system

Abstract

The system according to this embodiment aims to learn the skills and knowledge of skilled craftsmen and effectively pass them on to the next generation. [Solution] The system according to the embodiment comprises a learning unit, an instruction unit, and a management unit. The learning unit learns the skills and knowledge of skilled craftsmen. The instruction unit provides technical guidance based on the skills and knowledge learned by the learning unit. The management unit performs quality control based on the skills instructed by the instruction 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to effectively inherit the skills and knowledge of skilled workers to the next generation.

[0005] The system according to the embodiment aims to learn the skills and knowledge of skilled workers and effectively inherit them to the next generation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning unit, an instruction unit, and a management unit. The learning unit learns the skills and knowledge of skilled workers. The instruction unit provides technical guidance based on the skills and knowledge learned by the learning unit. The management unit performs quality control based on the technology guided by the instruction unit.

Effects of the Invention

[0007] The system according to this embodiment can learn the skills and knowledge of skilled craftsmen and effectively pass them on to the next generation. [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 Traditional Craft Master AI System according to an embodiment of the present invention is a system for preserving and passing on Japanese traditional craft techniques to future generations. This system learns the skills and knowledge of skilled craftsmen and plays a role in effectively transmitting them to young craftsmen and learners. The Traditional Craft Master AI System analyzes the actions and thought processes of craftsmen in detail and systematizes the techniques, including tacit knowledge that is difficult to put into words. It then provides technical guidance in an optimal form according to the user's skill level and learning style. Furthermore, the Traditional Craft Master AI System can also be used for quality control of traditional craft products, contributing to maintaining brand quality by inspecting products and detecting defective items. For example, the Traditional Craft Master AI System learns the skills and knowledge of skilled craftsmen. The Traditional Craft Master AI System analyzes the actions and thought processes of craftsmen in detail and systematizes the techniques, including tacit knowledge that is difficult to put into words. Next, the Traditional Craft Master AI System provides technical guidance according to the user's skill level and learning style. For example, it can carefully teach basic techniques to beginners and efficiently transmit advanced techniques to experts. Furthermore, the Traditional Crafts Master AI System performs quality control on traditional crafts, inspecting products and detecting defects. This contributes to maintaining brand quality. The Traditional Crafts Master AI System also provides optimal learning programs tailored to the user's skill level and learning style. For example, it can carefully teach basic techniques to beginners and efficiently impart advanced techniques to experts. Additionally, the Traditional Crafts Master AI System offers immersive technology transmission experiences utilizing VR / AR. This allows users to learn while actually experiencing the techniques of artisans. Moreover, the Traditional Crafts Master AI System is also used for quality control of traditional crafts. It uses AI image recognition technology to inspect products and detect defects, contributing to maintaining brand quality. Furthermore, the Traditional Crafts Master AI System ensures the permanent preservation of traditional techniques through digital archiving. This prevents the loss of traditional techniques and ensures their transmission to future generations. In this way, the Traditional Crafts Master AI System accelerates the transmission of traditional craft techniques, protecting Japan's cultural heritage while also creating new value.This will enable the Traditional Crafts Master AI System to preserve and pass on Japan's traditional craft techniques to future generations.

[0029] The traditional craft master AI system according to this embodiment comprises a learning unit, an instruction unit, and a management unit. The learning unit learns the skills and knowledge of skilled craftsmen. The learning unit, for example, analyzes the actions and thought processes of craftsmen in detail and systematizes the skills, including tacit knowledge that is difficult to verbalize. The learning unit, for example, records in detail the hand movements of craftsmen and how they use the tools they use, and the AI ​​analyzes that data. The learning unit, for example, records in detail the work environment and work procedures of craftsmen, and the AI ​​analyzes that data. The instruction unit provides technical guidance based on the skills and knowledge learned by the learning unit. The instruction unit provides technical guidance in an optimal form according to the user's skill level and learning style. For example, the instruction unit can carefully teach basic skills to beginners and efficiently convey advanced skills to experts. For example, the instruction unit provides appropriate feedback according to the user's learning progress. The management unit performs quality control based on the skills taught by the instruction unit. For example, the management unit performs quality control of traditional craft products, including product inspection and detection of defective products. The management department, for example, uses AI image recognition technology to inspect products and detect defective items. The management department also performs quality control based on product quality evaluation standards. As a result, the traditional craft master AI system according to this embodiment can learn the skills and knowledge of skilled craftsmen and provide technical guidance and quality control.

[0030] The learning department studies the techniques and knowledge of skilled craftsmen. For example, it analyzes the movements and thought processes of craftsmen in detail, systematizing the techniques, including tacit knowledge that is difficult to verbalize. Specifically, it uses high-precision cameras and sensors to meticulously record the hand movements and tool usage of craftsmen while they work. This data is analyzed by AI to build models that help understand how the craftsmen's techniques are realized. For example, by recording with high precision the subtle movements of a potter's hands and the amount of force applied when turning a potter's wheel, or the angle and force applied when a sculptor uses a chisel, and then analyzing this data with AI, the essence of the technique can be captured. The craftsmen's work environment and procedures are also recorded in detail. For example, by recording the environment in which a craftsman works and the procedures they follow, and then analyzing this data with AI, the optimal work environment and procedures can be derived. Furthermore, the craftsmen's thought processes are also subject to learning. By recording how craftsmen plan their work and solve problems through interviews and observations, and then analyzing this data with AI, the knowledge and experience behind the techniques can be systematized. This will allow the learning department to comprehensively learn the skills and knowledge of experienced craftsmen and build a foundation for passing them on to the next generation.

[0031] The instruction department provides technical guidance based on the skills and knowledge acquired by the learning department. For example, the instruction department provides technical guidance in an optimal way, tailored to the user's skill level and learning style. Specifically, they can carefully teach basic techniques to beginners and efficiently impart advanced techniques to experienced users. For example, beginners can be given clear explanations of basic tool usage and work procedures using videos and diagrams, allowing them to learn by actually doing. On the other hand, experienced users can receive detailed explanations of more advanced techniques and applied work procedures, along with specific advice useful for actual work. Furthermore, the instruction department provides appropriate feedback according to the user's learning progress. For example, the AI ​​evaluates the user's work, specifically pointing out what is well done and what needs improvement. This allows users to understand their shortcomings and gain concrete guidance for the next step. In addition, the instruction department manages the user's learning history and provides an optimal learning plan for each individual user. For example, based on past learning history and feedback, they propose the most suitable learning content and assignments for the user, supporting them in efficiently acquiring skills. This allows the leadership to effectively support users in improving their skills and pass on traditional craft techniques to the next generation.

[0032] The Management Department performs quality control based on techniques taught by the Training Department. For example, the Management Department performs quality control on traditional crafts, including product inspection and defect detection. Specifically, it uses AI image recognition technology to inspect products and detect defects. For example, it uses a high-precision camera to photograph the surface of a product to check for scratches or chips and to check if the shape is accurate, and then the AI ​​analyzes the image to determine if it meets quality standards. It also performs quality control based on product quality evaluation standards. For example, it inspects products based on various evaluation standards such as product dimensions, weight, and material quality to confirm that they meet quality standards. Furthermore, the Management Department records the results of quality control and accumulates data for quality improvement. For example, it records inspection results and the occurrence of defects in a database and performs analysis for quality improvement. This allows the Management Department to build a foundation for continuously improving product quality. In addition, the Management Department introduces systems to streamline the quality control process. For example, it improves the efficiency of quality control by automating inspections and centralizing data management. This allows the management department to maintain a high standard of quality for traditional crafts and provide customers with high-quality products.

[0033] The judgment unit can determine the user's skill level. For example, the judgment unit conducts tests to evaluate the user's technical level. For example, the judgment unit analyzes the user's past learning history and deliverables to determine skill level. For example, the judgment unit evaluates the user's work speed and accuracy to determine skill level. By determining the user's skill level, appropriate technical guidance becomes possible. Some or all of the above processes in the judgment unit may be performed using AI or not. For example, the judgment unit can input test data to evaluate the user's technical level into a generating AI and have the generating AI execute the evaluation results.

[0034] The service provider can provide learning programs. For example, the service provider can create learning programs tailored to the user's skill level and learning style. For example, the service provider can provide programs for beginners to learn basic techniques and programs for experts to learn advanced techniques. For example, the service provider can adjust the learning program according to the user's learning progress. This makes it possible to provide learning programs tailored to the user. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's learning data into a generating AI and have the generating AI create an optimal learning program.

[0035] The Experience Department can provide experiences utilizing VR / AR. For example, the Experience Department can provide an immersive skills transfer experience in which users can actually experience the skills of a craftsman. For example, the Experience Department can use a VR headset to recreate the work of a craftsman in a virtual space. For example, the Experience Department can use AR technology to overlay virtual instruction onto the real work environment. In this way, an immersive skills transfer experience becomes possible by utilizing VR / AR. Some or all of the above processes in the Experience Department may be performed using AI or not. For example, the Experience Department can input user experience data into a generating AI and have the generating AI create the optimal experience content.

[0036] The inspection department can perform inspections using AI image recognition technology. For example, the inspection department uses AI image recognition technology to detect scratches and defects on the surface of a product. For example, the inspection department uses AI image recognition technology to inspect the dimensions and shape of a product. For example, the inspection department uses AI image recognition technology to verify the consistency of the color and pattern of a product. As a result, using AI image recognition technology makes product inspection and the detection of defective products more efficient. Some or all of the above processes in the inspection department may be performed using AI or not. For example, the inspection department can input image data of a product into a generating AI and have the generating AI execute the inspection results.

[0037] The preservation unit can perform digital archiving. For example, the preservation unit can record detailed procedures and materials used in traditional techniques as digital data. For example, the preservation unit can record the working environment and procedures of traditional techniques as digital data. For example, the preservation unit can record video and audio of traditional techniques as digital data. This makes it possible to permanently preserve traditional techniques by performing digital archiving. Some or all of the above-mentioned processes in the preservation unit may be performed using AI or not. For example, the preservation unit can input digital data of traditional techniques into a generating AI and have the generating AI perform digital archiving.

[0038] The learning unit can detect subtle differences in the movements of craftsmen during the learning process and analyze differences in technique in detail. For example, the learning unit can detect differences in the speed and angle of the craftsmen's hand movements and analyze differences in technique. For example, the learning unit can detect differences in how craftsmen hold and apply force to the tools they use and analyze differences in technique. For example, the learning unit can detect differences in the posture and body movements of craftsmen while they are working and analyze differences in technique. In this way, by detecting subtle differences in the movements of craftsmen, differences in technique can be analyzed in detail. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input craftsman movement data into a generating AI and have the generating AI perform the analysis of differences in technique.

[0039] The learning unit can perform learning while taking into account differences in the work environment and tools used by craftsmen. For example, the learning unit can take into account differences in temperature and humidity in the craftsmen's work environment. For example, the learning unit can take into account differences in the type and condition of the tools used by craftsmen. For example, the learning unit can take into account differences in lighting and sound in the craftsmen's workspace. This makes effective learning possible by taking into account differences in the work environment and tools used by craftsmen. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input craftsmen's work environment data into a generating AI and have the generating AI perform adjustments to the learning content.

[0040] The learning unit can analyze the evolution of techniques by referring to the craftsman's past works during the learning process. For example, the learning unit can analyze changes in the design of the craftsman's past works. For example, the learning unit can analyze the evolution of techniques in the craftsman's past works. For example, the learning unit can analyze changes in the quality of the craftsman's past works. In this way, the evolution of techniques can be analyzed by referring to the craftsman's past works. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input data on the craftsman's past works into a generating AI and have the generating AI perform the analysis of the evolution of techniques.

[0041] The learning unit can perform learning while taking into account the region-specific techniques and culture of the craftsmen. For example, the learning unit can perform learning while taking into account the differences in the region-specific techniques of the craftsmen. For example, the learning unit can perform learning while taking into account the differences in the region-specific culture of the craftsmen. For example, the learning unit can perform learning while taking into account the differences in the region-specific materials of the craftsmen. This makes effective learning possible by taking into account the region-specific techniques and culture of the craftsmen. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input region-specific data of the craftsmen into a generating AI and have the generating AI perform adjustments to the learning content.

[0042] The instruction unit can adjust the level of detail in the instruction content according to the learner's level of understanding. For example, if the learner has a high level of understanding, the instruction unit will provide instruction that includes detailed technical explanations. For example, if the learner has a moderate level of understanding, the instruction unit will provide instruction that includes standard technical explanations. For example, if the learner has a low level of understanding, the instruction unit will provide instruction that includes concise technical explanations. By adjusting the level of detail in the instruction content according to the learner's level of understanding, effective instruction becomes possible. Some or all of the above processing in the instruction unit may be performed using AI or not. For example, the instruction unit can input learner understanding data into a generating AI and have the generating AI perform the adjustment of the instruction content.

[0043] The instruction unit can select the optimal teaching method by referring to the learner's past learning history during instruction. For example, the instruction unit selects the optimal teaching method from the learner's past learning history. For example, the instruction unit analyzes the evolution of technology from the learner's past learning history. For example, the instruction unit analyzes the level of understanding from the learner's past learning history. This allows the instruction unit to select the optimal teaching method by referring to the learner's past learning history. Some or all of the above processes in the instruction unit may be performed using AI or not. For example, the instruction unit can input the learner's past learning history data into a generating AI and have the generating AI perform the selection of the teaching method.

[0044] The instruction department can provide instruction while taking into account the learner's living environment and time of day. For example, the instruction department can provide instruction while taking into account differences in the learner's living environment. For example, the instruction department can provide instruction while taking into account differences in the learner's time of day. For example, the instruction department can provide instruction while taking into account differences in the learner's daily rhythm. By taking into account the learner's living environment and time of day, effective instruction becomes possible. Some or all of the above processing in the instruction department may be performed using AI or not. For example, the instruction department can input learner's living environment data into a generating AI and have the generating AI adjust the instruction content.

[0045] The instructional department can customize the content of instruction to take into account the cultural backgrounds of the learners. For example, the instructional department can customize the content to take into account differences in the cultural backgrounds of the learners. For example, the instructional department can customize the content to take into account differences in the language of the learners. For example, the instructional department can customize the content to take into account differences in the values ​​of the learners. This makes effective instruction possible by taking into account the cultural backgrounds of the learners. Some or all of the above processes in the instructional department may be performed using AI or not. For example, the instructional department can input learner cultural background data into a generating AI and have the generating AI perform the customization of the instructional content.

[0046] The management department can maintain quality consistency by meticulously recording the product manufacturing process during management. For example, the management department can meticulously record each step of the product manufacturing process. For example, the management department can meticulously record the materials used in the product manufacturing process. For example, the management department can meticulously record the working environment in the product manufacturing process. This allows for quality consistency to be maintained by meticulously recording the product manufacturing process. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input product manufacturing process data into a generating AI and have the generating AI perform the record management.

[0047] The management department can perform quality assessments during management, taking into account the environment and conditions in which the product is used. For example, the management department may consider the temperature and humidity of the environment in which the product is used when performing quality assessments. For example, the management department may consider the frequency and intensity of the conditions under which the product is used when performing quality assessments. For example, the management department may consider the lighting and sound of the location where the product is used when performing quality assessments. This allows for appropriate quality assessments by considering the environment and conditions in which the product is used. Some or all of the above processes in the management department may be performed using AI, or they may be performed without AI. For example, the management department may input product usage environment data into a generating AI and have the generating AI perform adjustments to the quality assessment.

[0048] The management department can perform quality control by referring to the market evaluation and feedback of the product during the management process. For example, the management department can perform quality control by referring to the market evaluation of the product. For example, the management department can perform quality control by referring to the market feedback of the product. For example, the management department can perform quality control by comprehensively referring to the market evaluation and feedback of the product. This makes it possible to perform appropriate quality control by referring to the market evaluation and feedback of the product. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input market evaluation data into a generating AI and have the generating AI perform quality control adjustments.

[0049] The management department can perform quality assessments during management, taking into account the transportation and storage conditions of the products. For example, the management department can perform quality assessments considering the temperature and humidity of the product's transportation conditions. For example, the management department can perform quality assessments considering the frequency and intensity of the product's storage conditions. For example, the management department can perform quality assessments considering the lighting and sound of the product's transportation and storage locations. This makes it possible to perform appropriate quality assessments by taking into account the transportation and storage conditions of the products. Some or all of the above processes in the management department may be performed using AI, or they may be performed without AI. For example, the management department can input transportation and storage condition data into a generating AI and have the generating AI perform adjustments to the quality assessment.

[0050] The judgment unit can analyze in detail the learner's past learning history and deliverables at the time of judgment. For example, the judgment unit may analyze the learner's past learning history in detail. For example, the judgment unit may analyze the learner's past deliverables in detail. For example, the judgment unit may comprehensively analyze the learner's past learning history and deliverables. This makes it possible to determine an appropriate level of proficiency by analyzing the learner's past learning history and deliverables in detail. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may input the learner's past learning history data into a generating AI and have the generating AI perform the analysis.

[0051] The judgment unit can make judgments while taking into account the learner's living environment and time of day. For example, the judgment unit can make judgments while taking into account differences in the learner's living environment. For example, the judgment unit can make judgments while taking into account differences in the learner's time of day. For example, the judgment unit can make judgments while taking into account differences in the learner's daily rhythm. This makes it possible to make an appropriate determination of proficiency level by taking into account the learner's living environment and time of day. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input learner's living environment data into a generating AI and have the generating AI perform adjustments to the judgment content.

[0052] The service provider can select the optimal learning program by referring to the learner's past learning history at the time of delivery. For example, the service provider selects the optimal learning program from the learner's past learning history. For example, the service provider analyzes the evolution of technology from the learner's past learning history. For example, the service provider analyzes the level of understanding from the learner's past learning history. In this way, the optimal learning program can be selected by referring to the learner's past learning history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the learner's past learning history data into a generating AI and have the generating AI perform the selection of a learning program.

[0053] The service provider can provide learning programs that take into account the learner's living environment and time of day. For example, the service provider can provide learning programs that take into account differences in the learner's living environment. For example, the service provider can provide learning programs that take into account differences in the learner's time of day. For example, the service provider can provide learning programs that take into account differences in the learner's daily rhythm. This makes effective learning possible by taking into account the learner's living environment and time of day. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input learner's living environment data into a generating AI and have the generating AI perform adjustments to the learning program.

[0054] The experience unit can select the most suitable experience content by referring to the learner's past experience history during the experience. For example, the experience unit selects the most suitable experience content from the learner's past experience history. For example, the experience unit analyzes the evolution of technology from the learner's past experience history. For example, the experience unit analyzes the level of understanding from the learner's past experience history. In this way, the optimal experience content can be selected by referring to the learner's past experience history. Some or all of the above processes in the experience unit may be performed using AI or not. For example, the experience unit can input the learner's past experience history data into a generating AI and have the generating AI perform the selection of the experience content.

[0055] The experience unit can provide experiences that take into account the learner's living environment and time of day. For example, the experience unit can provide experiences that take into account differences in the learner's living environment. For example, the experience unit can provide experiences that take into account differences in the learner's time of day. For example, the experience unit can provide experiences that take into account differences in the learner's daily rhythm. By taking into account the learner's living environment and time of day, an effective experience becomes possible. Some or all of the above processing in the experience unit may be performed using AI or not. For example, the experience unit can input learner's living environment data into a generating AI and have the generating AI adjust the content of the experience.

[0056] The inspection department can maintain quality consistency by meticulously recording the product manufacturing process during inspection. For example, the inspection department can meticulously record each step of the product manufacturing process. For example, the inspection department can meticulously record the materials used in the product manufacturing process. For example, the inspection department can meticulously record the working environment in the product manufacturing process. This allows for quality consistency to be maintained by meticulously recording the product manufacturing process. Some or all of the above processes in the inspection department may be performed using AI or not. For example, the inspection department can input product manufacturing process data into a generating AI and have the generating AI manage the records.

[0057] The inspection department can perform quality control by referring to the market evaluation and feedback of the product during inspection. For example, the inspection department can perform quality control by referring to the market evaluation of the product. For example, the inspection department can perform quality control by referring to the market feedback of the product. For example, the inspection department can perform quality control by comprehensively referring to the market evaluation and feedback of the product. This makes it possible to perform appropriate quality control by referring to the market evaluation and feedback of the product. Some or all of the above processes in the inspection department may be performed using AI or not. For example, the inspection department can input market evaluation data into a generating AI and have the generating AI perform quality control adjustments.

[0058] The storage unit can record detailed technical information during storage and accurately transmit it to future generations. For example, the storage unit can record detailed technical procedures. For example, the storage unit can record the materials used in the technical process. For example, the storage unit can record the working environment of the technical process. By doing so, detailed technical information can be accurately transmitted to future generations. Some or all of the above-described processes in the storage unit may be performed using AI or not. For example, the storage unit can input detailed technical record data into a generating AI and have the generating AI manage the records.

[0059] The preservation unit can create a digital archive of the technology during preservation and make it widely accessible. For example, the preservation unit can create a digital archive of the technology and make it accessible online. For example, the preservation unit can create a digital archive of the technology and make it accessible in educational institutions. For example, the preservation unit can create a digital archive of the technology and make it accessible in museums. In this way, the digital archive of the technology can be made widely accessible. Some or all of the above processes in the preservation unit may be performed using AI or not. For example, the preservation unit can input the digital archive data of the technology into a generating AI and have the generating AI perform the management of the archive.

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

[0061] The traditional crafts master AI system can include an evaluation unit in addition to a learning unit, instruction unit, and management unit. The evaluation unit regularly assesses the user's learning progress and skill acquisition level and provides feedback. For example, the evaluation unit can evaluate the accuracy and efficiency of the techniques the user has learned and point out areas for improvement. The evaluation unit can analyze the user's learning history and suggest the next learning steps. The evaluation unit can provide appropriate challenges according to the user's skill acquisition level. This allows the user to understand their own progress and learn effectively.

[0062] The traditional crafts master AI system can include a communication section in addition to the learning, instruction, and management sections. The communication section facilitates interaction among users and supports the sharing of skills and knowledge. For example, the communication section can provide a forum where users can exchange questions and opinions. The communication section can implement mentorship programs between skilled craftsmen and learners. The communication section can provide a platform for users to work on projects together. This allows users to collaborate with other learners and craftsmen and gain deeper learning.

[0063] The traditional crafts master AI system can include an inspiration unit in addition to the learning, instruction, and management units. The inspiration unit provides users with new ideas and creative inspiration. For example, the inspiration unit can provide information on the history and culture of traditional crafts. The inspiration unit can introduce the works and techniques of other artisans, providing users with new perspectives. The inspiration unit can suggest new techniques and designs that users can incorporate into their own work. This allows users to enhance their creativity and create unique works.

[0064] The traditional craft master AI system can include a learning unit, instruction unit, management unit, and a resources unit. The resources unit provides information on materials and tools that the user needs. For example, the resources unit can provide information on where to obtain and the price of materials required for a specific technique. The resources unit can provide information on how to maintain and use the tools the user uses. The resources unit can provide samples for the user to try out new materials and tools. This allows the user to efficiently obtain the necessary resources and concentrate on mastering the techniques.

[0065] The Traditional Crafts Master AI System can include a marketing department in addition to its learning, instruction, and management departments. The marketing department supports users in bringing their work to market. For example, it can assist with photography and catalog creation for users' works. It can provide advice on setting up online shops. It can provide opportunities for users to showcase their work at exhibitions and events. This allows users to raise awareness of their work and increase sales opportunities.

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

[0067] Step 1: The learning unit learns the skills and knowledge of skilled craftsmen. For example, it analyzes the craftsman's movements and thought processes in detail, systematizing the techniques, including tacit knowledge that is difficult to put into words. Furthermore, it meticulously records the craftsman's hand movements, how they use the tools they employ, the work environment, and the work procedures, and the AI ​​analyzes this data. Step 2: The instruction team provides technical guidance based on the skills and knowledge acquired by the learning team. For example, they provide technical guidance in an optimal way that suits the user's skill level and learning style. They can carefully teach basic techniques to beginners and efficiently impart advanced techniques to experienced users. They also provide appropriate feedback according to the user's learning progress. Step 3: The management department performs quality control based on the techniques taught by the leadership department. For example, they perform quality control on traditional crafts, including product inspection and defect detection. They use AI image recognition technology to inspect products and detect defects, and perform quality control based on product quality evaluation standards.

[0068] (Example of form 2) The Traditional Craft Master AI System according to an embodiment of the present invention is a system for preserving and passing on Japanese traditional craft techniques to future generations. This system learns the skills and knowledge of skilled craftsmen and plays a role in effectively transmitting them to young craftsmen and learners. The Traditional Craft Master AI System analyzes the actions and thought processes of craftsmen in detail and systematizes the techniques, including tacit knowledge that is difficult to put into words. It then provides technical guidance in an optimal form according to the user's skill level and learning style. Furthermore, the Traditional Craft Master AI System can also be used for quality control of traditional craft products, contributing to maintaining brand quality by inspecting products and detecting defective items. For example, the Traditional Craft Master AI System learns the skills and knowledge of skilled craftsmen. The Traditional Craft Master AI System analyzes the actions and thought processes of craftsmen in detail and systematizes the techniques, including tacit knowledge that is difficult to put into words. Next, the Traditional Craft Master AI System provides technical guidance according to the user's skill level and learning style. For example, it can carefully teach basic techniques to beginners and efficiently transmit advanced techniques to experts. Furthermore, the Traditional Crafts Master AI System performs quality control on traditional crafts, inspecting products and detecting defects. This contributes to maintaining brand quality. The Traditional Crafts Master AI System also provides optimal learning programs tailored to the user's skill level and learning style. For example, it can carefully teach basic techniques to beginners and efficiently impart advanced techniques to experts. Additionally, the Traditional Crafts Master AI System offers immersive technology transmission experiences utilizing VR / AR. This allows users to learn while actually experiencing the techniques of artisans. Moreover, the Traditional Crafts Master AI System is also used for quality control of traditional crafts. It uses AI image recognition technology to inspect products and detect defects, contributing to maintaining brand quality. Furthermore, the Traditional Crafts Master AI System ensures the permanent preservation of traditional techniques through digital archiving. This prevents the loss of traditional techniques and ensures their transmission to future generations. In this way, the Traditional Crafts Master AI System accelerates the transmission of traditional craft techniques, protecting Japan's cultural heritage while also creating new value.This will enable the Traditional Crafts Master AI System to preserve and pass on Japan's traditional craft techniques to future generations.

[0069] The traditional craft master AI system according to this embodiment comprises a learning unit, an instruction unit, and a management unit. The learning unit learns the skills and knowledge of skilled craftsmen. The learning unit, for example, analyzes the actions and thought processes of craftsmen in detail and systematizes the skills, including tacit knowledge that is difficult to verbalize. The learning unit, for example, records in detail the hand movements of craftsmen and how they use the tools they use, and the AI ​​analyzes that data. The learning unit, for example, records in detail the work environment and work procedures of craftsmen, and the AI ​​analyzes that data. The instruction unit provides technical guidance based on the skills and knowledge learned by the learning unit. The instruction unit provides technical guidance in an optimal form according to the user's skill level and learning style. For example, the instruction unit can carefully teach basic skills to beginners and efficiently convey advanced skills to experts. For example, the instruction unit provides appropriate feedback according to the user's learning progress. The management unit performs quality control based on the skills taught by the instruction unit. For example, the management unit performs quality control of traditional craft products, including product inspection and detection of defective products. The management department, for example, uses AI image recognition technology to inspect products and detect defective items. The management department also performs quality control based on product quality evaluation standards. As a result, the traditional craft master AI system according to this embodiment can learn the skills and knowledge of skilled craftsmen and provide technical guidance and quality control.

[0070] The learning department studies the techniques and knowledge of skilled craftsmen. For example, it analyzes the movements and thought processes of craftsmen in detail, systematizing the techniques, including tacit knowledge that is difficult to verbalize. Specifically, it uses high-precision cameras and sensors to meticulously record the hand movements and tool usage of craftsmen while they work. This data is analyzed by AI to build models that help understand how the craftsmen's techniques are realized. For example, by recording with high precision the subtle movements of a potter's hands and the amount of force applied when turning a potter's wheel, or the angle and force applied when a sculptor uses a chisel, and then analyzing this data with AI, the essence of the technique can be captured. The craftsmen's work environment and procedures are also recorded in detail. For example, by recording the environment in which a craftsman works and the procedures they follow, and then analyzing this data with AI, the optimal work environment and procedures can be derived. Furthermore, the craftsmen's thought processes are also subject to learning. By recording how craftsmen plan their work and solve problems through interviews and observations, and then analyzing this data with AI, the knowledge and experience behind the techniques can be systematized. This will allow the learning department to comprehensively learn the skills and knowledge of experienced craftsmen and build a foundation for passing them on to the next generation.

[0071] The instruction department provides technical guidance based on the skills and knowledge acquired by the learning department. For example, the instruction department provides technical guidance in an optimal way, tailored to the user's skill level and learning style. Specifically, they can carefully teach basic techniques to beginners and efficiently impart advanced techniques to experienced users. For example, beginners can be given clear explanations of basic tool usage and work procedures using videos and diagrams, allowing them to learn by actually doing. On the other hand, experienced users can receive detailed explanations of more advanced techniques and applied work procedures, along with specific advice useful for actual work. Furthermore, the instruction department provides appropriate feedback according to the user's learning progress. For example, the AI ​​evaluates the user's work, specifically pointing out what is well done and what needs improvement. This allows users to understand their shortcomings and gain concrete guidance for the next step. In addition, the instruction department manages the user's learning history and provides an optimal learning plan for each individual user. For example, based on past learning history and feedback, they propose the most suitable learning content and assignments for the user, supporting them in efficiently acquiring skills. This allows the leadership to effectively support users in improving their skills and pass on traditional craft techniques to the next generation.

[0072] The Management Department performs quality control based on techniques taught by the Training Department. For example, the Management Department performs quality control on traditional crafts, including product inspection and defect detection. Specifically, it uses AI image recognition technology to inspect products and detect defects. For example, it uses a high-precision camera to photograph the surface of a product to check for scratches or chips and to check if the shape is accurate, and then the AI ​​analyzes the image to determine if it meets quality standards. It also performs quality control based on product quality evaluation standards. For example, it inspects products based on various evaluation standards such as product dimensions, weight, and material quality to confirm that they meet quality standards. Furthermore, the Management Department records the results of quality control and accumulates data for quality improvement. For example, it records inspection results and the occurrence of defects in a database and performs analysis for quality improvement. This allows the Management Department to build a foundation for continuously improving product quality. In addition, the Management Department introduces systems to streamline the quality control process. For example, it improves the efficiency of quality control by automating inspections and centralizing data management. This allows the management department to maintain a high standard of quality for traditional crafts and provide customers with high-quality products.

[0073] The judgment unit can determine the user's skill level. For example, the judgment unit conducts tests to evaluate the user's technical level. For example, the judgment unit analyzes the user's past learning history and deliverables to determine skill level. For example, the judgment unit evaluates the user's work speed and accuracy to determine skill level. By determining the user's skill level, appropriate technical guidance becomes possible. Some or all of the above processes in the judgment unit may be performed using AI or not. For example, the judgment unit can input test data to evaluate the user's technical level into a generating AI and have the generating AI execute the evaluation results.

[0074] The service provider can provide learning programs. For example, the service provider can create learning programs tailored to the user's skill level and learning style. For example, the service provider can provide programs for beginners to learn basic techniques and programs for experts to learn advanced techniques. For example, the service provider can adjust the learning program according to the user's learning progress. This makes it possible to provide learning programs tailored to the user. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's learning data into a generating AI and have the generating AI create an optimal learning program.

[0075] The Experience Department can provide experiences utilizing VR / AR. For example, the Experience Department can provide an immersive skills transfer experience in which users can actually experience the skills of a craftsman. For example, the Experience Department can use a VR headset to recreate the work of a craftsman in a virtual space. For example, the Experience Department can use AR technology to overlay virtual instruction onto the real work environment. In this way, an immersive skills transfer experience becomes possible by utilizing VR / AR. Some or all of the above processes in the Experience Department may be performed using AI or not. For example, the Experience Department can input user experience data into a generating AI and have the generating AI create the optimal experience content.

[0076] The inspection department can perform inspections using AI image recognition technology. For example, the inspection department uses AI image recognition technology to detect scratches and defects on the surface of a product. For example, the inspection department uses AI image recognition technology to inspect the dimensions and shape of a product. For example, the inspection department uses AI image recognition technology to verify the consistency of the color and pattern of a product. As a result, using AI image recognition technology makes product inspection and the detection of defective products more efficient. Some or all of the above processes in the inspection department may be performed using AI or not. For example, the inspection department can input image data of a product into a generating AI and have the generating AI execute the inspection results.

[0077] The preservation unit can perform digital archiving. For example, the preservation unit can record detailed procedures and materials used in traditional techniques as digital data. For example, the preservation unit can record the working environment and procedures of traditional techniques as digital data. For example, the preservation unit can record video and audio of traditional techniques as digital data. This makes it possible to permanently preserve traditional techniques by performing digital archiving. Some or all of the above-mentioned processes in the preservation unit may be performed using AI or not. For example, the preservation unit can input digital data of traditional techniques into a generating AI and have the generating AI perform digital archiving.

[0078] The learning unit can estimate the emotions of the craftsman and select training data based on the estimated emotions. For example, if the craftsman is relaxed, the learning unit will select training data that includes detailed technical explanations. For example, if the craftsman is focused, the learning unit will select training data that emphasizes subtle differences in technique. For example, if the craftsman is tired, the learning unit will select training data that is concise and to the point. This allows for effective learning by selecting training data based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the craftsman's emotion data into a generative AI and have the generative AI perform the selection of training data.

[0079] The learning unit can detect subtle differences in the movements of craftsmen during the learning process and analyze differences in technique in detail. For example, the learning unit can detect differences in the speed and angle of the craftsmen's hand movements and analyze differences in technique. For example, the learning unit can detect differences in how craftsmen hold and apply force to the tools they use and analyze differences in technique. For example, the learning unit can detect differences in the posture and body movements of craftsmen while they are working and analyze differences in technique. In this way, by detecting subtle differences in the movements of craftsmen, differences in technique can be analyzed in detail. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input craftsman movement data into a generating AI and have the generating AI perform the analysis of differences in technique.

[0080] The learning unit can perform learning while taking into account differences in the work environment and tools used by craftsmen. For example, the learning unit can take into account differences in temperature and humidity in the craftsmen's work environment. For example, the learning unit can take into account differences in the type and condition of the tools used by craftsmen. For example, the learning unit can take into account differences in lighting and sound in the craftsmen's workspace. This makes effective learning possible by taking into account differences in the work environment and tools used by craftsmen. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input craftsmen's work environment data into a generating AI and have the generating AI perform adjustments to the learning content.

[0081] The learning unit can estimate the emotions of the craftsman and adjust the learning pace based on the estimated emotions. For example, if the craftsman is relaxed, the learning unit will proceed at a slow pace. If the craftsman is focused, the learning unit will proceed at a normal pace. If the craftsman is tired, the learning unit will proceed in a shorter time. This allows for effective learning by adjusting the learning pace based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the craftsman's emotion data into the generative AI and have the generative AI adjust the learning pace.

[0082] The learning unit can analyze the evolution of techniques by referring to the craftsman's past works during the learning process. For example, the learning unit can analyze changes in the design of the craftsman's past works. For example, the learning unit can analyze the evolution of techniques in the craftsman's past works. For example, the learning unit can analyze changes in the quality of the craftsman's past works. In this way, the evolution of techniques can be analyzed by referring to the craftsman's past works. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input data on the craftsman's past works into a generating AI and have the generating AI perform the analysis of the evolution of techniques.

[0083] The learning unit can perform learning while taking into account the region-specific techniques and culture of the craftsmen. For example, the learning unit can perform learning while taking into account the differences in the region-specific techniques of the craftsmen. For example, the learning unit can perform learning while taking into account the differences in the region-specific culture of the craftsmen. For example, the learning unit can perform learning while taking into account the differences in the region-specific materials of the craftsmen. This makes effective learning possible by taking into account the region-specific techniques and culture of the craftsmen. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input region-specific data of the craftsmen into a generating AI and have the generating AI perform adjustments to the learning content.

[0084] The instruction unit can estimate the learner's emotions and adjust the instruction method based on the estimated emotions. For example, if the learner is relaxed, the instruction unit will teach at a slow pace. For example, if the learner is focused, the instruction unit will teach at a normal pace. For example, if the learner is tired, the instruction unit will teach in a short time. This allows for effective instruction by adjusting the instruction method based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI or not. For example, the instruction unit can input learner emotion data into a generative AI and have the generative AI adjust the instruction method.

[0085] The instruction unit can adjust the level of detail in the instruction content according to the learner's level of understanding. For example, if the learner has a high level of understanding, the instruction unit will provide instruction that includes detailed technical explanations. For example, if the learner has a moderate level of understanding, the instruction unit will provide instruction that includes standard technical explanations. For example, if the learner has a low level of understanding, the instruction unit will provide instruction that includes concise technical explanations. By adjusting the level of detail in the instruction content according to the learner's level of understanding, effective instruction becomes possible. Some or all of the above processing in the instruction unit may be performed using AI or not. For example, the instruction unit can input learner understanding data into a generating AI and have the generating AI perform the adjustment of the instruction content.

[0086] The instruction unit can select the optimal teaching method by referring to the learner's past learning history during instruction. For example, the instruction unit selects the optimal teaching method from the learner's past learning history. For example, the instruction unit analyzes the evolution of technology from the learner's past learning history. For example, the instruction unit analyzes the level of understanding from the learner's past learning history. This allows the instruction unit to select the optimal teaching method by referring to the learner's past learning history. Some or all of the above processes in the instruction unit may be performed using AI or not. For example, the instruction unit can input the learner's past learning history data into a generating AI and have the generating AI perform the selection of the teaching method.

[0087] The instruction unit can estimate the learner's emotions and adjust the timing of instruction based on the estimated emotions. For example, if the learner is relaxed, the instruction unit will provide instruction at a slow pace. For example, if the learner is focused, the instruction unit will provide instruction at a normal pace. For example, if the learner is tired, the instruction unit will provide instruction in a short time. This allows for effective instruction by adjusting the timing of instruction based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI or not. For example, the instruction unit can input learner emotion data into a generative AI and have the generative AI adjust the timing of instruction.

[0088] The instruction department can provide instruction while taking into account the learner's living environment and time of day. For example, the instruction department can provide instruction while taking into account differences in the learner's living environment. For example, the instruction department can provide instruction while taking into account differences in the learner's time of day. For example, the instruction department can provide instruction while taking into account differences in the learner's daily rhythm. By taking into account the learner's living environment and time of day, effective instruction becomes possible. Some or all of the above processing in the instruction department may be performed using AI or not. For example, the instruction department can input learner's living environment data into a generating AI and have the generating AI adjust the instruction content.

[0089] The instructional department can customize the content of instruction to take into account the cultural backgrounds of the learners. For example, the instructional department can customize the content to take into account differences in the cultural backgrounds of the learners. For example, the instructional department can customize the content to take into account differences in the language of the learners. For example, the instructional department can customize the content to take into account differences in the values ​​of the learners. This makes effective instruction possible by taking into account the cultural backgrounds of the learners. Some or all of the above processes in the instructional department may be performed using AI or not. For example, the instructional department can input learner cultural background data into a generating AI and have the generating AI perform the customization of the instructional content.

[0090] The management department can estimate the emotions of craftsmen when evaluating product quality and adjust the evaluation criteria based on the estimated emotions. For example, if a craftsman is relaxed, the management department applies normal evaluation criteria. For example, if a craftsman is focused, the management department applies strict evaluation criteria. For example, if a craftsman is tired, the management department applies lenient evaluation criteria. This allows for appropriate quality evaluation by adjusting the evaluation criteria based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input craftsman emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0091] The management department can maintain quality consistency by meticulously recording the product manufacturing process during management. For example, the management department can meticulously record each step of the product manufacturing process. For example, the management department can meticulously record the materials used in the product manufacturing process. For example, the management department can meticulously record the working environment in the product manufacturing process. This allows for quality consistency to be maintained by meticulously recording the product manufacturing process. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input product manufacturing process data into a generating AI and have the generating AI perform the record management.

[0092] The management department can perform quality assessments during management, taking into account the environment and conditions in which the product is used. For example, the management department may consider the temperature and humidity of the environment in which the product is used when performing quality assessments. For example, the management department may consider the frequency and intensity of the conditions under which the product is used when performing quality assessments. For example, the management department may consider the lighting and sound of the location where the product is used when performing quality assessments. This allows for appropriate quality assessments by considering the environment and conditions in which the product is used. Some or all of the above processes in the management department may be performed using AI, or they may be performed without AI. For example, the management department may input product usage environment data into a generating AI and have the generating AI perform adjustments to the quality assessment.

[0093] The management department can estimate the emotions of craftsmen when evaluating product quality and adjust the frequency of evaluations based on the estimated emotions. For example, if a craftsman is relaxed, the management department will perform evaluations at a normal frequency. For example, if a craftsman is focused, the management department will perform evaluations frequently. For example, if a craftsman is tired, the management department will reduce the frequency of evaluations. This allows for appropriate quality evaluation by adjusting the frequency of evaluations based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input craftsman emotion data into a generative AI and have the generative AI adjust the frequency of evaluations.

[0094] The management department can perform quality control by referring to the market evaluation and feedback of the product during the management process. For example, the management department can perform quality control by referring to the market evaluation of the product. For example, the management department can perform quality control by referring to the market feedback of the product. For example, the management department can perform quality control by comprehensively referring to the market evaluation and feedback of the product. This makes it possible to perform appropriate quality control by referring to the market evaluation and feedback of the product. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input market evaluation data into a generating AI and have the generating AI perform quality control adjustments.

[0095] The management department can perform quality assessments during management, taking into account the transportation and storage conditions of the products. For example, the management department can perform quality assessments considering the temperature and humidity of the product's transportation conditions. For example, the management department can perform quality assessments considering the frequency and intensity of the product's storage conditions. For example, the management department can perform quality assessments considering the lighting and sound of the product's transportation and storage locations. This makes it possible to perform appropriate quality assessments by taking into account the transportation and storage conditions of the products. Some or all of the above processes in the management department may be performed using AI, or they may be performed without AI. For example, the management department can input transportation and storage condition data into a generating AI and have the generating AI perform adjustments to the quality assessment.

[0096] The assessment unit can estimate the learner's emotions and adjust the proficiency assessment criteria based on the estimated emotions. For example, if the learner is relaxed, the assessment unit applies normal criteria. For example, if the learner is focused, the assessment unit applies strict criteria. For example, if the learner is tired, the assessment unit applies lenient criteria. This allows for an appropriate assessment of proficiency by adjusting the proficiency assessment criteria based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the assessment unit may be performed using AI or not. For example, the assessment unit can input learner emotion data into a generative AI and have the generative AI perform the adjustment of the assessment criteria.

[0097] The judgment unit can analyze in detail the learner's past learning history and deliverables at the time of judgment. For example, the judgment unit may analyze the learner's past learning history in detail. For example, the judgment unit may analyze the learner's past deliverables in detail. For example, the judgment unit may comprehensively analyze the learner's past learning history and deliverables. This makes it possible to determine an appropriate level of proficiency by analyzing the learner's past learning history and deliverables in detail. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may input the learner's past learning history data into a generating AI and have the generating AI perform the analysis.

[0098] The judgment unit can estimate the learner's emotions and adjust the timing of judgments based on the estimated emotions. For example, if the learner is relaxed, the judgment unit will make judgments at normal intervals. For example, if the learner is focused, the judgment unit will make judgments frequently. For example, if the learner is tired, the judgment unit will reduce the frequency of judgments. By adjusting the timing of judgments based on the learner's emotions, it becomes possible to determine an appropriate level of proficiency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input learner emotion data into the generative AI and have the generative AI adjust the timing of judgments.

[0099] The judgment unit can make judgments while taking into account the learner's living environment and time of day. For example, the judgment unit can make judgments while taking into account differences in the learner's living environment. For example, the judgment unit can make judgments while taking into account differences in the learner's time of day. For example, the judgment unit can make judgments while taking into account differences in the learner's daily rhythm. This makes it possible to make an appropriate determination of proficiency level by taking into account the learner's living environment and time of day. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input learner's living environment data into a generating AI and have the generating AI perform adjustments to the judgment content.

[0100] The service provider can estimate the learner's emotions and adjust the content of the learning program based on the estimated emotions. For example, if the learner is relaxed, the service provider can provide a learning program that includes detailed technical explanations. For example, if the learner is focused, the service provider can provide a learning program that includes standard technical explanations. For example, if the learner is tired, the service provider can provide a learning program that includes concise technical explanations. This allows for effective learning by adjusting the content of the learning program based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input learner emotion data into a generative AI and have the generative AI adjust the content of the learning program.

[0101] The service provider can select the optimal learning program by referring to the learner's past learning history at the time of delivery. For example, the service provider selects the optimal learning program from the learner's past learning history. For example, the service provider analyzes the evolution of technology from the learner's past learning history. For example, the service provider analyzes the level of understanding from the learner's past learning history. In this way, the optimal learning program can be selected by referring to the learner's past learning history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the learner's past learning history data into a generating AI and have the generating AI perform the selection of a learning program.

[0102] The service provider can estimate the learner's emotions and adjust the pace of the learning program based on the estimated emotions. For example, if the learner is relaxed, the service provider will proceed with the learning program at a slow pace. For example, if the learner is focused, the service provider will proceed with the learning program at a normal pace. For example, if the learner is tired, the service provider will proceed with the learning program in a shorter time. This allows for effective learning by adjusting the pace of the learning program based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input learner emotion data into a generative AI and have the generative AI adjust the pace of the learning program.

[0103] The service provider can provide learning programs that take into account the learner's living environment and time of day. For example, the service provider can provide learning programs that take into account differences in the learner's living environment. For example, the service provider can provide learning programs that take into account differences in the learner's time of day. For example, the service provider can provide learning programs that take into account differences in the learner's daily rhythm. This makes effective learning possible by taking into account the learner's living environment and time of day. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input learner's living environment data into a generating AI and have the generating AI perform adjustments to the learning program.

[0104] The experience unit can estimate the learner's emotions and adjust the experience content based on those emotions. For example, if the learner is relaxed, the experience unit will provide the experience at a slow pace. If the learner is focused, the experience unit will provide the experience at a normal pace. If the learner is tired, the experience unit will provide the experience in a short amount of time. By adjusting the experience content based on the learner's emotions, an effective experience becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the experience unit may be performed using AI or not. For example, the experience unit can input learner emotion data into a generative AI and have the generative AI adjust the experience content.

[0105] The experience unit can select the most suitable experience content by referring to the learner's past experience history during the experience. For example, the experience unit selects the most suitable experience content from the learner's past experience history. For example, the experience unit analyzes the evolution of technology from the learner's past experience history. For example, the experience unit analyzes the level of understanding from the learner's past experience history. In this way, the optimal experience content can be selected by referring to the learner's past experience history. Some or all of the above processes in the experience unit may be performed using AI or not. For example, the experience unit can input the learner's past experience history data into a generating AI and have the generating AI perform the selection of the experience content.

[0106] The experience unit can estimate the learner's emotions and adjust the pace of the experience based on the estimated emotions. For example, if the learner is relaxed, the experience unit will proceed at a slow pace. If the learner is focused, the experience unit will proceed at a normal pace. If the learner is tired, the experience unit will proceed in a shorter time. By adjusting the pace of the experience based on the learner's emotions, an effective experience is possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the experience unit may be performed using AI or not. For example, the experience unit can input learner emotion data into a generative AI and have the generative AI adjust the pace of the experience.

[0107] The experience unit can provide experiences that take into account the learner's living environment and time of day. For example, the experience unit can provide experiences that take into account differences in the learner's living environment. For example, the experience unit can provide experiences that take into account differences in the learner's time of day. For example, the experience unit can provide experiences that take into account differences in the learner's daily rhythm. By taking into account the learner's living environment and time of day, an effective experience becomes possible. Some or all of the above processing in the experience unit may be performed using AI or not. For example, the experience unit can input learner's living environment data into a generating AI and have the generating AI adjust the content of the experience.

[0108] The inspection department can estimate the emotions of the craftsman when evaluating product quality and adjust inspection standards based on the estimated emotions. For example, if the craftsman is relaxed, the inspection department applies normal inspection standards. For example, if the craftsman is focused, the inspection department applies strict inspection standards. For example, if the craftsman is tired, the inspection department applies lenient inspection standards. This allows for appropriate quality evaluation by adjusting inspection standards based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inspection department may be performed using AI or not. For example, the inspection department can input craftsman emotion data into a generative AI and have the generative AI adjust the inspection standards.

[0109] The inspection department can maintain quality consistency by meticulously recording the product manufacturing process during inspection. For example, the inspection department can meticulously record each step of the product manufacturing process. For example, the inspection department can meticulously record the materials used in the product manufacturing process. For example, the inspection department can meticulously record the working environment in the product manufacturing process. This allows for quality consistency to be maintained by meticulously recording the product manufacturing process. Some or all of the above processes in the inspection department may be performed using AI or not. For example, the inspection department can input product manufacturing process data into a generating AI and have the generating AI manage the records.

[0110] The inspection department can estimate the emotions of the craftsman when evaluating product quality and adjust the inspection frequency based on the estimated emotions. For example, if the craftsman is relaxed, the inspection department will perform inspections at the normal frequency. For example, if the craftsman is focused, the inspection department will perform inspections frequently. For example, if the craftsman is tired, the inspection department will reduce the inspection frequency. This allows for appropriate quality evaluation by adjusting the inspection frequency based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inspection department may be performed using AI or not. For example, the inspection department can input craftsman emotion data into a generative AI and have the generative AI adjust the inspection frequency.

[0111] The inspection department can perform quality control by referring to the market evaluation and feedback of the product during inspection. For example, the inspection department can perform quality control by referring to the market evaluation of the product. For example, the inspection department can perform quality control by referring to the market feedback of the product. For example, the inspection department can perform quality control by comprehensively referring to the market evaluation and feedback of the product. This makes it possible to perform appropriate quality control by referring to the market evaluation and feedback of the product. Some or all of the above processes in the inspection department may be performed using AI or not. For example, the inspection department can input market evaluation data into a generating AI and have the generating AI perform quality control adjustments.

[0112] The preservation unit can estimate the emotions of the craftsman when preserving traditional techniques and adjust the preservation method based on the estimated emotions. For example, if the craftsman is relaxed, the preservation unit applies a normal preservation method. For example, if the craftsman is focused, the preservation unit applies a strict preservation method. For example, if the craftsman is tired, the preservation unit applies a gentle preservation method. This allows for appropriate preservation by adjusting the preservation method based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the preservation unit may be performed using AI or not. For example, the preservation unit can input the craftsman's emotion data into the generative AI and have the generative AI perform the adjustment of the preservation method.

[0113] The storage unit can record detailed technical information during storage and accurately transmit it to future generations. For example, the storage unit can record detailed technical procedures. For example, the storage unit can record the materials used in the technical process. For example, the storage unit can record the working environment of the technical process. By doing so, detailed technical information can be accurately transmitted to future generations. Some or all of the above-described processes in the storage unit may be performed using AI or not. For example, the storage unit can input detailed technical record data into a generating AI and have the generating AI manage the records.

[0114] The preservation unit can estimate the emotions of the craftsman when preserving traditional techniques and adjust the frequency of preservation based on the estimated emotions. For example, if the craftsman is relaxed, the preservation unit will perform preservation at a normal frequency. For example, if the craftsman is concentrating, the preservation unit will perform preservation frequently. For example, if the craftsman is tired, the preservation unit will reduce the frequency of preservation. This allows for appropriate preservation by adjusting the frequency of preservation based on the craftsman's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the preservation unit may be performed using AI or not. For example, the preservation unit can input the craftsman's emotion data into the generative AI and have the generative AI adjust the frequency of preservation.

[0115] The preservation unit can create a digital archive of the technology during preservation and make it widely accessible. For example, the preservation unit can create a digital archive of the technology and make it accessible online. For example, the preservation unit can create a digital archive of the technology and make it accessible in educational institutions. For example, the preservation unit can create a digital archive of the technology and make it accessible in museums. In this way, the digital archive of the technology can be made widely accessible. Some or all of the above processes in the preservation unit may be performed using AI or not. For example, the preservation unit can input the digital archive data of the technology into a generating AI and have the generating AI perform the management of the archive.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The traditional crafts master AI system can include an evaluation unit in addition to a learning unit, instruction unit, and management unit. The evaluation unit regularly assesses the user's learning progress and skill acquisition level and provides feedback. For example, the evaluation unit can evaluate the accuracy and efficiency of the techniques the user has learned and point out areas for improvement. The evaluation unit can analyze the user's learning history and suggest the next learning steps. The evaluation unit can provide appropriate challenges according to the user's skill acquisition level. This allows the user to understand their own progress and learn effectively.

[0118] The traditional crafts master AI system can include a communication section in addition to the learning, instruction, and management sections. The communication section facilitates interaction among users and supports the sharing of skills and knowledge. For example, the communication section can provide a forum where users can exchange questions and opinions. The communication section can implement mentorship programs between skilled craftsmen and learners. The communication section can provide a platform for users to work on projects together. This allows users to collaborate with other learners and craftsmen and gain deeper learning.

[0119] The traditional crafts master AI system can include an inspiration unit in addition to the learning, instruction, and management units. The inspiration unit provides users with new ideas and creative inspiration. For example, the inspiration unit can provide information on the history and culture of traditional crafts. The inspiration unit can introduce the works and techniques of other artisans, providing users with new perspectives. The inspiration unit can suggest new techniques and designs that users can incorporate into their own work. This allows users to enhance their creativity and create unique works.

[0120] The traditional craft master AI system can include a learning unit, instruction unit, management unit, and a resources unit. The resources unit provides information on materials and tools that the user needs. For example, the resources unit can provide information on where to obtain and the price of materials required for a specific technique. The resources unit can provide information on how to maintain and use the tools the user uses. The resources unit can provide samples for the user to try out new materials and tools. This allows the user to efficiently obtain the necessary resources and concentrate on mastering the techniques.

[0121] The Traditional Crafts Master AI System can include a marketing department in addition to its learning, instruction, and management departments. The marketing department supports users in bringing their work to market. For example, it can assist with photography and catalog creation for users' works. It can provide advice on setting up online shops. It can provide opportunities for users to showcase their work at exhibitions and events. This allows users to raise awareness of their work and increase sales opportunities.

[0122] The traditional crafts master AI system can include a learning unit, a teaching unit, a management unit, and a feedback unit equipped with emotion estimation capabilities. The feedback unit estimates the user's emotions and provides feedback based on those emotions. For example, if the user is relaxed, the feedback unit will emphasize positive feedback. If the user is focused, the feedback unit will point out specific areas for improvement. If the user is tired, the feedback unit will offer words of encouragement. This allows users to receive appropriate feedback tailored to their emotions and maintain their motivation to learn.

[0123] The traditional crafts master AI system can include a learning unit, a teaching unit, a management unit, and a motivation unit equipped with emotion estimation capabilities. The motivation unit estimates the user's emotions and provides support to enhance motivation based on those estimates. For example, if the user is relaxed, the motivation unit emphasizes the joy of achieving goals. If the user is focused, the motivation unit provides challenging tasks. If the user is tired, the motivation unit encourages breaks and provides advice to refresh. This allows users to receive appropriate support tailored to their emotions, thereby increasing their motivation to learn.

[0124] The traditional crafts master AI system can include a learning unit, a teaching unit, a management unit, and a customization unit equipped with emotion estimation capabilities. The customization unit estimates the user's emotions and customizes the learning content based on those emotions. For example, if the user is relaxed, the customization unit provides learning content that includes detailed technical explanations. If the user is focused, the customization unit provides learning content that emphasizes subtle differences in techniques. If the user is tired, the customization unit provides concise and to-the-point learning content. This allows the user to receive optimal learning content tailored to their emotions and to learn effectively.

[0125] The traditional crafts master AI system can include a learning unit, a teaching unit, a management unit, and a support unit equipped with emotion estimation capabilities. The support unit estimates the user's emotions and provides learning support based on those emotions. For example, if the user is relaxed, the support unit provides advice to support the learning process. If the user is focused, the support unit provides detailed explanations of the techniques. If the user is tired, the support unit temporarily pauses the learning process and provides advice to refresh. This allows the user to receive appropriate support according to their emotions and to progress in their learning effectively.

[0126] The traditional crafts master AI system can include a learning unit, a teaching unit, a management unit, and a monitoring unit equipped with emotion estimation capabilities. The monitoring unit estimates the user's emotions and monitors the learning progress based on those emotions. For example, if the user is relaxed, the monitoring unit periodically checks the learning progress. If the user is focused, the monitoring unit records the learning progress in detail. If the user is tired, the monitoring unit temporarily suspends the learning progress and prompts them to take a break. This allows the user to receive appropriate monitoring tailored to their emotions and effectively manage their learning progress.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The learning unit learns the skills and knowledge of skilled craftsmen. For example, it analyzes the craftsman's movements and thought processes in detail, systematizing the techniques, including tacit knowledge that is difficult to put into words. Furthermore, it meticulously records the craftsman's hand movements, how they use the tools they employ, the work environment, and the work procedures, and the AI ​​analyzes this data. Step 2: The instruction team provides technical guidance based on the skills and knowledge acquired by the learning team. For example, they provide technical guidance in an optimal way that suits the user's skill level and learning style. They can carefully teach basic techniques to beginners and efficiently impart advanced techniques to experienced users. They also provide appropriate feedback according to the user's learning progress. Step 3: The management department performs quality control based on the techniques taught by the leadership department. For example, they perform quality control on traditional crafts, including product inspection and defect detection. They use AI image recognition technology to inspect products and detect defects, and perform quality control based on product quality evaluation standards.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the learning unit, instruction unit, management unit, judgment unit, provision unit, experience unit, inspection unit, and storage unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the learning unit records the actions and thought processes of a craftsman using the camera 42 and microphone 38B of the smart device 14 and analyzes them with the control unit 46A. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides technical guidance based on the learned skills and knowledge. The management unit is implemented by, for example, the control unit 46A of the smart device 14 and performs product inspection and detection of defective products. The judgment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and determines the user's skill level. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides a learning program according to the user's skill level and learning style. The experience unit is implemented by, for example, the control unit 46A of the smart device 14 and provides an immersive skill transfer experience utilizing VR / AR. The inspection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI image recognition technology to inspect products and detect defective items. The storage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and performs digital archiving of traditional technologies. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the learning unit, instruction unit, management unit, judgment unit, provision unit, experience unit, inspection unit, and storage unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit records the actions and thought processes of a craftsman using the camera 42 and microphone 238 of the smart glasses 214 and analyzes them with the control unit 46A. The instruction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides technical guidance based on the learned skills and knowledge. The management unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs product inspection and detection of defective products. The judgment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and determines the user's skill level. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides a learning program according to the user's skill level and learning style. The experience unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an immersive skill transfer experience utilizing VR / AR. The inspection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI image recognition technology to inspect products and detect defective items. The storage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and performs digital archiving of traditional technologies. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the learning unit, instruction unit, management unit, judgment unit, provision unit, experience unit, inspection unit, and storage unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit records the actions and thought processes of the craftsman using the camera 42 and microphone 238 of the headset terminal 314 and analyzes them with the control unit 46A. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides technical guidance based on the learned skills and knowledge. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs product inspection and detection of defective products. The judgment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and determines the user's skill level. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides a learning program according to the user's skill level and learning style. The experience section is implemented, for example, by the control unit 46A of the headset terminal 314, and provides an immersive technology transmission experience utilizing VR / AR. The inspection section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and performs product inspection and detection of defective products using AI image recognition technology. The storage section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and creates a digital archive of traditional technologies. The correspondence between each section and the devices and control units is not limited to the examples described above, and various changes are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the learning unit, instruction unit, management unit, judgment unit, provision unit, experience unit, inspection unit, and storage unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit records the actions and thought processes of a craftsman using the camera 42 and microphone 238 of the robot 414 and analyzes them with the control unit 46A. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides technical guidance based on the learned skills and knowledge. The management unit is implemented by, for example, the control unit 46A of the robot 414 and performs product inspection and detection of defective products. The judgment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and determines the user's skill level. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides a learning program according to the user's skill level and learning style. The experience unit is implemented by, for example, the control unit 46A of the robot 414 and provides an immersive skill transfer experience utilizing VR / AR. The inspection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI image recognition technology to inspect products and detect defective items. The storage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and performs digital archiving of traditional technologies. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The learning department provides training in the skills and knowledge of experienced craftsmen, The instruction department provides technical guidance based on the skills and knowledge acquired by the aforementioned learning department, The system includes a management department that performs quality control based on the technology instructed by the aforementioned leadership department. A system characterized by the following features. (Note 2) It includes a determination unit that determines the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a department that provides learning programs. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has an experience department that provides experiences utilizing VR / AR. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features an inspection unit that utilizes AI image recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) Equipped with a storage unit for digital archiving. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, The system estimates the emotions of the craftsmen and selects training data based on the estimated emotions of the craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During learning, the system detects subtle differences in the movements of craftsmen and analyzes the differences in their techniques in detail. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During the learning process, we will take into account the differences in the working environment and tools used by craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, It estimates the emotions of the craftsman and adjusts the learning progress based on the estimated emotions of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During the learning process, analyze the evolution of techniques by referring to the past works of craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During the learning process, the techniques and culture unique to the region of the craftsman will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned leadership, The system estimates the learner's emotions and adjusts the teaching method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned leadership, During instruction, adjust the level of detail in the instruction content according to the learner's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned leadership, During instruction, the optimal teaching method is selected by referring to the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned leadership, The system estimates the learner's emotions and adjusts the timing of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned leadership, When providing instruction, we take into consideration the learner's living environment and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned leadership, When teaching, customize the lesson content to take into account the cultural background of the learners. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, When evaluating product quality, we estimate the emotions of the craftsman and adjust the evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the product manufacturing process is recorded in detail to maintain quality consistency. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, quality assessments are conducted taking into account the environment and conditions in which the product is used. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When evaluating product quality, we estimate the emotions of the craftsmen and adjust the frequency of evaluation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, quality control is performed by referring to the product's market evaluation and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, quality assessments are conducted taking into account the transportation and storage conditions of the products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, The system estimates the learner's emotions and adjusts the proficiency assessment criteria based on the estimated learner emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The determination unit, During the assessment, the learner's past learning history and achievements are analyzed in detail. The system described in Appendix 2, characterized by the features described herein. (Note 27) The determination unit, The system estimates the learner's emotions and adjusts the timing of decisions based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The determination unit, When making a decision, the learner's living environment and time of day should be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates learners' emotions and adjusts the content of the learning program based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the program, the system selects the most suitable learning program by referring to the learner's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned supply unit is, The system estimates the learner's emotions and adjusts the pace of the learning program based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the program, we will create a learning program that takes into account the learner's living environment and time of day. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned experience section is, The system estimates the learner's emotions and adjusts the experience based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned experience section is, During the experience, the most suitable content is selected by referring to the learner's past experience history. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned experience section is, The system estimates the learner's emotions and adjusts the pace of the experience based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned experience section is, When providing an experience, we take into consideration the learner's living environment and time of day. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned inspection unit, When evaluating product quality, we estimate the emotions of the craftsman and adjust inspection standards based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned inspection unit, During inspection, the manufacturing process of the product is recorded in detail to maintain consistency in quality. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned inspection unit, When evaluating product quality, we estimate the emotions of the craftsmen and adjust the inspection frequency based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned inspection unit, During inspection, quality control is performed by referring to the product's market evaluation and feedback. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned storage unit is When preserving traditional techniques, we estimate the emotions of the craftsmen and adjust the preservation methods based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 42) The aforementioned storage unit is During preservation, detailed records of the techniques are kept to accurately pass them on to future generations. The system described in Appendix 6, characterized by the features described herein. (Note 43) The aforementioned storage unit is When preserving traditional techniques, the emotions of the craftsmen are estimated, and the frequency of preservation is adjusted based on these estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 44) The aforementioned storage unit is During preservation, create a digital archive of the technology and make it widely accessible. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The learning department provides training in the skills and knowledge of experienced craftsmen, The instruction department provides technical guidance based on the skills and knowledge acquired by the aforementioned learning department, The system includes a management department that performs quality control based on the technology instructed by the aforementioned leadership department. A system characterized by the following features.

2. It includes a determination unit that determines the user's skill level. The system according to feature 1.

3. It includes a department that provides learning programs. The system according to feature 1.

4. It has an experience department that provides experiences utilizing VR / AR. The system according to feature 1.

5. It is equipped with an inspection unit that uses AI image recognition technology. The system according to feature 1.

6. Equipped with a storage unit for digital archiving. The system according to feature 1.

7. The aforementioned learning unit, The system estimates the emotions of the craftsmen and selects training data based on the estimated emotions of the craftsmen. The system according to feature 1.

8. The aforementioned learning unit, During learning, the system detects subtle differences in the movements of craftsmen and analyzes the differences in their techniques in detail. The system according to feature 1.

9. The aforementioned learning unit, During the learning process, we will take into account the differences in the working environment and tools used by craftsmen. The system according to feature 1.

10. The aforementioned learning unit, It estimates the emotions of the craftsman and adjusts the learning progress based on the estimated emotions of the craftsman. The system according to feature 1.

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