduces a series of movements in the craftsmanship. Furthermore, the learning unit can use the generated training data to simulate the movements of the craftsmanship and find the optimal movement pattern. This allows the learning unit to efficiently learn from the training data of the craftsmanship and perform highly accurate motion analysis. Furthermore, the learning unit can constantly adapt to the latest technologies and methods by updating and adding training data. For example, if a new craftsmanship technique is added, the system can quickly learn from that data and integrate it with existing data to improve the overall accuracy and flexibility of the system. This allows the learning unit to continuously learn from the training data of craftsmanship techniques and improve the overall performance of the system.

[0031] The instruction unit provides production guidance based on data learned by the learning unit. For example, the instruction unit analyzes the movements of craftsmen using image recognition technology and generates instructional content using natural language processing technology. Specifically, the instruction unit performs image recognition using CNN (Convolutional Neural Network) and object detection algorithms to analyze the craftsmen's movements in real time. For example, when a craftsman is carving wood, the instruction unit analyzes the movements of their hands and the use of tools in detail and provides accurate feedback. The instruction unit can also generate instructional content using natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. For example, based on the results of analyzing the craftsman's movements, it generates appropriate instructional content and presents the user with specific advice and areas for improvement. Furthermore, the instruction unit can respond to user questions and concerns in real time using speech recognition technology. This allows the instruction unit to provide individually customized instruction to users and support the acquisition of craftsmanship skills. In addition, the instruction unit can monitor the user's progress and provide feedback at the appropriate time. For example, if a user is repeatedly practicing a particular movement, the instruction unit evaluates the accuracy and speed of that movement and points out areas for improvement. Furthermore, the training department can adjust the instruction content according to the user's skill level and provide an optimal learning plan. This allows the training department to provide effective instruction to users and efficiently support the acquisition of craftsmanship skills.

[0032] The Learning Support Department allows users to learn the content taught by the Instruction Department experientially using VR / AR technology. For example, the Learning Support Department uses VR technology to simulate craftsmanship, allowing users to experience it firsthand. Specifically, users wearing VR headsets can practice craftsmanship in a virtual space. For instance, a user learning woodworking techniques can experience carving wood on a virtual workbench and receive realistic feedback. The Learning Support Department can also provide interactive learning content on craftsmanship using AR technology. For example, when a user wearing AR glasses learns craftsmanship in a real work environment, they can proceed with the work while referring to guidelines and animations displayed overlaid on their field of view. This allows users to experientially learn craftsmanship in a real work environment. Furthermore, the Learning Support Department can monitor user actions in real time and provide appropriate feedback. For example, when a user carves wood in a virtual space, the department evaluates the precision and speed of their actions and points out areas for improvement. The Learning Support Department can also record user progress and manage learning history. This allows users to check their own growth and set their next learning goals. Furthermore, the learning support department can provide an environment where multiple users can learn simultaneously, promoting collaborative learning and competition. For example, users can increase their motivation to learn by working together or competing in skills within a virtual space. In this way, the learning support department can provide users with an effective learning environment and support the acquisition of craftsmanship skills.

[0033] The instruction department can provide instruction using image recognition and natural language processing. For example, the instruction department can analyze the movements of craftsmanship using image recognition technology. For example, the instruction department can perform image recognition using a CNN (Convolutional Neural Network). The instruction department can also analyze the movements of craftsmanship using object detection algorithms. Furthermore, the instruction department can generate instructional content using natural language processing technology. For example, the instruction department can analyze text data using morphological analysis and generate instructional content. The instruction department can also analyze text data using grammatical analysis and generate instructional content. Furthermore, the instruction department can analyze text data using semantic analysis and generate instructional content. As a result, the accuracy of instruction is improved by using image recognition and natural language processing.

[0034] The Learning Support Department can support experiential learning using VR / AR technology. For example, the Learning Support Department can use VR technology to simulate craftsmanship and allow users to experience it firsthand. For example, the Learning Support Department can use a VR headset to allow users to experientially learn craftsmanship. The Learning Support Department can also use AR technology to provide interactive learning content about craftsmanship. For example, the Learning Support Department can use AR glasses to allow users to experientially learn craftsmanship. In this way, experiential learning becomes possible by using VR / AR technology. Some or all of the above processes in the Learning Support Department may be performed using, for example, generative AI, or not using generative AI. For example, the Learning Support Department can have a generative AI perform the generation of simulations using VR technology.

[0035] The Quality Control Department can perform quality control. For example, the Quality Control Department can evaluate the quality of manufactured goods. For example, the Quality Control Department can evaluate the quality of manufactured goods based on quality evaluation indicators. The Quality Control Department can also evaluate the quality of manufactured goods according to inspection procedures. Furthermore, the Quality Control Department can manage the quality of manufactured goods based on the results of the quality evaluation. For example, the Quality Control Department can record the results of the quality evaluation and set quality control standards. In this way, the quality of manufactured goods can be maintained through quality control. Some or all of the above processes in the Quality Control Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Quality Control Department can have a generative AI perform a quality evaluation based on quality evaluation indicators.

[0036] The real-time feedback unit can provide feedback in real time. For example, the real-time feedback unit can provide real-time feedback to a user during learning. For example, the real-time feedback unit can monitor the user's learning progress and provide feedback at the appropriate time. The real-time feedback unit can also provide feedback based on the user's learning content. Furthermore, the real-time feedback unit can provide feedback according to the user's learning progress. For example, the real-time feedback unit can evaluate the user's learning progress and provide appropriate feedback. By providing feedback in real time, the learning effect is improved. Some or all of the above processing in the real-time feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the real-time feedback unit can input the user's learning status into a generative AI and have the generative AI execute appropriate feedback.

[0037] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can refer to past learning data. For example, the learning unit can refer to the past learning database to analyze the user's strengths and weaknesses. The learning unit can also evaluate the progress of learning based on past learning data. Furthermore, the learning unit can adjust the number of learning iterations by referring to past learning data. Next, the learning unit optimizes the learning algorithm based on past learning data. For example, the learning unit adjusts hyperparameters to improve the accuracy of the learning algorithm. The learning unit can also improve the model to enhance the performance of the learning algorithm. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0038] The learning unit can perform detailed motion analysis of the craftsman's skills during the learning process and generate more precise learning data. For example, the learning unit can capture the craftsman's movements with a high-resolution camera and perform motion analysis. The learning unit can also measure the craftsman's movements using sensors and perform motion analysis. Furthermore, the learning unit can analyze the craftsman's movements using 3D scanning. Next, the learning unit generates more precise learning data based on the results of the motion analysis. For example, the learning unit generates high-resolution video data. The learning unit can also generate detailed sensor data. This allows for the generation of precise learning data by performing detailed motion analysis of the craftsman's skills. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the results of the motion analysis into a generative AI and have the generative AI generate precise learning data.

[0039] The learning unit can diversify the learning data during training by considering regional variations in craftsmanship. For example, the learning unit can analyze the characteristics of craftsmanship in each region and reflect them in the learning data. The learning unit can also compare the differences in craftsmanship in each region and diversify the learning data. Furthermore, the learning unit can incorporate the history of craftsmanship in each region into the learning data. This allows for diversification of the learning data by considering regional variations. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input the characteristics of craftsmanship in each region into a generative AI and have the generative AI perform the diversification of the learning data.

[0040] The learning unit can perform comparative learning with other traditional craft techniques during the learning process to clarify the commonalities and differences between the techniques. For example, the learning unit can refer to the learning data of other traditional craft techniques and analyze the commonalities. The learning unit can also refer to the learning data of other traditional craft techniques and analyze the differences. Furthermore, the learning unit can perform comparative learning with other traditional craft techniques to integrate the techniques. In this way, by performing comparative learning with other traditional craft techniques, the commonalities and differences between the techniques can be clarified. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the learning data of other traditional craft techniques into a generative AI and have the generative AI perform comparative learning.

[0041] The instruction department can customize the instruction content by emphasizing key points of the craftsmanship during instruction. For example, the instruction department can customize the instruction content by emphasizing key points of the craftsmanship. For example, the instruction department can customize the instruction content by emphasizing key points of the craftsmanship. The instruction department can also customize the instruction content by emphasizing the detailed movements of the craftsmanship. Furthermore, the instruction department can customize the instruction content by emphasizing the historical background of the craftsmanship. This enriches the instruction content by emphasizing key points of the craftsmanship. Some or all of the above processing in the instruction department may be performed using, for example, a generative AI, or not using a generative AI. For example, the instruction department can input key points of the craftsmanship into a generative AI and have the generative AI perform the customization of the instruction content.

[0042] The instruction unit can adjust the instruction content in real time according to the user's progress during instruction. For example, the instruction unit can monitor the user's progress in real time and adjust the instruction content. For example, the instruction unit can monitor the user's progress in real time and adjust the instruction content. The instruction unit can also change the instruction content in real time according to the user's progress. Furthermore, the instruction unit can analyze the user's progress and optimize the instruction content. This improves the effectiveness of instruction by adjusting the instruction content according to the user's progress. Some or all of the above processes in the instruction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the instruction unit can input user progress data into a generative AI and have the generative AI perform the adjustment of the instruction content.

[0043] The instruction department can provide instruction that includes the historical background and cultural significance of the craftsmanship. For example, the instruction department can provide instruction while explaining the historical background of the craftsmanship. For example, the instruction department can provide instruction while explaining the historical background of the craftsmanship. The instruction department can also provide instruction while explaining the cultural significance of the craftsmanship. Furthermore, the instruction department can provide instruction that integrates the history and culture of the craftsmanship. This enriches the content of instruction by including the historical background and cultural significance of the craftsmanship. Some or all of the above processing in the instruction department may be performed using, for example, a generative AI, or not using a generative AI. For example, the instruction department can input data on the historical background and cultural significance of the craftsmanship into a generative AI and have the generative AI generate the instruction content.

[0044] The training department can enrich its training content by referring to the success stories of other craftsmen. For example, the training department can provide training while introducing the success stories of other craftsmen. The training department can also analyze the success stories of other craftsmen and reflect them in its training content. Furthermore, the training department can customize the training content by referring to the success stories of other craftsmen. In this way, the training content is enriched by referring to the success stories of other craftsmen. Some or all of the above processes in the training department may be performed using, for example, a generative AI, or not using a generative AI. For example, the training department can input data on the success stories of other craftsmen into a generative AI and have the generative AI generate the training content.

[0045] The learning support unit can provide the optimal learning scenario by referring to the user's operation history during learning support. For example, the learning support unit can analyze the user's operation history and provide the optimal learning scenario. For example, the learning support unit can analyze the user's operation history and provide the optimal learning scenario. The learning support unit can also customize the learning scenario based on the user's operation history. Furthermore, the learning support unit can optimize the learning scenario by referring to the user's operation history. In this way, the optimal learning scenario can be provided by referring to the user's operation history. Some or all of the above processing in the learning support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning support unit can input user operation history data into a generative AI and have the generative AI execute the generation of the optimal learning scenario.

[0046] The learning support unit can provide a more realistic experience by recreating the actual work environment of the craftsman during learning support. For example, the learning support unit can recreate the work environment of the craftsman in VR to provide a realistic experience. For example, the learning support unit can recreate the work environment of the craftsman in VR to provide a realistic experience. The learning support unit can also recreate the work environment of the craftsman in AR to provide a realistic experience. Furthermore, the learning support unit can recreate the work environment of the craftsman in 3D model to provide a realistic experience. In this way, a more realistic experience is provided by recreating the actual work environment of the craftsman. Some or all of the above processing in the learning support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning support unit can input the work environment data of the craftsman into a generative AI and have the generative AI recreate the work environment.

[0047] The learning support unit can provide collaborative work experiences with other users during learning support, thereby promoting cooperative learning. For example, the learning support unit can provide VR / AR experiences in which users work together. For example, the learning support unit can provide VR / AR experiences in which users work together. The learning support unit can also provide VR / AR experiences in which users work together to solve problems. Furthermore, the learning support unit can provide VR / AR experiences in which users learn while communicating with other users. In this way, collaborative learning is promoted by providing collaborative work experiences with other users. Some or all of the above processing in the learning support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning support unit can input collaborative work data with other users into a generative AI and have the generative AI execute the generation of a collaborative work experience.

[0048] The Learning Support Department can promote the fusion of technologies by providing experiences that combine different traditional craft techniques during learning support. For example, the Learning Support Department can provide VR / AR experiences that combine different traditional craft techniques. For example, the Learning Support Department can provide VR / AR experiences that combine different traditional craft techniques. The Learning Support Department can also provide VR / AR experiences that allow users to experience the fusion of different traditional craft techniques. Furthermore, the Learning Support Department can provide VR / AR experiences that promote the discovery of new technologies by learning different traditional craft techniques. In this way, the fusion of technologies is promoted by providing experiences that combine different traditional craft techniques. Some or all of the above processing in the Learning Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Learning Support Department can input data on different traditional craft techniques into a generative AI and have the generative AI execute the generation of a technology fusion experience.

[0049] The Quality Control Department can optimize the quality evaluation algorithm by referring to past quality data during quality control. For example, the Quality Control Department can refer to past quality data. For example, the Quality Control Department can analyze past quality data and optimize the quality evaluation algorithm. The Quality Control Department can also adjust the quality evaluation algorithm based on past quality data. Furthermore, the Quality Control Department can improve the quality evaluation algorithm by referring to past quality data. As a result, the accuracy of the quality evaluation algorithm is improved by referring to past quality data. Some or all of the above processes in the Quality Control Department may be performed using, for example, a generative AI, or without using a generative AI. For example, the Quality Control Department can input past quality data into a generative AI and have the generative AI perform the optimization of the quality evaluation algorithm.

[0050] The Quality Control Department can perform quality evaluations by comparing the work of other craftsmen during quality control. For example, the Quality Control Department can perform quality evaluations by comparing the work of other craftsmen. For example, the Quality Control Department can perform quality evaluations by comparing the work of other craftsmen. The Quality Control Department can also perform quality evaluations by referring to the quality data of other craftsmen's works. Furthermore, the Quality Control Department can perform quality evaluations by referring to the evaluation criteria of other craftsmen's works. This improves the accuracy of quality evaluations by comparing the work of other craftsmen. Some or all of the above processes in the Quality Control Department may be performed using, for example, a generative AI, or without using a generative AI. For example, the Quality Control Department can input data of other craftsmen's works into a generative AI and have the generative AI perform the quality evaluation.

[0051] The real-time feedback unit can provide optimal feedback by referring to the user's past work history at the time of feedback. For example, the real-time feedback unit can analyze the user's past work history and provide optimal feedback. For example, the real-time feedback unit can analyze the user's past work history and provide optimal feedback. The real-time feedback unit can also customize the feedback content based on the user's past work history. Furthermore, the real-time feedback unit can optimize the feedback content by referring to the user's past work history. This allows the unit to provide optimal feedback by referring to the user's past work history. Some or all of the above processing in the real-time feedback unit may be performed using, for example, a generation AI, or without a generation AI. For example, the real-time feedback unit can input the user's past work history data into a generation AI and have the generation AI generate optimal feedback.

[0052] The real-time feedback unit can enrich its feedback by referring to the feedback content of other users. For example, the real-time feedback unit can enrich its feedback by referring to the feedback content of other users. The real-time feedback unit can also analyze the feedback content of other users and reflect it in its own feedback. Furthermore, the real-time feedback unit can customize its feedback by referring to the feedback content of other users. In this way, the feedback content is enriched by referring to the feedback content of other users. Some or all of the above processing in the real-time feedback unit may be performed using, for example, a generation AI, or without a generation AI. For example, the real-time feedback unit can input the feedback content data of other users into a generation AI and have the generation AI generate the feedback content.

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

[0054] The AI ​​agent system can further provide customized learning plans based on the user's individual learning style. For example, the learning unit analyzes the user's past learning history and performance data to identify the most effective way for the user to learn. The instruction unit adjusts the instruction content to match the user's learning style, using images and videos extensively for visual learners and providing audio guides for auditory learners. Furthermore, the learning support unit can customize the content of the VR / AR experience based on the user's learning style, providing an environment in which the user can learn most effectively. As a result, learning effectiveness is improved by providing customized learning plans tailored to the user's individual learning style.

[0055] The AI ​​agent system can further incorporate gamification elements to maintain user motivation. For example, the learning unit can award points or badges based on the goals and progress the user achieves. The instruction unit can provide rewards each time the user completes a specific task, motivating them to progress to the next stage. Furthermore, the learning support unit can enhance the enjoyment of learning by making VR / AR experiences feel like games. In this way, introducing gamification elements helps maintain user motivation and improves learning effectiveness.

[0056] The AI ​​agent system can also provide a dashboard that visualizes the user's learning progress. For example, the learning unit collects the user's learning data and displays the progress in graphs and charts. The instruction unit displays a list of goals the user has achieved and tasks that remain unfinished, making it clear what needs to be done next. Furthermore, the learning support unit can highlight content the user has previously learned and points that need review, helping to create an effective learning plan. By visualizing learning progress, users can more easily understand their own learning status, thereby improving learning effectiveness.

[0057] The AI ​​agent system can further provide personalized review plans based on the user's learning history. For example, the learning unit can analyze the user's past learning data and identify points that need review. The instruction unit can create review plans that focus on areas where the user struggles, supporting effective learning. Furthermore, the learning support unit can provide VR / AR experiences to review previously learned material, promoting practical learning. As a result, the learning effect is improved by providing personalized review plans.

[0058] The AI ​​agent system can further set personalized learning goals based on the user's learning data. For example, the learning unit analyzes the user's past learning data and sets achievable learning goals. The instruction unit can provide specific steps for the user to progress towards their set goals and support goal achievement. Furthermore, the learning support unit can visualize the user's progress towards their goals and provide real-time feedback on their progress. As a result, setting personalized learning goals improves learning effectiveness.

[0059] The AI ​​agent system can further provide personalized learning resources based on the user's learning data. For example, the learning unit can analyze the user's past learning data and select the optimal learning resources. The instruction unit can provide the learning resources the user needs and support effective learning. Furthermore, the learning support unit can guide the user on how to use the learning resources effectively. As a result, learning effectiveness is improved by providing personalized learning resources.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The learning unit learns from training data of craftsmanship. This training data includes video data, sensor data, text data, etc. The learning unit can learn from the training data of craftsmanship using machine learning and deep learning, perform motion analysis of the craftsmanship, and generate training data. Step 2: The instruction department provides production instruction based on the data learned by the learning department. The instruction department analyzes the movements of craftsmanship using image recognition technology and generates instructional content using natural language processing technology. For example, image recognition can be performed using CNN (Convolutional Neural Network) or object detection algorithms, and instructional content can be generated using natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The Learning Support Department uses VR / AR technology to allow users to experientially learn the content taught by the Instruction Department. The Learning Support Department uses VR technology to simulate craftsmanship, enabling users to actually experience it. In addition, the Learning Support Department uses AR technology to provide interactive learning content about craftsmanship. For example, VR headsets and AR glasses can be used to allow users to experientially learn about craftsmanship.

[0062] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that reproduces the techniques and knowledge of traditional crafts using AI and provides 24 / 7 support to young craftspeople, consumers, and local governments. This AI agent system learns from training data of craftspeople's skills and provides production guidance using image recognition and natural language processing. Furthermore, it supports experiential learning by utilizing VR and AR technologies. For example, the AI ​​agent system learns from training data of craftspeople's skills. For example, the AI ​​agent system learns from training data of craftspeople's skills using video data, sensor data, text data, etc. Next, the AI ​​agent system provides production guidance based on the learned data. For example, the AI ​​agent system analyzes the movements of craftspeople's skills using image recognition technology and generates instruction content using natural language processing technology. Furthermore, the AI ​​agent system supports experiential learning by utilizing VR and AR technologies. For example, the AI ​​agent system simulates craftspeople's skills using VR technology so that users can actually experience them. In addition, the AI ​​agent system provides interactive learning content of craftspeople's skills using AR technology. As a result, the AI ​​agent system simultaneously realizes the transmission of skills and the preservation of culture, and supports the training of young craftspeople, the improvement of consumer satisfaction, and the cultural preservation activities of local governments. This allows the AI ​​agent system to replicate the techniques and knowledge of traditional crafts and provide 24 / 7 support to young artisans, consumers, and local governments.

[0063] The AI ​​agent system according to this embodiment comprises a learning unit, an instruction unit, and a learning support unit. The learning unit learns training data of craftsmanship. The training data of craftsmanship includes, but is not limited to, video data, sensor data, and text data. The learning unit learns the training data of craftsmanship using, for example, machine learning or deep learning. The learning unit can also perform motion analysis of craftsmanship and generate learning data. The instruction unit provides production instruction based on the data learned by the learning unit. The instruction unit analyzes the motion of craftsmanship using, for example, image recognition technology and generates instruction content using natural language processing technology. The instruction unit performs image recognition using, for example, CNN (convolutional neural network) or object detection algorithms. The instruction unit can also generate instruction content using natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. The learning support unit learns the content instructed by the instruction unit experientially using VR / AR technology. The learning support unit simulates craftsmanship using, for example, VR technology, allowing the user to actually experience it. Furthermore, the Learning Support Department can also provide interactive learning content on craftsmanship using AR technology. For example, the Learning Support Department can enable users to experience and learn craftsmanship using VR headsets or AR glasses. This allows the AI ​​agent system according to the embodiment to learn from training data on craftsmanship, provide production guidance, and learn experientially.

[0064] The learning unit learns from training data of craftsmanship. This training data includes, but is not limited to, video data, sensor data, and text data. The learning unit learns from this training data using, for example, machine learning or deep learning. Specifically, video data records in detail the movements of the craftsman's hands and how they use tools, while sensor data quantifies the amount of force applied and the speed of movement. Text data verbalizes the craftsman's techniques and procedures. By integrating this data, the learning unit grasps the overall picture of the craftsmanship. The learning unit can also use this data to perform motion analysis of the craftsmanship and generate training data. For example, it can use a recurrent neural network (RNN), a type of deep learning, to learn patterns of consecutive movements and build a model that reproduces a series of movements in the craftsmanship. Furthermore, the learning unit can use the generated training data to simulate the movements of the craftsmanship and find the optimal movement pattern. This allows the learning unit to efficiently learn from the training data of the craftsmanship and perform highly accurate motion analysis. Furthermore, the learning unit can constantly adapt to the latest technologies and methods by updating and adding training data. For example, if a new craftsmanship technique is added, the system can quickly learn from that data and integrate it with existing data to improve the overall accuracy and flexibility of the system. This allows the learning unit to continuously learn from the training data of craftsmanship techniques and improve the overall performance of the system.

[0065] The instruction unit provides production guidance based on data learned by the learning unit. For example, the instruction unit analyzes the movements of craftsmen using image recognition technology and generates instructional content using natural language processing technology. Specifically, the instruction unit performs image recognition using CNN (Convolutional Neural Network) and object detection algorithms to analyze the craftsmen's movements in real time. For example, when a craftsman is carving wood, the instruction unit analyzes the movements of their hands and the use of tools in detail and provides accurate feedback. The instruction unit can also generate instructional content using natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. For example, based on the results of analyzing the craftsman's movements, it generates appropriate instructional content and presents the user with specific advice and areas for improvement. Furthermore, the instruction unit can respond to user questions and concerns in real time using speech recognition technology. This allows the instruction unit to provide individually customized instruction to users and support the acquisition of craftsmanship skills. In addition, the instruction unit can monitor the user's progress and provide feedback at the appropriate time. For example, if a user is repeatedly practicing a particular movement, the instruction unit evaluates the accuracy and speed of that movement and points out areas for improvement. Furthermore, the training department can adjust the instruction content according to the user's skill level and provide an optimal learning plan. This allows the training department to provide effective instruction to users and efficiently support the acquisition of craftsmanship skills.

[0066] The Learning Support Department allows users to learn the content taught by the Instruction Department experientially using VR / AR technology. For example, the Learning Support Department uses VR technology to simulate craftsmanship, allowing users to experience it firsthand. Specifically, users wearing VR headsets can practice craftsmanship in a virtual space. For instance, a user learning woodworking techniques can experience carving wood on a virtual workbench and receive realistic feedback. The Learning Support Department can also provide interactive learning content on craftsmanship using AR technology. For example, when a user wearing AR glasses learns craftsmanship in a real work environment, they can proceed with the work while referring to guidelines and animations displayed overlaid on their field of view. This allows users to experientially learn craftsmanship in a real work environment. Furthermore, the Learning Support Department can monitor user actions in real time and provide appropriate feedback. For example, when a user carves wood in a virtual space, the department evaluates the precision and speed of their actions and points out areas for improvement. The Learning Support Department can also record user progress and manage learning history. This allows users to check their own growth and set their next learning goals. Furthermore, the learning support department can provide an environment where multiple users can learn simultaneously, promoting collaborative learning and competition. For example, users can increase their motivation to learn by working together or competing in skills within a virtual space. In this way, the learning support department can provide users with an effective learning environment and support the acquisition of craftsmanship skills.

[0067] The instruction department can provide instruction using image recognition and natural language processing. For example, the instruction department can analyze the movements of craftsmanship using image recognition technology. For example, the instruction department can perform image recognition using a CNN (Convolutional Neural Network). The instruction department can also analyze the movements of craftsmanship using object detection algorithms. Furthermore, the instruction department can generate instructional content using natural language processing technology. For example, the instruction department can analyze text data using morphological analysis and generate instructional content. The instruction department can also analyze text data using grammatical analysis and generate instructional content. Furthermore, the instruction department can analyze text data using semantic analysis and generate instructional content. As a result, the accuracy of instruction is improved by using image recognition and natural language processing.

[0068] The Learning Support Department can support experiential learning using VR / AR technology. For example, the Learning Support Department can use VR technology to simulate craftsmanship and allow users to experience it firsthand. For example, the Learning Support Department can use a VR headset to allow users to experientially learn craftsmanship. The Learning Support Department can also use AR technology to provide interactive learning content about craftsmanship. For example, the Learning Support Department can use AR glasses to allow users to experientially learn craftsmanship. In this way, experiential learning becomes possible by using VR / AR technology. Some or all of the above processes in the Learning Support Department may be performed using, for example, generative AI, or not using generative AI. For example, the Learning Support Department can have a generative AI perform the generation of simulations using VR technology.

[0069] The Quality Control Department can perform quality control. For example, the Quality Control Department can evaluate the quality of manufactured goods. For example, the Quality Control Department can evaluate the quality of manufactured goods based on quality evaluation indicators. The Quality Control Department can also evaluate the quality of manufactured goods according to inspection procedures. Furthermore, the Quality Control Department can manage the quality of manufactured goods based on the results of the quality evaluation. For example, the Quality Control Department can record the results of the quality evaluation and set quality control standards. In this way, the quality of manufactured goods can be maintained through quality control. Some or all of the above processes in the Quality Control Department may be performed using, for example, a generative AI, or not using a generative AI. For example, the Quality Control Department can have a generative AI perform a quality evaluation based on quality evaluation indicators.

[0070] The real-time feedback unit can provide feedback in real time. For example, the real-time feedback unit can provide real-time feedback to a user during learning. For example, the real-time feedback unit can monitor the user's learning progress and provide feedback at the appropriate time. The real-time feedback unit can also provide feedback based on the user's learning content. Furthermore, the real-time feedback unit can provide feedback according to the user's learning progress. For example, the real-time feedback unit can evaluate the user's learning progress and provide appropriate feedback. By providing feedback in real time, the learning effect is improved. Some or all of the above processing in the real-time feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the real-time feedback unit can input the user's learning status into a generative AI and have the generative AI execute appropriate feedback.

[0071] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the learning unit selects training data based on the estimated emotions. For example, if the user is excited, the learning unit will select training data with a high difficulty level. If the user is tired, the learning unit will also select training data with a low difficulty level. Furthermore, if the user is relaxed, the learning unit will also select normal training data. By selecting training data according to the user's emotions, the learning effect is improved. 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 learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of learning data.

[0072] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can refer to past learning data. For example, the learning unit can refer to the past learning database to analyze the user's strengths and weaknesses. The learning unit can also evaluate the progress of learning based on past learning data. Furthermore, the learning unit can adjust the number of learning iterations by referring to past learning data. Next, the learning unit optimizes the learning algorithm based on past learning data. For example, the learning unit adjusts hyperparameters to improve the accuracy of the learning algorithm. The learning unit can also improve the model to enhance the performance of the learning algorithm. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0073] The learning unit can perform detailed motion analysis of the craftsman's skills during the learning process and generate more precise learning data. For example, the learning unit can capture the craftsman's movements with a high-resolution camera and perform motion analysis. The learning unit can also measure the craftsman's movements using sensors and perform motion analysis. Furthermore, the learning unit can analyze the craftsman's movements using 3D scanning. Next, the learning unit generates more precise learning data based on the results of the motion analysis. For example, the learning unit generates high-resolution video data. The learning unit can also generate detailed sensor data. This allows for the generation of precise learning data by performing detailed motion analysis of the craftsman's skills. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the results of the motion analysis into a generative AI and have the generative AI generate precise learning data.

[0074] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the learning unit adjusts the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit reduces the learning frequency. It can also increase the learning frequency if the user is relaxed. Furthermore, it can maintain the learning frequency if the user is focused. By adjusting the learning frequency according to the user's emotions, the learning effect is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0075] The learning unit can diversify the learning data during training by considering regional variations in craftsmanship. For example, the learning unit can analyze the characteristics of craftsmanship in each region and reflect them in the learning data. The learning unit can also compare the differences in craftsmanship in each region and diversify the learning data. Furthermore, the learning unit can incorporate the history of craftsmanship in each region into the learning data. This allows for diversification of the learning data by considering regional variations. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input the characteristics of craftsmanship in each region into a generative AI and have the generative AI perform the diversification of the learning data.

[0076] The learning unit can perform comparative learning with other traditional craft techniques during the learning process to clarify the commonalities and differences between the techniques. For example, the learning unit can refer to the learning data of other traditional craft techniques and analyze the commonalities. The learning unit can also refer to the learning data of other traditional craft techniques and analyze the differences. Furthermore, the learning unit can perform comparative learning with other traditional craft techniques to integrate the techniques. In this way, by performing comparative learning with other traditional craft techniques, the commonalities and differences between the techniques can be clarified. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the learning data of other traditional craft techniques into a generative AI and have the generative AI perform comparative learning.

[0077] The instruction system can estimate the user's emotions and adjust the method of instruction based on the estimated emotions. For example, the instruction system can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the instruction system adjusts the method of instruction based on the estimated emotions. For example, if the user is nervous, the instruction system will use a gentle tone. If the user is relaxed, the instruction system can use a normal tone. Furthermore, if the user is excited, the instruction system can use a calm tone. By adjusting the method of instruction according to the user's emotions, the effectiveness of the instruction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the instruction department may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction department may input user emotion data into a generative AI and have the generative AI adjust the way the instruction is expressed.

[0078] The instruction department can customize the instruction content by emphasizing key points of the craftsmanship during instruction. For example, the instruction department can customize the instruction content by emphasizing key points of the craftsmanship. For example, the instruction department can customize the instruction content by emphasizing key points of the craftsmanship. The instruction department can also customize the instruction content by emphasizing the detailed movements of the craftsmanship. Furthermore, the instruction department can customize the instruction content by emphasizing the historical background of the craftsmanship. This enriches the instruction content by emphasizing key points of the craftsmanship. Some or all of the above processing in the instruction department may be performed using, for example, a generative AI, or not using a generative AI. For example, the instruction department can input key points of the craftsmanship into a generative AI and have the generative AI perform the customization of the instruction content.

[0079] The instruction unit can adjust the instruction content in real time according to the user's progress during instruction. For example, the instruction unit can monitor the user's progress in real time and adjust the instruction content. For example, the instruction unit can monitor the user's progress in real time and adjust the instruction content. The instruction unit can also change the instruction content in real time according to the user's progress. Furthermore, the instruction unit can analyze the user's progress and optimize the instruction content. This improves the effectiveness of instruction by adjusting the instruction content according to the user's progress. Some or all of the above processes in the instruction unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the instruction unit can input user progress data into a generative AI and have the generative AI perform the adjustment of the instruction content.

[0080] The instruction unit can estimate the user's emotions and adjust the length of the instruction based on the estimated emotions. For example, the instruction unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the instruction unit adjusts the length of the instruction based on the estimated emotions. For example, if the user is tired, the instruction unit may shorten the length of the instruction. It can also lengthen the instruction if the user is focused. Furthermore, it can return the instruction length to normal if the user is relaxed. By adjusting the length of the instruction according to the user's emotions, the effectiveness of the instruction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using, for example, generative AI, or without generative AI. For example, the instruction department can input user emotion data into a generating AI and have the AI ​​adjust the length of the instruction.

[0081] The instruction department can provide instruction that includes the historical background and cultural significance of the craftsmanship. For example, the instruction department can provide instruction while explaining the historical background of the craftsmanship. For example, the instruction department can provide instruction while explaining the historical background of the craftsmanship. The instruction department can also provide instruction while explaining the cultural significance of the craftsmanship. Furthermore, the instruction department can provide instruction that integrates the history and culture of the craftsmanship. This enriches the content of instruction by including the historical background and cultural significance of the craftsmanship. Some or all of the above processing in the instruction department may be performed using, for example, a generative AI, or not using a generative AI. For example, the instruction department can input data on the historical background and cultural significance of the craftsmanship into a generative AI and have the generative AI generate the instruction content.

[0082] The training department can enrich its training content by referring to the success stories of other craftsmen. For example, the training department can provide training while introducing the success stories of other craftsmen. The training department can also analyze the success stories of other craftsmen and reflect them in its training content. Furthermore, the training department can customize the training content by referring to the success stories of other craftsmen. In this way, the training content is enriched by referring to the success stories of other craftsmen. Some or all of the above processes in the training department may be performed using, for example, a generative AI, or not using a generative AI. For example, the training department can input data on the success stories of other craftsmen into a generative AI and have the generative AI generate the training content.

[0083] The learning support unit can estimate the user's emotions and adjust the content of the VR / AR experience based on the estimated emotions. For example, the learning support unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the learning support unit adjusts the content of the VR / AR experience based on the estimated emotions. For example, if the user is excited, the learning support unit can provide a stimulating VR / AR experience. If the user is relaxed, the learning support unit can provide a calming VR / AR experience. Furthermore, if the user is tired, the learning support unit can provide a simple VR / AR experience. By adjusting the content of the VR / AR experience according to the user's emotions, the learning effect is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning support unit can input user emotion data into a generative AI and have the generative AI adjust the content of the VR / AR experience.

[0084] The learning support unit can provide the optimal learning scenario by referring to the user's operation history during learning support. For example, the learning support unit can analyze the user's operation history and provide the optimal learning scenario. For example, the learning support unit can analyze the user's operation history and provide the optimal learning scenario. The learning support unit can also customize the learning scenario based on the user's operation history. Furthermore, the learning support unit can optimize the learning scenario by referring to the user's operation history. In this way, the optimal learning scenario can be provided by referring to the user's operation history. Some or all of the above processing in the learning support unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning support unit can input user operation history data into a generative AI and have the generative AI execute the generation of the optimal learning scenario.

[0085] The learning support unit can provide a more realistic experience by recreating the actual work environment of the craftsman during learning support. For example, the learning support unit can recreate the work environment of the craftsman in VR to provide a realistic experience. For example, the learning support unit can recreate the work environment of the craftsman in VR to provide a realistic experience. The learning support unit can also recreate the work environment of the craftsman in AR to provide a realistic experience. Furthermore, the learning support unit can recreate the work environment of the craftsman in 3D model to provide a realistic experience. In this way, a more realistic experience is provided by recreating the actual work environment of the craftsman. Some or all of the above processing in the learning support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning support unit can input the work environment data of the craftsman into a generative AI and have the generative AI recreate the work environment.

[0086] The learning support unit can estimate the user's emotions and adjust the difficulty level of the VR / AR experience based on the estimated emotions. For example, the learning support unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the learning support unit adjusts the difficulty level of the VR / AR experience based on the estimated emotions. For example, if the user is excited, the learning support unit can provide a high-difficulty VR / AR experience. If the user is relaxed, the learning support unit can provide a low-difficulty VR / AR experience. Furthermore, if the user is tired, the learning support unit can provide an easy VR / AR experience. By adjusting the difficulty level of the VR / AR experience according to the user's emotions, the learning effect is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the processing described above in the learning support unit may be performed using a generative AI, or not using a generative AI. For example, the learning support unit can input user emotion data into a generative AI and have the generative AI adjust the difficulty level of the VR / AR experience.

[0087] The learning support unit can provide collaborative work experiences with other users during learning support, thereby promoting cooperative learning. For example, the learning support unit can provide VR / AR experiences in which users work together. For example, the learning support unit can provide VR / AR experiences in which users work together. The learning support unit can also provide VR / AR experiences in which users work together to solve problems. Furthermore, the learning support unit can provide VR / AR experiences in which users learn while communicating with other users. In this way, collaborative learning is promoted by providing collaborative work experiences with other users. Some or all of the above processing in the learning support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning support unit can input collaborative work data with other users into a generative AI and have the generative AI execute the generation of a collaborative work experience.

[0088] The Learning Support Department can promote the fusion of technologies by providing experiences that combine different traditional craft techniques during learning support. For example, the Learning Support Department can provide VR / AR experiences that combine different traditional craft techniques. For example, the Learning Support Department can provide VR / AR experiences that combine different traditional craft techniques. The Learning Support Department can also provide VR / AR experiences that allow users to experience the fusion of different traditional craft techniques. Furthermore, the Learning Support Department can provide VR / AR experiences that promote the discovery of new technologies by learning different traditional craft techniques. In this way, the fusion of technologies is promoted by providing experiences that combine different traditional craft techniques. Some or all of the above processing in the Learning Support Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Learning Support Department can input data on different traditional craft techniques into a generative AI and have the generative AI execute the generation of a technology fusion experience.

[0089] The Quality Control Department can estimate user emotions and adjust quality control standards based on the estimated emotions. For example, the Quality Control Department can estimate user emotions using facial recognition technology. It can also estimate user emotions using voice analysis technology. Furthermore, it can estimate user emotions using biosensor data. Next, the Quality Control Department adjusts quality control standards based on the estimated user emotions. For example, if the user gives a harsh evaluation, the Quality Control Department will tighten the quality control standards. Conversely, if the user gives a generous evaluation, the Quality Control Department can loosen the quality control standards. Furthermore, if the user gives a neutral evaluation, the Quality Control Department can return the quality control standards to normal. In this way, the accuracy of quality control is improved by adjusting quality control standards according to user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generative AI may include, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processes described above in the quality control department may be performed using, for example, a generative AI, or not using a generative AI. For example, the quality control department may input user sentiment data into a generative AI and have the generative AI adjust the quality control standards.

[0090] The Quality Control Department can optimize the quality evaluation algorithm by referring to past quality data during quality control. For example, the Quality Control Department can refer to past quality data. For example, the Quality Control Department can analyze past quality data and optimize the quality evaluation algorithm. The Quality Control Department can also adjust the quality evaluation algorithm based on past quality data. Furthermore, the Quality Control Department can improve the quality evaluation algorithm by referring to past quality data. As a result, the accuracy of the quality evaluation algorithm is improved by referring to past quality data. Some or all of the above processes in the Quality Control Department may be performed using, for example, a generative AI, or without using a generative AI. For example, the Quality Control Department can input past quality data into a generative AI and have the generative AI perform the optimization of the quality evaluation algorithm.

[0091] The Quality Control Department can estimate the user's emotions and adjust the frequency of quality control based on the estimated emotions. For example, the Quality Control Department can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the Quality Control Department adjusts the frequency of quality control based on the estimated emotions. For example, if the user gives a harsh evaluation, the Quality Control Department can increase the frequency of quality control. If the user gives a generous evaluation, the Quality Control Department can decrease the frequency of quality control. Furthermore, if the user gives a neutral evaluation, the Quality Control Department can return the frequency of quality control to normal. In this way, the accuracy of quality control is improved by adjusting the frequency of quality control according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. The generative AI may include, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processes described above in the quality control department may be performed using, for example, a generative AI, or not using a generative AI. For example, the quality control department may input user sentiment data into a generative AI and have the generative AI adjust the frequency of quality control.

[0092] The Quality Control Department can perform quality evaluations by comparing the work of other craftsmen during quality control. For example, the Quality Control Department can perform quality evaluations by comparing the work of other craftsmen. For example, the Quality Control Department can perform quality evaluations by comparing the work of other craftsmen. The Quality Control Department can also perform quality evaluations by referring to the quality data of other craftsmen's works. Furthermore, the Quality Control Department can perform quality evaluations by referring to the evaluation criteria of other craftsmen's works. This improves the accuracy of quality evaluations by comparing the work of other craftsmen. Some or all of the above processes in the Quality Control Department may be performed using, for example, a generative AI, or without using a generative AI. For example, the Quality Control Department can input data of other craftsmen's works into a generative AI and have the generative AI perform the quality evaluation.

[0093] The real-time feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, the real-time feedback unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the real-time feedback unit adjusts the content of the feedback based on the estimated emotions. For example, if the user is tense, the real-time feedback unit provides gentle feedback. It can also provide detailed feedback if the user is relaxed. Furthermore, it can provide calming feedback if the user is excited. In this way, the effectiveness of the feedback is improved by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the real-time feedback unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the real-time feedback unit can input user emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0094] The real-time feedback unit can provide optimal feedback by referring to the user's past work history at the time of feedback. For example, the real-time feedback unit can analyze the user's past work history and provide optimal feedback. For example, the real-time feedback unit can analyze the user's past work history and provide optimal feedback. The real-time feedback unit can also customize the feedback content based on the user's past work history. Furthermore, the real-time feedback unit can optimize the feedback content by referring to the user's past work history. This allows the unit to provide optimal feedback by referring to the user's past work history. Some or all of the above processing in the real-time feedback unit may be performed using, for example, a generation AI, or without a generation AI. For example, the real-time feedback unit can input the user's past work history data into a generation AI and have the generation AI generate optimal feedback.

[0095] The real-time feedback unit can estimate the user's emotions and adjust the timing of the feedback based on the estimated emotions. For example, the real-time feedback unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biosensor data. Next, the real-time feedback unit adjusts the timing of the feedback based on the estimated emotions. For example, if the user is tense, the real-time feedback unit delays the timing of the feedback. Conversely, if the user is relaxed, the real-time feedback unit can speed up the timing of the feedback. Furthermore, if the user is excited, the real-time feedback unit can return the timing of the feedback to normal. This improves the effectiveness of the feedback by adjusting the timing of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 real-time feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time feedback unit can input user emotion data into a generative AI and have the generative AI adjust the timing of the feedback.

[0096] The real-time feedback unit can enrich its feedback by referring to the feedback content of other users. For example, the real-time feedback unit can enrich its feedback by referring to the feedback content of other users. The real-time feedback unit can also analyze the feedback content of other users and reflect it in its own feedback. Furthermore, the real-time feedback unit can customize its feedback by referring to the feedback content of other users. In this way, the feedback content is enriched by referring to the feedback content of other users. Some or all of the above processing in the real-time feedback unit may be performed using, for example, a generation AI, or without a generation AI. For example, the real-time feedback unit can input the feedback content data of other users into a generation AI and have the generation AI generate the feedback content.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The AI ​​agent system can further provide customized learning plans based on the user's individual learning style. For example, the learning unit analyzes the user's past learning history and performance data to identify the most effective way for the user to learn. The instruction unit adjusts the instruction content to match the user's learning style, using images and videos extensively for visual learners and providing audio guides for auditory learners. Furthermore, the learning support unit can customize the content of the VR / AR experience based on the user's learning style, providing an environment in which the user can learn most effectively. As a result, learning effectiveness is improved by providing customized learning plans tailored to the user's individual learning style.

[0099] The AI ​​agent system can further incorporate gamification elements to maintain user motivation. For example, the learning unit can award points or badges based on the goals and progress the user achieves. The instruction unit can provide rewards each time the user completes a specific task, motivating them to progress to the next stage. Furthermore, the learning support unit can enhance the enjoyment of learning by making VR / AR experiences feel like games. In this way, introducing gamification elements helps maintain user motivation and improves learning effectiveness.

[0100] The AI ​​agent system can further estimate the user's emotions and adjust the learning environment based on those emotions. For example, the learning unit can estimate the user's emotions and, if the user is feeling stressed, provide relaxing music or videos. The instruction unit can minimize notifications and interruptions when the user is concentrating, reducing distractions to learning. Furthermore, the learning support unit can display messages encouraging the user to take a break if they are tired, supporting them in getting adequate rest. By adjusting the learning environment according to the user's emotions, learning effectiveness is improved.

[0101] The AI ​​agent system can also provide a dashboard that visualizes the user's learning progress. For example, the learning unit collects the user's learning data and displays the progress in graphs and charts. The instruction unit displays a list of goals the user has achieved and tasks that remain unfinished, making it clear what needs to be done next. Furthermore, the learning support unit can highlight content the user has previously learned and points that need review, helping to create an effective learning plan. By visualizing learning progress, users can more easily understand their own learning status, thereby improving learning effectiveness.

[0102] The AI ​​agent system can further estimate the user's emotions and personalize the feedback based on those emotions. For example, the real-time feedback unit can estimate the user's emotions and provide encouraging messages if the user is feeling down. If the user is confident, it can point out specific areas for improvement to encourage further growth. Furthermore, if the user is feeling anxious, it can provide advice to help them calm down and adjust the learning pace. This personalizes the feedback according to the user's emotions, thereby improving the effectiveness of the feedback.

[0103] The AI ​​agent system can further provide personalized review plans based on the user's learning history. For example, the learning unit can analyze the user's past learning data and identify points that need review. The instruction unit can create review plans that focus on areas where the user struggles, supporting effective learning. Furthermore, the learning support unit can provide VR / AR experiences to review previously learned material, promoting practical learning. As a result, the learning effect is improved by providing personalized review plans.

[0104] The AI ​​agent system can further estimate the user's emotions and adjust the learning timing based on those emotions. For example, the learning unit can estimate the user's emotions and, if the user is relaxed, it can start learning earlier. Conversely, if the user is tired, it can delay the start of learning. Furthermore, if the user is concentrating, it can suppress notifications to avoid interruptions. By adjusting the learning timing according to the user's emotions, the learning effect is improved.

[0105] The AI ​​agent system can further set personalized learning goals based on the user's learning data. For example, the learning unit analyzes the user's past learning data and sets achievable learning goals. The instruction unit can provide specific steps for the user to progress towards their set goals and support goal achievement. Furthermore, the learning support unit can visualize the user's progress towards their goals and provide real-time feedback on their progress. As a result, setting personalized learning goals improves learning effectiveness.

[0106] The AI ​​agent system can further estimate the user's emotions and adjust the learning content based on those emotions. For example, the learning unit can estimate the user's emotions and, if the user is excited, provide more challenging learning content. If the user is relaxed, it can provide normal learning content. Furthermore, if the user is tired, it can provide easy learning content. By adjusting the learning content according to the user's emotions, the learning effect is improved.

[0107] The AI ​​agent system can further provide personalized learning resources based on the user's learning data. For example, the learning unit can analyze the user's past learning data and select the optimal learning resources. The instruction unit can provide the learning resources the user needs and support effective learning. Furthermore, the learning support unit can guide the user on how to use the learning resources effectively. As a result, learning effectiveness is improved by providing personalized learning resources.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The learning unit learns from training data of craftsmanship. This training data includes video data, sensor data, text data, etc. The learning unit can learn from the training data of craftsmanship using machine learning and deep learning, perform motion analysis of the craftsmanship, and generate training data. Step 2: The instruction department provides production instruction based on the data learned by the learning department. The instruction department analyzes the movements of craftsmanship using image recognition technology and generates instructional content using natural language processing technology. For example, image recognition can be performed using CNN (Convolutional Neural Network) or object detection algorithms, and instructional content can be generated using natural language processing technologies such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The Learning Support Department uses VR / AR technology to allow users to experientially learn the content taught by the Instruction Department. The Learning Support Department uses VR technology to simulate craftsmanship, enabling users to actually experience it. In addition, the Learning Support Department uses AR technology to provide interactive learning content about craftsmanship. For example, VR headsets and AR glasses can be used to allow users to experientially learn about craftsmanship.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the learning unit, instruction unit, learning support unit, quality control unit, and real-time feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides instruction using image recognition technology and natural language processing technology. The learning support unit is implemented by the control unit 46A of the smart device 14 and supports experiential learning using VR and AR technology. The quality control unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates and manages the quality of the manufactured product. The real-time feedback unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and provides real-time feedback according to the user's learning status. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the learning unit, instruction unit, learning support unit, quality control unit, and real-time feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides instruction in production using image recognition technology and natural language processing technology. The learning support unit is implemented by the control unit 46A of the smart glasses 214 and supports experiential learning using VR and AR technology. The quality control unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates and manages the quality of the manufactured product. The real-time feedback unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and provides real-time feedback according to the user's learning status. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the learning unit, instruction unit, learning support unit, quality control unit, and real-time feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides instruction on production using image recognition technology and natural language processing technology. The learning support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and supports experiential learning using VR and AR technology. The quality control unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates and manages the quality of the produced goods. The real-time feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and provides real-time feedback according to the user's learning status. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the learning unit, instruction unit, learning support unit, quality control unit, and real-time feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides instruction on manufacturing using image recognition technology and natural language processing technology. The learning support unit is implemented by the control unit 46A of the robot 414 and supports experiential learning using VR and AR technology. The quality control unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates and manages the quality of the manufactured product. The real-time feedback unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and provides real-time feedback according to the user's learning progress. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The learning unit learns from training data of skilled craftsmen, The instruction unit provides production guidance based on the data learned by the aforementioned learning unit, The learning support unit includes a unit that allows users to experience and learn the content taught by the aforementioned instruction unit using VR / AR technology. A system characterized by the following features. (Note 2) The aforementioned leadership, We provide instruction on production using image recognition and natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Learning Support Department, Supporting experiential learning using VR / AR technology The system described in Appendix 1, characterized by the features described herein. (Note 4) The company has a quality control department that is responsible for quality management. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a real-time feedback unit that provides feedback in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During training, the system performs detailed motion analysis of skilled craftsmanship to generate more precise training data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During training, the training data is diversified to take into account regional variations in craftsmanship. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During the learning process, students will compare and contrast this technique with other traditional craft techniques to clarify the similarities and differences between them. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned leadership, The system estimates the user's emotions and adjusts the way instructions are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned leadership, During instruction, we customize the content by emphasizing key points of the craftsmanship. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned leadership, During instruction, the content of the instruction is adjusted in real time according to the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned leadership, It estimates the user's emotions and adjusts the length of instruction based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned leadership, During instruction, the historical background and cultural significance of the craftsmanship should also be included. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned leadership, During instruction, enrich the content of the training by referring to the success stories of other craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Learning Support Department, It estimates the user's emotions and adjusts the VR / AR experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Learning Support Department, When providing learning support, the system provides the optimal learning scenario by referring to the user's operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Learning Support Department, During learning support sessions, we recreate the actual work environment of skilled craftsmen to provide a more realistic experience. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Learning Support Department, It estimates the user's emotions and adjusts the difficulty level of the VR / AR experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Learning Support Department, During learning support, we provide collaborative work experiences with other users to promote cooperative learning. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Learning Support Department, During learning support, we provide experiences that combine different traditional craft techniques, promoting the integration of technologies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Quality Control Department We estimate user sentiment and adjust quality control standards based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Quality Control Department During quality control, the quality evaluation algorithm is optimized by referring to past quality data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Quality Control Department We estimate user sentiment and adjust the frequency of quality control based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Quality Control Department During quality control, the quality is evaluated by comparing it with the work of other craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 28) The real-time feedback unit described above is: It estimates the user'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 29) The real-time feedback unit described above is: When providing feedback, we refer to the user's past work history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The real-time feedback unit described above is: It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The real-time feedback unit described above is: When providing feedback, refer to the feedback from other users to enrich your own feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 learning unit learns from training data of skilled craftsmen, The instruction unit provides production guidance based on the data learned by the aforementioned learning unit, The learning support unit includes a unit that allows users to experience and learn the content taught by the aforementioned instruction unit using VR / AR technology. A system characterized by the following features.

2. The aforementioned leadership, We provide instruction on production using image recognition and natural language processing. The system according to feature 1.

3. The aforementioned Learning Support Department Supporting experiential learning using VR / AR technology The system according to feature 1.

4. The company has a quality control department that is responsible for quality management. The system according to feature 1.

5. It features a real-time feedback unit that provides feedback in real time. The system according to feature 1.

6. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

7. The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system according to feature 1.

8. The aforementioned learning unit, During training, the system performs detailed motion analysis of skilled craftsmanship to generate more precise training data. The system according to feature 1.

9. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system according to feature 1.

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