system

Abstract

The system according to this embodiment aims to reduce the training time required for workers on assembly lines in the manufacturing industry. [Solution] The system according to the embodiment comprises a registration unit, an analysis unit, and a generation unit. The registration unit registers 3D CAD data of parts. The analysis unit analyzes the assembly sequence based on the data registered by the registration unit. The generation unit generates educational videos based on the data analyzed by the analysis 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, including 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that a great deal of man-hours were required for the education of workers on the assembly line in the manufacturing industry.

[0005] The system according to the embodiment aims to reduce the man-hours for educating workers on the assembly line in the manufacturing industry.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, an analysis unit, and a generation unit. The registration unit registers 3D CAD data of parts. The analysis unit analyzes the assembly order based on the data registered by the registration unit. The generation unit generates an educational video based on the data analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can reduce the training time required for workers on assembly lines in manufacturing. [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 educational support system according to an embodiment of the present invention is a system for reducing the training time required for workers on assembly lines in the manufacturing industry. This system registers 3D CAD data of parts created during the design phase and constraints on the assembly sequence, and a generating AI analyzes this data to generate an optimal assembly procedure. Based on the generated procedure, an educational video is created, and by specifying the positions of parts and workers, the movements of the right and left hands are naturally reproduced, realizing efficient standard work. For example, 3D CAD data of parts created during the design phase and constraints on the assembly sequence are registered in the system. For example, 3D CAD data of parts A, B, and C, and their assembly sequence are input into the system. This information is input into the generating AI. Next, the generating AI analyzes the input information and generates an optimal assembly procedure. The generating AI considers the constraints on the positions and assembly sequence of parts and calculates an efficient assembly procedure. For example, it generates a procedure in which part A is assembled first, and then parts B and C are assembled. Based on the generated procedure, an educational video is created. The generating AI specifies the positions of parts and workers and naturally reproduces the movements of the right and left hands. For example, the AI ​​can recreate the action of holding part A in the right hand and part B in the left hand in a video. This allows workers to learn assembly tasks with natural movements. Furthermore, the generating AI has functions to realize efficient standard work. For example, it can eliminate unnecessary movements from the assembly sequence and generate efficient work procedures. This reduces the time spent training workers and improves the efficiency of the assembly line. This system allows for a clear vision of the ideal assembly line from the design stage, significantly reducing the time spent on training. For example, when a new worker joins the assembly line, they can efficiently learn the work procedures simply by watching the generated training video. This reduces the time and cost of training and improves the productivity of the assembly line. In short, the training support system can reduce the time spent training workers and improve the efficiency of the assembly line.

[0029] The educational support system according to this embodiment comprises a registration unit, an analysis unit, and a generation unit. The registration unit registers 3D CAD data of parts. For example, the registration unit can input 3D CAD data of parts created during the design phase into the system. The analysis unit analyzes the assembly sequence based on the data registered by the registration unit. For example, the analysis unit considers the constraints of part positions and assembly sequence to calculate an efficient assembly procedure. The generation unit generates educational videos based on the data analyzed by the analysis unit. For example, the generation unit uses generation AI to specify the positions of parts and workers and generates videos that naturally reproduce the movements of the right and left hands. For example, the generation unit can reproduce in a video the action of holding part A in the right hand and part B in the left hand. This allows workers to learn assembly work with natural movements. Furthermore, the generation unit has functions to realize efficient standard work. For example, the generation unit can eliminate unnecessary movements from the assembly sequence and generate an efficient work procedure. This reduces the training time for workers and improves the efficiency of the assembly line. As a result, the educational support system according to this embodiment can reduce the training time required for workers and improve the efficiency of the assembly line.

[0030] The registration unit registers 3D CAD data for parts. For example, the registration unit can input 3D CAD data of parts created during the design phase into the system. Specifically, the registration unit receives 3D model data exported from the CAD software used by the designer and stores it in the system's database. This allows for centralized management of detailed information such as the shape, dimensions, and material of the parts. Furthermore, the registration unit has a part version control function, allowing for comparison of past and new data to clearly identify changes even when design changes are made. This helps prevent design and assembly errors. The registration unit can also register part metadata (e.g., part number, manufacturing date, manufacturer, etc.) simultaneously, ensuring the traceability of the parts. In addition, the registration unit is designed to allow designers and engineers to easily register and update data through a user interface, improving usability. As a result, the registration unit can efficiently and accurately manage 3D CAD data for parts, improving the overall data quality of the system.

[0031] The analysis unit analyzes the assembly sequence based on data registered by the registration unit. For example, the analysis unit considers constraints on part position and assembly sequence to calculate an efficient assembly procedure. Specifically, the analysis unit extracts the shape and connection points of parts from 3D CAD data and analyzes how each part fits together. Furthermore, the analysis unit considers physical constraints in the assembly work (e.g., part size and weight, workspace constraints, etc.) to derive the optimal assembly sequence. Using AI technology, the analysis can learn from past assembly data and worker feedback to propose more efficient procedures. For example, the AI ​​analyzes data from past assembly work to identify frequently occurring errors and wasted movements and generates procedures to avoid them. The analysis unit also has a simulation function that allows the assembly procedure to be verified in a virtual environment. This allows problems to be discovered and corrected in advance before actual assembly work begins. In addition, the analysis unit can update data in real time and respond to design changes and the addition or deletion of parts. As a result, the analysis unit can always provide the optimal assembly procedure based on the latest data, improving the efficiency and accuracy of the assembly work.

[0032] The generation unit generates educational videos based on data analyzed by the analysis unit. For example, the generation unit uses a generation AI to specify the positions of parts and workers and generate videos that naturally reproduce the movements of the right and left hands. Specifically, the generation AI constructs a virtual work environment based on assembly procedure data provided by the analysis unit and simulates the worker's movements. Using deep learning technology, the generation AI learns from past work videos and actual work data, enabling it to reproduce natural and efficient movements. For example, it can faithfully reproduce the action of holding part A in the right hand and part B in the left hand, down to the fine movements of the hands and fingers. The generation unit can also use multiple camera angles and zoom functions to clearly display the details of the work. This allows workers to receive training in an environment close to the actual work environment. Furthermore, the generation unit has functions to realize efficient standard work. For example, the generation unit can eliminate unnecessary movements from the assembly sequence and generate efficient work procedures. This reduces the time required to train workers and improves the efficiency of the assembly line. The generation unit can also save the generated videos to the cloud, allowing workers to access them at any time. This allows workers to learn at their own pace, increasing the flexibility of education. Furthermore, the production unit can collect user feedback and continuously improve the content and quality of the videos. As a result, the production unit can always provide the latest and highest quality educational content, supporting the skill development of workers.

[0033] The generation unit includes a reproduction unit that specifies the positions of parts and workers and naturally reproduces the movements of the right and left hands. The reproduction unit can, for example, reproduce in a video the action of holding part A in the right hand and part B in the left hand. The reproduction unit can, for example, specify the positions of parts and naturally reproduce the movements of the right and left hands. The reproduction unit can, for example, specify the positions of workers and naturally reproduce the movements of the right and left hands. This makes it possible to reproduce natural movements and teach efficient assembly procedures by specifying the positions of parts and workers. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input the positions of parts and workers into the generation AI and cause the generation AI to execute a video that naturally reproduces the movements of the right and left hands.

[0034] The generation unit includes a standard work generation unit that generates efficient standard work from the assembly sequence. The standard work generation unit can, for example, eliminate unnecessary movements from the assembly sequence and generate an efficient work procedure. For example, the standard work generation unit can generate a procedure in which part A is assembled first, followed by parts B and C. For example, the standard work generation unit can optimize the assembly sequence and generate efficient standard work. By generating efficient standard work, an efficient assembly procedure can be achieved. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the standard work generation unit can input the assembly sequence into the generation AI and have the generation AI execute efficient standard work.

[0035] The registration unit can determine data priority based on the importance of the parts during registration. For example, the registration unit can prioritize the registration of high-importance parts and register lower-importance parts later. For example, the registration unit can divide the registration process according to the importance of the parts, allowing for the rapid registration of high-importance parts. For example, the registration unit can adjust the verification procedure during registration based on the importance of the parts, allowing for detailed verification of high-importance parts. This enables the priority registration of important parts by determining data priority based on the importance of the parts. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input part importance data into a generating AI and have the generating AI perform the determination of data priority.

[0036] The registration unit can apply different registration algorithms depending on the shape and size of the part during registration. For example, the registration unit can apply a dedicated registration algorithm to large parts to register detailed shape information. For example, the registration unit can apply a simplified registration algorithm to small parts to perform registration quickly. For example, the registration unit can perform shape analysis on parts with complex shapes and apply the optimal registration algorithm. This enables efficient data registration by applying the optimal registration algorithm according to the shape and size of the part. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input the shape and size data of the part into a generating AI and have the generating AI execute the application of the registration algorithm.

[0037] The registration unit can prioritize the registration of highly relevant data by considering the manufacturer information of the parts during registration. For example, the registration unit can prioritize the registration of parts from the same manufacturer to maintain data consistency. For example, the registration unit can group highly relevant parts based on manufacturer information and register them efficiently. For example, the registration unit can optimize the registration order of parts by considering manufacturer information to achieve efficient data management. This ensures data consistency by prioritizing the registration of highly relevant data by considering the manufacturer information of the parts. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the manufacturer information of the parts into a generating AI and have the generating AI perform the priority registration of highly relevant data.

[0038] The registration unit can analyze the usage history of parts and register relevant data during registration. For example, the registration unit can analyze the usage history of parts and prioritize the registration of frequently used parts. For example, the registration unit can automatically suggest relevant part data based on the usage history and perform registration efficiently. For example, the registration unit can optimize the registration order of parts by considering the usage history, thereby achieving efficient data management. This enables efficient data management by analyzing the usage history of parts and registering relevant data. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the usage history data of parts into a generating AI and have the generating AI perform the registration of relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the parts during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance parts to provide highly accurate results. For example, the analysis unit can perform a simplified analysis on low-importance parts to provide results quickly. For example, the analysis unit can determine the priority of the analysis according to the importance of the parts and perform the analysis efficiently. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the parts. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input part importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the part during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to mechanical parts to perform a detailed analysis. For example, the analysis unit can apply a dedicated analysis algorithm to electronic parts to perform a rapid analysis. For example, the analysis unit can select the optimal analysis algorithm according to the category of the part and perform the analysis efficiently. This enables efficient analysis by applying the optimal analysis algorithm according to the category of the part. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input part category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the manufacturing date of the parts during the analysis. For example, the analysis unit can prioritize the analysis of the newest parts and provide results quickly. For example, the analysis unit can determine the priority of analysis based on the manufacturing date and perform analysis efficiently. For example, the analysis unit can adjust the level of detail of the analysis considering the manufacturing date and provide optimal results. In this way, efficient analysis can be achieved by determining the priority of analysis based on the manufacturing date of the parts. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input manufacturing date data of the parts into a generating AI and have the generating AI perform the determination of the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relationships between parts during the analysis. For example, the analysis unit can prioritize the analysis of highly related parts and provide results efficiently. For example, the analysis unit can optimize the order of analysis based on the relationships between parts and perform analysis quickly. For example, the analysis unit can group highly related parts and perform analysis in a batch. This enables efficient analysis by adjusting the order of analysis based on the relationships between parts. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relationship data of parts into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The generation unit can adjust the level of detail in the video based on the importance of the parts during generation. For example, the generation unit can generate videos with detailed explanations for high-importance parts. For example, the generation unit can generate simplified videos for low-importance parts and provide them quickly. For example, the generation unit can adjust the level of detail in the video according to the importance of the parts to provide optimal content. This makes it possible to provide efficient educational videos by adjusting the level of detail in the video based on the importance of the parts. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input part importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the video.

[0044] The generation unit can apply different generation algorithms depending on the category of the part during generation. For example, the generation unit can apply a dedicated generation algorithm to mechanical parts to generate detailed videos. For example, the generation unit can apply a dedicated generation algorithm to electronic parts to generate videos quickly. For example, the generation unit can select the optimal generation algorithm according to the category of the part and generate videos efficiently. This makes it possible to provide efficient educational videos by applying the optimal generation algorithm according to the category of the part. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input part category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0045] The generation unit can determine the priority of videos based on the manufacturing date of the parts during generation. For example, the generation unit can prioritize the inclusion of the latest parts in the videos and provide them quickly. For example, the generation unit can determine the priority of videos based on the manufacturing date and generate them efficiently. For example, the generation unit can adjust the level of detail in the videos, taking the manufacturing date into consideration, to provide optimal content. This allows for the efficient provision of educational videos by determining the priority of videos based on the manufacturing date of the parts. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input manufacturing date data of the parts into a generation AI and have the generation AI perform the determination of video priorities.

[0046] The generation unit can adjust the order of videos based on the relationships between parts during generation. For example, the generation unit can prioritize the inclusion of highly relevant parts in the videos and provide them efficiently. For example, the generation unit can optimize the order of videos based on the relationships between parts and generate them quickly. For example, the generation unit can group highly relevant parts and generate videos in batches. This allows for the efficient provision of educational videos by adjusting the order of videos based on the relationships between parts. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the relationship data of parts into a generation AI and have the generation AI perform the adjustment of the order of videos.

[0047] The reproduction unit can adjust the level of detail of the operation based on the importance of the parts during reproduction. For example, the reproduction unit can reproduce detailed operation for high-importance parts, providing highly accurate results. For example, the reproduction unit can reproduce simplified operation for low-importance parts, providing results quickly. For example, the reproduction unit can adjust the level of detail of the operation according to the importance of the parts to provide optimal content. This allows for efficient operation reproduction by adjusting the level of detail of the operation based on the importance of the parts. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input part importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the operation.

[0048] The reproduction unit can apply different reproduction algorithms depending on the category of the part during reproduction. For example, the reproduction unit can apply a dedicated reproduction algorithm to mechanical parts to reproduce detailed operation. For example, the reproduction unit can apply a dedicated reproduction algorithm to electronic parts to reproduce operation quickly. For example, the reproduction unit can select the optimal reproduction algorithm according to the category of the part to reproduce operation efficiently. This allows for efficient operation reproduction by applying the optimal reproduction algorithm according to the category of the part. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input part category data into a generation AI and have the generation AI execute the application of the reproduction algorithm.

[0049] The reproduction unit can determine the priority of operations based on the manufacturing date of the parts during reproduction. The reproduction unit can, for example, prioritize the inclusion of the newest parts in the operations and provide them quickly. The reproduction unit can, for example, determine the priority of operations based on the manufacturing date and perform reproduction efficiently. The reproduction unit can, for example, adjust the level of detail of the operations considering the manufacturing date and provide the optimal content. This allows for efficient operation reproduction by determining the priority of operations based on the manufacturing date of the parts. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input manufacturing date data of the parts into a generation AI and have the generation AI perform the determination of the priority of operations.

[0050] The reproduction unit can adjust the sequence of operations based on the relationships between parts during reproduction. For example, the reproduction unit can prioritize the inclusion of highly related parts in the operations and provide them efficiently. For example, the reproduction unit can optimize the sequence of operations based on the relationships between parts and perform reproduction quickly. For example, the reproduction unit can group highly related parts and reproduce their operations in a batch. This allows for efficient operation reproduction by adjusting the sequence of operations based on the relationships between parts. Some or all of the above-described processes in the reproduction unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the reproduction unit can input the relationship data of parts into a generation AI and have the generation AI perform the adjustment of the sequence of operations.

[0051] The standard work generation unit can adjust the level of detail of a work based on the importance of the parts when generating a standard work. For example, the standard work generation unit can generate detailed work procedures for high-importance parts, providing highly accurate results. For example, the standard work generation unit can generate simplified work procedures for low-importance parts, providing results quickly. For example, the standard work generation unit can adjust the level of detail of the work procedures according to the importance of the parts to provide optimal content. In this way, efficient standard work can be provided by adjusting the level of detail of the work based on the importance of the parts. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the standard work generation unit can input part importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the work procedures.

[0052] The standard work generation unit can apply different generation algorithms depending on the category of the part when generating standard work. For example, the standard work generation unit can apply a dedicated generation algorithm to mechanical parts to generate detailed work procedures. For example, the standard work generation unit can apply a dedicated generation algorithm to electronic parts to quickly generate work procedures. For example, the standard work generation unit can select the optimal generation algorithm according to the category of the part and efficiently generate work procedures. This allows for efficient standard work by applying the optimal generation algorithm according to the category of the part. Some or all of the above-described processes in the standard work generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the standard work generation unit can input part category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0053] The standard work generation unit can determine the priority of tasks based on the manufacturing date of the parts when generating standard work. The standard work generation unit can, for example, prioritize the inclusion of the latest parts in the work procedure and provide it quickly. The standard work generation unit can, for example, determine the priority of the work procedure based on the manufacturing date and generate it efficiently. The standard work generation unit can, for example, adjust the level of detail of the work procedure considering the manufacturing date and provide the optimal content. As a result, by determining the priority of tasks based on the manufacturing date of the parts, it is possible to provide efficient standard work. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the standard work generation unit can input the manufacturing date data of the parts into the generation AI and have the generation AI perform the determination of the priority of the work procedure.

[0054] The standard work generation unit can adjust the order of tasks based on the relationships between parts when generating standard tasks. For example, the standard work generation unit can prioritize the inclusion of highly related parts in the work procedure and provide it efficiently. For example, the standard work generation unit can optimize the order of work procedures based on the relationships between parts and generate them quickly. For example, the standard work generation unit can group highly related parts and generate work procedures in a batch. This allows for the provision of efficient standard tasks by adjusting the order of tasks based on the relationships between parts. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the standard work generation unit can input part relationship data into a generation AI and have the generation AI perform the adjustment of the order of work procedures.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The educational support system may further include a feedback unit. The feedback unit can collect data from the worker's actual assembly work and compare it with the generated educational video. For example, the feedback unit can use sensors to detect the worker's hand movements and the position of parts and determine if they match the video. If the worker makes an incorrect procedure, the feedback unit can provide corrective instructions in real time. This allows the worker to receive immediate feedback during actual work and learn efficiently. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input work data into a generating AI and have the generating AI generate the feedback.

[0057] The training support system may also include a customization section. This customization section can adjust the content of training videos based on the worker's skill level and experience. For example, the customization section can generate videos that explain basic procedures in detail for beginners. For experienced workers, it can generate videos that emphasize efficient procedures. The customization section can collect worker feedback and incorporate it into future training videos. This allows for the provision of optimal training tailored to the worker's skill level. Some or all of the above-described processes in the customization section may be performed using AI or not. For example, the customization section can input worker skill data into a generating AI and have the generating AI perform video customization.

[0058] The educational support system may further include a prediction unit. The prediction unit can predict future work efficiency based on the worker's past work data. For example, the prediction unit can analyze the procedures a worker has followed in the past and suggest efficient procedures. It can also analyze the worker's skill improvement trends and suggest future training plans. Furthermore, it can consider the worker's fatigue level and concentration to suggest optimal break times. This maximizes worker efficiency and enhances training effectiveness. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input work data into a generating AI and have the generating AI generate predictions.

[0059] The educational support system may also include an interactive section. The interactive section provides the ability for workers to ask questions and provide feedback in real time while watching educational videos. For example, the interactive section allows workers to ask questions about points they are unsure of and receive immediate answers. The interactive section allows workers to provide feedback on the content of the videos, which can then be incorporated into future videos. The interactive section allows workers to exchange opinions with other workers and advance their learning collaboratively. This enables workers to gain a deeper understanding and enhance the educational effect. Some or all of the above-described processes in the interactive section may be performed using AI or not. For example, the interactive section may input worker question data into a generating AI and have the generating AI generate answers.

[0060] The educational support system may further include a performance evaluation unit. The performance evaluation unit can collect worker work data and evaluate the effectiveness of educational videos. For example, the performance evaluation unit can measure the work efficiency of workers after they have watched educational videos and identify areas for improvement. The performance evaluation unit can evaluate the degree of skill improvement of workers and reflect this in the next training plan. The performance evaluation unit can collect worker feedback and provide data to improve the content of educational videos. This allows for continuous improvement of the effectiveness of educational videos. Some or all of the above processes in the performance evaluation unit may be performed using AI or not. For example, the performance evaluation unit can input work data into a generating AI and have the generating AI perform the generation of evaluations.

[0061] The educational support system may further include a training unit. The training unit can provide training in a simulation environment before workers actually perform assembly work. For example, the training unit can use virtual reality (VR) to provide an environment where workers can experience actual assembly work. If a worker makes an incorrect procedure in the simulation environment, the training unit can provide immediate corrective instructions. The training unit allows workers to learn efficient procedures in the simulation environment, enabling a smooth transition to actual work. This ensures that workers receive sufficient training before starting actual work, thereby enhancing the educational effect. Some or all of the above processes in the training unit may be performed using AI or not. For example, the training unit can input simulation data into a generating AI and have the generating AI generate the training.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The registration unit registers the 3D CAD data of the part. For example, 3D CAD data of a part created during the design phase can be entered into the system. Step 2: The analysis unit analyzes the assembly sequence based on the data registered by the registration unit. For example, it calculates an efficient assembly procedure, taking into account constraints on the position of parts and the assembly sequence. Step 3: The generation unit generates educational videos based on the data analyzed by the analysis unit. For example, using the generation AI, it can specify the positions of parts and workers and generate videos that naturally reproduce the movements of the right and left hands. For example, the generation unit can reproduce in a video the action of holding part A in the right hand and part B in the left hand. This allows workers to learn assembly work with natural movements. Furthermore, the generation unit has functions to realize efficient standard work. For example, it can eliminate unnecessary movements from the assembly sequence and generate efficient work procedures. This reduces the time required to train workers and improves the efficiency of the assembly line.

[0064] (Example of form 2) The educational support system according to an embodiment of the present invention is a system for reducing the training time required for workers on assembly lines in the manufacturing industry. This system registers 3D CAD data of parts created during the design phase and constraints on the assembly sequence, and a generating AI analyzes this data to generate an optimal assembly procedure. Based on the generated procedure, an educational video is created, and by specifying the positions of parts and workers, the movements of the right and left hands are naturally reproduced, realizing efficient standard work. For example, 3D CAD data of parts created during the design phase and constraints on the assembly sequence are registered in the system. For example, 3D CAD data of parts A, B, and C, and their assembly sequence are input into the system. This information is input into the generating AI. Next, the generating AI analyzes the input information and generates an optimal assembly procedure. The generating AI considers the constraints on the positions and assembly sequence of parts and calculates an efficient assembly procedure. For example, it generates a procedure in which part A is assembled first, and then parts B and C are assembled. Based on the generated procedure, an educational video is created. The generating AI specifies the positions of parts and workers and naturally reproduces the movements of the right and left hands. For example, the AI ​​can recreate the action of holding part A in the right hand and part B in the left hand in a video. This allows workers to learn assembly tasks with natural movements. Furthermore, the generating AI has functions to realize efficient standard work. For example, it can eliminate unnecessary movements from the assembly sequence and generate efficient work procedures. This reduces the time spent training workers and improves the efficiency of the assembly line. This system allows for a clear vision of the ideal assembly line from the design stage, significantly reducing the time spent on training. For example, when a new worker joins the assembly line, they can efficiently learn the work procedures simply by watching the generated training video. This reduces the time and cost of training and improves the productivity of the assembly line. In short, the training support system can reduce the time spent training workers and improve the efficiency of the assembly line.

[0065] The educational support system according to this embodiment comprises a registration unit, an analysis unit, and a generation unit. The registration unit registers 3D CAD data of parts. For example, the registration unit can input 3D CAD data of parts created during the design phase into the system. The analysis unit analyzes the assembly sequence based on the data registered by the registration unit. For example, the analysis unit considers the constraints of part positions and assembly sequence to calculate an efficient assembly procedure. The generation unit generates educational videos based on the data analyzed by the analysis unit. For example, the generation unit uses generation AI to specify the positions of parts and workers and generates videos that naturally reproduce the movements of the right and left hands. For example, the generation unit can reproduce in a video the action of holding part A in the right hand and part B in the left hand. This allows workers to learn assembly work with natural movements. Furthermore, the generation unit has functions to realize efficient standard work. For example, the generation unit can eliminate unnecessary movements from the assembly sequence and generate an efficient work procedure. This reduces the training time for workers and improves the efficiency of the assembly line. As a result, the educational support system according to this embodiment can reduce the training time required for workers and improve the efficiency of the assembly line.

[0066] The registration unit registers 3D CAD data for parts. For example, the registration unit can input 3D CAD data of parts created during the design phase into the system. Specifically, the registration unit receives 3D model data exported from the CAD software used by the designer and stores it in the system's database. This allows for centralized management of detailed information such as the shape, dimensions, and material of the parts. Furthermore, the registration unit has a part version control function, allowing for comparison of past and new data to clearly identify changes even when design changes are made. This helps prevent design and assembly errors. The registration unit can also register part metadata (e.g., part number, manufacturing date, manufacturer, etc.) simultaneously, ensuring the traceability of the parts. In addition, the registration unit is designed to allow designers and engineers to easily register and update data through a user interface, improving usability. As a result, the registration unit can efficiently and accurately manage 3D CAD data for parts, improving the overall data quality of the system.

[0067] The analysis unit analyzes the assembly sequence based on data registered by the registration unit. For example, the analysis unit considers constraints on part position and assembly sequence to calculate an efficient assembly procedure. Specifically, the analysis unit extracts the shape and connection points of parts from 3D CAD data and analyzes how each part fits together. Furthermore, the analysis unit considers physical constraints in the assembly work (e.g., part size and weight, workspace constraints, etc.) to derive the optimal assembly sequence. Using AI technology, the analysis can learn from past assembly data and worker feedback to propose more efficient procedures. For example, the AI ​​analyzes data from past assembly work to identify frequently occurring errors and wasted movements and generates procedures to avoid them. The analysis unit also has a simulation function that allows the assembly procedure to be verified in a virtual environment. This allows problems to be discovered and corrected in advance before actual assembly work begins. In addition, the analysis unit can update data in real time and respond to design changes and the addition or deletion of parts. As a result, the analysis unit can always provide the optimal assembly procedure based on the latest data, improving the efficiency and accuracy of the assembly work.

[0068] The generation unit generates educational videos based on data analyzed by the analysis unit. For example, the generation unit uses a generation AI to specify the positions of parts and workers and generate videos that naturally reproduce the movements of the right and left hands. Specifically, the generation AI constructs a virtual work environment based on assembly procedure data provided by the analysis unit and simulates the worker's movements. Using deep learning technology, the generation AI learns from past work videos and actual work data, enabling it to reproduce natural and efficient movements. For example, it can faithfully reproduce the action of holding part A in the right hand and part B in the left hand, down to the fine movements of the hands and fingers. The generation unit can also use multiple camera angles and zoom functions to clearly display the details of the work. This allows workers to receive training in an environment close to the actual work environment. Furthermore, the generation unit has functions to realize efficient standard work. For example, the generation unit can eliminate unnecessary movements from the assembly sequence and generate efficient work procedures. This reduces the time required to train workers and improves the efficiency of the assembly line. The generation unit can also save the generated videos to the cloud, allowing workers to access them at any time. This allows workers to learn at their own pace, increasing the flexibility of education. Furthermore, the production unit can collect user feedback and continuously improve the content and quality of the videos. As a result, the production unit can always provide the latest and highest quality educational content, supporting the skill development of workers.

[0069] The generation unit includes a reproduction unit that specifies the positions of parts and workers and naturally reproduces the movements of the right and left hands. The reproduction unit can, for example, reproduce in a video the action of holding part A in the right hand and part B in the left hand. The reproduction unit can, for example, specify the positions of parts and naturally reproduce the movements of the right and left hands. The reproduction unit can, for example, specify the positions of workers and naturally reproduce the movements of the right and left hands. This makes it possible to reproduce natural movements and teach efficient assembly procedures by specifying the positions of parts and workers. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input the positions of parts and workers into the generation AI and cause the generation AI to execute a video that naturally reproduces the movements of the right and left hands.

[0070] The generation unit includes a standard work generation unit that generates efficient standard work from the assembly sequence. The standard work generation unit can, for example, eliminate unnecessary movements from the assembly sequence and generate an efficient work procedure. For example, the standard work generation unit can generate a procedure in which part A is assembled first, followed by parts B and C. For example, the standard work generation unit can optimize the assembly sequence and generate efficient standard work. By generating efficient standard work, an efficient assembly procedure can be achieved. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the standard work generation unit can input the assembly sequence into the generation AI and have the generation AI execute efficient standard work.

[0071] The registration unit can estimate the user's emotions and adjust the timing of 3D CAD data registration based on the estimated emotions. For example, if the user is stressed, the registration unit can simplify the registration procedure to allow for quick data registration. For example, if the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. For example, if the user is in a hurry, the registration unit can prioritize voice input to allow for quick 3D CAD data registration. This enables efficient data registration by adjusting the registration timing according to the user'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 registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The registration unit can determine data priority based on the importance of the parts during registration. For example, the registration unit can prioritize the registration of high-importance parts and register lower-importance parts later. For example, the registration unit can divide the registration process according to the importance of the parts, allowing for the rapid registration of high-importance parts. For example, the registration unit can adjust the verification procedure during registration based on the importance of the parts, allowing for detailed verification of high-importance parts. This enables the priority registration of important parts by determining data priority based on the importance of the parts. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input part importance data into a generating AI and have the generating AI perform the determination of data priority.

[0073] The registration unit can apply different registration algorithms depending on the shape and size of the part during registration. For example, the registration unit can apply a dedicated registration algorithm to large parts to register detailed shape information. For example, the registration unit can apply a simplified registration algorithm to small parts to perform registration quickly. For example, the registration unit can perform shape analysis on parts with complex shapes and apply the optimal registration algorithm. This enables efficient data registration by applying the optimal registration algorithm according to the shape and size of the part. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input the shape and size data of the part into a generating AI and have the generating AI execute the application of the registration algorithm.

[0074] The registration unit can estimate the user's emotions and determine the priority of part data to register based on the estimated user emotions. For example, if the user is stressed, the registration unit can prioritize the registration of high-priority parts to complete the task quickly. For example, if the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. For example, if the user is in a hurry, the registration unit can prioritize voice input to enable quick registration of part data. This enables efficient data registration by determining the priority of part data according to the user'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 registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The registration unit can prioritize the registration of highly relevant data by considering the manufacturer information of the parts during registration. For example, the registration unit can prioritize the registration of parts from the same manufacturer to maintain data consistency. For example, the registration unit can group highly relevant parts based on manufacturer information and register them efficiently. For example, the registration unit can optimize the registration order of parts by considering manufacturer information to achieve efficient data management. This ensures data consistency by prioritizing the registration of highly relevant data by considering the manufacturer information of the parts. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input the manufacturer information of the parts into a generating AI and have the generating AI perform the priority registration of highly relevant data.

[0076] The registration unit can analyze the usage history of parts and register relevant data during registration. For example, the registration unit can analyze the usage history of parts and prioritize the registration of frequently used parts. For example, the registration unit can automatically suggest relevant part data based on the usage history and perform registration efficiently. For example, the registration unit can optimize the registration order of parts by considering the usage history, thereby achieving efficient data management. This enables efficient data management by analyzing the usage history of parts and registering relevant data. Some or all of the above processes in the registration unit may be performed using AI or not. For example, the registration unit can input the usage history data of parts into a generating AI and have the generating AI perform the registration of relevant data.

[0077] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy to understand. Emotion estimation is achieved using an emotion estimation function with 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the parts during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance parts to provide highly accurate results. For example, the analysis unit can perform a simplified analysis on low-importance parts to provide results quickly. For example, the analysis unit can determine the priority of the analysis according to the importance of the parts and perform the analysis efficiently. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the parts. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input part importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the part during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to mechanical parts to perform a detailed analysis. For example, the analysis unit can apply a dedicated analysis algorithm to electronic parts to perform a rapid analysis. For example, the analysis unit can select the optimal analysis algorithm according to the category of the part and perform the analysis efficiently. This enables efficient analysis by applying the optimal analysis algorithm according to the category of the part. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input part category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a longer analysis result that includes detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows for efficient analysis by adjusting the length of the analysis according to the user'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-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can determine the priority of analysis based on the manufacturing date of the parts during the analysis. For example, the analysis unit can prioritize the analysis of the newest parts and provide results quickly. For example, the analysis unit can determine the priority of analysis based on the manufacturing date and perform analysis efficiently. For example, the analysis unit can adjust the level of detail of the analysis considering the manufacturing date and provide optimal results. In this way, efficient analysis can be achieved by determining the priority of analysis based on the manufacturing date of the parts. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input manufacturing date data of the parts into a generating AI and have the generating AI perform the determination of the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relationships between parts during the analysis. For example, the analysis unit can prioritize the analysis of highly related parts and provide results efficiently. For example, the analysis unit can optimize the order of analysis based on the relationships between parts and perform analysis quickly. For example, the analysis unit can group highly related parts and perform analysis in a batch. This enables efficient analysis by adjusting the order of analysis based on the relationships between parts. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relationship data of parts into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The generation unit can estimate the user's emotions and adjust the presentation of the generated video based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. For example, if the user is excited, the generation unit can generate a video with visually stimulating effects. By adjusting the presentation of the video according to the user's emotions, it is possible to provide educational videos that are easy to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0084] The generation unit can adjust the level of detail in the video based on the importance of the parts during generation. For example, the generation unit can generate videos with detailed explanations for high-importance parts. For example, the generation unit can generate simplified videos for low-importance parts and provide them quickly. For example, the generation unit can adjust the level of detail in the video according to the importance of the parts to provide optimal content. This makes it possible to provide efficient educational videos by adjusting the level of detail in the video based on the importance of the parts. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input part importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the video.

[0085] The generation unit can apply different generation algorithms depending on the category of the part during generation. For example, the generation unit can apply a dedicated generation algorithm to mechanical parts to generate detailed videos. For example, the generation unit can apply a dedicated generation algorithm to electronic parts to generate videos quickly. For example, the generation unit can select the optimal generation algorithm according to the category of the part and generate videos efficiently. This makes it possible to provide efficient educational videos by applying the optimal generation algorithm according to the category of the part. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input part category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0086] The generation unit can estimate the user's emotions and adjust the length of the generated video based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise video. If the user is relaxed, the generation unit can generate a longer video with detailed explanations. If the user is excited, the generation unit can generate a video with visually stimulating effects. By adjusting the video length according to the user's emotions, it is possible to provide efficient educational videos. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0087] The generation unit can determine the priority of videos based on the manufacturing date of the parts during generation. For example, the generation unit can prioritize the inclusion of the latest parts in the videos and provide them quickly. For example, the generation unit can determine the priority of videos based on the manufacturing date and generate them efficiently. For example, the generation unit can adjust the level of detail in the videos, taking the manufacturing date into consideration, to provide optimal content. This allows for the efficient provision of educational videos by determining the priority of videos based on the manufacturing date of the parts. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input manufacturing date data of the parts into a generation AI and have the generation AI perform the determination of video priorities.

[0088] The generation unit can adjust the order of videos based on the relationships between parts during generation. For example, the generation unit can prioritize the inclusion of highly relevant parts in the videos and provide them efficiently. For example, the generation unit can optimize the order of videos based on the relationships between parts and generate them quickly. For example, the generation unit can group highly relevant parts and generate videos in batches. This allows for the efficient provision of educational videos by adjusting the order of videos based on the relationships between parts. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the relationship data of parts into a generation AI and have the generation AI perform the adjustment of the order of videos.

[0089] The reproduction unit can estimate the user's emotions and adjust the way the reproduced actions are represented based on the estimated emotions. For example, if the user is relaxed, the reproduction unit can reproduce actions that proceed at a leisurely pace. For example, if the user is in a hurry, the reproduction unit can reproduce quick and efficient actions. For example, if the user is excited, the reproduction unit can reproduce actions with visually stimulating effects. By adjusting the way actions are represented according to the user's emotions, it is possible to provide action reproductions that are easy to understand. 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-described processing in the reproduction unit may be performed using a generative AI or not. For example, the reproduction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The reproduction unit can adjust the level of detail of the operation based on the importance of the parts during reproduction. For example, the reproduction unit can reproduce detailed operation for high-importance parts, providing highly accurate results. For example, the reproduction unit can reproduce simplified operation for low-importance parts, providing results quickly. For example, the reproduction unit can adjust the level of detail of the operation according to the importance of the parts to provide optimal content. This allows for efficient operation reproduction by adjusting the level of detail of the operation based on the importance of the parts. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input part importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the operation.

[0091] The reproduction unit can apply different reproduction algorithms depending on the category of the part during reproduction. For example, the reproduction unit can apply a dedicated reproduction algorithm to mechanical parts to reproduce detailed operation. For example, the reproduction unit can apply a dedicated reproduction algorithm to electronic parts to reproduce operation quickly. For example, the reproduction unit can select the optimal reproduction algorithm according to the category of the part to reproduce operation efficiently. This allows for efficient operation reproduction by applying the optimal reproduction algorithm according to the category of the part. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input part category data into a generation AI and have the generation AI execute the application of the reproduction algorithm.

[0092] The reproduction unit can estimate the user's emotions and adjust the length of the reproduced actions based on the estimated emotions. For example, if the user is in a hurry, the reproduction unit can reproduce short, concise actions. For example, if the user is relaxed, the reproduction unit can reproduce longer actions that include detailed explanations. For example, if the user is excited, the reproduction unit can reproduce actions with visually stimulating effects. This allows for efficient action reproduction by adjusting the length of actions according to the user'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-described processes in the reproduction unit may be performed using or without a generative AI. For example, the reproduction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The reproduction unit can determine the priority of operations based on the manufacturing date of the parts during reproduction. The reproduction unit can, for example, prioritize the inclusion of the newest parts in the operations and provide them quickly. The reproduction unit can, for example, determine the priority of operations based on the manufacturing date and perform reproduction efficiently. The reproduction unit can, for example, adjust the level of detail of the operations considering the manufacturing date and provide the optimal content. This allows for efficient operation reproduction by determining the priority of operations based on the manufacturing date of the parts. Some or all of the above processing in the reproduction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reproduction unit can input manufacturing date data of the parts into a generation AI and have the generation AI perform the determination of the priority of operations.

[0094] The reproduction unit can adjust the sequence of operations based on the relationships between parts during reproduction. For example, the reproduction unit can prioritize the inclusion of highly related parts in the operations and provide them efficiently. For example, the reproduction unit can optimize the sequence of operations based on the relationships between parts and perform reproduction quickly. For example, the reproduction unit can group highly related parts and reproduce their operations in a batch. This allows for efficient operation reproduction by adjusting the sequence of operations based on the relationships between parts. Some or all of the above-described processes in the reproduction unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the reproduction unit can input the relationship data of parts into a generation AI and have the generation AI perform the adjustment of the sequence of operations.

[0095] The standard task generation unit can estimate the user's emotions and adjust the way the standard task is presented based on the estimated user emotions. For example, if the user is relaxed, the standard task generation unit can generate a standard task that proceeds at a relaxed pace. For example, if the user is in a hurry, the standard task generation unit can generate a standard task that is quick and efficient. For example, if the user is excited, the standard task generation unit can generate a standard task with visually stimulating effects. In this way, by adjusting the way the standard task is presented according to the user's emotions, it is possible to provide a standard task that is easy to understand. 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 standard task generation unit may be performed using the generative AI or not. For example, the standard task generation unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0096] The standard work generation unit can adjust the level of detail of a work based on the importance of the parts when generating a standard work. For example, the standard work generation unit can generate detailed work procedures for high-importance parts, providing highly accurate results. For example, the standard work generation unit can generate simplified work procedures for low-importance parts, providing results quickly. For example, the standard work generation unit can adjust the level of detail of the work procedures according to the importance of the parts to provide optimal content. In this way, efficient standard work can be provided by adjusting the level of detail of the work based on the importance of the parts. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the standard work generation unit can input part importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the work procedures.

[0097] The standard work generation unit can apply different generation algorithms depending on the category of the part when generating standard work. For example, the standard work generation unit can apply a dedicated generation algorithm to mechanical parts to generate detailed work procedures. For example, the standard work generation unit can apply a dedicated generation algorithm to electronic parts to quickly generate work procedures. For example, the standard work generation unit can select the optimal generation algorithm according to the category of the part and efficiently generate work procedures. This allows for efficient standard work by applying the optimal generation algorithm according to the category of the part. Some or all of the above-described processes in the standard work generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the standard work generation unit can input part category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0098] The standard task generation unit can estimate the user's emotions and adjust the length of the standard task based on the estimated emotions. For example, if the user is in a hurry, the standard task generation unit can generate a short, concise standard task. For example, if the user is relaxed, the standard task generation unit can generate a longer standard task that includes detailed explanations. For example, if the user is excited, the standard task generation unit can generate a standard task with visually stimulating effects. This allows for efficient standard tasks to be provided by adjusting the length of the standard task according to the user'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-described processes in the standard task generation unit may be performed using or without a generative AI. For example, the standard task generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The standard work generation unit can determine the priority of tasks based on the manufacturing date of the parts when generating standard work. The standard work generation unit can, for example, prioritize the inclusion of the latest parts in the work procedure and provide it quickly. The standard work generation unit can, for example, determine the priority of the work procedure based on the manufacturing date and generate it efficiently. The standard work generation unit can, for example, adjust the level of detail of the work procedure considering the manufacturing date and provide the optimal content. As a result, by determining the priority of tasks based on the manufacturing date of the parts, it is possible to provide efficient standard work. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the standard work generation unit can input the manufacturing date data of the parts into the generation AI and have the generation AI perform the determination of the priority of the work procedure.

[0100] The standard work generation unit can adjust the order of tasks based on the relationships between parts when generating standard tasks. For example, the standard work generation unit can prioritize the inclusion of highly related parts in the work procedure and provide it efficiently. For example, the standard work generation unit can optimize the order of work procedures based on the relationships between parts and generate them quickly. For example, the standard work generation unit can group highly related parts and generate work procedures in a batch. This allows for the provision of efficient standard tasks by adjusting the order of tasks based on the relationships between parts. Some or all of the above processing in the standard work generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the standard work generation unit can input part relationship data into a generation AI and have the generation AI perform the adjustment of the order of work procedures.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The educational support system may further include a feedback unit. The feedback unit can collect data from the worker's actual assembly work and compare it with the generated educational video. For example, the feedback unit can use sensors to detect the worker's hand movements and the position of parts and determine if they match the video. If the worker makes an incorrect procedure, the feedback unit can provide corrective instructions in real time. This allows the worker to receive immediate feedback during actual work and learn efficiently. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input work data into a generating AI and have the generating AI generate the feedback.

[0103] The training support system may also include a customization section. This customization section can adjust the content of training videos based on the worker's skill level and experience. For example, the customization section can generate videos that explain basic procedures in detail for beginners. For experienced workers, it can generate videos that emphasize efficient procedures. The customization section can collect worker feedback and incorporate it into future training videos. This allows for the provision of optimal training tailored to the worker's skill level. Some or all of the above-described processes in the customization section may be performed using AI or not. For example, the customization section can input worker skill data into a generating AI and have the generating AI perform video customization.

[0104] The educational support system may further include a prediction unit. The prediction unit can predict future work efficiency based on the worker's past work data. For example, the prediction unit can analyze the procedures a worker has followed in the past and suggest efficient procedures. It can also analyze the worker's skill improvement trends and suggest future training plans. Furthermore, it can consider the worker's fatigue level and concentration to suggest optimal break times. This maximizes worker efficiency and enhances training effectiveness. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input work data into a generating AI and have the generating AI generate predictions.

[0105] The educational support system may further include an emotion estimation unit. The emotion estimation unit can estimate emotions from the worker's facial expressions and voice and adjust the content of the educational video accordingly. For example, if the worker is feeling stressed, the emotion estimation unit can provide a video that helps them relax. If the worker is excited, the emotion estimation unit can provide a video that helps them concentrate. If the worker is tired, the emotion estimation unit can provide a video that encourages them to take a break. This allows for the provision of optimal education tailored to the worker's emotions. Some or all of the above processing in the emotion estimation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the emotion estimation unit can input the worker's emotional data into a generative AI and have the generative AI perform the emotion estimation.

[0106] The educational support system may also include an interactive section. The interactive section provides the ability for workers to ask questions and provide feedback in real time while watching educational videos. For example, the interactive section allows workers to ask questions about points they are unsure of and receive immediate answers. The interactive section allows workers to provide feedback on the content of the videos, which can then be incorporated into future videos. The interactive section allows workers to exchange opinions with other workers and advance their learning collaboratively. This enables workers to gain a deeper understanding and enhance the educational effect. Some or all of the above-described processes in the interactive section may be performed using AI or not. For example, the interactive section may input worker question data into a generating AI and have the generating AI generate answers.

[0107] The educational support system may further include a motivation unit. The motivation unit can estimate the worker's emotions and provide content to enhance motivation based on the estimated emotions. For example, if the worker is tired, the motivation unit can provide encouraging messages or refreshing content. If the worker is stressed, the motivation unit can provide relaxing music or videos. If the worker is unmotivated, the motivation unit can provide success stories or stories of achieving goals. This helps maintain worker motivation and enhances the educational effect. Some or all of the above processing in the motivation unit may be performed using or without a generative AI. For example, the motivation unit can input worker emotion data into a generative AI and have the generative AI generate motivational content.

[0108] The educational support system may further include a performance evaluation unit. The performance evaluation unit can collect worker work data and evaluate the effectiveness of educational videos. For example, the performance evaluation unit can measure the work efficiency of workers after they have watched educational videos and identify areas for improvement. The performance evaluation unit can evaluate the degree of skill improvement of workers and reflect this in the next training plan. The performance evaluation unit can collect worker feedback and provide data to improve the content of educational videos. This allows for continuous improvement of the effectiveness of educational videos. Some or all of the above processes in the performance evaluation unit may be performed using AI or not. For example, the performance evaluation unit can input work data into a generating AI and have the generating AI perform the generation of evaluations.

[0109] The educational support system may further include an emotional feedback unit. The emotional feedback unit can estimate the worker's emotions and provide feedback based on the estimated emotions. For example, if the worker is feeling stressed, the emotional feedback unit can provide advice to help them relax. If the worker is excited, the emotional feedback unit can provide advice to help them concentrate. If the worker is tired, the emotional feedback unit can provide advice to encourage them to take a break. This allows for the provision of optimal feedback tailored to the worker's emotions. Some or all of the above processing in the emotional feedback unit may be performed using a generative AI, or not. For example, the emotional feedback unit can input the worker's emotional data into a generative AI and have the generative AI generate the feedback.

[0110] The educational support system may further include a training unit. The training unit can provide training in a simulation environment before workers actually perform assembly work. For example, the training unit can use virtual reality (VR) to provide an environment where workers can experience actual assembly work. If a worker makes an incorrect procedure in the simulation environment, the training unit can provide immediate corrective instructions. The training unit allows workers to learn efficient procedures in the simulation environment, enabling a smooth transition to actual work. This ensures that workers receive sufficient training before starting actual work, thereby enhancing the educational effect. Some or all of the above processes in the training unit may be performed using AI or not. For example, the training unit can input simulation data into a generating AI and have the generating AI generate the training.

[0111] The educational support system may further include an emotion monitoring unit. The emotion monitoring unit can monitor the worker's emotions in real time and dynamically adjust the content of the educational video. For example, if the worker is feeling stressed, the emotion monitoring unit can switch to relaxing content. If the worker is excited, the emotion monitoring unit can switch to content that enhances concentration. If the worker is tired, the emotion monitoring unit can switch to content that encourages a break. This allows for the provision of optimal education in real time, tailored to the worker's emotions. Some or all of the above processing in the emotion monitoring unit may be performed using a generative AI, or not. For example, the emotion monitoring unit can input the worker's emotional data into a generative AI and have the generative AI perform the generation of monitoring data.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The registration unit registers the 3D CAD data of the part. For example, 3D CAD data of a part created during the design phase can be entered into the system. Step 2: The analysis unit analyzes the assembly sequence based on the data registered by the registration unit. For example, it calculates an efficient assembly procedure, taking into account constraints on the position of parts and the assembly sequence. Step 3: The generation unit generates educational videos based on the data analyzed by the analysis unit. For example, using the generation AI, it can specify the positions of parts and workers and generate videos that naturally reproduce the movements of the right and left hands. For example, the generation unit can reproduce in a video the action of holding part A in the right hand and part B in the left hand. This allows workers to learn assembly work with natural movements. Furthermore, the generation unit has functions to realize efficient standard work. For example, it can eliminate unnecessary movements from the assembly sequence and generate efficient work procedures. This reduces the time required to train workers and improves the efficiency of the assembly line.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the registration unit, analysis unit, generation unit, reproduction unit, and standard work generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and inputs 3D CAD data of parts into the system. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the assembly sequence based on the registered data. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates educational videos based on the analyzed data. The reproduction unit is implemented by the control unit 46A of the smart device 14 and naturally reproduces the movements of the right and left hands by specifying the positions of parts and workers. The standard work generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates efficient standard work from the assembly sequence. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the registration unit, analysis unit, generation unit, reproduction unit, and standard work generation unit, is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and inputs 3D CAD data of parts into the system. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the assembly sequence based on the registered data. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates educational videos based on the analyzed data. The reproduction unit is implemented by the control unit 46A of the smart glasses 214 and specifies the positions of parts and workers, and naturally reproduces the movements of the right and left hands. The standard work generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates efficient standard work from the assembly sequence. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the registration unit, analysis unit, generation unit, reproduction unit, and standard work generation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and inputs 3D CAD data of parts into the system. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the assembly sequence based on the registered data. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates educational videos based on the analyzed data. The reproduction unit is implemented by the control unit 46A of the headset terminal 314 and naturally reproduces the movements of the right and left hands by specifying the positions of parts and workers. The standard work generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates efficient standard work from the assembly sequence. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the registration unit, analysis unit, generation unit, reproduction unit, and standard work generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and inputs 3D CAD data of parts into the system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the assembly sequence based on the registered data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates educational videos based on the analyzed data. The reproduction unit is implemented by the control unit 46A of the robot 414 and naturally reproduces the movements of the right and left hands by specifying the positions of parts and workers. The standard work generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates efficient standard work from the assembly sequence. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A registration unit for registering 3D CAD data of parts, An analysis unit analyzes the assembly sequence based on the data registered by the registration unit, The system includes a generation unit that generates educational videos based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The generating unit is It features a reproduction unit that allows you to specify the position of parts and workers and naturally reproduce the movements of the right and left hands. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes a standard work generation unit that generates efficient standard work procedures based on the assembly sequence. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of 3D CAD data registration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned registration unit is During registration, data prioritization is determined based on the importance of the parts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned registration unit is During registration, different registration algorithms are applied depending on the shape and size of the part. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and determines the priority of the component data to be registered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is During registration, the system prioritizes registering highly relevant data, taking into account the manufacturer information of the parts. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is During registration, the usage history of the parts is analyzed, and relevant data is registered. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the component. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the part. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the manufacturing date of the parts. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the parts. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the way the generated video is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, adjust the video detail level based on the importance of the components. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the category of the component. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the length of the generated video based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the priority of videos is determined based on the manufacturing date of the parts. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the order of the videos is adjusted based on the relationships between the parts. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reproduction unit is, It estimates the user's emotions and adjusts the way actions are represented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 23) The reproduction unit is, During reproduction, adjust the level of detail of the operation based on the importance of the parts. The system described in Appendix 2, characterized by the features described herein. (Note 24) The reproduction unit is, During reproduction, different reproduction algorithms are applied depending on the category of the part. The system described in Appendix 2, characterized by the features described herein. (Note 25) The reproduction unit is, It estimates the user's emotions and adjusts the length of the actions to be reproduced based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The reproduction unit is, During reproduction, the priority of operations is determined based on the manufacturing date of the parts. The system described in Appendix 2, characterized by the features described herein. (Note 27) The reproduction unit is, During reproduction, the sequence of operations is adjusted based on the relationships between the parts. The system described in Appendix 2, characterized by the features described herein. (Note 28) The standard work generation unit is, The system estimates user emotions and adjusts the way standard operations are represented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The standard work generation unit is, When generating standard work, adjust the level of detail of the work based on the importance of the parts. The system described in Appendix 3, characterized by the features described herein. (Note 30) The standard work generation unit is, When generating standard work, different generation algorithms are applied depending on the part category. The system described in Appendix 3, characterized by the features described herein. (Note 31) The standard work generation unit is, It estimates the user's emotions and adjusts the standard work length based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The standard work generation unit is, When generating standard work, prioritize tasks based on the manufacturing date of the parts. The system described in Appendix 3, characterized by the features described herein. (Note 33) The standard work generation unit is, When generating standard work, the order of tasks is adjusted based on the relationships between parts. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0186] 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. A registration unit for registering 3D CAD data of parts, An analysis unit analyzes the assembly sequence based on the data registered by the registration unit, The system includes a generation unit that generates educational videos based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The generating unit is It features a reproduction unit that allows you to specify the position of parts and workers and naturally reproduce the movements of the right and left hands. The system according to feature 1.

3. The generating unit is It includes a standard work generation unit that generates efficient standard work procedures based on the assembly sequence. The system according to feature 1.

4. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of 3D CAD data registration based on the estimated emotions. The system according to feature 1.

5. The aforementioned registration unit is During registration, data prioritization is determined based on the importance of the parts. The system according to feature 1.

6. The aforementioned registration unit is During registration, different registration algorithms are applied depending on the shape and size of the part. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and determines the priority of the component data to be registered based on the estimated user emotions. The system according to feature 1.

8. The aforementioned registration unit is During registration, the system prioritizes registering highly relevant data, taking into account the manufacturer information of the parts. The system according to feature 1.

9. The aforementioned registration unit is During registration, the usage history of the parts is analyzed, and relevant data is registered. The system according to feature 1.

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