system

Abstract

The system according to this embodiment aims to streamline technology transfer and improve the quality and efficiency of work processes. [Solution] The system according to the embodiment comprises a generation unit, a training unit, a display unit, a work unit, and an analysis unit. The generation unit video analyzes the work of craftsmen and creates a digitized training manual. The training unit automates the training of new employees based on the training manual created by the generation unit. The display unit displays the procedures and placement of tools in the workspace. The work unit performs the work based on the procedures and placement of tools displayed by the display unit. The analysis unit analyzes the properties of the material and proposes the optimal processing procedure.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 are problems that technology inheritance is inefficient and costly, and there are variations in the quality and efficiency of work processes.

[0005] The system according to the embodiment aims to improve the efficiency of technology inheritance and improve the quality and efficiency of work processes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a training unit, a display unit, a work unit, and an analysis unit. The generation unit video-analyzes the work of a craftsman and creates a digitized training manual. The training unit automates the training of new employees based on the training manual created by the generation unit. The display unit displays the procedures and tool placement in the workspace. The work unit performs the work based on the procedures and tool placement displayed by the display unit. The analysis unit analyzes the properties of the material and proposes the optimal processing procedure. [Effects of the Invention]

[0007] The system according to this embodiment can streamline technology transfer and improve the quality and efficiency of work processes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The technology transfer system according to an embodiment of the present invention is a system that utilizes generative AI and AR to improve the transfer of craftsmanship skills and work efficiency. This technology transfer system promotes the automation of new employee training by video analyzing the work of craftsmen and creating a digitized training manual using generative AI. Furthermore, it uses AR to display procedures and the placement of tools in the workspace, supporting craftsmen in performing their work efficiently. For example, it can display drying time and mixing ratios in real time to a lacquer craftsman. It also uses AI to analyze the properties of materials and propose the optimal processing procedure. This provides the optimal procedure based on data including that of experienced workers, improving accuracy and efficiency. For example, the technology transfer system video analyzes the work of craftsmen and creates a digitized training manual using generative AI. In this process, the craftsman's work procedures and techniques are recorded in detail and analyzed by the generative AI. For example, the work of a bamboo craftsman is videotaped, and the generative AI analyzes the procedure to create a digital manual. This promotes the automation of new employee training and enables efficient transfer of skills. Next, the technology transfer system uses AR to display procedures and the placement of tools in the workspace. For example, when a lacquer craftsman is working, AR is used to display drying time and mixing ratios in real time. This allows craftsmen to work more efficiently, improving accuracy and efficiency. Furthermore, the technology transfer system uses AI to analyze the properties of materials and propose the optimal processing procedure. For example, the AI ​​analyzes the properties of bamboo and lacquer and proposes the optimal processing procedure. This provides the optimal procedure based on data, including that of experienced craftsmen, improving accuracy and efficiency. This system enables faster and more efficient skill acquisition through digital manuals and AR guidance. It also improves the uniformity of work and unifies quality assurance, and enables support for the development of new technologies that exceed normal efficiency. For example, when a lacquer craftsman introduces a new technique, using AI and AR can provide more accurate drying time management and mixing ratio advice. This allows for the production of higher quality lacquerware products. In this way, the technology transfer system enables the transfer of craftsmanship and improves work efficiency.

[0029] The technology transfer system according to this embodiment comprises a generation unit, a training unit, a display unit, a work unit, and an analysis unit. The generation unit video-analyzes the work of a craftsman and creates a digitized training manual. For example, the generation unit records the work procedures and techniques of a craftsman in detail, and the generation AI analyzes this to create a digital manual. For example, the generation unit videotapes the work of a bamboo craftsman, and the generation AI analyzes the procedures to create a digital manual. The generation unit can also videotape the work of a lacquer craftsman, and the generation AI can analyze the procedures to create a digital manual. Furthermore, the generation unit can videotape the work of a ceramic craftsman, and the generation AI can analyze the procedures to create a digital manual. The training unit automates the training of new recruits based on the training manual created by the generation unit. For example, the training unit automates the training program for new recruits using the digital manual created by the generation unit. For example, the training unit instructs new recruits on work procedures and monitors their progress based on the digital manual. The training unit can also evaluate the work of new recruits and provide feedback based on the digital manual. Furthermore, the training department can provide customized training programs tailored to the skill level of new recruits, based on digital manuals. The display unit shows procedures and tool placement in the workspace. The display unit can, for example, use augmented reality (AR) to show procedures and tool placement in the workspace. For example, the display unit can show drying time and mixing ratio in real time when a lacquer craftsman is working. The display unit can also show the placement of tools and work procedures when a bamboo craftsman is working. Furthermore, the display unit can also show work procedures and information on materials used when a potter is working. The work unit performs work based on the procedures and tool placement displayed by the display unit. For example, the work unit helps craftsmen perform work according to the procedures displayed by the display unit. For example, the work unit supports lacquer craftsmen in performing work according to drying time and mixing ratio. The work unit can also support bamboo craftsmen in performing work according to the placement of tools they use. Furthermore, the work unit can also support pottery craftsmen in performing work according to information on materials they use.The analysis unit analyzes the properties of materials and proposes the optimal processing procedure. For example, the analysis unit uses AI to analyze the properties of bamboo and lacquer and proposes the optimal processing procedure. For instance, the analysis unit analyzes the physical and chemical properties of bamboo and proposes the optimal processing procedure. The analysis unit can also analyze the properties of lacquer and propose the optimal drying time and mixing ratio. Furthermore, the analysis unit can analyze the properties of clay used in pottery and propose the optimal molding procedure and firing conditions. As a result, the technology transfer system according to this embodiment can achieve the transfer of craftsmanship and improvement of work efficiency.

[0030] The generation unit analyzes the work of craftsmen via video and creates digitized training manuals. For example, the generation unit meticulously records the work procedures and techniques of craftsmen, and the generation AI analyzes this information to create digital manuals. Specifically, the generation unit uses high-resolution cameras to film the craftsmen's work from multiple angles, and the generation AI analyzes the footage. The generation AI analyzes the movements in the video frame by frame, meticulously recording the start and end times of each movement, the types of tools and materials used, and the working environment conditions. For example, when videotaping a bamboo craftsman, the system meticulously records a series of work procedures such as bamboo selection, cutting, carving, and assembly, and the generation AI analyzes these procedures to create a digital manual. Similarly, when videotaping a lacquer craftsman, the system meticulously records processes such as lacquer mixing, application, and drying, and the generation AI analyzes these procedures to create a digital manual. Furthermore, when videotaping a pottery craftsman, the system meticulously records processes such as clay kneading, shaping, and firing, and the generation AI analyzes these procedures to create a digital manual. The generating AI analyzes these work procedures, evaluating their accuracy and efficiency, and extracting the optimal procedure. Furthermore, the generating AI highlights particularly important points and precautions within the work procedures, reflecting them in the digital manual. This allows the generating unit to accurately digitize the advanced skills of craftsmen and provide them as training manuals.

[0031] The Training Department automates new employee training based on training manuals created by the Production Department. For example, the Training Department automates the new employee training program using digital manuals created by the Production Department. Specifically, the Training Department instructs new employees on work procedures based on the digital manuals and monitors their progress. The Training Department uses AI to monitor new employees' work in real time and evaluate the accuracy and efficiency of their work procedures. For example, when a new employee is working on bamboo crafts, the AI ​​instructs them on procedures such as selecting, cutting, carving, and assembling bamboo based on the digital manual, and monitors and evaluates the new employee's work. Similarly, when a new employee is working with lacquer, the AI ​​can instruct them on procedures such as mixing, applying, and drying lacquer based on the digital manual, and monitor and evaluate the new employee's work. Furthermore, when a new employee is working on pottery, the AI ​​can instruct them on procedures such as kneading, shaping, and firing clay based on the digital manual, and monitor and evaluate the new employee's work. When evaluating new employees' work, the Training Department evaluates not only the accuracy and efficiency of the work, but also the safety and quality of the work. Furthermore, the training department can provide customized training programs tailored to the skill level of new recruits. For example, if a new recruit has a low skill level, the training department will focus on teaching basic work procedures, while if they have a high skill level, they will be taught more advanced techniques and applied work procedures. In this way, the training department can efficiently support the skill improvement of new recruits and ensure the transmission of skilled craftsmanship.

[0032] The display unit shows procedures and tool placement in the workspace. For example, the display unit uses augmented reality (AR) to show procedures and tool placement in the workspace. Specifically, the display unit uses AR glasses or a projector to display procedures and tool placement in the workspace in real time. For example, when a lacquer craftsman is working, wearing AR glasses will display drying time and mixing ratios in real time within their field of view. Similarly, when a bamboo craftsman is working, a projector can be used to display the placement of tools used on the workbench and the work procedures. Furthermore, when a potter is working, AR glasses or a projector can be used to display information about the work procedures and materials used. The display unit can not only show work procedures and tool placement, but also the progress of the work and points to note. For example, when a lacquer craftsman is working, it will display the elapsed drying time and the timing of the next work procedure. Similarly, when a bamboo craftsman is working, it can display the placement of tools used and the progress of the work procedures. Furthermore, when a potter is working, it can display not only the work procedures and information about materials used, but also the progress of the work and points to note. This allows the display unit to support craftsmen in performing their work efficiently, improving the accuracy and quality of their work.

[0033] The work area performs tasks based on the procedures and tool placements displayed by the display unit. For example, the work area supports craftsmen in performing tasks according to the procedures displayed by the display unit. Specifically, the work area provides support to craftsmen as they perform tasks based on the procedures and tool placements displayed by the display unit. For example, to support lacquer craftsmen in performing tasks according to drying time and mixing ratios, the work area monitors the progress of drying time and adjustments to mixing ratios in real time and provides feedback to the craftsman. Similarly, to support bamboo craftsmen in performing tasks according to the placement of their tools, the work area can monitor the placement and use of tools in real time and provide feedback to the craftsman. Furthermore, to support pottery craftsmen in performing tasks according to information on the materials they use, the work area can monitor the usage of materials and work procedures in real time and provide feedback to the craftsman. In addition to providing support to craftsmen as they perform tasks, the work area can monitor the progress and quality of the work and make adjustments and improvements as needed. This allows the work area to support craftsmen in performing tasks efficiently and accurately, thereby improving the quality and efficiency of the work.

[0034] The analysis unit analyzes the properties of materials and proposes the optimal processing procedure. For example, the analysis unit uses AI to analyze the properties of bamboo and lacquer and propose the optimal processing procedure. Specifically, the analysis unit uses AI to analyze the physical and chemical properties of bamboo in detail and propose the optimal processing procedure. For example, it analyzes the properties of bamboo such as hardness, elasticity, and moisture content, and proposes the optimal cutting and carving methods based on that. It can also analyze the properties of lacquer and propose the optimal drying time and mixing ratio. For example, it analyzes the properties of lacquer such as viscosity, drying speed, and mixing ratio, and proposes the optimal drying time and mixing ratio based on that. Furthermore, the analysis unit can analyze the properties of clay used in pottery and propose the optimal molding procedure and firing conditions. For example, it analyzes the properties of clay such as particle size, moisture content, and firing temperature, and proposes the optimal molding procedure and firing conditions based on that. When analyzing these properties, the analysis unit utilizes past data and statistical information to perform more accurate analyses. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data, and issue warnings early. This allows the analysis unit to analyze the material properties in detail and propose the optimal processing procedure, thereby improving work efficiency and quality.

[0035] The generation unit can meticulously record the work procedures and techniques of craftsmen, and the generation AI can analyze this information to create digital manuals. For example, the generation unit can meticulously record the work procedures and techniques of craftsmen. For example, the generation unit can film the craftsmen's work using a video camera and analyze the footage. The generation unit can also record the craftsmen's work procedures as text. For example, the generation unit can meticulously record each step performed by the craftsman and transcribe the procedure into text. Furthermore, the generation unit can meticulously record the craftsmen's work techniques. For example, the generation unit can meticulously record the types of tools and materials used by the craftsman and how to use them. The generation AI analyzes the recorded work procedures and techniques to create digital manuals. For example, the generation AI can analyze video footage and extract the craftsmen's movements. For example, the generation AI can analyze the craftsmen's hand movements from video footage and reflect those movements in the digital manual. The generation AI can also analyze text data and organize work procedures. For example, the generation AI can analyze recorded text data and organize work procedures in an easy-to-understand manner. Furthermore, the generating AI can also analyze the skills of craftsmen and reflect them in digital manuals. For example, the generating AI can analyze how craftsmen use the tools and materials they employ and include that information in the digital manual. This allows for detailed recording of craftsmen's work procedures and techniques, facilitating the automation of new employee training by creating digital manuals. Some or all of the above-described processes in the generation unit may be performed using the generating AI, or they may not. For example, the generation unit can input video footage captured by a video camera into the generating AI, which can then analyze the footage to create a digital manual.

[0036] The display unit can provide real-time information on drying time and mixing ratios to lacquer artisans while they are working. For example, the display unit can use augmented reality (AR) to provide real-time information on drying time and mixing ratios to lacquer artisans while they are working. For instance, the display unit can show the drying time in real time when a lacquer artisan is applying lacquer. The display unit can also show the mixing ratio in real time when a lacquer artisan is mixing lacquer. Furthermore, the display unit can also display the arrangement of tools and work procedures used by the lacquer artisan while they are working. For example, the display unit can show the arrangement of brushes and spatulas used by the lacquer artisan and provide real-time information on work procedures. This allows the lacquer artisan to work more efficiently, improving the accuracy and efficiency of their work. Some or all of the above-described processes in the display unit may be performed using augmented reality (AR), or they may not. For example, the display unit can use augmented reality (AR) to show drying time and mixing ratios in real time when a lacquer artisan is working.

[0037] The analysis unit can analyze the properties of bamboo and lacquer and propose the optimal processing procedure. For example, the analysis unit can use AI to analyze the properties of bamboo and lacquer. For instance, it can analyze the physical and chemical properties of bamboo and propose the optimal processing procedure. It can also analyze the properties of lacquer and propose the optimal drying time and mixing ratio. Furthermore, it can analyze the properties of clay used in pottery and propose the optimal molding procedure and firing conditions. This improves accuracy and efficiency by analyzing the properties of materials and proposing the optimal processing procedure. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can use AI to analyze data in order to analyze the properties of bamboo and lacquer and propose the optimal processing procedure.

[0038] The training department can automate new employee training based on digital manuals created by the generation department. For example, the training department can automate the new employee training program using the digital manuals created by the generation department. For example, the training department can instruct new employees on work procedures and monitor their progress based on the digital manuals. The training department can also evaluate the new employees' work and provide feedback based on the digital manuals. Furthermore, the training department can provide customized training programs tailored to the new employees' skill levels based on the digital manuals. This improves the efficiency of training by automating new employee training based on digital manuals. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department can input the digital manuals created by the generation department into an AI, which can then automate the new employee training program.

[0039] The work unit can perform tasks based on procedures and tool placements displayed by the display unit. For example, the work unit can help a craftsman perform tasks according to procedures displayed by the display unit. For example, the work unit can support a lacquer craftsman in performing tasks according to drying times and mixing ratios. The work unit can also support a bamboo craftsman in performing tasks according to the placement of tools used. Furthermore, the work unit can support a potter in performing tasks according to information on the materials used. This improves the efficiency and accuracy of the work by performing tasks based on displayed procedures and tool placements. Some or all of the above processes in the work unit may be performed using AI, for example, or not. For example, the work unit can input the procedures and tool placements displayed by the display unit into the AI, which can then support the work.

[0040] The generation unit can analyze a craftsman's past work history and select the optimal video analysis method. For example, the generation unit uses AI to analyze a craftsman's past work history. For example, the generation unit prioritizes analyzing similar procedures based on the craftsman's past successful work procedures. The generation unit can also avoid work procedures that the craftsman has failed at in the past and analyze procedures with a high success rate. Furthermore, the generation unit can select a video analysis method specialized for a specific technique from the craftsman's work history. In this way, the optimal video analysis method can be selected by analyzing the craftsman's past work history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the craftsman's past work history data into a generation AI, which can then select the optimal video analysis method.

[0041] The generation unit can perform filtering during video analysis based on the craftsman's work environment and the tools they use. For example, the generation unit uses AI to perform filtering based on the craftsman's work environment and tools. For instance, the generation unit filters the video analysis according to the type of tools the craftsman uses. The generation unit can also filter the video analysis according to the craftsman's work environment (indoors, outdoors, etc.). Furthermore, the generation unit can filter the video analysis according to the size of the craftsman's workspace. This improves the accuracy of the analysis by filtering based on the craftsman's work environment and tools. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the craftsman's work environment and tools into the generation AI, which can then perform the filtering.

[0042] The generation unit can prioritize the analysis of highly relevant tasks by considering the geographical location information of the craftsman during video analysis. For example, the generation unit uses AI to consider the geographical location information of the craftsman. For example, the generation unit prioritizes the analysis of videos related to tasks performed by the craftsman in a specific region. The generation unit can also prioritize the analysis of videos that include region-specific techniques based on the geographical location information of the craftsman. Furthermore, if the craftsman is on the move, the generation unit can prioritize the analysis of tasks related to their current location. This allows for the prioritization of highly relevant tasks by considering the geographical location information of the craftsman. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the geographical location information of the craftsman into a generation AI, which can then prioritize the analysis of highly relevant tasks.

[0043] The generation unit can analyze a craftsman's social media activity during video analysis and analyze related tasks. For example, the generation unit uses AI to analyze a craftsman's social media activity. For example, the generation unit analyzes related videos based on the work content shared by the craftsman on social media. The generation unit can also analyze videos related to technologies the craftsman is interested in from the craftsman's social media activity. Furthermore, the generation unit can analyze related videos by referring to the work of other craftsmen that the craftsman follows on social media. In this way, related tasks can be analyzed by analyzing the craftsman's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the craftsman's social media activity data into a generation AI, which can then analyze related tasks.

[0044] The training department can analyze a new employee's past learning history and select the optimal training method. For example, the training department can use AI to analyze a new employee's past learning history. For instance, the training department can prioritize providing similar methods based on learning methods that have worked well for the new employee in the past. It can also avoid learning methods that have failed in the past and provide methods with a higher success rate. Furthermore, the training department can select training methods specialized for specific skills based on the new employee's learning history. In this way, the optimal training method can be selected by analyzing a new employee's past learning history. Some or all of the above processes in the training department may be performed using, for example, generative AI, or not using generative AI. For example, the training department can input data on a new employee's past learning history into a generative AI, which can then select the optimal training method.

[0045] The training department can filter new recruits based on their current skill level and areas of interest during training. For example, the training department can use AI to filter based on the new recruits' current skill level and areas of interest. For example, the training department can provide training content of appropriate difficulty according to the new recruits' skill level. The training department can also provide training content that will interest the new recruits based on their areas of interest. Furthermore, the training department can combine the new recruits' skill level and areas of interest to provide optimal training content. In this way, optimal training content can be provided by filtering based on the new recruits' skill level and areas of interest. Some or all of the above processing in the training department may be performed using, for example, a generative AI, or without a generative AI. For example, the training department can input data on the new recruits' skill levels and areas of interest into a generative AI, which can then perform the filtering.

[0046] The training department can prioritize providing training content that is highly relevant to new recruits, taking into account their geographical location. For example, the training department can use AI to consider the geographical location of new recruits. For instance, the training department can prioritize providing training content related to the work the new recruit will be doing in a specific region. The training department can also provide training content that includes region-specific technologies based on the new recruit's geographical location. Furthermore, if the new recruit is on the move, the training department can provide training content related to their current location. This allows the training department to provide highly relevant training content by considering the new recruit's geographical location. Some or all of the above processing in the training department may be performed using, for example, a generative AI, or without a generative AI. For example, the training department can input the new recruit's geographical location into a generative AI, which can then prioritize providing highly relevant training content.

[0047] The training department can analyze the social media activities of new recruits during their training and provide relevant training content. For example, the training department can use AI to analyze the social media activities of new recruits. For instance, the training department can provide relevant training content based on the interests that new recruits share on social media. The training department can also provide training content related to technologies that new recruits are interested in, based on their social media activities. Furthermore, the training department can provide relevant training content by referencing the technologies of other professionals that new recruits follow on social media. In this way, relevant training content can be provided by analyzing the social media activities of new recruits. Some or all of the above processes in the training department may be performed using, for example, generative AI, or not using generative AI. For example, the training department can input new recruit social media activity data into a generative AI, and the generative AI can provide relevant training content.

[0048] The display unit can analyze the craftsman's past work history and select the optimal AR display method. For example, the display unit uses AI to analyze the craftsman's past work history. For example, the display unit prioritizes displaying similar procedures based on the craftsman's past successful work procedures. The display unit can also avoid work procedures that the craftsman has failed at in the past and display procedures with a high success rate. Furthermore, the display unit can select an AR display method specialized for a specific technique from the craftsman's work history. In this way, the optimal AR display method can be selected by analyzing the craftsman's past work history. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input the craftsman's past work history data into a generative AI, which can then select the optimal AR display method.

[0049] The display unit can filter AR displays based on the craftsman's work environment and the tools they use. For example, the display unit uses AI to filter based on the craftsman's work environment and tools. For example, the display unit filters the AR display according to the type of tools the craftsman uses. The display unit can also filter the AR display according to the craftsman's work environment (indoors, outdoors, etc.). Furthermore, the display unit can filter the AR display according to the size of the craftsman's workspace. This improves the accuracy of the display by filtering based on the craftsman's work environment and tools. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input data on the craftsman's work environment and tools into a generative AI, which can then perform the filtering.

[0050] The display unit can prioritize displaying highly relevant information by considering the geographical location of the craftsman during AR display. For example, the display unit uses AI to consider the geographical location of the craftsman. For example, the display unit prioritizes displaying information related to the work the craftsman is doing in a specific area. The display unit can also display information including region-specific techniques based on the craftsman's geographical location. Furthermore, if the craftsman is on the move, the display unit can prioritize displaying information related to their current location. In this way, highly relevant information can be prioritized by considering the craftsman's geographical location. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input the craftsman's geographical location information into a generative AI, which can then prioritize displaying highly relevant information.

[0051] The display unit can analyze the craftsman's social media activity during AR display and display relevant information. For example, the display unit uses AI to analyze the craftsman's social media activity. For example, the display unit displays relevant information based on the work content shared by the craftsman on social media. The display unit can also display information related to technologies the craftsman is interested in, based on their social media activity. Furthermore, the display unit can display relevant information by referencing the work of other craftsmen that the craftsman follows on social media. In this way, relevant information can be displayed by analyzing the craftsman's social media activity. Some or all of the above processing in the display unit may be performed using, for example, generative AI, or without generative AI. For example, the display unit can input the craftsman's social media activity data into generative AI, and the generative AI can display relevant information.

[0052] The work unit can analyze a craftsman's past work history and select the optimal work method. For example, the work unit can use AI to analyze a craftsman's past work history. For example, the work unit can prioritize performing similar procedures based on the craftsman's past successful procedures. The work unit can also avoid procedures that the craftsman has failed at in the past and perform procedures with a higher success rate. Furthermore, the work unit can select work methods specialized for specific skills from the craftsman's work history. In this way, the optimal work method can be selected by analyzing a craftsman's past work history. Some or all of the above processing in the work unit may be performed using, for example, generative AI, or without generative AI. For example, the work unit can input the craftsman's past work history data into a generative AI, and the generative AI can select the optimal work method.

[0053] The work unit can filter tasks based on the craftsman's current skill level and areas of interest during the task. For example, the work unit uses AI to filter tasks based on the craftsman's current skill level and areas of interest. For example, the work unit provides tasks of appropriate difficulty according to the craftsman's skill level. The work unit can also provide tasks that are of interest to the craftsman based on their areas of interest. Furthermore, the work unit can combine the craftsman's skill level and areas of interest to provide the optimal task. This allows the work unit to provide the optimal task by filtering based on the craftsman's skill level and areas of interest. Some or all of the above processing in the work unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the work unit can input data on the craftsman's skill level and areas of interest into a generative AI, which can then perform the filtering.

[0054] The work unit can prioritize tasks that are highly relevant to the work, taking into account the geographical location of the craftsman. For example, the work unit can use AI to consider the geographical location of the craftsman. For example, the work unit can prioritize tasks related to work performed by the craftsman in a specific region. The work unit can also perform tasks that include region-specific techniques based on the craftsman's geographical location. Furthermore, if the craftsman is on the move, the work unit can prioritize tasks related to their current location. In this way, by considering the geographical location of the craftsman, highly relevant tasks can be prioritized. Some or all of the above processing in the work unit may be performed using, for example, a generative AI, or without a generative AI. For example, the work unit can input the craftsman's geographical location into a generative AI, which can then prioritize tasks that are highly relevant.

[0055] The work unit can analyze the social media activity of craftsmen during work and perform related tasks. For example, the work unit can use AI to analyze the social media activity of craftsmen. For example, the work unit can perform related tasks based on the work content that craftsmen have shared on social media. The work unit can also perform tasks related to technologies that the craftsmen are interested in, based on their social media activity. Furthermore, the work unit can perform related tasks by referring to the work of other craftsmen that the craftsmen follow on social media. In this way, related tasks can be performed by analyzing the social media activity of craftsmen. Some or all of the above processing in the work unit may be performed using, for example, generative AI, or not using generative AI. For example, the work unit can input the craftsmen's social media activity data into generative AI, and the generative AI can perform related tasks.

[0056] The analysis unit can select the optimal analysis method by referring to past analysis data when analyzing the properties of a material. For example, the analysis unit uses AI to refer to past analysis data. For example, the analysis unit selects the optimal analysis method based on past analysis data. The analysis unit can also select an analysis method with a high success rate from past analysis data. Furthermore, the analysis unit can analyze past analysis data and select an analysis method specialized for a particular material. In this way, the optimal analysis method can be selected by referring to past analysis data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can select the optimal analysis method.

[0057] The analysis unit can filter the material's properties based on its usage environment and purpose. For example, the analysis unit uses AI to filter based on the material's usage environment and purpose. For instance, the analysis unit filters the analysis according to the material's usage environment (indoors, outdoors, etc.). The analysis unit can also filter the analysis according to the material's purpose. Furthermore, the analysis unit can filter the analysis according to the material's properties. This improves the accuracy of the analysis by filtering based on the material's usage environment and purpose. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input data on the material's usage environment and purpose into a generating AI, which can then perform the filtering.

[0058] The analysis unit, when analyzing the properties of materials, can prioritize the analysis of highly relevant materials by considering their geographical distribution. For example, the analysis unit uses AI to consider the geographical distribution of materials. For instance, the analysis unit prioritizes the analysis of highly relevant materials based on their geographical distribution. The analysis unit can also prioritize the analysis of region-specific materials based on their geographical distribution. Furthermore, the analysis unit can analyze the geographical distribution of materials and select the optimal analysis method. This allows for the priority analysis of highly relevant materials by considering their geographical distribution. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input geographical distribution data of materials into a generative AI, which can then prioritize the analysis of highly relevant materials.

[0059] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing the properties of a material. For example, the analysis unit uses AI to refer to relevant literature. For example, the analysis unit can refer to relevant literature to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method from the relevant literature. Furthermore, the analysis unit can analyze relevant literature and select an analysis method specific to a particular material. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input relevant literature data into a generating AI, and the generating AI can improve the accuracy of the analysis.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The technology transfer system can analyze a craftsman's past work history and select the optimal video analysis method. For example, the generation unit uses AI to analyze a craftsman's past work history. The generation unit prioritizes analyzing similar procedures based on the craftsman's past successful work procedures. It can also avoid work procedures that the craftsman has failed at in the past and analyze procedures with a high success rate. Furthermore, the generation unit can select a video analysis method specialized for a particular technique from the craftsman's work history. In this way, the optimal video analysis method can be selected by analyzing a craftsman's past work history.

[0062] The technology transfer system can prioritize the analysis of highly relevant tasks by considering the geographical location of the craftsman. For example, the generation unit uses AI to consider the craftsman's geographical location. The generation unit prioritizes the analysis of videos related to tasks performed by the craftsman in a specific region. The generation unit can also prioritize the analysis of videos containing region-specific techniques based on the craftsman's geographical location. Furthermore, if the craftsman is on the move, the generation unit can prioritize the analysis of tasks related to their current location. In this way, by considering the craftsman's geographical location, the system can prioritize the analysis of highly relevant tasks.

[0063] The technology transfer system can analyze the social media activities of craftsmen and identify related tasks. For example, the generation unit uses AI to analyze craftsmen's social media activities. The generation unit analyzes related videos based on the work content shared by craftsmen on social media. It can also analyze videos related to technologies that craftsmen are interested in from their social media activities. Furthermore, the generation unit can analyze related videos by referring to the work of other craftsmen that the craftsmen follow on social media. In this way, by analyzing the social media activities of craftsmen, it is possible to identify related tasks.

[0064] A skills transfer system can analyze a craftsman's past learning history and select the optimal training method. For example, the training department uses AI to analyze a new recruit's past learning history. Based on the learning methods that the new recruit has succeeded with in the past, the training department prioritizes providing similar methods. It can also avoid learning methods that the new recruit has failed with in the past and provide methods with a higher success rate. Furthermore, the training department can select training methods specialized for specific skills based on the new recruit's learning history. In this way, the optimal training method can be selected by analyzing the new recruit's past learning history.

[0065] The technology transfer system can select the optimal analysis method by referring to past analysis data when analyzing the properties of materials. For example, the analysis unit uses AI to refer to past analysis data. The analysis unit selects the optimal analysis method based on past analysis data. The analysis unit can also select analysis methods with a high success rate from past analysis data. Furthermore, the analysis unit can analyze past analysis data and select analysis methods specialized for specific materials. In this way, the optimal analysis method can be selected by referring to past analysis data.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The generation unit analyzes the work of craftsmen via video and creates digitized training manuals. For example, the work of bamboo craftsmen, lacquerware craftsmen, and pottery craftsmen is videotaped, and the generation AI analyzes the procedures to create digital manuals. Step 2: The training department automates new employee training based on the training manuals created by the generation department. For example, it uses digital manuals to instruct new employees on work procedures, monitor their progress, evaluate their work, and provide feedback. It also provides customized training programs tailored to the new employees' skill levels. Step 3: The display unit shows the procedures and tool placement in the workspace. For example, using AR, it can display drying time and mixing ratios in real time when a lacquer craftsman is working, the placement of tools and work procedures used by a bamboo craftsman can be displayed, and information on work procedures and materials used by a pottery craftsman can be displayed when a potter is working. Step 4: The work area performs the work based on the procedures and tool placement displayed by the display area. For example, it supports lacquer craftsmen in working according to drying time and mixing ratios, bamboo craftsmen in working according to the placement of their tools, and potters in working according to information on the materials they use. Step 5: The analysis unit analyzes the properties of the materials and proposes the optimal processing procedure. For example, it uses AI to analyze the properties of bamboo and lacquer and proposes the optimal processing procedure. It analyzes the physical and chemical properties of bamboo and proposes the optimal processing procedure, analyzes the properties of lacquer and proposes the optimal drying time and mixing ratio, and analyzes the properties of clay used in pottery and proposes the optimal molding procedure and firing conditions.

[0068] (Example of form 2) The technology transfer system according to an embodiment of the present invention is a system that utilizes generative AI and AR to improve the transfer of craftsmanship skills and work efficiency. This technology transfer system promotes the automation of new employee training by video analyzing the work of craftsmen and creating a digitized training manual using generative AI. Furthermore, it uses AR to display procedures and the placement of tools in the workspace, supporting craftsmen in performing their work efficiently. For example, it can display drying time and mixing ratios in real time to a lacquer craftsman. It also uses AI to analyze the properties of materials and propose the optimal processing procedure. This provides the optimal procedure based on data including that of experienced workers, improving accuracy and efficiency. For example, the technology transfer system video analyzes the work of craftsmen and creates a digitized training manual using generative AI. In this process, the craftsman's work procedures and techniques are recorded in detail and analyzed by the generative AI. For example, the work of a bamboo craftsman is videotaped, and the generative AI analyzes the procedure to create a digital manual. This promotes the automation of new employee training and enables efficient transfer of skills. Next, the technology transfer system uses AR to display procedures and the placement of tools in the workspace. For example, when a lacquer craftsman is working, AR is used to display drying time and mixing ratios in real time. This allows craftsmen to work more efficiently, improving accuracy and efficiency. Furthermore, the technology transfer system uses AI to analyze the properties of materials and propose the optimal processing procedure. For example, the AI ​​analyzes the properties of bamboo and lacquer and proposes the optimal processing procedure. This provides the optimal procedure based on data, including that of experienced craftsmen, improving accuracy and efficiency. This system enables faster and more efficient skill acquisition through digital manuals and AR guidance. It also improves the uniformity of work and unifies quality assurance, and enables support for the development of new technologies that exceed normal efficiency. For example, when a lacquer craftsman introduces a new technique, using AI and AR can provide more accurate drying time management and mixing ratio advice. This allows for the production of higher quality lacquerware products. In this way, the technology transfer system enables the transfer of craftsmanship and improves work efficiency.

[0069] The technology transfer system according to this embodiment comprises a generation unit, a training unit, a display unit, a work unit, and an analysis unit. The generation unit video-analyzes the work of a craftsman and creates a digitized training manual. For example, the generation unit records the work procedures and techniques of a craftsman in detail, and the generation AI analyzes this to create a digital manual. For example, the generation unit videotapes the work of a bamboo craftsman, and the generation AI analyzes the procedures to create a digital manual. The generation unit can also videotape the work of a lacquer craftsman, and the generation AI can analyze the procedures to create a digital manual. Furthermore, the generation unit can videotape the work of a ceramic craftsman, and the generation AI can analyze the procedures to create a digital manual. The training unit automates the training of new recruits based on the training manual created by the generation unit. For example, the training unit automates the training program for new recruits using the digital manual created by the generation unit. For example, the training unit instructs new recruits on work procedures and monitors their progress based on the digital manual. The training unit can also evaluate the work of new recruits and provide feedback based on the digital manual. Furthermore, the training department can provide customized training programs tailored to the skill level of new recruits, based on digital manuals. The display unit shows procedures and tool placement in the workspace. The display unit can, for example, use augmented reality (AR) to show procedures and tool placement in the workspace. For example, the display unit can show drying time and mixing ratio in real time when a lacquer craftsman is working. The display unit can also show the placement of tools and work procedures when a bamboo craftsman is working. Furthermore, the display unit can also show work procedures and information on materials used when a potter is working. The work unit performs work based on the procedures and tool placement displayed by the display unit. For example, the work unit helps craftsmen perform work according to the procedures displayed by the display unit. For example, the work unit supports lacquer craftsmen in performing work according to drying time and mixing ratio. The work unit can also support bamboo craftsmen in performing work according to the placement of tools they use. Furthermore, the work unit can also support pottery craftsmen in performing work according to information on materials they use.The analysis unit analyzes the properties of materials and proposes the optimal processing procedure. For example, the analysis unit uses AI to analyze the properties of bamboo and lacquer and proposes the optimal processing procedure. For instance, the analysis unit analyzes the physical and chemical properties of bamboo and proposes the optimal processing procedure. The analysis unit can also analyze the properties of lacquer and propose the optimal drying time and mixing ratio. Furthermore, the analysis unit can analyze the properties of clay used in pottery and propose the optimal molding procedure and firing conditions. As a result, the technology transfer system according to this embodiment can achieve the transfer of craftsmanship and improvement of work efficiency.

[0070] The generation unit analyzes the work of craftsmen via video and creates digitized training manuals. For example, the generation unit meticulously records the work procedures and techniques of craftsmen, and the generation AI analyzes this information to create digital manuals. Specifically, the generation unit uses high-resolution cameras to film the craftsmen's work from multiple angles, and the generation AI analyzes the footage. The generation AI analyzes the movements in the video frame by frame, meticulously recording the start and end times of each movement, the types of tools and materials used, and the working environment conditions. For example, when videotaping a bamboo craftsman, the system meticulously records a series of work procedures such as bamboo selection, cutting, carving, and assembly, and the generation AI analyzes these procedures to create a digital manual. Similarly, when videotaping a lacquer craftsman, the system meticulously records processes such as lacquer mixing, application, and drying, and the generation AI analyzes these procedures to create a digital manual. Furthermore, when videotaping a pottery craftsman, the system meticulously records processes such as clay kneading, shaping, and firing, and the generation AI analyzes these procedures to create a digital manual. The generating AI analyzes these work procedures, evaluating their accuracy and efficiency, and extracting the optimal procedure. Furthermore, the generating AI highlights particularly important points and precautions within the work procedures, reflecting them in the digital manual. This allows the generating unit to accurately digitize the advanced skills of craftsmen and provide them as training manuals.

[0071] The Training Department automates new employee training based on training manuals created by the Production Department. For example, the Training Department automates the new employee training program using digital manuals created by the Production Department. Specifically, the Training Department instructs new employees on work procedures based on the digital manuals and monitors their progress. The Training Department uses AI to monitor new employees' work in real time and evaluate the accuracy and efficiency of their work procedures. For example, when a new employee is working on bamboo crafts, the AI ​​instructs them on procedures such as selecting, cutting, carving, and assembling bamboo based on the digital manual, and monitors and evaluates the new employee's work. Similarly, when a new employee is working with lacquer, the AI ​​can instruct them on procedures such as mixing, applying, and drying lacquer based on the digital manual, and monitor and evaluate the new employee's work. Furthermore, when a new employee is working on pottery, the AI ​​can instruct them on procedures such as kneading, shaping, and firing clay based on the digital manual, and monitor and evaluate the new employee's work. When evaluating new employees' work, the Training Department evaluates not only the accuracy and efficiency of the work, but also the safety and quality of the work. Furthermore, the training department can provide customized training programs tailored to the skill level of new recruits. For example, if a new recruit has a low skill level, the training department will focus on teaching basic work procedures, while if they have a high skill level, they will be taught more advanced techniques and applied work procedures. In this way, the training department can efficiently support the skill improvement of new recruits and ensure the transmission of skilled craftsmanship.

[0072] The display unit shows procedures and tool placement in the workspace. For example, the display unit uses augmented reality (AR) to show procedures and tool placement in the workspace. Specifically, the display unit uses AR glasses or a projector to display procedures and tool placement in the workspace in real time. For example, when a lacquer craftsman is working, wearing AR glasses will display drying time and mixing ratios in real time within their field of view. Similarly, when a bamboo craftsman is working, a projector can be used to display the placement of tools used on the workbench and the work procedures. Furthermore, when a potter is working, AR glasses or a projector can be used to display information about the work procedures and materials used. The display unit can not only show work procedures and tool placement, but also the progress of the work and points to note. For example, when a lacquer craftsman is working, it will display the elapsed drying time and the timing of the next work procedure. Similarly, when a bamboo craftsman is working, it can display the placement of tools used and the progress of the work procedures. Furthermore, when a potter is working, it can display not only the work procedures and information about materials used, but also the progress of the work and points to note. This allows the display unit to support craftsmen in performing their work efficiently, improving the accuracy and quality of their work.

[0073] The work area performs tasks based on the procedures and tool placements displayed by the display unit. For example, the work area supports craftsmen in performing tasks according to the procedures displayed by the display unit. Specifically, the work area provides support to craftsmen as they perform tasks based on the procedures and tool placements displayed by the display unit. For example, to support lacquer craftsmen in performing tasks according to drying time and mixing ratios, the work area monitors the progress of drying time and adjustments to mixing ratios in real time and provides feedback to the craftsman. Similarly, to support bamboo craftsmen in performing tasks according to the placement of their tools, the work area can monitor the placement and use of tools in real time and provide feedback to the craftsman. Furthermore, to support pottery craftsmen in performing tasks according to information on the materials they use, the work area can monitor the usage of materials and work procedures in real time and provide feedback to the craftsman. In addition to providing support to craftsmen as they perform tasks, the work area can monitor the progress and quality of the work and make adjustments and improvements as needed. This allows the work area to support craftsmen in performing tasks efficiently and accurately, thereby improving the quality and efficiency of the work.

[0074] The analysis unit analyzes the properties of materials and proposes the optimal processing procedure. For example, the analysis unit uses AI to analyze the properties of bamboo and lacquer and propose the optimal processing procedure. Specifically, the analysis unit uses AI to analyze the physical and chemical properties of bamboo in detail and propose the optimal processing procedure. For example, it analyzes the properties of bamboo such as hardness, elasticity, and moisture content, and proposes the optimal cutting and carving methods based on that. It can also analyze the properties of lacquer and propose the optimal drying time and mixing ratio. For example, it analyzes the properties of lacquer such as viscosity, drying speed, and mixing ratio, and proposes the optimal drying time and mixing ratio based on that. Furthermore, the analysis unit can analyze the properties of clay used in pottery and propose the optimal molding procedure and firing conditions. For example, it analyzes the properties of clay such as particle size, moisture content, and firing temperature, and proposes the optimal molding procedure and firing conditions based on that. When analyzing these properties, the analysis unit utilizes past data and statistical information to perform more accurate analyses. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns or abnormal data, and issue warnings early. This allows the analysis unit to analyze the material properties in detail and propose the optimal processing procedure, thereby improving work efficiency and quality.

[0075] The generation unit can meticulously record the work procedures and techniques of craftsmen, and the generation AI can analyze this information to create digital manuals. For example, the generation unit can meticulously record the work procedures and techniques of craftsmen. For example, the generation unit can film the craftsmen's work using a video camera and analyze the footage. The generation unit can also record the craftsmen's work procedures as text. For example, the generation unit can meticulously record each step performed by the craftsman and transcribe the procedure into text. Furthermore, the generation unit can meticulously record the craftsmen's work techniques. For example, the generation unit can meticulously record the types of tools and materials used by the craftsman and how to use them. The generation AI analyzes the recorded work procedures and techniques to create digital manuals. For example, the generation AI can analyze video footage and extract the craftsmen's movements. For example, the generation AI can analyze the craftsmen's hand movements from video footage and reflect those movements in the digital manual. The generation AI can also analyze text data and organize work procedures. For example, the generation AI can analyze recorded text data and organize work procedures in an easy-to-understand manner. Furthermore, the generating AI can also analyze the skills of craftsmen and reflect them in digital manuals. For example, the generating AI can analyze how craftsmen use the tools and materials they employ and include that information in the digital manual. This allows for detailed recording of craftsmen's work procedures and techniques, facilitating the automation of new employee training by creating digital manuals. Some or all of the above-described processes in the generation unit may be performed using the generating AI, or they may not. For example, the generation unit can input video footage captured by a video camera into the generating AI, which can then analyze the footage to create a digital manual.

[0076] The display unit can provide real-time information on drying time and mixing ratios to lacquer artisans while they are working. For example, the display unit can use augmented reality (AR) to provide real-time information on drying time and mixing ratios to lacquer artisans while they are working. For instance, the display unit can show the drying time in real time when a lacquer artisan is applying lacquer. The display unit can also show the mixing ratio in real time when a lacquer artisan is mixing lacquer. Furthermore, the display unit can also display the arrangement of tools and work procedures used by the lacquer artisan while they are working. For example, the display unit can show the arrangement of brushes and spatulas used by the lacquer artisan and provide real-time information on work procedures. This allows the lacquer artisan to work more efficiently, improving the accuracy and efficiency of their work. Some or all of the above-described processes in the display unit may be performed using augmented reality (AR), or they may not. For example, the display unit can use augmented reality (AR) to show drying time and mixing ratios in real time when a lacquer artisan is working.

[0077] The analysis unit can analyze the properties of bamboo and lacquer and propose the optimal processing procedure. For example, the analysis unit can use AI to analyze the properties of bamboo and lacquer. For instance, it can analyze the physical and chemical properties of bamboo and propose the optimal processing procedure. It can also analyze the properties of lacquer and propose the optimal drying time and mixing ratio. Furthermore, it can analyze the properties of clay used in pottery and propose the optimal molding procedure and firing conditions. This improves accuracy and efficiency by analyzing the properties of materials and proposing the optimal processing procedure. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can use AI to analyze data in order to analyze the properties of bamboo and lacquer and propose the optimal processing procedure.

[0078] The training department can automate new employee training based on digital manuals created by the generation department. For example, the training department can automate the new employee training program using the digital manuals created by the generation department. For example, the training department can instruct new employees on work procedures and monitor their progress based on the digital manuals. The training department can also evaluate the new employees' work and provide feedback based on the digital manuals. Furthermore, the training department can provide customized training programs tailored to the new employees' skill levels based on the digital manuals. This improves the efficiency of training by automating new employee training based on digital manuals. Some or all of the above processes in the training department may be performed using AI, for example, or not using AI. For example, the training department can input the digital manuals created by the generation department into an AI, which can then automate the new employee training program.

[0079] The work unit can perform tasks based on procedures and tool placements displayed by the display unit. For example, the work unit can help a craftsman perform tasks according to procedures displayed by the display unit. For example, the work unit can support a lacquer craftsman in performing tasks according to drying times and mixing ratios. The work unit can also support a bamboo craftsman in performing tasks according to the placement of tools used. Furthermore, the work unit can support a potter in performing tasks according to information on the materials used. This improves the efficiency and accuracy of the work by performing tasks based on displayed procedures and tool placements. Some or all of the above processes in the work unit may be performed using AI, for example, or not. For example, the work unit can input the procedures and tool placements displayed by the display unit into the AI, which can then support the work.

[0080] The generation unit can estimate the emotions of the craftsman and adjust the timing of video analysis based on the estimated emotions. For example, the generation unit uses an emotion estimation algorithm to estimate the emotions of the craftsman. For example, the generation unit can capture the craftsman's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the craftsman's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the generation unit adjusts the timing of video analysis. For example, if the craftsman is concentrating, the generation unit will perform video analysis continuously without interruption. If the craftsman is tired, the generation unit can pause the video analysis and resume it after a break. Furthermore, if the craftsman is stressed, the generation unit can reduce the frequency of video analysis and resume it in a relaxed state. This improves the efficiency of the analysis by adjusting the timing of video analysis based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input image data of a craftsman captured by a camera into the generation AI, which can then estimate the craftsman's emotions and adjust the timing of the video analysis.

[0081] The generation unit can analyze a craftsman's past work history and select the optimal video analysis method. For example, the generation unit uses AI to analyze a craftsman's past work history. For example, the generation unit prioritizes analyzing similar procedures based on the craftsman's past successful work procedures. The generation unit can also avoid work procedures that the craftsman has failed at in the past and analyze procedures with a high success rate. Furthermore, the generation unit can select a video analysis method specialized for a specific technique from the craftsman's work history. In this way, the optimal video analysis method can be selected by analyzing the craftsman's past work history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the craftsman's past work history data into a generation AI, which can then select the optimal video analysis method.

[0082] The generation unit can perform filtering during video analysis based on the craftsman's work environment and the tools they use. For example, the generation unit uses AI to perform filtering based on the craftsman's work environment and tools. For instance, the generation unit filters the video analysis according to the type of tools the craftsman uses. The generation unit can also filter the video analysis according to the craftsman's work environment (indoors, outdoors, etc.). Furthermore, the generation unit can filter the video analysis according to the size of the craftsman's workspace. This improves the accuracy of the analysis by filtering based on the craftsman's work environment and tools. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the craftsman's work environment and tools into the generation AI, which can then perform the filtering.

[0083] The generation unit can estimate the emotions of the craftsman and determine the priority of the digital manuals to generate based on the estimated emotions. For example, the generation unit uses an emotion estimation algorithm to estimate the emotions of the craftsman. For instance, the generation unit can capture the craftsman's facial expressions with a camera and estimate their emotions using the emotion estimation algorithm. The generation unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the generation unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using the emotion estimation algorithm. Based on the estimated emotions of the craftsman, the generation unit determines the priority of the digital manuals to generate. For example, if the craftsman is relaxed, the generation unit will prioritize generating detailed digital manuals. If the craftsman is in a hurry, the generation unit can prioritize generating concise digital manuals. Furthermore, if the craftsman is excited, the generation unit can prioritize generating visually stimulating digital manuals. This allows for efficient training by prioritizing digital manuals based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. The 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input image data of a craftsman captured by a camera into the generative AI, which can then estimate the craftsman's emotions and determine the priority of the digital manual.

[0084] The generation unit can prioritize the analysis of highly relevant tasks by considering the geographical location information of the craftsman during video analysis. For example, the generation unit uses AI to consider the geographical location information of the craftsman. For example, the generation unit prioritizes the analysis of videos related to tasks performed by the craftsman in a specific region. The generation unit can also prioritize the analysis of videos that include region-specific techniques based on the geographical location information of the craftsman. Furthermore, if the craftsman is on the move, the generation unit can prioritize the analysis of tasks related to their current location. This allows for the prioritization of highly relevant tasks by considering the geographical location information of the craftsman. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the geographical location information of the craftsman into a generation AI, which can then prioritize the analysis of highly relevant tasks.

[0085] The generation unit can analyze a craftsman's social media activity during video analysis and analyze related tasks. For example, the generation unit uses AI to analyze a craftsman's social media activity. For example, the generation unit analyzes related videos based on the work content shared by the craftsman on social media. The generation unit can also analyze videos related to technologies the craftsman is interested in from the craftsman's social media activity. Furthermore, the generation unit can analyze related videos by referring to the work of other craftsmen that the craftsman follows on social media. In this way, related tasks can be analyzed by analyzing the craftsman's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the craftsman's social media activity data into a generation AI, which can then analyze related tasks.

[0086] The training department can estimate the emotions of new recruits and adjust the progress of the training program based on those estimated emotions. For example, the training department uses an emotion estimation algorithm to estimate the emotions of new recruits. For instance, the training department can capture the new recruit's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the training department can record the new recruit's voice and estimate their emotions using voice analysis technology. Furthermore, the training department can collect the new recruit's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the new recruit, the training department adjusts the progress of the training program. For example, if the new recruit is nervous, the training department will proceed with the training program slowly. If the new recruit is relaxed, the training department can proceed with the training program at a normal pace. Furthermore, if the new recruit is excited, the training department can proceed with the training program quickly. This allows for efficient training by adjusting the progress of the training program based on the new recruit's emotions. Emotion estimation is achieved using emotion estimation functions, such as 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 processes described above in the training department may be performed using AI, or not using AI. For example, the training department can input image data of a new employee taken with a camera into a generative AI, which can then estimate the new employee's emotions and adjust the progress of the training program.

[0087] The training department can analyze a new employee's past learning history and select the optimal training method. For example, the training department can use AI to analyze a new employee's past learning history. For instance, the training department can prioritize providing similar methods based on learning methods that have worked well for the new employee in the past. It can also avoid learning methods that have failed in the past and provide methods with a higher success rate. Furthermore, the training department can select training methods specialized for specific skills based on the new employee's learning history. In this way, the optimal training method can be selected by analyzing a new employee's past learning history. Some or all of the above processes in the training department may be performed using, for example, generative AI, or not using generative AI. For example, the training department can input data on a new employee's past learning history into a generative AI, which can then select the optimal training method.

[0088] The training department can filter new recruits based on their current skill level and areas of interest during training. For example, the training department can use AI to filter based on the new recruits' current skill level and areas of interest. For example, the training department can provide training content of appropriate difficulty according to the new recruits' skill level. The training department can also provide training content that will interest the new recruits based on their areas of interest. Furthermore, the training department can combine the new recruits' skill level and areas of interest to provide optimal training content. In this way, optimal training content can be provided by filtering based on the new recruits' skill level and areas of interest. Some or all of the above processing in the training department may be performed using, for example, a generative AI, or without a generative AI. For example, the training department can input data on the new recruits' skill levels and areas of interest into a generative AI, which can then perform the filtering.

[0089] The training department can estimate the emotions of new recruits and prioritize training programs based on those estimated emotions. For example, the training department might use an emotion estimation algorithm to estimate a recruit's emotions. For instance, it could capture a recruit's facial expression with a camera and use the emotion estimation algorithm to estimate their emotions. Alternatively, it could record a recruit's voice and use voice analysis technology to estimate their emotions. Furthermore, it could collect biometric data (heart rate and skin electrical activity) from a recruit using sensors and use the emotion estimation algorithm to estimate their emotions. Based on the estimated emotions, the training department prioritizes training programs. For example, if a recruit is relaxed, the training department prioritizes providing a detailed training program. If a recruit is in a hurry, it prioritizes providing a concise training program. If a recruit is excited, it prioritizes providing a visually stimulating training program. This allows for efficient training by prioritizing training programs based on the recruit's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, 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 training department may be performed using AI, or not using AI. For example, the training department can input image data of new recruits taken with a camera into a generative AI, which can then estimate the new recruits' emotions and determine the priorities of the training program.

[0090] The training department can prioritize providing training content that is highly relevant to new recruits, taking into account their geographical location. For example, the training department can use AI to consider the geographical location of new recruits. For instance, the training department can prioritize providing training content related to the work the new recruit will be doing in a specific region. The training department can also provide training content that includes region-specific technologies based on the new recruit's geographical location. Furthermore, if the new recruit is on the move, the training department can provide training content related to their current location. This allows the training department to provide highly relevant training content by considering the new recruit's geographical location. Some or all of the above processing in the training department may be performed using, for example, a generative AI, or without a generative AI. For example, the training department can input the new recruit's geographical location into a generative AI, which can then prioritize providing highly relevant training content.

[0091] The training department can analyze the social media activities of new recruits during their training and provide relevant training content. For example, the training department can use AI to analyze the social media activities of new recruits. For instance, the training department can provide relevant training content based on the interests that new recruits share on social media. The training department can also provide training content related to technologies that new recruits are interested in, based on their social media activities. Furthermore, the training department can provide relevant training content by referencing the technologies of other professionals that new recruits follow on social media. In this way, relevant training content can be provided by analyzing the social media activities of new recruits. Some or all of the above processes in the training department may be performed using, for example, generative AI, or not using generative AI. For example, the training department can input new recruit social media activity data into a generative AI, and the generative AI can provide relevant training content.

[0092] The display unit can estimate the emotions of the craftsman and adjust the timing of the AR display based on the estimated emotions. For example, the display unit uses an emotion estimation algorithm to estimate the emotions of the craftsman. For example, the display unit can capture the craftsman's facial expression with a camera and estimate the emotions using the emotion estimation algorithm. The display unit can also record the craftsman's voice and estimate the emotions using voice analysis technology. Furthermore, the display unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using the emotion estimation algorithm. Based on the estimated emotions of the craftsman, the display unit adjusts the timing of the AR display. For example, if the craftsman is concentrating, the display unit will continuously display the AR without interruption. If the craftsman is tired, the display unit can temporarily pause the AR display and resume it after a break. Furthermore, if the craftsman is stressed, the display unit can reduce the frequency of the AR display and resume it in a relaxed state. This allows for more efficient work by adjusting the timing of the AR display based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the display unit may be performed using AI, or not using AI. For example, the display unit can input image data of a craftsman captured by a camera into the generating AI, which can then estimate the craftsman's emotions and adjust the timing of the AR display.

[0093] The display unit can analyze the craftsman's past work history and select the optimal AR display method. For example, the display unit uses AI to analyze the craftsman's past work history. For example, the display unit prioritizes displaying similar procedures based on the craftsman's past successful work procedures. The display unit can also avoid work procedures that the craftsman has failed at in the past and display procedures with a high success rate. Furthermore, the display unit can select an AR display method specialized for a specific technique from the craftsman's work history. In this way, the optimal AR display method can be selected by analyzing the craftsman's past work history. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input the craftsman's past work history data into a generative AI, which can then select the optimal AR display method.

[0094] The display unit can filter AR displays based on the craftsman's work environment and the tools they use. For example, the display unit uses AI to filter based on the craftsman's work environment and tools. For example, the display unit filters the AR display according to the type of tools the craftsman uses. The display unit can also filter the AR display according to the craftsman's work environment (indoors, outdoors, etc.). Furthermore, the display unit can filter the AR display according to the size of the craftsman's workspace. This improves the accuracy of the display by filtering based on the craftsman's work environment and tools. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input data on the craftsman's work environment and tools into a generative AI, which can then perform the filtering.

[0095] The display unit can estimate the emotions of the craftsman and determine the priority of AR displays based on the estimated emotions. For example, the display unit uses an emotion estimation algorithm to estimate the emotions of the craftsman. For example, the display unit can capture the craftsman's facial expression with a camera and estimate the emotions using the emotion estimation algorithm. The display unit can also record the craftsman's voice and estimate the emotions using voice analysis technology. Furthermore, the display unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using the emotion estimation algorithm. Based on the estimated emotions of the craftsman, the display unit determines the priority of AR displays. For example, if the craftsman is relaxed, the display unit will prioritize detailed AR displays. If the craftsman is in a hurry, the display unit will prioritize concise AR displays. Furthermore, if the craftsman is excited, the display unit will prioritize visually stimulating AR displays. This allows for more efficient work by prioritizing AR displays based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the display unit may be performed using AI, or not using AI. For example, the display unit can input image data of a craftsman captured by a camera into the generating AI, which can then estimate the craftsman's emotions and determine the priority of AR display.

[0096] The display unit can prioritize displaying highly relevant information by considering the geographical location of the craftsman during AR display. For example, the display unit uses AI to consider the geographical location of the craftsman. For example, the display unit prioritizes displaying information related to the work the craftsman is doing in a specific area. The display unit can also display information including region-specific techniques based on the craftsman's geographical location. Furthermore, if the craftsman is on the move, the display unit can prioritize displaying information related to their current location. In this way, highly relevant information can be prioritized by considering the craftsman's geographical location. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or without a generative AI. For example, the display unit can input the craftsman's geographical location information into a generative AI, which can then prioritize displaying highly relevant information.

[0097] The display unit can analyze the craftsman's social media activity during AR display and display relevant information. For example, the display unit uses AI to analyze the craftsman's social media activity. For example, the display unit displays relevant information based on the work content shared by the craftsman on social media. The display unit can also display information related to technologies the craftsman is interested in, based on their social media activity. Furthermore, the display unit can display relevant information by referencing the work of other craftsmen that the craftsman follows on social media. In this way, relevant information can be displayed by analyzing the craftsman's social media activity. Some or all of the above processing in the display unit may be performed using, for example, generative AI, or without generative AI. For example, the display unit can input the craftsman's social media activity data into generative AI, and the generative AI can display relevant information.

[0098] The work unit can estimate the emotions of the craftsman and adjust the progress of the work based on the estimated emotions. For example, the work unit uses an emotion estimation algorithm to estimate the emotions of the craftsman. For example, the work unit can capture the craftsman's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The work unit can also record the craftsman's voice and estimate the emotions using voice analysis technology. Furthermore, the work unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the work unit adjusts the progress of the work. For example, if the craftsman is concentrating, the work unit will continue the work without interruption. If the craftsman is tired, the work unit can temporarily pause the work and resume it after a break. Furthermore, if the craftsman is stressed, the work unit can slow down the progress of the work and resume it in a relaxed state. In this way, by adjusting the progress of the work based on the craftsman's emotions, efficient work becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the work area may be performed using AI, or not using AI. For example, the work area can input image data of a craftsman captured by a camera into a generative AI, which can then estimate the craftsman's emotions and adjust the progress of the work.

[0099] The work unit can analyze a craftsman's past work history and select the optimal work method. For example, the work unit can use AI to analyze a craftsman's past work history. For example, the work unit can prioritize performing similar procedures based on the craftsman's past successful procedures. The work unit can also avoid procedures that the craftsman has failed at in the past and perform procedures with a higher success rate. Furthermore, the work unit can select work methods specialized for specific skills from the craftsman's work history. In this way, the optimal work method can be selected by analyzing a craftsman's past work history. Some or all of the above processing in the work unit may be performed using, for example, generative AI, or without generative AI. For example, the work unit can input the craftsman's past work history data into a generative AI, and the generative AI can select the optimal work method.

[0100] The work unit can filter tasks based on the craftsman's current skill level and areas of interest during the task. For example, the work unit uses AI to filter tasks based on the craftsman's current skill level and areas of interest. For example, the work unit provides tasks of appropriate difficulty according to the craftsman's skill level. The work unit can also provide tasks that are of interest to the craftsman based on their areas of interest. Furthermore, the work unit can combine the craftsman's skill level and areas of interest to provide the optimal task. This allows the work unit to provide the optimal task by filtering based on the craftsman's skill level and areas of interest. Some or all of the above processing in the work unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the work unit can input data on the craftsman's skill level and areas of interest into a generative AI, which can then perform the filtering.

[0101] The work unit can estimate the emotions of craftsmen and determine work priorities based on those estimated emotions. For example, the work unit uses an emotion estimation algorithm to estimate the emotions of craftsmen. For instance, the work unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the work unit can record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the work unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the work unit determines work priorities. For example, if the craftsman is relaxed, the work unit prioritizes detailed tasks. If the craftsman is in a hurry, the work unit prioritizes simpler tasks. Furthermore, if the craftsman is excited, the work unit prioritizes visually stimulating tasks. This allows for more efficient work by prioritizing tasks based on the craftsman's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the work area may be performed using AI, or not using AI. For example, the work area can input image data of a craftsman captured by a camera into a generative AI, which can then estimate the craftsman's emotions and determine the priority of the tasks.

[0102] The work unit can prioritize tasks that are highly relevant to the work, taking into account the geographical location of the craftsman. For example, the work unit can use AI to consider the geographical location of the craftsman. For example, the work unit can prioritize tasks related to work performed by the craftsman in a specific region. The work unit can also perform tasks that include region-specific techniques based on the craftsman's geographical location. Furthermore, if the craftsman is on the move, the work unit can prioritize tasks related to their current location. In this way, by considering the geographical location of the craftsman, highly relevant tasks can be prioritized. Some or all of the above processing in the work unit may be performed using, for example, a generative AI, or without a generative AI. For example, the work unit can input the craftsman's geographical location into a generative AI, which can then prioritize tasks that are highly relevant.

[0103] The work unit can analyze the social media activity of craftsmen during work and perform related tasks. For example, the work unit can use AI to analyze the social media activity of craftsmen. For example, the work unit can perform related tasks based on the work content that craftsmen have shared on social media. The work unit can also perform tasks related to technologies that the craftsmen are interested in, based on their social media activity. Furthermore, the work unit can perform related tasks by referring to the work of other craftsmen that the craftsmen follow on social media. In this way, related tasks can be performed by analyzing the social media activity of craftsmen. Some or all of the above processing in the work unit may be performed using, for example, generative AI, or not using generative AI. For example, the work unit can input the craftsmen's social media activity data into generative AI, and the generative AI can perform related tasks.

[0104] The analysis unit can estimate the emotions of a craftsman when analyzing the properties of a material, and adjust the timing of the analysis based on the estimated emotions. For example, the analysis unit uses an emotion estimation algorithm to estimate the emotions of a craftsman. For example, the analysis unit can capture the craftsman's facial expressions with a camera and estimate their emotions using the emotion estimation algorithm. The analysis unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using the emotion estimation algorithm. Based on the estimated emotions of the craftsman, the analysis unit adjusts the timing of the analysis. For example, if the craftsman is concentrating, the analysis unit will perform the material property analysis continuously without interruption. If the craftsman is tired, the analysis unit can temporarily suspend the material property analysis and resume it after a break. Furthermore, if the craftsman is stressed, the analysis unit can reduce the frequency of material property analysis and resume it in a relaxed state. In this way, adjusting the timing of the analysis based on the craftsman's emotions enables efficient analysis. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of a craftsman captured by a camera into the generative AI, which can estimate the craftsman's emotions and adjust the timing of the analysis.

[0105] The analysis unit can select the optimal analysis method by referring to past analysis data when analyzing the properties of a material. For example, the analysis unit uses AI to refer to past analysis data. For example, the analysis unit selects the optimal analysis method based on past analysis data. The analysis unit can also select an analysis method with a high success rate from past analysis data. Furthermore, the analysis unit can analyze past analysis data and select an analysis method specialized for a particular material. In this way, the optimal analysis method can be selected by referring to past analysis data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input past analysis data into a generating AI, and the generating AI can select the optimal analysis method.

[0106] The analysis unit can filter the material's properties based on its usage environment and purpose. For example, the analysis unit uses AI to filter based on the material's usage environment and purpose. For instance, the analysis unit filters the analysis according to the material's usage environment (indoors, outdoors, etc.). The analysis unit can also filter the analysis according to the material's purpose. Furthermore, the analysis unit can filter the analysis according to the material's properties. This improves the accuracy of the analysis by filtering based on the material's usage environment and purpose. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input data on the material's usage environment and purpose into a generating AI, which can then perform the filtering.

[0107] The analysis unit can estimate the emotions of a craftsman when analyzing the properties of a material, and determine the priority of the analysis based on the estimated emotions. For example, the analysis unit uses an emotion estimation algorithm to estimate the emotions of a craftsman. For example, the analysis unit can capture the craftsman's facial expressions with a camera and estimate the emotions using the emotion estimation algorithm. The analysis unit can also record the craftsman's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using the emotion estimation algorithm. Based on the estimated emotions of the craftsman, the analysis unit determines the priority of the analysis. For example, if the craftsman is relaxed, the analysis unit will prioritize detailed analysis. If the craftsman is in a hurry, the analysis unit will prioritize concise analysis. Furthermore, if the craftsman is excited, the analysis unit will prioritize visually stimulating analysis. This allows for efficient analysis by determining the priority of analysis based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of a craftsman captured by a camera into the generating AI, which can then estimate the craftsman's emotions and determine the priority of the analysis.

[0108] The analysis unit, when analyzing the properties of materials, can prioritize the analysis of highly relevant materials by considering their geographical distribution. For example, the analysis unit uses AI to consider the geographical distribution of materials. For instance, the analysis unit prioritizes the analysis of highly relevant materials based on their geographical distribution. The analysis unit can also prioritize the analysis of region-specific materials based on their geographical distribution. Furthermore, the analysis unit can analyze the geographical distribution of materials and select the optimal analysis method. This allows for the priority analysis of highly relevant materials by considering their geographical distribution. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input geographical distribution data of materials into a generative AI, which can then prioritize the analysis of highly relevant materials.

[0109] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing the properties of a material. For example, the analysis unit uses AI to refer to relevant literature. For example, the analysis unit can refer to relevant literature to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method from the relevant literature. Furthermore, the analysis unit can analyze relevant literature and select an analysis method specific to a particular material. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input relevant literature data into a generating AI, and the generating AI can improve the accuracy of the analysis.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The technology transfer system can estimate the emotions of skilled workers and adjust the training program based on those estimated emotions. For example, the training department can capture the new recruit's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The training department can also record the new recruit's voice and estimate their emotions using voice analysis technology. Furthermore, the training department can collect the new recruit's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the new recruit, the training department adjusts the training program. For example, if the new recruit is nervous, the training department slows down the program. If the new recruit is relaxed, the training department can proceed at a normal pace. If the new recruit is excited, the training department can speed up the program. This allows for efficient training by adjusting the program based on the new recruit's emotions.

[0112] The technology transfer system can analyze a craftsman's past work history and select the optimal video analysis method. For example, the generation unit uses AI to analyze a craftsman's past work history. The generation unit prioritizes analyzing similar procedures based on the craftsman's past successful work procedures. It can also avoid work procedures that the craftsman has failed at in the past and analyze procedures with a high success rate. Furthermore, the generation unit can select a video analysis method specialized for a particular technique from the craftsman's work history. In this way, the optimal video analysis method can be selected by analyzing a craftsman's past work history.

[0113] The technology transfer system can estimate the emotions of craftsmen and adjust the timing of AR displays based on those estimated emotions. For example, the display unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The display unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the display unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the display unit adjusts the timing of the AR displays. For example, if the craftsman is concentrating, the display unit will continuously display AR without interruption. If the craftsman is tired, the display unit can temporarily pause the AR display and resume it after a break. Furthermore, if the craftsman is stressed, the display unit can reduce the frequency of AR displays and resume them in a relaxed state. By adjusting the timing of AR displays based on the craftsman's emotions, efficient work becomes possible.

[0114] The technology transfer system can prioritize the analysis of highly relevant tasks by considering the geographical location of the craftsman. For example, the generation unit uses AI to consider the craftsman's geographical location. The generation unit prioritizes the analysis of videos related to tasks performed by the craftsman in a specific region. The generation unit can also prioritize the analysis of videos containing region-specific techniques based on the craftsman's geographical location. Furthermore, if the craftsman is on the move, the generation unit can prioritize the analysis of tasks related to their current location. In this way, by considering the craftsman's geographical location, the system can prioritize the analysis of highly relevant tasks.

[0115] The skills transfer system can estimate the emotions of craftsmen and adjust the pace of work based on those estimated emotions. For example, the work area can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The work area can also record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the work area can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the work area adjusts the pace of work. For example, if the craftsman is concentrating, the work area will continue the work without interruption. If the craftsman is tired, the work area can temporarily pause the work and resume it after a break. Furthermore, if the craftsman is stressed, the work area will slow down the pace of work and resume it in a relaxed state. In this way, adjusting the pace of work based on the craftsman's emotions enables more efficient work.

[0116] The technology transfer system can analyze the social media activities of craftsmen and identify related tasks. For example, the generation unit uses AI to analyze craftsmen's social media activities. The generation unit analyzes related videos based on the work content shared by craftsmen on social media. It can also analyze videos related to technologies that craftsmen are interested in from their social media activities. Furthermore, the generation unit can analyze related videos by referring to the work of other craftsmen that the craftsmen follow on social media. In this way, by analyzing the social media activities of craftsmen, it is possible to identify related tasks.

[0117] The technology transfer system can estimate the emotions of craftsmen and prioritize the digital manuals it generates based on those estimated emotions. For example, the generation unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the generation unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the generation unit determines the priority of the digital manuals it generates. For example, if the craftsman is relaxed, the generation unit prioritizes generating detailed digital manuals. If the craftsman is in a hurry, the generation unit can prioritize generating concise digital manuals. Furthermore, if the craftsman is excited, the generation unit can prioritize generating visually stimulating digital manuals. This allows for efficient training by prioritizing digital manuals based on the craftsman's emotions.

[0118] A skills transfer system can analyze a craftsman's past learning history and select the optimal training method. For example, the training department uses AI to analyze a new recruit's past learning history. Based on the learning methods that the new recruit has succeeded with in the past, the training department prioritizes providing similar methods. It can also avoid learning methods that the new recruit has failed with in the past and provide methods with a higher success rate. Furthermore, the training department can select training methods specialized for specific skills based on the new recruit's learning history. In this way, the optimal training method can be selected by analyzing the new recruit's past learning history.

[0119] The technology transfer system can estimate the emotions of craftsmen and adjust the timing of analysis based on those estimated emotions. For example, the analysis unit can capture the craftsman's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the craftsman's voice and estimate their emotions using voice analysis technology. Furthermore, the analysis unit can collect the craftsman's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. Based on the estimated emotions of the craftsman, the analysis unit adjusts the timing of the analysis. For example, if the craftsman is concentrating, the analysis unit will perform material property analysis continuously without interruption. If the craftsman is tired, the analysis unit can temporarily pause the material property analysis and resume it after a break. Furthermore, if the craftsman is stressed, the analysis unit can reduce the frequency of material property analysis and resume it in a relaxed state. This allows for efficient analysis by adjusting the timing of analysis based on the craftsman's emotions.

[0120] The technology transfer system can select the optimal analysis method by referring to past analysis data when analyzing the properties of materials. For example, the analysis unit uses AI to refer to past analysis data. The analysis unit selects the optimal analysis method based on past analysis data. The analysis unit can also select analysis methods with a high success rate from past analysis data. Furthermore, the analysis unit can analyze past analysis data and select analysis methods specialized for specific materials. In this way, the optimal analysis method can be selected by referring to past analysis data.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The generation unit analyzes the work of craftsmen via video and creates digitized training manuals. For example, the work of bamboo craftsmen, lacquerware craftsmen, and pottery craftsmen is videotaped, and the generation AI analyzes the procedures to create digital manuals. Step 2: The training department automates new employee training based on the training manuals created by the generation department. For example, it uses digital manuals to instruct new employees on work procedures, monitor their progress, evaluate their work, and provide feedback. It also provides customized training programs tailored to the new employees' skill levels. Step 3: The display unit shows the procedures and tool placement in the workspace. For example, using AR, it can display drying time and mixing ratios in real time when a lacquer craftsman is working, the placement of tools and work procedures used by a bamboo craftsman can be displayed, and information on work procedures and materials used by a pottery craftsman can be displayed when a potter is working. Step 4: The work area performs the work based on the procedures and tool placement displayed by the display area. For example, it supports lacquer craftsmen in working according to drying time and mixing ratios, bamboo craftsmen in working according to the placement of their tools, and potters in working according to information on the materials they use. Step 5: The analysis unit analyzes the properties of the materials and proposes the optimal processing procedure. For example, it uses AI to analyze the properties of bamboo and lacquer and proposes the optimal processing procedure. It analyzes the physical and chemical properties of bamboo and proposes the optimal processing procedure, analyzes the properties of lacquer and proposes the optimal drying time and mixing ratio, and analyzes the properties of clay used in pottery and proposes the optimal molding procedure and firing conditions.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the generation unit, training unit, display unit, work unit, and analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the generation unit uses the camera 42 of the smart device 14 to video record the work of a craftsman, and the generation AI analyzes the video using the specific processing unit 290 of the data processing unit 12 to create a digital manual. The training unit automates the training of new employees based on the digital manual generated by the specific processing unit 290 of the data processing unit 12. The display unit uses augmented reality (AR) to display procedures and the placement of tools in the workspace using the control unit 46A of the smart device 14. The work unit supports craftsmen in performing tasks according to the procedures displayed by the control unit 46A of the smart device 14. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the properties of the material and propose the optimal processing procedure. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the generation unit, training unit, display unit, work unit, and analysis unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit uses the camera 42 of the smart glasses 214 to video record the work of a craftsman, and the generation AI analyzes the video using the specific processing unit 290 of the data processing unit 12 to create a digital manual. The training unit automates the training of new employees based on the digital manual generated by the specific processing unit 290 of the data processing unit 12. The display unit uses the control unit 46A of the smart glasses 214 to display procedures and the placement of tools in the workspace using augmented reality (AR). The work unit supports craftsmen in performing tasks according to the procedures displayed by the control unit 46A of the smart glasses 214. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the properties of the material and propose the optimal processing procedure. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In 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.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 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.

[0158] Each of the multiple elements described above, including the generation unit, training unit, display unit, work unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit uses the camera 42 of the headset terminal 314 to video record the work of a craftsman, and the generation AI analyzes the data using the specific processing unit 290 of the data processing unit 12 to create a digital manual. The training unit automates the training of new employees based on the digital manual generated by the specific processing unit 290 of the data processing unit 12. The display unit uses augmented reality (AR) to display procedures and the placement of tools in the workspace using the control unit 46A of the headset terminal 314. The work unit supports craftsmen in performing tasks according to the procedures displayed by the control unit 46A of the headset terminal 314. The analysis unit uses the specific processing unit 290 of the data processing unit 12 to analyze the properties of the material and propose the optimal processing procedure. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the generation unit, training unit, display unit, work unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the generation unit uses the camera 42 of the robot 414 to video record the work of a craftsman, and the generation AI is analyzed by the specific processing unit 290 of the data processing unit 12 to create a digital manual. The training unit automates the training of new employees based on the digital manual generated by the specific processing unit 290 of the data processing unit 12. The display unit uses augmented reality (AR) to display procedures and the placement of tools in the workspace, for example, by the control unit 46A of the robot 414. The work unit supports craftsmen in performing tasks according to the procedures displayed by the control unit 46A of the robot 414. The analysis unit analyzes the properties of the material using the specific processing unit 290 of the data processing unit 12 and proposes the optimal processing procedure. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The production unit analyzes the work of craftsmen via video and creates digitized training manuals, A training unit that automates new employee training based on the training manual created by the generation unit, A display unit that shows the procedure and placement of tools in the workspace, A work unit that performs work based on the procedures and arrangement of tools displayed by the aforementioned display unit, It includes an analysis unit that analyzes the properties of the material and proposes the optimal processing procedure. A system characterized by the following features. (Note 2) The generating unit is The process and techniques of craftsmen are recorded in detail, and a generating AI analyzes them to create a digital manual. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The system displays drying time and mixing ratios in real time while the lacquer craftsman is working. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze the properties of bamboo and lacquer and propose the optimal processing procedure. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned training department, Automate new employee training based on digital manuals created by the generation unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned work unit is Perform the work based on the procedures and tool placement displayed on the display unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system estimates the emotions of the craftsman and adjusts the timing of the video analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We analyze the craftsman's past work history and select the optimal video analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During video analysis, filtering is performed based on the craftsman's work environment and the tools they use. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the emotions of craftsmen and prioritizes the digital manuals generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During video analysis, the system prioritizes analyzing highly relevant tasks by considering the geographical location of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During video analysis, the social media activity of the craftsman is analyzed, and related tasks are identified. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned training department, The system estimates the emotions of new employees and adjusts the progress of the training program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned training department, Analyze the past learning history of new employees and select the optimal training method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned training department, During training, new recruits are filtered based on their current skill level and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned training department, The system estimates the emotions of new employees and prioritizes training programs based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned training department, During training, the program prioritizes providing highly relevant training content, taking into account the geographical location of new recruits. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned training department, During training, we analyze the social media activities of new employees and provide relevant training content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is The system estimates the emotions of the craftsman and adjusts the timing of the AR display based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is Analyze the craftsman's past work history and select the optimal AR display method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying in AR, filtering is performed based on the craftsman's work environment and the tools they use. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is The system estimates the emotions of the craftsmen and determines the priority of AR displays based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying AR content, the system prioritizes showing highly relevant information, taking into account the geographical location of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When AR is displayed, the social media activity of the craftsman is analyzed and relevant information is shown. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned work unit is The system estimates the emotions of the craftsmen and adjusts the progress of the work based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned work unit is Analyze the craftsman's past work history and select the optimal work method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned work unit is During the process, filtering is performed based on the craftsman's current skill level and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned work unit is The system estimates the emotions of the craftsmen and determines the priority of tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned work unit is During work, the geographical location of the craftsman is taken into consideration, and tasks with high relevance are prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned work unit is During the work process, we analyze the social media activity of the craftsmen and perform related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, When analyzing the properties of materials, the emotions of the craftsman are estimated, and the timing of the analysis is adjusted based on the estimated emotions of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit, When analyzing the properties of a material, the optimal analysis method is selected by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit, When analyzing the properties of a material, filtering is performed based on the material's usage environment and intended use. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit, When analyzing the properties of materials, we estimate the emotions of the craftsmen and determine the priority of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, When analyzing the properties of materials, the geographical distribution of the materials is taken into consideration, and materials with high relevance are prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, When analyzing the properties of materials, referencing relevant literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 production unit analyzes the work of craftsmen via video and creates digitized training manuals, A training unit that automates new employee training based on the training manual created by the generation unit, A display unit that shows the procedure and placement of tools in the workspace, A work unit that performs work based on the procedures and arrangement of tools displayed by the aforementioned display unit, It includes an analysis unit that analyzes the properties of the material and proposes the optimal processing procedure. A system characterized by the following features.

2. The generating unit is The process of a craftsman's work is recorded in detail, and a generating AI analyzes this information to create a digital manual. The system according to feature 1.

3. The aforementioned display unit is The system displays drying time and mixing ratios in real time while the lacquer craftsman is working. The system according to feature 1.

4. The aforementioned analysis unit, We analyze the properties of bamboo and lacquer and propose the optimal processing procedure. The system according to feature 1.

5. The aforementioned training department, The training of new employees is automated based on the digital manual created by the aforementioned generation unit. The system according to feature 1.

6. The aforementioned work unit is The work is performed based on the procedures and tool placement displayed by the aforementioned display unit. The system according to feature 1.

7. The generating unit is The system estimates the emotions of the craftsman and adjusts the timing of the video analysis based on the estimated emotions. The system according to feature 1.

8. The generating unit is We analyze the craftsman's past work history and select the optimal video analysis method. The system according to feature 1.

9. The generating unit is During video analysis, filtering is performed based on the craftsman's work environment and the tools they use. The system according to feature 1.

10. The generating unit is The system estimates the emotions of craftsmen and prioritizes the digital manuals generated based on those estimated emotions. The system according to feature 1.

Images