system

Abstract

The system according to this embodiment aims to efficiently provide technical guidance and support the smooth transfer of technology. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a quantification unit, and a teaching unit. The recording unit records video of the technical instruction. The analysis unit analyzes the video recorded by the recording unit. The quantification unit quantifies each step of the technique analyzed by the analysis unit. The teaching unit provides technical instruction based on the technical information quantified by the quantification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to inherit traditional technologies, and there is a problem that it takes a long time for technical guidance in particular.

[0005] The system according to the embodiment aims to efficiently perform technical guidance and support smooth inheritance of technologies.

Means for Solving the Problems

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a quantification unit, and a guidance unit. The recording unit records a video of technical guidance. The analysis unit analyzes the video recorded by the recording unit. The quantification unit quantifies each step of the technology analyzed by the analysis unit. The guidance unit performs technical guidance based on the technical information quantified by the quantification unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide technical guidance and support the smooth transfer of technology. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The technology transfer support agent according to an embodiment of the present invention is a system for facilitating technology transfer. This system records video of a craftsman giving technical instruction, analyzes the recorded video, and quantifies each step of the technique. Furthermore, it provides technical instruction to a successor based on the quantified technical information. At this time, the AI ​​analyzes each step of the technique and provides appropriate instruction to the successor. This facilitates technology transfer and prevents the loss of techniques due to the aging of craftsmen and a lack of successors. For example, when a craftsman gives technical instruction, the video is recorded. For example, when teaching traditional craft techniques, the movements of the craftsman's hands and how to use the tools are recorded in detail. This video is used as reference material when the successor learns the technique. Next, the recorded video is analyzed and each step of the technique is quantified. The AI ​​analyzes the craftsman's movements from the video and quantifies each step of the technique. For example, it analyzes how much force the craftsman is using the tools and at what timing the movements are performed. This clarifies each step of the technique, making it easier for the successor to acquire the technique. Furthermore, the system provides technical guidance to successors based on quantified technical information. The AI ​​provides appropriate guidance to successors based on this quantified technical information. For example, when a successor practices a technique, the AI ​​provides real-time feedback and points out areas for improvement. This allows successors to acquire the technique efficiently. This system ensures smooth technology transfer and prevents the loss of techniques due to the aging of craftsmen and a lack of successors. For example, even if craftsmen with traditional craft skills age, utilizing a technology transfer support agent makes it easier for successors to acquire the skills, preventing the loss of those skills. Also, when accepting successors from overseas, utilizing a technology transfer support agent ensures smooth technical guidance. In this way, the technology transfer support agent can facilitate the smooth transfer of techniques.

[0029] The technology transfer support agent according to this embodiment comprises a recording unit, an analysis unit, a quantification unit, and a teaching unit. The recording unit records video of the technology instruction. For example, the recording unit records video when a craftsman provides technology instruction. For example, when teaching traditional craft techniques, the recording unit records in detail the movements of the craftsman's hands and how to use the tools they use. For example, the recording unit can record video in high resolution and use it as reference material when a successor learns the technology. The analysis unit analyzes the video recorded by the recording unit. For example, the analysis unit analyzes the craftsman's movements from the video and quantifies each step of the technology. For example, the analysis unit analyzes how much force the craftsman is using the tools and at what timing they are performing the actions. For example, the analysis unit can use video analysis technology to analyze the craftsman's movements in detail. The quantification unit quantifies each step of the technology analyzed by the analysis unit. For example, the quantification unit quantifies each step of the technology to make it easier for a successor to acquire the technology. The quantification unit can, for example, quantify the actions of a craftsman and clarify each step of the technique. The instruction unit provides technical guidance based on the technical information quantified by the quantification unit. The instruction unit can, for example, provide real-time feedback when a successor is practicing the technique and point out areas for improvement. The instruction unit can, for example, provide appropriate guidance when a successor is acquiring the technique. As a result, the technology transfer support agent according to the embodiment can facilitate technology transfer.

[0030] The recording unit records video of technical instruction. For example, when a craftsman provides technical instruction, the recording unit records the video. Specifically, the recording unit uses a high-resolution camera to meticulously record the craftsman's hand movements and how they use the tools. This allows successors to use the video as reference material when learning the techniques. The recording unit can use multiple camera angles to record the craftsman's actions from various perspectives. For example, by combining a camera that takes close-up shots of hand movements with a camera that captures the overall movement, it is possible to record both the overall picture and the details of the technique simultaneously. The recording unit also records audio simultaneously, allowing successors to review the craftsman's instruction and advice later. Furthermore, the recording unit saves the recorded video as digital data and uploads it to cloud storage, making it accessible to successors anytime, anywhere. This allows for the efficient recording of technical instruction videos, which can be used as valuable resources for successors to learn the techniques.

[0031] The analysis unit analyzes the video recorded by the recording unit. For example, the analysis unit analyzes the movements of a craftsman from the video and quantifies each step of the technique. Specifically, the analysis unit uses video analysis technology to analyze the movements of a craftsman in detail. For example, it analyzes how much force the craftsman is using the tools and at what timing the movements are being performed. The analysis unit can use AI to analyze the video and automatically recognize the movements of a craftsman. For example, the AI ​​tracks the movements of the craftsman's hands from the video and analyzes what kind of movements are being performed. The AI ​​can also analyze the speed and force of the craftsman's movements and quantify each step of the technique. Furthermore, the analysis unit saves the analysis results in a database so that successors can refer to them when learning the technique. This allows the analysis unit to efficiently analyze the recorded video and quantify each step of the technique.

[0032] The quantification unit quantifies each step of the technique analyzed by the analysis unit. For example, the quantification unit quantifies each step of the technique, making it easier for successors to learn the technique. Specifically, the quantification unit quantifies the actions of the craftsman, clarifying each step of the technique. For example, it quantifies the angle at which the craftsman holds the tool and the amount of force applied when operating it. The quantification unit also illustrates each step of the technique in an easy-to-understand diagram, making it easy for successors to visually understand. Furthermore, the quantification unit stores the quantified technical information as digital data, allowing successors to refer to it at any time. In this way, the quantification unit clarifies each step of the technique, making it easier for successors to learn the technique.

[0033] The training department provides technical guidance based on technical information quantified by the quantification department. For example, the training department provides real-time feedback to successors as they practice the techniques, pointing out areas for improvement. Specifically, the training department provides appropriate guidance to successors as they practice the techniques, referring to the quantified technical information. For example, they check whether the successor is holding the tool at the correct angle and using the appropriate amount of force, and point out areas for improvement as needed. The training department also records the process of successors acquiring the techniques and monitors their progress. Furthermore, the training department provides training programs to enable appropriate guidance to successors as they acquire the techniques. This allows the training department to provide appropriate guidance and point out areas for improvement as successors acquire the techniques.

[0034] The feedback unit can provide real-time feedback to successors as they practice the technology. For example, the feedback unit can provide real-time feedback to successors as they practice the technology and point out areas for improvement. For example, the AI ​​can provide real-time feedback to successors as they practice the technology and point out areas for improvement. By providing real-time feedback to successors and pointing out areas for improvement, the AI ​​can provide real-time feedback to successors as they practice the technology, enabling them to acquire the technology efficiently. This allows successors to acquire the technology efficiently by providing real-time feedback. The specific definition and criteria of real-time include, for example, the feedback delay time and the method for evaluating real-timeness.

[0035] The management department can manage the progress of technical training. For example, the management department can improve the efficiency of technology transfer by managing the progress of technical training. For example, the management department can understand the progress of successors acquiring the technology. For example, by managing the progress of technical training and understanding the progress of successors acquiring the technology, the management department can improve the efficiency of technology transfer. Thus, by managing the progress of technical training, the efficiency of technology transfer is improved. Specific details of progress and evaluation criteria include, for example, the stages of progress, methods for evaluating progress, and methods for reporting progress.

[0036] The recording unit can meticulously record the movements of a craftsman's hands and how they use their tools. For example, the recording unit can meticulously record the movements of a craftsman's hands and how they use their tools. For example, the recording unit can record the movements of a craftsman's hands and how they use their tools in high resolution, which can then be used as reference material for successors learning the techniques. By meticulously recording the movements of a craftsman's hands and how they use their tools, the recording unit can serve as reference material for successors learning the techniques. Specific methods and criteria for detailed recording include, for example, the items to be recorded, the accuracy of the recording, and the frequency of recording.

[0037] The analysis unit can analyze the movements of a craftsman from video and quantify each step of the technique. For example, the analysis unit can analyze the movements of a craftsman from video and quantify each step of the technique. For example, the analysis unit can analyze how much force the craftsman is using the tools and the timing of their movements. For example, the analysis unit can use video analysis technology to analyze the movements of a craftsman in detail and quantify each step of the technique. By analyzing the movements of a craftsman from video and quantifying each step of the technique, each step of the technique becomes clearer, making it easier for successors to acquire the technique. Specific methods and criteria for quantification include, for example, the items to be quantified, the measurement methods used, and the accuracy of the quantification.

[0038] The training department can provide appropriate guidance to successors based on quantified technical information. For example, the training department can provide appropriate guidance to successors based on quantified technical information. For example, the training department can provide real-time feedback to successors as they practice the technology and point out areas for improvement. For example, the training department can provide appropriate guidance to successors as they acquire the technology. This allows successors to acquire the technology efficiently by providing guidance based on quantified technical information. Specific content and criteria for appropriate guidance include, for example, the method of instruction, the content of instruction, and the method of evaluating instruction.

[0039] The recording unit can analyze the craftsman's past technical instruction history and select the optimal recording method during recording. For example, the recording unit can prioritize recording methods that the craftsman has successfully used in the past. For example, the recording unit can avoid recording methods that the craftsman has failed at in the past. For example, the recording unit can suggest the most effective recording method based on the craftsman's technical instruction history. In this way, by analyzing the craftsman's past technical instruction history and selecting the optimal recording method, effective video recording of technical instruction can be achieved. Specific details and criteria for the optimal recording method include, for example, the format of the recording, the timing of the recording, and the method of evaluating the recording.

[0040] The recording unit can filter recordings based on the craftsman's current projects and areas of interest. For example, the recording unit prioritizes recording techniques related to the craftsman's current project. For example, the recording unit selects and records relevant techniques based on the craftsman's areas of interest. For example, the recording unit adjusts the content of the recordings according to the progress of the craftsman's current project. This allows for the recording of highly relevant technical instruction videos by filtering based on the craftsman's current projects and areas of interest. Specific filtering methods and criteria include, for example, filtering conditions, filtering accuracy, and filtering evaluation methods.

[0041] The recording unit can prioritize recording highly relevant footage by considering the geographical location information of the craftsman during recording. For example, the recording unit can prioritize recording techniques performed by a craftsman in a specific region. For example, the recording unit can select relevant footage based on the craftsman's geographical location information. For example, if the craftsman is traveling, the recording unit can record techniques performed at the destination. In this way, by prioritizing the recording of highly relevant footage by considering the craftsman's geographical location information, it is possible to record footage of region-specific technical instruction. Specific details and usage methods of geographical location information include, for example, how the location information is acquired, the accuracy of the location information, and how the location information is used.

[0042] The recording unit can analyze the craftsman's social media activity during recording and record relevant videos. For example, the recording unit can record videos related to techniques shared by the craftsman on social media. For example, the recording unit can select and record techniques of high interest from the craftsman's social media activity. For example, the recording unit can record techniques from other craftsmen that the craftsman follows on social media as reference. In this way, by analyzing the craftsman's social media activity and recording relevant videos, it is possible to record videos of technical instruction based on the craftsman's interests. Specific details of social media activity and analysis methods include, for example, the type of activity, the method of analyzing the activity, and the method of evaluating the activity.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the technology during the analysis. For example, the analysis unit performs a detailed analysis for important technologies. For example, the analysis unit performs a simplified analysis for less important technologies. The analysis unit adjusts the depth of the analysis according to the importance of the technology. In this way, by adjusting the level of detail of the analysis based on the importance of the technology, a detailed analysis can be performed for important technologies. Specific evaluation criteria and methods for technology importance include, for example, evaluation criteria for importance, methods for determining importance, and methods for changing importance.

[0044] The analysis unit can apply different analysis algorithms depending on the category of technology during analysis. For example, for woodworking technology, the analysis unit applies an analysis algorithm that takes into account the properties of wood. For example, for pottery technology, the analysis unit applies an analysis algorithm that takes into account the properties of clay. For example, for dyeing technology, the analysis unit applies an analysis algorithm that takes into account the properties of dyes. In this way, by applying different analysis algorithms depending on the category of technology, analysis can be performed according to the characteristics of the technology. Specific types and implementation methods of analysis algorithms include, for example, the type of algorithm used, the method of applying the algorithm, and the method of evaluating the algorithm.

[0045] The analysis unit can determine the priority of analyses based on the submission date of the technologies during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted technologies. For example, the analysis unit may postpone the analysis of older technologies. For example, the analysis unit may adjust the order of analyses according to the submission date. This allows for the prioritization of the latest technologies by determining the analysis priority based on the submission date of the technologies. Specific details and evaluation criteria for submission dates include, for example, evaluation criteria for submission dates, methods for determining submission dates, and methods for changing submission dates.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the technologies during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant technologies. For example, the analysis unit postpones the analysis of less relevant technologies. The analysis unit adjusts the order of analysis according to the relevance of the technologies. In this way, by adjusting the order of analysis based on the relevance of the technologies, highly relevant technologies can be prioritized for analysis. Specific evaluation criteria and methods for relevance include, for example, evaluation criteria for relevance, methods for determining relevance, and methods for changing relevance.

[0047] The quantification unit can improve the accuracy of quantification by considering the interrelationships between technologies during the quantification process. For example, the quantification unit analyzes the interrelationships between technologies to improve the accuracy of quantification. For example, the quantification unit adjusts the quantification criteria by considering the relationships between technologies. For example, the quantification unit modifies the quantification results based on the interrelationships between technologies. This allows for more accurate quantification results by improving the accuracy of quantification by considering the interrelationships between technologies. Specific details and evaluation methods of the interrelationships between technologies include, for example, the types of interrelationships, evaluation methods for interrelationships, and methods for utilizing interrelationships.

[0048] The quantification unit can perform quantification while considering the attribute information of the technology submitter. For example, the quantification unit can perform quantification while considering the technology submitter's years of experience. For example, the quantification unit can perform quantification while considering the technology submitter's field of expertise. For example, the quantification unit can perform quantification while considering the technology submitter's past achievements. In this way, by performing quantification while considering the attribute information of the technology submitter, it is possible to provide appropriate quantification results tailored to the submitter. Specific details and usage methods of attribute information include, for example, the type of attribute information, the method of acquiring the attribute information, and the method of using the attribute information.

[0049] The quantification unit can perform quantification while considering the geographical distribution of the technology. For example, the quantification unit can analyze the geographical distribution of the technology to improve the accuracy of quantification. For example, the quantification unit can adjust the quantification criteria based on the geographical distribution. For example, the quantification unit can modify the quantification results according to the geographical distribution. In this way, by performing quantification while considering the geographical distribution of the technology, it is possible to provide appropriate quantification results for each region. Specific details and evaluation methods of geographical distribution include, for example, the type of distribution, the method of evaluating the distribution, and the method of using the distribution.

[0050] The quantification unit can improve the accuracy of quantification by referring to relevant technical literature during the quantification process. For example, the quantification unit can improve the accuracy of quantification by referring to relevant technical literature. For example, the quantification unit can adjust the quantification criteria based on the relevant literature. For example, the quantification unit can modify the quantification results according to the relevant literature. This allows for more accurate quantification results by improving the accuracy of quantification through the referencing of relevant technical literature. Specific details of the relevant literature and the methods of referencing it include, for example, the type of literature, how the literature was obtained, and how the literature was used.

[0051] The instruction department can optimize current instruction by referring to past instructional data. For example, the instruction department can analyze past instructional data to optimize current instructional content. For example, the instruction department can adjust instructional methods based on past instructional data. For example, the instruction department can improve the effectiveness of instruction by referring to past instructional data. In this way, effective instruction can be provided by optimizing current instruction by referring to past instructional data. Specific details of past instructional data and methods of referencing it include, for example, the type of data, how the data was acquired, and how the data was used.

[0052] The instruction department can apply different teaching methods to each category of technology during instruction. For example, for woodworking techniques, the instruction department will apply teaching methods that take into account the properties of wood. For pottery techniques, the instruction department will apply teaching methods that take into account the properties of clay. For dyeing techniques, the instruction department will apply teaching methods that take into account the properties of dyes. In this way, by applying different teaching methods to each category of technology, instruction can be provided that is appropriate to the characteristics of the technology. The specific content and application methods of the teaching methods include, for example, the type of method, the method of application of the method, and the method of evaluation of the method.

[0053] The instruction department can analyze changes in instruction based on the timing of technical submissions. For example, the instruction department can apply the latest instruction methods to recently submitted technical skills. For example, the instruction department can apply conventional instruction methods to older technical skills. For example, the instruction department can adjust the content of instruction according to the submission timing. This allows for appropriate instruction of the latest technical skills by analyzing changes in instruction based on the timing of technical skill submissions. Specific details and evaluation methods of changes in instruction include, for example, the type of change, the method of evaluating the change, and how the change is utilized.

[0054] The training department can analyze the training by referring to relevant market data on the technology during the training process. For example, the training department can optimize the training content by referring to relevant market data on the technology. For example, the training department can adjust the training methods based on market data. For example, the training department can improve the effectiveness of the training by referring to market data. In this way, the effectiveness of training can be improved by analyzing the training by referring to relevant market data on the technology. Specific details of the relevant market data and how it is referenced include, for example, the type of data, how the data is acquired, and how the data is used.

[0055] The feedback unit can provide optimal feedback by referring to the successor's past learning history during the feedback process. For example, the feedback unit can analyze the successor's past learning history and provide optimal feedback. For example, the feedback unit can adjust the content of the feedback based on the learning history. For example, the feedback unit can improve the effectiveness of the feedback by referring to the learning history. In this way, by providing optimal feedback by referring to the successor's past learning history, the successor's learning effectiveness can be improved. Specific details of the past learning history and how it is referenced include, for example, the type of history, how the history is acquired, and how the history is used.

[0056] The feedback unit can provide optimal feedback by considering the successor's device information. For example, if the successor is using a smartphone, the feedback unit will provide feedback tailored to the screen size. If the successor is using a tablet, the feedback unit will provide feedback optimized for a larger screen. If the successor is using a smartwatch, the feedback unit will provide concise and highly visible feedback. By providing optimal feedback while considering the successor's device information, feedback can be provided in the most suitable format for the successor. Specific details of device information, such as device type, how the device was acquired, and how the device is used, may be included.

[0057] The management department can select the optimal management method by referring to the successor's past learning history when managing progress. For example, the management department can analyze the successor's past learning history and select the optimal management method. For example, the management department can adjust the management method based on the learning history. For example, the management department can improve the effectiveness of management by referring to the learning history. In this way, by selecting the optimal management method by referring to the successor's past learning history, the successor's learning effectiveness can be improved. Specific details and criteria for the optimal management method include, for example, the type of method, how to apply the method, and how to evaluate the method.

[0058] The management department can select the optimal management method when managing progress, taking into account the successor's geographical location. For example, the management department can analyze the successor's geographical location and select the optimal management method. For example, the management department can adjust the management method based on the geographical location. For example, the management department can improve the effectiveness of management by referring to the geographical location. In this way, by selecting the optimal management method while considering the successor's geographical location, appropriate progress management can be carried out according to the region. Specific details and usage methods of geographical location information include, for example, how the location information is acquired, the accuracy of the location information, and how the location information is used.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The technology transfer support agent can be equipped with a learning management unit that manages the successor's learning history, in addition to a recording unit, analysis unit, quantification unit, and instruction unit. The learning management unit refers to the successor's past learning history and reflects it in current technology instruction. For example, it provides focused instruction on technologies that the successor has struggled with in the past. Conversely, it provides more advanced technology instruction on technologies that the successor excels at. This enables optimal technology instruction tailored to each successor's learning situation, improving the efficiency of technology acquisition.

[0061] The technology transfer support agent can include a recording unit, analysis unit, quantification unit, and instruction unit, as well as a progress management unit to manage the progress of technology instruction. The progress management unit grasps the progress of the successor's technology acquisition and provides feedback at the appropriate time. For example, it monitors the successor's progress in acquiring technology in real time and adjusts the instruction content as needed. This allows for efficient management of the progress of technology acquisition and improves the efficiency of technology transfer.

[0062] In addition to recording, analysis, quantification, and instruction units, the technology transfer support agent can be equipped with a recording optimization unit that analyzes the craftsman's past technical instruction history and selects the optimal recording method when recording video of technical instruction. For example, it can prioritize recording methods that have worked well for the craftsman in the past and avoid those that have failed. This allows for the recording of effective technical instruction videos, improving the efficiency of skill acquisition for successors.

[0063] In addition to the recording, analysis, quantification, and instruction units, the technology transfer support agent can be equipped with a recording filtering unit that filters the recording of technical instruction videos based on the craftsman's current projects and areas of interest. For example, it can prioritize recording technologies related to the craftsman's current projects and select and record relevant technologies based on their areas of interest. This allows for the recording of highly relevant technical instruction videos, improving the efficiency of technology acquisition for successors.

[0064] In addition to the recording, analysis, quantification, and instruction units, the technology transfer support agent can be equipped with a geographic information recording unit that prioritizes recording highly relevant videos by considering the geographic location information of the craftsman when recording videos of technical instruction. For example, it can prioritize recording techniques performed by craftsmen in a specific region and select videos of related techniques based on geographic location information. This makes it possible to record region-specific videos of technical instruction, improving the efficiency of skill acquisition for successors.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The recording unit records video of the technical instruction. For example, when a craftsman provides technical instruction, the video is recorded in high resolution, detailing the craftsman's hand movements and how they use the tools. This can then be used as reference material when successors learn the techniques. Step 2: The analysis unit analyzes the video recorded by the recording unit. For example, it analyzes the movements of the craftsman from the video and quantifies each step of the technique. This allows for a detailed analysis of how much force the craftsman is using the tools, the timing of their movements, and so on. Step 3: The quantification unit quantifies each step of the technique analyzed by the analysis unit. For example, it quantifies each step of the technique to make it easier for successors to learn the technique. This makes it possible to quantify the actions of the craftsman and clarify each step of the technique. Step 4: The training department provides technical guidance based on the technical information quantified by the quantification department. For example, when a successor practices the technology, the training department provides real-time feedback and points out areas for improvement. This allows for appropriate guidance when successors acquire the technology.

[0067] (Example of form 2) The technology transfer support agent according to an embodiment of the present invention is a system for facilitating technology transfer. This system records video of a craftsman giving technical instruction, analyzes the recorded video, and quantifies each step of the technique. Furthermore, it provides technical instruction to a successor based on the quantified technical information. At this time, the AI ​​analyzes each step of the technique and provides appropriate instruction to the successor. This facilitates technology transfer and prevents the loss of techniques due to the aging of craftsmen and a lack of successors. For example, when a craftsman gives technical instruction, the video is recorded. For example, when teaching traditional craft techniques, the movements of the craftsman's hands and how to use the tools are recorded in detail. This video is used as reference material when the successor learns the technique. Next, the recorded video is analyzed and each step of the technique is quantified. The AI ​​analyzes the craftsman's movements from the video and quantifies each step of the technique. For example, it analyzes how much force the craftsman is using the tools and at what timing the movements are performed. This clarifies each step of the technique, making it easier for the successor to acquire the technique. Furthermore, the system provides technical guidance to successors based on quantified technical information. The AI ​​provides appropriate guidance to successors based on this quantified technical information. For example, when a successor practices a technique, the AI ​​provides real-time feedback and points out areas for improvement. This allows successors to acquire the technique efficiently. This system ensures smooth technology transfer and prevents the loss of techniques due to the aging of craftsmen and a lack of successors. For example, even if craftsmen with traditional craft skills age, utilizing a technology transfer support agent makes it easier for successors to acquire the skills, preventing the loss of those skills. Also, when accepting successors from overseas, utilizing a technology transfer support agent ensures smooth technical guidance. In this way, the technology transfer support agent can facilitate the smooth transfer of techniques.

[0068] The technology transfer support agent according to this embodiment comprises a recording unit, an analysis unit, a quantification unit, and a teaching unit. The recording unit records video of the technology instruction. For example, the recording unit records video when a craftsman provides technology instruction. For example, when teaching traditional craft techniques, the recording unit records in detail the movements of the craftsman's hands and how to use the tools they use. For example, the recording unit can record video in high resolution and use it as reference material when a successor learns the technology. The analysis unit analyzes the video recorded by the recording unit. For example, the analysis unit analyzes the craftsman's movements from the video and quantifies each step of the technology. For example, the analysis unit analyzes how much force the craftsman is using the tools and at what timing they are performing the actions. For example, the analysis unit can use video analysis technology to analyze the craftsman's movements in detail. The quantification unit quantifies each step of the technology analyzed by the analysis unit. For example, the quantification unit quantifies each step of the technology to make it easier for a successor to acquire the technology. The quantification unit can, for example, quantify the actions of a craftsman and clarify each step of the technique. The instruction unit provides technical guidance based on the technical information quantified by the quantification unit. The instruction unit can, for example, provide real-time feedback when a successor is practicing the technique and point out areas for improvement. The instruction unit can, for example, provide appropriate guidance when a successor is acquiring the technique. As a result, the technology transfer support agent according to the embodiment can facilitate technology transfer.

[0069] The recording unit records video of technical instruction. For example, when a craftsman provides technical instruction, the recording unit records the video. Specifically, the recording unit uses a high-resolution camera to meticulously record the craftsman's hand movements and how they use the tools. This allows successors to use the video as reference material when learning the techniques. The recording unit can use multiple camera angles to record the craftsman's actions from various perspectives. For example, by combining a camera that takes close-up shots of hand movements with a camera that captures the overall movement, it is possible to record both the overall picture and the details of the technique simultaneously. The recording unit also records audio simultaneously, allowing successors to review the craftsman's instruction and advice later. Furthermore, the recording unit saves the recorded video as digital data and uploads it to cloud storage, making it accessible to successors anytime, anywhere. This allows for the efficient recording of technical instruction videos, which can be used as valuable resources for successors to learn the techniques.

[0070] The analysis unit analyzes the video recorded by the recording unit. For example, the analysis unit analyzes the movements of a craftsman from the video and quantifies each step of the technique. Specifically, the analysis unit uses video analysis technology to analyze the movements of a craftsman in detail. For example, it analyzes how much force the craftsman is using the tools and at what timing the movements are being performed. The analysis unit can use AI to analyze the video and automatically recognize the movements of a craftsman. For example, the AI ​​tracks the movements of the craftsman's hands from the video and analyzes what kind of movements are being performed. The AI ​​can also analyze the speed and force of the craftsman's movements and quantify each step of the technique. Furthermore, the analysis unit saves the analysis results in a database so that successors can refer to them when learning the technique. This allows the analysis unit to efficiently analyze the recorded video and quantify each step of the technique.

[0071] The quantification unit quantifies each step of the technique analyzed by the analysis unit. For example, the quantification unit quantifies each step of the technique, making it easier for successors to learn the technique. Specifically, the quantification unit quantifies the actions of the craftsman, clarifying each step of the technique. For example, it quantifies the angle at which the craftsman holds the tool and the amount of force applied when operating it. The quantification unit also illustrates each step of the technique in an easy-to-understand diagram, making it easy for successors to visually understand. Furthermore, the quantification unit stores the quantified technical information as digital data, allowing successors to refer to it at any time. In this way, the quantification unit clarifies each step of the technique, making it easier for successors to learn the technique.

[0072] The training department provides technical guidance based on technical information quantified by the quantification department. For example, the training department provides real-time feedback to successors as they practice the techniques, pointing out areas for improvement. Specifically, the training department provides appropriate guidance to successors as they practice the techniques, referring to the quantified technical information. For example, they check whether the successor is holding the tool at the correct angle and using the appropriate amount of force, and point out areas for improvement as needed. The training department also records the process of successors acquiring the techniques and monitors their progress. Furthermore, the training department provides training programs to enable appropriate guidance to successors as they acquire the techniques. This allows the training department to provide appropriate guidance and point out areas for improvement as successors acquire the techniques.

[0073] The feedback unit can provide real-time feedback to successors as they practice the technology. For example, the feedback unit can provide real-time feedback to successors as they practice the technology and point out areas for improvement. For example, the AI ​​can provide real-time feedback to successors as they practice the technology and point out areas for improvement. By providing real-time feedback to successors and pointing out areas for improvement, the AI ​​can provide real-time feedback to successors as they practice the technology, enabling them to acquire the technology efficiently. This allows successors to acquire the technology efficiently by providing real-time feedback. The specific definition and criteria of real-time include, for example, the feedback delay time and the method for evaluating real-timeness.

[0074] The management department can manage the progress of technical training. For example, the management department can improve the efficiency of technology transfer by managing the progress of technical training. For example, the management department can understand the progress of successors acquiring the technology. For example, by managing the progress of technical training and understanding the progress of successors acquiring the technology, the management department can improve the efficiency of technology transfer. Thus, by managing the progress of technical training, the efficiency of technology transfer is improved. Specific details of progress and evaluation criteria include, for example, the stages of progress, methods for evaluating progress, and methods for reporting progress.

[0075] The recording unit can meticulously record the movements of a craftsman's hands and how they use their tools. For example, the recording unit can meticulously record the movements of a craftsman's hands and how they use their tools. For example, the recording unit can record the movements of a craftsman's hands and how they use their tools in high resolution, which can then be used as reference material for successors learning the techniques. By meticulously recording the movements of a craftsman's hands and how they use their tools, the recording unit can serve as reference material for successors learning the techniques. Specific methods and criteria for detailed recording include, for example, the items to be recorded, the accuracy of the recording, and the frequency of recording.

[0076] The analysis unit can analyze the movements of a craftsman from video and quantify each step of the technique. For example, the analysis unit can analyze the movements of a craftsman from video and quantify each step of the technique. For example, the analysis unit can analyze how much force the craftsman is using the tools and the timing of their movements. For example, the analysis unit can use video analysis technology to analyze the movements of a craftsman in detail and quantify each step of the technique. By analyzing the movements of a craftsman from video and quantifying each step of the technique, each step of the technique becomes clearer, making it easier for successors to acquire the technique. Specific methods and criteria for quantification include, for example, the items to be quantified, the measurement methods used, and the accuracy of the quantification.

[0077] The training department can provide appropriate guidance to successors based on quantified technical information. For example, the training department can provide appropriate guidance to successors based on quantified technical information. For example, the training department can provide real-time feedback to successors as they practice the technology and point out areas for improvement. For example, the training department can provide appropriate guidance to successors as they acquire the technology. This allows successors to acquire the technology efficiently by providing guidance based on quantified technical information. Specific content and criteria for appropriate guidance include, for example, the method of instruction, the content of instruction, and the method of evaluating instruction.

[0078] The recording unit can estimate the emotions of the craftsman and adjust the timing of recording based on the estimated emotions. For example, if the craftsman is concentrating, the recording unit will record continuously without interruption. If the craftsman is tired, the recording unit will take a break and then resume recording. If the craftsman is tense, the recording unit will allow time for relaxation before starting recording. By adjusting the timing of recording based on the craftsman's emotions, the video of the technical instruction can be recorded at the optimal time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, or not using AI. For example, the recording unit can input image data of the craftsman captured by a camera into the generative AI and have the generative AI perform the estimation of the craftsman's emotions.

[0079] The recording unit can analyze the craftsman's past technical instruction history and select the optimal recording method during recording. For example, the recording unit can prioritize recording methods that the craftsman has successfully used in the past. For example, the recording unit can avoid recording methods that the craftsman has failed at in the past. For example, the recording unit can suggest the most effective recording method based on the craftsman's technical instruction history. In this way, by analyzing the craftsman's past technical instruction history and selecting the optimal recording method, effective video recording of technical instruction can be achieved. Specific details and criteria for the optimal recording method include, for example, the format of the recording, the timing of the recording, and the method of evaluating the recording.

[0080] The recording unit can filter recordings based on the craftsman's current projects and areas of interest. For example, the recording unit prioritizes recording techniques related to the craftsman's current project. For example, the recording unit selects and records relevant techniques based on the craftsman's areas of interest. For example, the recording unit adjusts the content of the recordings according to the progress of the craftsman's current project. This allows for the recording of highly relevant technical instruction videos by filtering based on the craftsman's current projects and areas of interest. Specific filtering methods and criteria include, for example, filtering conditions, filtering accuracy, and filtering evaluation methods.

[0081] The recording unit can estimate the emotions of the craftsman and determine the priority of the footage to be recorded based on the estimated emotions of the craftsman. For example, if the craftsman is excited, the recording unit will prioritize recording footage of important techniques. For example, if the craftsman is relaxed, the recording unit will record footage of detailed techniques. For example, if the craftsman is tired, the recording unit will record footage of simple techniques. In this way, by determining the priority of the footage to be recorded based on the emotions of the craftsman, footage of important techniques can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input image data of the craftsman captured by the camera into the generative AI and have the generative AI perform the estimation of the craftsman's emotions.

[0082] The recording unit can prioritize recording highly relevant footage by considering the geographical location information of the craftsman during recording. For example, the recording unit can prioritize recording techniques performed by a craftsman in a specific region. For example, the recording unit can select relevant footage based on the craftsman's geographical location information. For example, if the craftsman is traveling, the recording unit can record techniques performed at the destination. In this way, by prioritizing the recording of highly relevant footage by considering the craftsman's geographical location information, it is possible to record footage of region-specific technical instruction. Specific details and usage methods of geographical location information include, for example, how the location information is acquired, the accuracy of the location information, and how the location information is used.

[0083] The recording unit can analyze the craftsman's social media activity during recording and record relevant videos. For example, the recording unit can record videos related to techniques shared by the craftsman on social media. For example, the recording unit can select and record techniques of high interest from the craftsman's social media activity. For example, the recording unit can record techniques from other craftsmen that the craftsman follows on social media as reference. In this way, by analyzing the craftsman's social media activity and recording relevant videos, it is possible to record videos of technical instruction based on the craftsman's interests. Specific details of social media activity and analysis methods include, for example, the type of activity, the method of analyzing the activity, and the method of evaluating the activity.

[0084] The analysis unit can estimate the emotions of the craftsman and adjust the presentation of the analysis based on the estimated emotions. For example, if the craftsman is relaxed, the analysis unit will provide detailed analysis results. For example, if the craftsman is tense, the analysis unit will provide concise analysis results. For example, if the craftsman is excited, the analysis unit will provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis based on the craftsman's emotions, it is possible to provide analysis results that are easy for the craftsman to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Specific content and criteria for the presentation of the analysis include, for example, the format of the expression, the accuracy of the expression, and the method of evaluating the expression.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the technology during the analysis. For example, the analysis unit performs a detailed analysis for important technologies. For example, the analysis unit performs a simplified analysis for less important technologies. The analysis unit adjusts the depth of the analysis according to the importance of the technology. In this way, by adjusting the level of detail of the analysis based on the importance of the technology, a detailed analysis can be performed for important technologies. Specific evaluation criteria and methods for technology importance include, for example, evaluation criteria for importance, methods for determining importance, and methods for changing importance.

[0086] The analysis unit can apply different analysis algorithms depending on the category of technology during analysis. For example, for woodworking technology, the analysis unit applies an analysis algorithm that takes into account the properties of wood. For example, for pottery technology, the analysis unit applies an analysis algorithm that takes into account the properties of clay. For example, for dyeing technology, the analysis unit applies an analysis algorithm that takes into account the properties of dyes. In this way, by applying different analysis algorithms depending on the category of technology, analysis can be performed according to the characteristics of the technology. Specific types and implementation methods of analysis algorithms include, for example, the type of algorithm used, the method of applying the algorithm, and the method of evaluating the algorithm.

[0087] The analysis unit can estimate the emotions of the craftsman and adjust the length of the analysis based on the estimated emotions. For example, if the craftsman is in a hurry, the analysis unit will provide a short analysis result. For example, if the craftsman is relaxed, the analysis unit will provide a detailed analysis result. For example, if the craftsman is excited, the analysis unit will provide a visually appealing analysis result. By adjusting the length of the analysis based on the craftsman's emotions, it is possible to provide an analysis result of the optimal length for the craftsman. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining the length of the analysis include, for example, the time of the analysis, the scope of the analysis, and the method for evaluating the analysis.

[0088] The analysis unit can determine the priority of analyses based on the submission date of the technologies during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted technologies. For example, the analysis unit may postpone the analysis of older technologies. For example, the analysis unit may adjust the order of analyses according to the submission date. This allows for the prioritization of the latest technologies by determining the analysis priority based on the submission date of the technologies. Specific details and evaluation criteria for submission dates include, for example, evaluation criteria for submission dates, methods for determining submission dates, and methods for changing submission dates.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the technologies during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant technologies. For example, the analysis unit postpones the analysis of less relevant technologies. The analysis unit adjusts the order of analysis according to the relevance of the technologies. In this way, by adjusting the order of analysis based on the relevance of the technologies, highly relevant technologies can be prioritized for analysis. Specific evaluation criteria and methods for relevance include, for example, evaluation criteria for relevance, methods for determining relevance, and methods for changing relevance.

[0090] The quantification unit can estimate the emotions of the craftsman and adjust the quantification criteria based on the estimated emotions. For example, if the craftsman is relaxed, the quantification unit applies detailed quantification criteria. For example, if the craftsman is tense, the quantification unit applies concise quantification criteria. For example, if the craftsman is excited, the quantification unit applies visually appealing quantification criteria. This allows for optimal quantification results for the craftsman by adjusting the quantification criteria based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The specific content and determination methods of the quantification criteria include, for example, the type of criteria, the method of determining the criteria, and the method of changing the criteria.

[0091] The quantification unit can improve the accuracy of quantification by considering the interrelationships between technologies during the quantification process. For example, the quantification unit analyzes the interrelationships between technologies to improve the accuracy of quantification. For example, the quantification unit adjusts the quantification criteria by considering the relationships between technologies. For example, the quantification unit modifies the quantification results based on the interrelationships between technologies. This allows for more accurate quantification results by improving the accuracy of quantification by considering the interrelationships between technologies. Specific details and evaluation methods of the interrelationships between technologies include, for example, the types of interrelationships, evaluation methods for interrelationships, and methods for utilizing interrelationships.

[0092] The quantification unit can perform quantification while considering the attribute information of the technology submitter. For example, the quantification unit can perform quantification while considering the technology submitter's years of experience. For example, the quantification unit can perform quantification while considering the technology submitter's field of expertise. For example, the quantification unit can perform quantification while considering the technology submitter's past achievements. In this way, by performing quantification while considering the attribute information of the technology submitter, it is possible to provide appropriate quantification results tailored to the submitter. Specific details and usage methods of attribute information include, for example, the type of attribute information, the method of acquiring the attribute information, and the method of using the attribute information.

[0093] The quantification unit can estimate the craftsman's emotions and adjust the order in which the quantification results are displayed based on the estimated emotions. For example, if the craftsman is in a hurry, the quantification unit will prioritize displaying important results. For example, if the craftsman is relaxed, the quantification unit will display detailed results. For example, if the craftsman is excited, the quantification unit will display visually appealing results. This allows for the provision of results that are easy for the craftsman to understand by adjusting the order in which the quantification results are displayed based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining the display order include, for example, order determination criteria, methods for changing the order, and methods for evaluating the order.

[0094] The quantification unit can perform quantification while considering the geographical distribution of the technology. For example, the quantification unit can analyze the geographical distribution of the technology to improve the accuracy of quantification. For example, the quantification unit can adjust the quantification criteria based on the geographical distribution. For example, the quantification unit can modify the quantification results according to the geographical distribution. In this way, by performing quantification while considering the geographical distribution of the technology, it is possible to provide appropriate quantification results for each region. Specific details and evaluation methods of geographical distribution include, for example, the type of distribution, the method of evaluating the distribution, and the method of using the distribution.

[0095] The quantification unit can improve the accuracy of quantification by referring to relevant technical literature during the quantification process. For example, the quantification unit can improve the accuracy of quantification by referring to relevant technical literature. For example, the quantification unit can adjust the quantification criteria based on the relevant literature. For example, the quantification unit can modify the quantification results according to the relevant literature. This allows for more accurate quantification results by improving the accuracy of quantification through the referencing of relevant technical literature. Specific details of the relevant literature and the methods of referencing it include, for example, the type of literature, how the literature was obtained, and how the literature was used.

[0096] The instruction system can estimate the emotions of the craftsman and adjust the way the instructions are displayed based on the estimated emotions. For example, if the craftsman is relaxed, the instruction system will display detailed instructions. If the craftsman is tense, the instruction system will display concise instructions. If the craftsman is excited, the instruction system will display visually appealing instructions. By adjusting the way the instructions are displayed based on the craftsman's emotions, the instruction system can provide instructions that are easy for the craftsman to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific details and criteria for the display method include, for example, the display format, the accuracy of the display, and the method for evaluating the display.

[0097] The instruction department can optimize current instruction by referring to past instructional data. For example, the instruction department can analyze past instructional data to optimize current instructional content. For example, the instruction department can adjust instructional methods based on past instructional data. For example, the instruction department can improve the effectiveness of instruction by referring to past instructional data. In this way, effective instruction can be provided by optimizing current instruction by referring to past instructional data. Specific details of past instructional data and methods of referencing it include, for example, the type of data, how the data was acquired, and how the data was used.

[0098] The instruction department can apply different teaching methods to each category of technology during instruction. For example, for woodworking techniques, the instruction department will apply teaching methods that take into account the properties of wood. For pottery techniques, the instruction department will apply teaching methods that take into account the properties of clay. For dyeing techniques, the instruction department will apply teaching methods that take into account the properties of dyes. In this way, by applying different teaching methods to each category of technology, instruction can be provided that is appropriate to the characteristics of the technology. The specific content and application methods of the teaching methods include, for example, the type of method, the method of application of the method, and the method of evaluation of the method.

[0099] The instruction system can estimate the emotions of the craftsman and adjust the importance of instruction based on the estimated emotions. For example, if the craftsman is relaxed, the instruction system will provide detailed instruction. If the craftsman is tense, the instruction system will provide concise instruction. If the craftsman is excited, the instruction system will provide visually appealing instruction. By adjusting the importance of instruction based on the craftsman's emotions, the system can provide the most appropriate instruction for the craftsman. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific evaluation criteria and methods for instruction importance include, for example, importance evaluation criteria, importance determination methods, and importance modification methods.

[0100] The instruction department can analyze changes in instruction based on the timing of technical submissions. For example, the instruction department can apply the latest instruction methods to recently submitted technical skills. For example, the instruction department can apply conventional instruction methods to older technical skills. For example, the instruction department can adjust the content of instruction according to the submission timing. This allows for appropriate instruction of the latest technical skills by analyzing changes in instruction based on the timing of technical skill submissions. Specific details and evaluation methods of changes in instruction include, for example, the type of change, the method of evaluating the change, and how the change is utilized.

[0101] The training department can analyze the training by referring to relevant market data on the technology during the training process. For example, the training department can optimize the training content by referring to relevant market data on the technology. For example, the training department can adjust the training methods based on market data. For example, the training department can improve the effectiveness of the training by referring to market data. In this way, the effectiveness of training can be improved by analyzing the training by referring to relevant market data on the technology. Specific details of the relevant market data and how it is referenced include, for example, the type of data, how the data is acquired, and how the data is used.

[0102] The feedback unit can estimate the successor's emotions and adjust the feedback method based on the estimated emotions. For example, if the successor is relaxed, the feedback unit provides detailed feedback. For example, if the successor is nervous, the feedback unit provides concise feedback. For example, if the successor is excited, the feedback unit provides visually appealing feedback. This allows the system to provide optimal feedback to the successor by adjusting the feedback method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific details and criteria for the feedback method include, for example, the type of method, how to apply the method, and how to evaluate the method.

[0103] The feedback unit can provide optimal feedback by referring to the successor's past learning history during the feedback process. For example, the feedback unit can analyze the successor's past learning history and provide optimal feedback. For example, the feedback unit can adjust the content of the feedback based on the learning history. For example, the feedback unit can improve the effectiveness of the feedback by referring to the learning history. In this way, by providing optimal feedback by referring to the successor's past learning history, the successor's learning effectiveness can be improved. Specific details of the past learning history and how it is referenced include, for example, the type of history, how the history is acquired, and how the history is used.

[0104] The feedback unit can estimate the successor's emotions and prioritize feedback based on the estimated emotions. For example, if the successor is relaxed, the feedback unit will prioritize providing detailed feedback. If the successor is nervous, the feedback unit will prioritize providing concise feedback. If the successor is excited, the feedback unit will prioritize providing visually appealing feedback. In this way, by prioritizing feedback based on the successor's emotions, the system can prioritize providing the most appropriate feedback for the successor. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining feedback priorities include, for example, evaluation criteria for prioritization, methods for determining priorities, and methods for changing priorities.

[0105] The feedback unit can provide optimal feedback by considering the successor's device information. For example, if the successor is using a smartphone, the feedback unit will provide feedback tailored to the screen size. If the successor is using a tablet, the feedback unit will provide feedback optimized for a larger screen. If the successor is using a smartwatch, the feedback unit will provide concise and highly visible feedback. By providing optimal feedback while considering the successor's device information, feedback can be provided in the most suitable format for the successor. Specific details of device information, such as device type, how the device was acquired, and how the device is used, may be included.

[0106] The management department can estimate the successor's emotions and adjust the progress management method based on the estimated emotions. For example, if the successor is relaxed, the management department will perform detailed progress management. If the successor is tense, the management department will perform concise progress management. If the successor is excited, the management department will perform visually appealing progress management. By adjusting the progress management method based on the successor's emotions, the management department can provide optimal progress management for the successor. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific details and criteria for the progress management method include, for example, the type of method, how to apply the method, and how to evaluate the method.

[0107] The management department can select the optimal management method by referring to the successor's past learning history when managing progress. For example, the management department can analyze the successor's past learning history and select the optimal management method. For example, the management department can adjust the management method based on the learning history. For example, the management department can improve the effectiveness of management by referring to the learning history. In this way, by selecting the optimal management method by referring to the successor's past learning history, the successor's learning effectiveness can be improved. Specific details and criteria for the optimal management method include, for example, the type of method, how to apply the method, and how to evaluate the method.

[0108] The management department can estimate the successor's emotions and determine the priority of progress management based on the estimated emotions. For example, if the successor is relaxed, the management department will prioritize detailed progress management. If the successor is stressed, the management department will prioritize concise progress management. If the successor is excited, the management department will prioritize visually appealing progress management. This allows for optimal progress management for the successor by determining the priority of progress management based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Specific methods and criteria for determining progress management priorities include, for example, priority evaluation criteria, priority determination methods, and priority modification methods.

[0109] The management department can select the optimal management method when managing progress, taking into account the successor's geographical location. For example, the management department can analyze the successor's geographical location and select the optimal management method. For example, the management department can adjust the management method based on the geographical location. For example, the management department can improve the effectiveness of management by referring to the geographical location. In this way, by selecting the optimal management method while considering the successor's geographical location, appropriate progress management can be carried out according to the region. Specific details and usage methods of geographical location information include, for example, how the location information is acquired, the accuracy of the location information, and how the location information is used.

[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 technical succession support agent includes a recording unit that records video of the craftsman's technical instruction, an analysis unit that analyzes the recorded video, a quantification unit that quantifies the analysis results, and an instruction unit that provides technical instruction to the successor based on the quantified technical information. Furthermore, the technical succession support agent can be equipped with an emotion estimation function that estimates the successor's emotions and adjusts the instruction content based on the estimated emotions. For example, if the successor is relaxed, detailed instruction content is provided, and if the successor is tense, concise instruction content is provided. This enables optimal instruction tailored to the successor's emotions, improving the efficiency of skill acquisition.

[0112] The technology transfer support agent can be equipped with a learning management unit that manages the successor's learning history, in addition to a recording unit, analysis unit, quantification unit, and instruction unit. The learning management unit refers to the successor's past learning history and reflects it in current technology instruction. For example, it provides focused instruction on technologies that the successor has struggled with in the past. Conversely, it provides more advanced technology instruction on technologies that the successor excels at. This enables optimal technology instruction tailored to each successor's learning situation, improving the efficiency of technology acquisition.

[0113] In addition to recording, analysis, quantification, and instruction units, the technology transfer support agent may also include a feedback unit that estimates the successor's emotions and adjusts the feedback method based on those emotions. For example, if the successor is relaxed, detailed feedback is provided; if the successor is tense, concise feedback is provided. This enables optimal feedback tailored to the successor's emotions, improving the efficiency of technology acquisition.

[0114] The technology transfer support agent can include a recording unit, analysis unit, quantification unit, and instruction unit, as well as a progress management unit to manage the progress of technology instruction. The progress management unit grasps the progress of the successor's technology acquisition and provides feedback at the appropriate time. For example, it monitors the successor's progress in acquiring technology in real time and adjusts the instruction content as needed. This allows for efficient management of the progress of technology acquisition and improves the efficiency of technology transfer.

[0115] In addition to recording, analysis, quantification, and instruction functions, the technology transfer support agent can be equipped with an emotion estimation function that estimates the successor's emotions and adjusts the progress management method based on the estimated emotions. For example, if the successor is relaxed, detailed progress management is performed; if the successor is tense, concise progress management is performed. This enables optimal progress management tailored to the successor's emotions, improving the efficiency of technology acquisition.

[0116] In addition to recording, analysis, quantification, and instruction units, the technology transfer support agent can be equipped with a recording optimization unit that analyzes the craftsman's past technical instruction history and selects the optimal recording method when recording video of technical instruction. For example, it can prioritize recording methods that have worked well for the craftsman in the past and avoid those that have failed. This allows for the recording of effective technical instruction videos, improving the efficiency of skill acquisition for successors.

[0117] In addition to recording, analysis, quantification, and instruction functions, the technology transfer support agent can be equipped with an emotion estimation function that estimates the successor's emotions and adjusts the timing of recording the technical instruction video based on the estimated emotions. For example, if the successor is concentrating, recording will continue without interruption, but if the successor is tired, recording will resume after a break. This allows for recording the technical instruction video at the optimal timing, improving the efficiency of the successor's skill acquisition.

[0118] In addition to the recording, analysis, quantification, and instruction units, the technology transfer support agent can be equipped with a recording filtering unit that filters the recording of technical instruction videos based on the craftsman's current projects and areas of interest. For example, it can prioritize recording technologies related to the craftsman's current projects and select and record relevant technologies based on their areas of interest. This allows for the recording of highly relevant technical instruction videos, improving the efficiency of technology acquisition for successors.

[0119] In addition to recording, analysis, quantification, and instruction functions, the technology transfer support agent can be equipped with an emotion estimation function that estimates the successor's emotions and prioritizes the recording of technical instruction videos based on those emotions. For example, if the successor is excited, it will prioritize recording videos of important techniques; if the successor is relaxed, it will prioritize recording videos of detailed techniques. This allows for the priority recording of videos of important techniques, improving the efficiency of the successor's skill acquisition.

[0120] In addition to the recording, analysis, quantification, and instruction units, the technology transfer support agent can be equipped with a geographic information recording unit that prioritizes recording highly relevant videos by considering the geographic location information of the craftsman when recording videos of technical instruction. For example, it can prioritize recording techniques performed by craftsmen in a specific region and select videos of related techniques based on geographic location information. This makes it possible to record region-specific videos of technical instruction, improving the efficiency of skill acquisition for successors.

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

[0122] Step 1: The recording unit records video of the technical instruction. For example, when a craftsman provides technical instruction, the video is recorded in high resolution, detailing the craftsman's hand movements and how they use the tools. This can then be used as reference material when successors learn the techniques. Step 2: The analysis unit analyzes the video recorded by the recording unit. For example, it analyzes the movements of the craftsman from the video and quantifies each step of the technique. This allows for a detailed analysis of how much force the craftsman is using the tools, the timing of their movements, and so on. Step 3: The quantification unit quantifies each step of the technique analyzed by the analysis unit. For example, it quantifies each step of the technique to make it easier for successors to learn the technique. This makes it possible to quantify the actions of the craftsman and clarify each step of the technique. Step 4: The training department provides technical guidance based on the technical information quantified by the quantification department. For example, when a successor practices the technology, the training department provides real-time feedback and points out areas for improvement. This allows for appropriate guidance when successors acquire the technology.

[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 recording unit, analysis unit, quantification unit, instruction unit, feedback unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records video of the technical instruction using the camera 42 of the smart device 14. The analysis unit analyzes the recorded video, for example, by the specific processing unit 290 of the data processing unit 12. The quantification unit quantifies each step of the technique, for example, by the specific processing unit 290 of the data processing unit 12. The instruction unit provides technical instruction based on the quantified technical information, for example, by the control unit 46A of the smart device 14. The feedback unit provides real-time feedback to the successor as they practice the technique, for example, by the control unit 46A of the smart device 14. The management unit manages the progress of the technical instruction, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 recording unit, analysis unit, quantification unit, instruction unit, feedback unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records video of the technical instruction using the camera 42 of the smart glasses 214. The analysis unit analyzes the recorded video, for example, by the specific processing unit 290 of the data processing unit 12. The quantification unit quantifies each step of the technique, for example, by the specific processing unit 290 of the data processing unit 12. The instruction unit provides technical instruction based on the quantified technical information, for example, by the control unit 46A of the smart glasses 214. The feedback unit provides real-time feedback to the successor as they practice the technique, for example, by the control unit 46A of the smart glasses 214. The management unit manages the progress of the technical instruction, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 recording unit, analysis unit, quantification unit, instruction unit, feedback unit, and management unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records video of the technical instruction using the camera 42 of the headset terminal 314. The analysis unit analyzes the recorded video, for example, by the specific processing unit 290 of the data processing unit 12. The quantification unit quantifies each step of the technique, for example, by the specific processing unit 290 of the data processing unit 12. The instruction unit provides technical instruction based on the quantified technical information, for example, by the control unit 46A of the headset terminal 314. The feedback unit provides real-time feedback to the successor as they practice the technique, for example, by the control unit 46A of the headset terminal 314. The management unit manages the progress of the technical instruction, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[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 recording unit, analysis unit, quantification unit, instruction unit, feedback unit, and management unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the recording unit records video of the technical instruction using the camera 42 of the robot 414. The analysis unit analyzes the recorded video, for example, by the specific processing unit 290 of the data processing unit 12. The quantification unit quantifies each step of the technique, for example, by the specific processing unit 290 of the data processing unit 12. The instruction unit provides technical instruction based on the quantified technical information, for example, by the control unit 46A of the robot 414. The feedback unit provides real-time feedback to the successor as they practice the technique, for example, by the control unit 46A of the robot 414. The management unit manages the progress of the technical instruction, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 recording department records the video of the technical instruction, An analysis unit that analyzes the video recorded by the recording unit, A quantification unit that quantifies each step of the technology analyzed by the analysis unit, A guidance unit provides technical guidance based on the technical information quantified by the aforementioned quantification unit, Equipped with A system characterized by the following features. (Note 2) It includes a feedback unit that provides real-time feedback to successors as they implement the technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes a management department that oversees the progress of technical guidance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recording unit is The movements of the craftsman's hands and the way he uses his tools are recorded in detail. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the movements of craftsmen from video footage and quantify each step of their technique. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned leadership, Based on quantified technical information, provide appropriate guidance to successors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is The system estimates the emotions of the craftsman and adjusts the timing of recordings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording unit is When recording, the craftsman's past technical instruction history is analyzed to select the most suitable recording method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is During recording, filtering is performed based on the craftsman's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is The system estimates the emotions of the craftsmen and prioritizes the video footage to record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is During recording, the system prioritizes recording highly relevant footage, taking into account the geographical location of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is During recording, analyze the craftsman's social media activity and record relevant footage. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of the craftsman and adjust the representation of the analysis based on the estimated emotions of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of technology. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the emotions of the craftsman and adjusts the length of the analysis based on the estimated emotions of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the timing of the submission of the technology. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the technologies. The system described in Appendix 1, characterized by the features described herein. (Note 19) The quantification unit is, We estimate the emotions of the craftsmen and adjust the quantification criteria based on the estimated emotions of the craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 20) The quantification unit is, When quantifying, consider the interrelationships between technologies to improve the accuracy of the quantification. The system described in Appendix 1, characterized by the features described herein. (Note 21) The quantification unit is, When quantifying the technology, the attribute information of the technology submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The quantification unit is, The system estimates the emotions of the craftsmen and adjusts the order in which the quantification results are displayed based on the estimated emotions of the craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 23) The quantification unit is, When quantifying, the geographical distribution of the technology should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The quantification unit is, When quantifying data, refer to relevant technical literature to improve the accuracy of the quantification. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned leadership, The system estimates the emotions of the craftsmen and adjusts the way instructions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned leadership, During instruction, refer to past instructional data to optimize current instruction. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned leadership, When providing instruction, different teaching methods are applied to each category of skill. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned leadership, The system estimates the emotions of the craftsmen and adjusts the importance of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned leadership, During instruction, analyze changes in instruction based on the timing of technical submissions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned leadership, During instruction, we analyze the instruction by referring to relevant market data on the technology. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is Estimate the successor's emotions and adjust the feedback method based on the estimated emotions of the successor. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, refer to the successor's past learning history to provide the most appropriate feedback. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned feedback unit is Estimate the successor's emotions and prioritize feedback based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When providing feedback, we take into account the successor's device information to provide optimal feedback. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned management department, Estimate the successor's emotions and adjust progress management methods based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned management department, When managing progress, refer to the successor's past learning history to select the most suitable management method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned management department, Estimate the successor's emotions and determine progress management priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned management department, When managing progress, the optimal management method will be selected, taking into account the geographical location of the successor. The system described in Appendix 3, 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 recording department records the video of the technical instruction, An analysis unit that analyzes the video recorded by the recording unit, A quantification unit that quantifies each step of the technology analyzed by the analysis unit, A guidance unit provides technical guidance based on the technical information quantified by the aforementioned quantification unit, Equipped with A system characterized by the following features.

2. It includes a feedback unit that provides real-time feedback to successors as they implement the technology. The system according to feature 1.

3. The facility includes a management department that oversees the progress of technical guidance. The system according to feature 1.

4. The aforementioned recording unit is The movements of the craftsman's hands and the way he uses his tools are recorded in detail. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the movements of craftsmen from video footage and quantify each step of their technique. The system according to feature 1.

6. The aforementioned leadership, Based on quantified technical information, provide appropriate guidance to successors. The system according to feature 1.

7. The aforementioned recording unit is The system estimates the emotions of the craftsman and adjusts the timing of recordings based on the estimated emotions. The system according to feature 1.

8. The aforementioned recording unit is When recording, the craftsman's past technical instruction history is analyzed to select the most suitable recording method. The system according to feature 1.

9. The aforementioned recording unit is During recording, filtering is performed based on the craftsman's current projects and areas of interest. The system according to feature 1.

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