system

Abstract

The system according to this embodiment aims to improve production efficiency and quality in the manufacture of traditional crafts. [Solution] The system according to the embodiment comprises a design support unit, a feedback unit, and an adjustment unit. The design support unit provides a data-driven design support tool. The feedback unit provides real-time feedback based on the design support provided by the design support unit. The adjustment unit automatically adjusts the manufacturing process based on the feedback provided by the feedback 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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, insufficient support has been provided for improving production efficiency and quality in the manufacture of traditional handicrafts, and there is room for improvement.

[0005] The system according to the embodiment aims to improve production efficiency and quality in the manufacture of traditional handicrafts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a design support unit, a feedback unit, and an adjustment unit. The design support unit provides a data-driven design support tool. The feedback unit provides real-time feedback based on the design support provided by the design support unit. The adjustment unit automatically adjusts the manufacturing process based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve production efficiency and quality in the manufacture of traditional crafts. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is designed to improve the production efficiency and quality of artisans who manufacture traditional crafts. This system provides a data-driven design support tool and digitizes artisans' skills to facilitate knowledge sharing. It also features real-time feedback and automatic process adjustment capabilities, and is expected to reduce manufacturing time by 30%, improve quality consistency and reproducibility, and increase new customer acquisition rates by 20%. Furthermore, it addresses the time and cost constraints faced by artisans and enables flexible production adjustments in response to market demand. It offers innovative applications of traditional techniques using AI, new possibilities for customization and personalization, and also enables the digitalization of education and training programs. For example, the AI ​​agent system allows artisans to receive real-time feedback at each stage of the manufacturing process. This enables artisans to immediately adjust the manufacturing process and maintain quality. The AI ​​agent system also promotes the inheritance and sharing of skills by digitizing artisans' skills and sharing them with other artisans. For example, it can save digital records of work performed by artisans, which other artisans can then refer to. Furthermore, the AI ​​agent system provides customization and personalization capabilities, enabling the delivery of products tailored to individual customer needs. For example, it can modify product designs according to customer requests. This can improve customer satisfaction. Furthermore, the AI ​​agent system digitizes education and training programs, supporting craftsmen in efficiently learning and acquiring skills. For example, craftsmen can take training programs online and acquire skills. This can improve the skills of craftsmen. As a result, the AI ​​agent system can improve the production efficiency and quality of craftsmen.

[0029] The AI ​​agent system according to this embodiment comprises a design support unit, a feedback unit, and an adjustment unit. The design support unit provides a data-driven design support tool. For example, when a craftsman designs a product, the design support unit proposes an optimal design based on past design data. The design support unit can analyze the design data using AI and suggest improvements to the design to the craftsman. For example, the design support unit proposes a new design based on past successful design patterns. The design support unit can also analyze failed design patterns and suggest improvements. Furthermore, the design support unit can propose design patterns suitable for specific materials or technologies. The feedback unit provides real-time feedback based on the design support provided by the design support unit. For example, the feedback unit provides real-time feedback when a craftsman proceeds with the manufacturing process. The feedback unit can monitor the manufacturing process using AI and suggest improvements to the manufacturing process to the craftsman. For example, the feedback unit monitors the progress of the manufacturing process in real time and suggests improvements to the manufacturing process to the craftsman. The feedback unit can also analyze the data of the manufacturing process and suggest methods for optimizing the manufacturing process to the craftsman. The adjustment unit automatically adjusts the manufacturing process based on the feedback provided by the feedback unit. The adjustment unit automatically performs adjustments at each stage of the manufacturing process, for example. The adjustment unit can monitor the manufacturing process using AI and automatically adjust it. For example, the adjustment unit can monitor the progress of the manufacturing process in real time and automatically adjust it. The adjustment unit can also analyze data from the manufacturing process and optimize it. As a result, the AI ​​agent system according to this embodiment enables data-driven design support, real-time feedback, and automatic adjustment of the manufacturing process.

[0030] The Design Support Department provides data-driven design support tools. Specifically, it suggests optimal designs based on past design data when craftsmen design products. The Design Support Department can use AI to analyze design data and suggest areas for design improvement to craftsmen. The AI ​​learns from past successful design patterns and utilizes these patterns to suggest new designs. For example, the AI ​​searches for similar designs in the past design database and shows craftsmen which parts of those designs were successful. It also analyzes failed design patterns and identifies areas for improvement. This allows craftsmen to receive specific advice based on past experience. Furthermore, the Design Support Department can suggest design patterns suitable for specific materials and technologies. For example, when using a new material, it suggests a design optimized for the properties of that material. This allows craftsmen to effectively utilize new materials and technologies. The Design Support Department provides these suggestions in real time, allowing craftsmen to receive immediate feedback as they progress through the design process. This improves the quality and efficiency of designs and shortens the time to market.

[0031] The Feedback Department provides real-time feedback based on the design support provided by the Design Support Department. Specifically, it provides real-time feedback as craftsmen proceed with the manufacturing process. The Feedback Department can use AI to monitor the manufacturing process and suggest areas for improvement to craftsmen. The AI ​​analyzes data collected at each stage of the manufacturing process in real time, identifying anomalies and areas where improvement is possible. For example, it analyzes data obtained from sensors on the manufacturing line to detect fluctuations that may affect product quality. This allows craftsmen to immediately understand which part of the manufacturing process is problematic and take appropriate measures. The Feedback Department can also analyze data from the manufacturing process and suggest ways to optimize the manufacturing process to craftsmen. For example, it can suggest specific steps to improve the efficiency of the manufacturing process or adjustments to improve quality. This allows craftsmen to continuously improve the manufacturing process and enhance product quality and productivity. Furthermore, the Feedback Department can improve the accuracy and usefulness of the feedback by collecting feedback from craftsmen and using it as training data for the AI. This allows the Feedback Department to provide more effective support to craftsmen and improve the overall performance of the manufacturing process.

[0032] The adjustment unit automatically adjusts the manufacturing process based on feedback provided by the feedback unit. Specifically, it automatically makes adjustments at each stage of the manufacturing process. The adjustment unit can monitor the manufacturing process using AI and perform automatic adjustments. The AI ​​monitors the progress of the manufacturing process in real time and automatically makes necessary adjustments at each stage of the manufacturing process. For example, it adjusts parameters such as the speed, temperature, and pressure of the manufacturing line in real time to maintain optimal manufacturing conditions. It can also analyze manufacturing process data and optimize the manufacturing process. For example, it can make adjustments to maximize the efficiency of the manufacturing process and fine-tune it to improve quality. As a result, the adjustment unit can achieve automation and optimization of the manufacturing process, improving product quality and productivity. Furthermore, by accumulating manufacturing process data and using it as training data for the AI, the adjustment unit can continuously improve the accuracy and effectiveness of its adjustments. As a result, the adjustment unit can continuously improve the automation and optimization of the manufacturing process and improve the overall performance of the manufacturing process. Through these functions, the adjustment unit can achieve increased efficiency and improved quality in the manufacturing process, thereby enhancing the market competitiveness of the product.

[0033] The Digitalization Department digitizes the skills of craftsmen and promotes knowledge sharing. For example, the Digitalization Department can save digital records of the work performed by craftsmen, allowing other craftsmen to refer to those records. The Digitalization Department can use AI to digitize craftsmen's work and create digital records. For example, the Digitalization Department can record craftsmen's work in real time and save it as digital data. It can also record craftsmen's work as videos, allowing other craftsmen to refer to those videos. Furthermore, the Digitalization Department can record craftsmen's work as 3D models, allowing other craftsmen to refer to those 3D models. This digitizes craftsmen's skills and promotes knowledge sharing, making the inheritance and sharing of skills easier. Some or all of the above processes in the Digitalization Department may be performed using AI or not. For example, when the Digitalization Department records craftsmen's work in real time and saves it as digital data, it can use AI to automatically extract important parts of the work and create digital records.

[0034] The customization department provides customization and personalization functions. For example, the customization department can change the design of a product according to customer requests. The customization department can use AI to analyze customer requests and propose the optimal design. For example, the customization department can change the design based on customer requests and propose the design to the customer. The customization department can also change the material and color of the product according to customer requests. Furthermore, the customization department can add functions to the product according to customer requests. In this way, by providing customization and personalization functions, it becomes possible to provide products that meet individual needs. Some or all of the above processes in the customization department may be performed using AI or not. For example, the customization department can input customer requests into AI, and the AI ​​can propose the optimal design.

[0035] The Ministry of Education will digitize education and training programs. For example, the Ministry of Education will enable artisans to take online training programs and acquire skills. The Ministry of Education can use AI to provide training programs and support artisans in acquiring skills. For example, the Ministry of Education will use AI to provide real-time feedback when artisans take online training programs. The Ministry of Education can also use AI to automatically adjust the training content as artisans take the training programs. Furthermore, the Ministry of Education can use AI to monitor the progress of artisans as they take the training programs and provide appropriate advice. By digitizing education and training programs, efficient learning and skill acquisition will be possible. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can improve the learning efficiency of artisans by having AI automatically adjust the training content as artisans take the training programs.

[0036] The design support department can analyze past design data and propose the optimal design pattern. For example, the design support department can propose a new design similar to a past successful design pattern. The design support department can use AI to analyze past design data and propose the optimal design pattern. For example, the design support department can propose a new design based on a past successful design pattern. The design support department can also analyze past failed design patterns and propose improvements. For example, the design support department can propose improvements based on a past failed design pattern. Furthermore, the design support department can propose a design pattern suitable for specific materials or technologies from past design data. For example, the design support department can propose a design pattern suitable for specific materials or technologies based on past design data. In this way, the optimal design pattern can be proposed by utilizing past design data. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input past design data into AI, and the AI ​​can propose the optimal design pattern.

[0037] The design support department can provide different support methods depending on the skill level of the craftsman during the design support process. For example, the design support department can provide basic design guidelines and step-by-step instructions to a novice craftsman. The design support department can use AI to assess the craftsman's skill level and provide appropriate support methods. For example, the design support department can provide basic design guidelines and step-by-step instructions to a novice craftsman. The design support department can also provide advanced design options and customizable tools to an intermediate craftsman. For example, the design support department can provide advanced design options and customizable tools to an intermediate craftsman. Furthermore, the design support department can provide a highly flexible design environment and advanced analytical tools to an advanced craftsman. For example, the design support department can provide a highly flexible design environment and advanced analytical tools to an advanced craftsman. This enables efficient design support by providing support methods tailored to the craftsman's skill level. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input the skill level of craftsmen into an AI, which can then propose the most suitable support method.

[0038] The design support department can prioritize providing highly relevant design patterns based on the geographical location information of craftsmen during the design support process. For example, if a craftsman is in a specific region, the design support department can suggest traditional design patterns for that region. The design support department uses AI to analyze the geographical location information of craftsmen and provide highly relevant design patterns. For example, the design support department suggests traditional design patterns for a region based on the craftsman's geographical location information. Furthermore, if a craftsman moves to a different region, the design support department can suggest design patterns based on the culture and customs of that region. For example, the design support department suggests design patterns based on the culture and customs of that region based on the craftsman's geographical location information. In addition, if a craftsman is targeting an international market, the design support department can suggest global design trends. For example, the design support department suggests global design trends based on the craftsman's geographical location information. This enables design that is appropriate for the region by providing design patterns based on the craftsman's geographical location information. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input the geographical location information of craftsmen into the AI, which can then propose the optimal design pattern.

[0039] The design support department can analyze the social media activities of craftsmen and provide relevant design patterns during the design support process. For example, the design support department can analyze works shared by craftsmen on social media and propose similar design patterns. The design support department can use AI to analyze the social media activities of craftsmen and provide relevant design patterns. For example, the design support department can propose similar design patterns based on works shared by craftsmen on social media. The design support department can also analyze the trends of design influencers followed by craftsmen and propose relevant design patterns. For example, the design support department can propose relevant design patterns based on the trends of design influencers followed by craftsmen. Furthermore, the design support department can analyze the activities of design communities in which craftsmen participate and propose relevant design patterns. For example, the design support department can propose relevant design patterns based on the activities of design communities in which craftsmen participate. This enables trend-aligned designs by providing design patterns based on the craftsmen's social media activities. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input the craftsmen's social media activity data into AI, which can then propose the optimal design pattern.

[0040] The feedback unit can adjust the level of detail in feedback based on the importance of the product. For example, the feedback unit can provide detailed feedback to high-value products to improve quality. The feedback unit uses AI to assess the importance of products and provide appropriate feedback detail. For example, the feedback unit can provide detailed feedback to high-value products to improve quality. The feedback unit can also provide standard feedback to general products to encourage efficient improvement. For example, the feedback unit can provide standard feedback to general products to encourage efficient improvement. Furthermore, the feedback unit can provide concise feedback to prototypes to support rapid improvement. For example, the feedback unit can provide concise feedback to prototypes to support rapid improvement. This enables efficient improvement by providing feedback detail according to the importance of the product. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input product importance data into AI, and the AI ​​can suggest the optimal level of feedback detail.

[0041] The feedback unit can apply different feedback algorithms depending on the product category during the feedback process. For example, the feedback unit provides feedback based on the characteristics of wood for woodworking products. The feedback unit uses AI to analyze the product category and apply an appropriate feedback algorithm. For example, the feedback unit provides feedback based on the characteristics of wood for woodworking products. The feedback unit can also provide feedback based on the firing process for ceramic products. For example, the feedback unit provides feedback based on the firing process for ceramic products. Furthermore, the feedback unit can provide feedback based on weaving and dyeing techniques for textile products. For example, the feedback unit provides feedback based on weaving and dyeing techniques for textile products. This enables appropriate feedback by providing feedback algorithms tailored to the product category. 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 product category data into AI, and the AI ​​can propose the optimal feedback algorithm.

[0042] The feedback unit can prioritize feedback based on the product's production stage. For example, the feedback unit will provide priority feedback to products with approaching deadlines. The feedback unit uses AI to evaluate the product's production stage and provide appropriate feedback priorities. For example, the feedback unit will provide priority feedback to products with approaching deadlines. The feedback unit can also provide basic feedback to products that have just begun production. For example, the feedback unit will provide basic feedback to products that have just begun production. Furthermore, the feedback unit can provide detailed feedback to completed products and indicate areas for improvement for the next time. For example, the feedback unit will provide detailed feedback to completed products and indicate areas for improvement for the next time. This enables efficient improvement by providing feedback priorities according to the product's production stage. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input product production stage data into AI, and the AI ​​can propose the optimal feedback priority.

[0043] The feedback unit can adjust the order of feedback based on the relevance of the products during the feedback process. For example, the feedback unit can provide common feedback to similar products to encourage efficient improvement. The feedback unit uses AI to evaluate the relevance of products and provide an appropriate order of feedback. For example, the feedback unit can provide common feedback to similar products to encourage efficient improvement. The feedback unit can also provide individual feedback to products for specific customers to improve customer satisfaction. For example, the feedback unit can provide individual feedback to products for specific customers to improve customer satisfaction. Furthermore, the feedback unit can provide detailed feedback to new products to improve quality. For example, the feedback unit can provide detailed feedback to new products to improve quality. This enables efficient improvement by providing an order of feedback according to the relevance of the products. 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 product relevance data into the AI, and the AI ​​can suggest the optimal order of feedback.

[0044] The adjustment unit can select the optimal adjustment method by referring to past manufacturing data during adjustment. For example, the adjustment unit proposes the optimal adjustment method based on past successful manufacturing processes. The adjustment unit uses AI to analyze past manufacturing data and select the optimal adjustment method. For example, the adjustment unit proposes the optimal adjustment method based on past successful manufacturing processes. The adjustment unit can also analyze past failed manufacturing processes and propose areas for improvement. For example, the adjustment unit proposes areas for improvement based on past failed manufacturing processes. Furthermore, the adjustment unit can propose adjustment methods suitable for specific materials or technologies based on past manufacturing data. For example, the adjustment unit proposes adjustment methods suitable for specific materials or technologies based on past manufacturing data. In this way, the optimal adjustment method can be provided by utilizing past manufacturing data. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input past manufacturing data into AI, and the AI ​​can propose the optimal adjustment method.

[0045] The adjustment unit can apply different adjustment algorithms depending on the product category during adjustment. For example, for woodworking products, the adjustment unit applies an adjustment algorithm based on the characteristics of the wood. The adjustment unit uses AI to analyze the product category and apply an appropriate adjustment algorithm. For example, the adjustment unit applies an adjustment algorithm based on the characteristics of the wood to woodworking products. The adjustment unit can also apply an adjustment algorithm based on the firing process to ceramic products. For example, the adjustment unit applies an adjustment algorithm based on the firing process to ceramic products. Furthermore, the adjustment unit can apply an adjustment algorithm based on the weaving method and dyeing technique to textile products. For example, the adjustment unit applies an adjustment algorithm based on the weaving method and dyeing technique to textile products. This enables appropriate adjustment by providing an adjustment algorithm according to the product category. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input product category data into AI, and the AI ​​can propose the optimal adjustment algorithm.

[0046] The adjustment unit can determine the priority of adjustments based on the product's manufacturing date during the adjustment process. For example, the adjustment unit prioritizes adjustments for products with approaching deadlines. The adjustment unit uses AI to evaluate the product's manufacturing date and provide appropriate adjustment priorities. For example, the adjustment unit prioritizes adjustments for products with approaching deadlines. The adjustment unit can also perform basic adjustments for products that have just begun production. For example, the adjustment unit performs basic adjustments for products that have just begun production. Furthermore, the adjustment unit can perform detailed adjustments for completed products and suggest areas for improvement for the next production cycle. For example, the adjustment unit performs detailed adjustments for completed products and suggests areas for improvement for the next production cycle. This enables efficient adjustments by providing adjustment priorities according to the product's manufacturing date. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input product manufacturing date data into the AI, which can then suggest the optimal adjustment priorities.

[0047] The adjustment unit can adjust the order of adjustments based on the relevance of the products during the adjustment process. For example, the adjustment unit can perform common adjustments on similar products to promote efficient improvement. The adjustment unit uses AI to evaluate the relevance of products and provide an appropriate order of adjustments. For example, the adjustment unit can perform common adjustments on similar products to promote efficient improvement. The adjustment unit can also perform individual adjustments on products for specific customers to improve customer satisfaction. For example, the adjustment unit can perform individual adjustments on products for specific customers to improve customer satisfaction. Furthermore, the adjustment unit can perform detailed adjustments on new products to improve quality. For example, the adjustment unit can perform detailed adjustments on new products to improve quality. This enables efficient adjustments by providing an order of adjustments according to the relevance of the products. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input product relevance data into the AI, and the AI ​​can propose the optimal order of adjustments.

[0048] The Digitalization Department can select the optimal digitalization method by referring to past technical data during the digitalization process. For example, the Digitalization Department can propose the optimal digitalization method based on past successful digitalization projects. The Digitalization Department can analyze past technical data using AI and select the optimal digitalization method. For example, the Digitalization Department can propose the optimal digitalization method based on past successful digitalization projects. The Digitalization Department can also analyze past unsuccessful digitalization projects and propose areas for improvement. For example, the Digitalization Department can propose areas for improvement based on past unsuccessful digitalization projects. Furthermore, the Digitalization Department can propose digitalization methods suitable for specific materials or technologies based on past technical data. For example, the Digitalization Department can propose digitalization methods suitable for specific materials or technologies based on past technical data. In this way, the Digitalization Department can provide the optimal digitalization method by utilizing past technical data. Some or all of the above processes in the Digitalization Department may be performed using AI or not. For example, the Digitalization Department can input past technical data into AI, and the AI ​​can propose the optimal digitalization method.

[0049] The digitalization department can adjust the order of digitalization based on the relevance of technologies during the digitalization process. For example, the digitalization department can perform common digitalization on similar technologies to promote efficient improvement. The digitalization department uses AI to evaluate the relevance of technologies and provide an appropriate order of digitalization. For example, the digitalization department can perform common digitalization on similar technologies to promote efficient improvement. The digitalization department can also perform individual digitalization on technologies for specific customers to improve customer satisfaction. For example, the digitalization department can perform individual digitalization on technologies for specific customers to improve customer satisfaction. Furthermore, the digitalization department can perform detailed digitalization on new technologies to improve quality. For example, the digitalization department can perform detailed digitalization on new technologies to improve quality. This enables efficient digitalization by providing an order of digitalization according to the relevance of technologies. Some or all of the above processes in the digitalization department may be performed using AI or not. For example, the digitalization department can input technology relevance data into AI, and the AI ​​can propose the optimal order of digitalization.

[0050] The customization department can select the optimal customization method by referring to past customization data during the customization process. For example, the customization department can propose the optimal customization method based on past successful customization projects. The customization department can analyze past customization data using AI to select the optimal customization method. For example, the customization department can propose the optimal customization method based on past successful customization projects. The customization department can also analyze past unsuccessful customization projects and propose areas for improvement. For example, the customization department can propose areas for improvement based on past unsuccessful customization projects. Furthermore, the customization department can propose customization methods suitable for specific materials or technologies based on past customization data. For example, the customization department can propose customization methods suitable for specific materials or technologies based on past customization data. In this way, the optimal customization method can be provided by utilizing past customization data. Some or all of the above processes in the customization department may be performed using AI or not. For example, the customization department can input past customization data into AI, and the AI ​​can propose the optimal customization method.

[0051] The customization unit can adjust the order of customization based on the relevance of products during the customization process. For example, the customization unit can perform common customizations on similar products to promote efficient improvement. The customization unit uses AI to evaluate the relevance of products and provide an appropriate customization order. For example, the customization unit can perform common customizations on similar products to promote efficient improvement. The customization unit can also perform individual customizations on products for specific customers to improve customer satisfaction. For example, the customization unit can perform individual customizations on products for specific customers to improve customer satisfaction. Furthermore, the customization unit can perform detailed customizations on new products to improve quality. For example, the customization unit can perform detailed customizations on new products to improve quality. This enables efficient customization by providing a customization order according to the relevance of products. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input product relevance data into the AI, and the AI ​​can propose the optimal customization order.

[0052] The Ministry of Education can select the optimal teaching method by referring to past educational data during the teaching process. For example, the Ministry of Education can propose the optimal teaching method based on past successful educational programs. The Ministry of Education can use AI to analyze past educational data and select the optimal teaching method. For example, the Ministry of Education can propose the optimal teaching method based on past successful educational programs. The Ministry of Education can also analyze past unsuccessful educational programs and propose areas for improvement. For example, the Ministry of Education can propose areas for improvement based on past unsuccessful educational programs. Furthermore, the Ministry of Education can propose teaching methods suitable for specific materials or technologies based on past educational data. For example, the Ministry of Education can propose teaching methods suitable for specific materials or technologies based on past educational data. In this way, the Ministry of Education can provide the optimal teaching method by utilizing past educational data. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input past educational data into AI, and the AI ​​can propose the optimal teaching method.

[0053] The Ministry of Education can adjust the order of educational programs based on the relevance of technologies during education. For example, the Ministry of Education can implement common educational programs for similar technologies to promote efficient learning. The Ministry of Education can use AI to evaluate the relevance of technologies and provide an appropriate order of educational programs. For example, the Ministry of Education can implement common educational programs for similar technologies to promote efficient learning. The Ministry of Education can also implement individualized educational programs for technologies aimed at specific customers to improve customer satisfaction. For example, the Ministry of Education can implement individualized educational programs for technologies aimed at specific customers to improve customer satisfaction. Furthermore, the Ministry of Education can implement detailed educational programs for new technologies to improve quality. For example, the Ministry of Education can implement detailed educational programs for new technologies to improve quality. This enables efficient learning by providing an order of educational programs according to the relevance of technologies. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input technology relevance data into AI, and the AI ​​can propose the optimal order of educational programs.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The design support department can provide different support methods depending on the skill level of the craftsman. For example, beginner craftsmen can be provided with basic design guidelines and step-by-step instructions. Intermediate craftsmen can be provided with advanced design options and customizable tools. Advanced craftsmen can be provided with a highly flexible design environment and advanced analysis tools. This allows for efficient design support by providing support methods tailored to the skill level of the craftsman.

[0056] The customization department can select the optimal customization method by referring to past customization data. For example, it can propose the best customization method based on past successful customization projects. It can also analyze past unsuccessful customization projects and propose improvements. It can also propose customization methods suitable for specific materials or technologies. In this way, by utilizing past customization data, it can provide the optimal customization method.

[0057] The feedback system can adjust the level of detail in feedback based on the importance of the product. For example, high-value products can receive detailed feedback to improve quality. General products can receive standard feedback to facilitate efficient improvement. Prototypes can receive concise feedback to support rapid improvement. This allows for efficient improvement by providing feedback with a level of detail appropriate to the importance of the product.

[0058] The design support department can prioritize providing highly relevant design patterns based on the geographical location of craftsmen. For example, if a craftsman is in a specific region, it can suggest traditional design patterns from that region. If a craftsman moves to a different region, it can suggest design patterns based on the culture and customs of that region. If a craftsman is targeting an international market, it can suggest global design trends. In this way, providing design patterns based on the geographical location of craftsmen enables designs that are appropriate for the region.

[0059] The adjustment unit can select the optimal adjustment method by referring to past manufacturing data. For example, it can propose the optimal adjustment method based on past successful manufacturing processes. It can also analyze past failed manufacturing processes and propose improvements. It can also propose adjustment methods suitable for specific materials or technologies. In this way, by utilizing past manufacturing data, the optimal adjustment method can be provided.

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

[0061] Step 1: The Design Support Department provides data-driven design support tools. When craftsmen design products, the Design Support Department proposes optimal designs based on past design data. Using AI, it can analyze design data and suggest design improvements to craftsmen. For example, it can propose new designs based on past successful design patterns, and analyze unsuccessful design patterns to suggest improvements. It can also propose design patterns suitable for specific materials or technologies. Step 2: The Feedback Department provides real-time feedback based on the design support provided by the Design Support Department. The Feedback Department provides real-time feedback as the craftsman proceeds with the manufacturing process. It can use AI to monitor the manufacturing process and suggest areas for improvement to the craftsman. For example, it can monitor the progress of the manufacturing process in real time and suggest areas for improvement. It can also analyze manufacturing process data and suggest ways to optimize the manufacturing process. Step 3: The adjustment unit automatically adjusts the manufacturing process based on the feedback provided by the feedback unit. The adjustment unit automatically makes adjustments at each stage of the manufacturing process. AI can be used to monitor the manufacturing process and automatically adjust it. For example, it can monitor the progress of the manufacturing process in real time and automatically adjust it. It can also analyze manufacturing process data and optimize the manufacturing process.

[0062] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is designed to improve the production efficiency and quality of artisans who manufacture traditional crafts. This system provides a data-driven design support tool and digitizes artisans' skills to facilitate knowledge sharing. It also features real-time feedback and automatic process adjustment capabilities, and is expected to reduce manufacturing time by 30%, improve quality consistency and reproducibility, and increase new customer acquisition rates by 20%. Furthermore, it addresses the time and cost constraints faced by artisans and enables flexible production adjustments in response to market demand. It offers innovative applications of traditional techniques using AI, new possibilities for customization and personalization, and also enables the digitalization of education and training programs. For example, the AI ​​agent system allows artisans to receive real-time feedback at each stage of the manufacturing process. This enables artisans to immediately adjust the manufacturing process and maintain quality. The AI ​​agent system also promotes the inheritance and sharing of skills by digitizing artisans' skills and sharing them with other artisans. For example, it can save digital records of work performed by artisans, which other artisans can then refer to. Furthermore, the AI ​​agent system provides customization and personalization capabilities, enabling the delivery of products tailored to individual customer needs. For example, it can modify product designs according to customer requests. This can improve customer satisfaction. Furthermore, the AI ​​agent system digitizes education and training programs, supporting craftsmen in efficiently learning and acquiring skills. For example, craftsmen can take training programs online and acquire skills. This can improve the skills of craftsmen. As a result, the AI ​​agent system can improve the production efficiency and quality of craftsmen.

[0063] The AI ​​agent system according to this embodiment comprises a design support unit, a feedback unit, and an adjustment unit. The design support unit provides a data-driven design support tool. For example, when a craftsman designs a product, the design support unit proposes an optimal design based on past design data. The design support unit can analyze the design data using AI and suggest improvements to the design to the craftsman. For example, the design support unit proposes a new design based on past successful design patterns. The design support unit can also analyze failed design patterns and suggest improvements. Furthermore, the design support unit can propose design patterns suitable for specific materials or technologies. The feedback unit provides real-time feedback based on the design support provided by the design support unit. For example, the feedback unit provides real-time feedback when a craftsman proceeds with the manufacturing process. The feedback unit can monitor the manufacturing process using AI and suggest improvements to the manufacturing process to the craftsman. For example, the feedback unit monitors the progress of the manufacturing process in real time and suggests improvements to the manufacturing process to the craftsman. The feedback unit can also analyze the data of the manufacturing process and suggest methods for optimizing the manufacturing process to the craftsman. The adjustment unit automatically adjusts the manufacturing process based on the feedback provided by the feedback unit. The adjustment unit automatically performs adjustments at each stage of the manufacturing process, for example. The adjustment unit can monitor the manufacturing process using AI and automatically adjust it. For example, the adjustment unit can monitor the progress of the manufacturing process in real time and automatically adjust it. The adjustment unit can also analyze data from the manufacturing process and optimize it. As a result, the AI ​​agent system according to this embodiment enables data-driven design support, real-time feedback, and automatic adjustment of the manufacturing process.

[0064] The Design Support Department provides data-driven design support tools. Specifically, it suggests optimal designs based on past design data when craftsmen design products. The Design Support Department can use AI to analyze design data and suggest areas for design improvement to craftsmen. The AI ​​learns from past successful design patterns and utilizes these patterns to suggest new designs. For example, the AI ​​searches for similar designs in the past design database and shows craftsmen which parts of those designs were successful. It also analyzes failed design patterns and identifies areas for improvement. This allows craftsmen to receive specific advice based on past experience. Furthermore, the Design Support Department can suggest design patterns suitable for specific materials and technologies. For example, when using a new material, it suggests a design optimized for the properties of that material. This allows craftsmen to effectively utilize new materials and technologies. The Design Support Department provides these suggestions in real time, allowing craftsmen to receive immediate feedback as they progress through the design process. This improves the quality and efficiency of designs and shortens the time to market.

[0065] The Feedback Department provides real-time feedback based on the design support provided by the Design Support Department. Specifically, it provides real-time feedback as craftsmen proceed with the manufacturing process. The Feedback Department can use AI to monitor the manufacturing process and suggest areas for improvement to craftsmen. The AI ​​analyzes data collected at each stage of the manufacturing process in real time, identifying anomalies and areas where improvement is possible. For example, it analyzes data obtained from sensors on the manufacturing line to detect fluctuations that may affect product quality. This allows craftsmen to immediately understand which part of the manufacturing process is problematic and take appropriate measures. The Feedback Department can also analyze data from the manufacturing process and suggest ways to optimize the manufacturing process to craftsmen. For example, it can suggest specific steps to improve the efficiency of the manufacturing process or adjustments to improve quality. This allows craftsmen to continuously improve the manufacturing process and enhance product quality and productivity. Furthermore, the Feedback Department can improve the accuracy and usefulness of the feedback by collecting feedback from craftsmen and using it as training data for the AI. This allows the Feedback Department to provide more effective support to craftsmen and improve the overall performance of the manufacturing process.

[0066] The adjustment unit automatically adjusts the manufacturing process based on feedback provided by the feedback unit. Specifically, it automatically makes adjustments at each stage of the manufacturing process. The adjustment unit can monitor the manufacturing process using AI and perform automatic adjustments. The AI ​​monitors the progress of the manufacturing process in real time and automatically makes necessary adjustments at each stage of the manufacturing process. For example, it adjusts parameters such as the speed, temperature, and pressure of the manufacturing line in real time to maintain optimal manufacturing conditions. It can also analyze manufacturing process data and optimize the manufacturing process. For example, it can make adjustments to maximize the efficiency of the manufacturing process and fine-tune it to improve quality. As a result, the adjustment unit can achieve automation and optimization of the manufacturing process, improving product quality and productivity. Furthermore, by accumulating manufacturing process data and using it as training data for the AI, the adjustment unit can continuously improve the accuracy and effectiveness of its adjustments. As a result, the adjustment unit can continuously improve the automation and optimization of the manufacturing process and improve the overall performance of the manufacturing process. Through these functions, the adjustment unit can achieve increased efficiency and improved quality in the manufacturing process, thereby enhancing the market competitiveness of the product.

[0067] The Digitalization Department digitizes the skills of craftsmen and promotes knowledge sharing. For example, the Digitalization Department can save digital records of the work performed by craftsmen, allowing other craftsmen to refer to those records. The Digitalization Department can use AI to digitize craftsmen's work and create digital records. For example, the Digitalization Department can record craftsmen's work in real time and save it as digital data. It can also record craftsmen's work as videos, allowing other craftsmen to refer to those videos. Furthermore, the Digitalization Department can record craftsmen's work as 3D models, allowing other craftsmen to refer to those 3D models. This digitizes craftsmen's skills and promotes knowledge sharing, making the inheritance and sharing of skills easier. Some or all of the above processes in the Digitalization Department may be performed using AI or not. For example, when the Digitalization Department records craftsmen's work in real time and saves it as digital data, it can use AI to automatically extract important parts of the work and create digital records.

[0068] The customization department provides customization and personalization functions. For example, the customization department can change the design of a product according to customer requests. The customization department can use AI to analyze customer requests and propose the optimal design. For example, the customization department can change the design based on customer requests and propose the design to the customer. The customization department can also change the material and color of the product according to customer requests. Furthermore, the customization department can add functions to the product according to customer requests. In this way, by providing customization and personalization functions, it becomes possible to provide products that meet individual needs. Some or all of the above processes in the customization department may be performed using AI or not. For example, the customization department can input customer requests into AI, and the AI ​​can propose the optimal design.

[0069] The Ministry of Education will digitize education and training programs. For example, the Ministry of Education will enable artisans to take online training programs and acquire skills. The Ministry of Education can use AI to provide training programs and support artisans in acquiring skills. For example, the Ministry of Education will use AI to provide real-time feedback when artisans take online training programs. The Ministry of Education can also use AI to automatically adjust the training content as artisans take the training programs. Furthermore, the Ministry of Education can use AI to monitor the progress of artisans as they take the training programs and provide appropriate advice. By digitizing education and training programs, efficient learning and skill acquisition will be possible. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can improve the learning efficiency of artisans by having AI automatically adjust the training content as artisans take the training programs.

[0070] The design support unit can estimate the emotions of craftsmen and adjust its design support approach based on those emotions. For example, if a craftsman is stressed, the design support unit can provide a simple design support interface to reduce the workload. The design support unit estimates the emotions of craftsmen using emotion estimation functions, such as an emotion engine or generative AI. For example, the design support unit can estimate emotions using the craftsman's facial expressions and voice data as input and provide a simple interface if they are stressed. The design support unit can also provide detailed design options to stimulate creativity if the craftsman is relaxed. For example, based on the craftsman's emotion data, the design support unit can provide detailed design options if they are relaxed. The design support unit can also prioritize providing automated tools to streamline work if the craftsman is tired. For example, based on the craftsman's emotion data, the design support unit can provide automated tools if they are tired. This improves work efficiency and creativity by providing a design support approach that is tailored to the craftsman's emotions. Some or all of the above processes in the design support unit may be performed using AI or not. For example, the design support department can input emotional data from craftsmen into an AI, which can then propose the optimal design support approach.

[0071] The design support department can analyze past design data and propose the optimal design pattern. For example, the design support department can propose a new design similar to a past successful design pattern. The design support department can use AI to analyze past design data and propose the optimal design pattern. For example, the design support department can propose a new design based on a past successful design pattern. The design support department can also analyze past failed design patterns and propose improvements. For example, the design support department can propose improvements based on a past failed design pattern. Furthermore, the design support department can propose a design pattern suitable for specific materials or technologies from past design data. For example, the design support department can propose a design pattern suitable for specific materials or technologies based on past design data. In this way, the optimal design pattern can be proposed by utilizing past design data. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input past design data into AI, and the AI ​​can propose the optimal design pattern.

[0072] The design support department can provide different support methods depending on the skill level of the craftsman during the design support process. For example, the design support department can provide basic design guidelines and step-by-step instructions to a novice craftsman. The design support department can use AI to assess the craftsman's skill level and provide appropriate support methods. For example, the design support department can provide basic design guidelines and step-by-step instructions to a novice craftsman. The design support department can also provide advanced design options and customizable tools to an intermediate craftsman. For example, the design support department can provide advanced design options and customizable tools to an intermediate craftsman. Furthermore, the design support department can provide a highly flexible design environment and advanced analytical tools to an advanced craftsman. For example, the design support department can provide a highly flexible design environment and advanced analytical tools to an advanced craftsman. This enables efficient design support by providing support methods tailored to the craftsman's skill level. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input the skill level of craftsmen into an AI, which can then propose the most suitable support method.

[0073] The design support unit can estimate the emotions of craftsmen and determine the priority of design support based on the estimated emotions. For example, if a craftsman is feeling stressed, the design support unit will prioritize assisting with the most important design tasks. The design support unit estimates the emotions of craftsmen using an emotion estimation function, such as an emotion engine or generative AI. For example, the design support unit estimates emotions using the craftsman's facial expressions and voice data as input, and if the craftsman is feeling stressed, it will prioritize assisting with the most important design tasks. The design support unit can also prioritize assisting with creative design tasks if the craftsman is relaxed. For example, based on the craftsman's emotional data, the design support unit will prioritize assisting with creative design tasks if the craftsman is relaxed. The design support unit can also prioritize assisting with simpler design tasks if the craftsman is tired. For example, based on the craftsman's emotional data, the design support unit will prioritize assisting with simpler design tasks if the craftsman is tired. This allows for more efficient work by determining the priority of design support according to the craftsman's emotions. Some or all of the above processing in the design support unit may be performed using AI or not. For example, the design support department can input emotional data from craftsmen into an AI, which can then suggest the optimal design support priorities.

[0074] The design support department can prioritize providing highly relevant design patterns based on the geographical location information of craftsmen during the design support process. For example, if a craftsman is in a specific region, the design support department can suggest traditional design patterns for that region. The design support department uses AI to analyze the geographical location information of craftsmen and provide highly relevant design patterns. For example, the design support department suggests traditional design patterns for a region based on the craftsman's geographical location information. Furthermore, if a craftsman moves to a different region, the design support department can suggest design patterns based on the culture and customs of that region. For example, the design support department suggests design patterns based on the culture and customs of that region based on the craftsman's geographical location information. In addition, if a craftsman is targeting an international market, the design support department can suggest global design trends. For example, the design support department suggests global design trends based on the craftsman's geographical location information. This enables design that is appropriate for the region by providing design patterns based on the craftsman's geographical location information. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input the geographical location information of craftsmen into the AI, which can then propose the optimal design pattern.

[0075] The design support department can analyze the social media activities of craftsmen and provide relevant design patterns during the design support process. For example, the design support department can analyze works shared by craftsmen on social media and propose similar design patterns. The design support department can use AI to analyze the social media activities of craftsmen and provide relevant design patterns. For example, the design support department can propose similar design patterns based on works shared by craftsmen on social media. The design support department can also analyze the trends of design influencers followed by craftsmen and propose relevant design patterns. For example, the design support department can propose relevant design patterns based on the trends of design influencers followed by craftsmen. Furthermore, the design support department can analyze the activities of design communities in which craftsmen participate and propose relevant design patterns. For example, the design support department can propose relevant design patterns based on the activities of design communities in which craftsmen participate. This enables trend-aligned designs by providing design patterns based on the craftsmen's social media activities. Some or all of the above processes in the design support department may be performed using AI or not. For example, the design support department can input the craftsmen's social media activity data into AI, which can then propose the optimal design pattern.

[0076] The feedback unit can estimate the craftsman's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the craftsman is stressed, the feedback unit will prioritize providing positive feedback. The feedback unit estimates the craftsman's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the feedback unit estimates emotions using the craftsman's facial expressions and voice data as input and provides positive feedback if the craftsman is stressed. The feedback unit can also provide detailed feedback and specifically indicate areas for improvement if the craftsman is relaxed. For example, based on the craftsman's emotional data, the feedback unit provides detailed feedback if the craftsman is relaxed. The feedback unit can also provide concise and to-the-point feedback if the craftsman is tired. For example, based on the craftsman's emotional data, the feedback unit provides concise and to-the-point feedback if the craftsman is tired. This enables effective feedback by providing feedback expressions that are appropriate to the craftsman's emotions. 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 the craftsman's emotional data into an AI, which can then suggest the most appropriate way to express the feedback.

[0077] The feedback unit can adjust the level of detail in feedback based on the importance of the product. For example, the feedback unit can provide detailed feedback to high-value products to improve quality. The feedback unit uses AI to assess the importance of products and provide appropriate feedback detail. For example, the feedback unit can provide detailed feedback to high-value products to improve quality. The feedback unit can also provide standard feedback to general products to encourage efficient improvement. For example, the feedback unit can provide standard feedback to general products to encourage efficient improvement. Furthermore, the feedback unit can provide concise feedback to prototypes to support rapid improvement. For example, the feedback unit can provide concise feedback to prototypes to support rapid improvement. This enables efficient improvement by providing feedback detail according to the importance of the product. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input product importance data into AI, and the AI ​​can suggest the optimal level of feedback detail.

[0078] The feedback unit can apply different feedback algorithms depending on the product category during the feedback process. For example, the feedback unit provides feedback based on the characteristics of wood for woodworking products. The feedback unit uses AI to analyze the product category and apply an appropriate feedback algorithm. For example, the feedback unit provides feedback based on the characteristics of wood for woodworking products. The feedback unit can also provide feedback based on the firing process for ceramic products. For example, the feedback unit provides feedback based on the firing process for ceramic products. Furthermore, the feedback unit can provide feedback based on weaving and dyeing techniques for textile products. For example, the feedback unit provides feedback based on weaving and dyeing techniques for textile products. This enables appropriate feedback by providing feedback algorithms tailored to the product category. 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 product category data into AI, and the AI ​​can propose the optimal feedback algorithm.

[0079] The feedback unit can estimate the craftsman's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the craftsman is stressed, the feedback unit provides short, concise feedback. The feedback unit estimates the craftsman's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the feedback unit estimates emotions using the craftsman's facial expressions and voice data as input, and provides short, concise feedback if the craftsman is stressed. The feedback unit can also provide detailed feedback and specifically indicate areas for improvement if the craftsman is relaxed. For example, based on the craftsman's emotional data, the feedback unit provides detailed feedback if the craftsman is relaxed. The feedback unit can also provide concise, concise feedback if the craftsman is tired. For example, based on the craftsman's emotional data, the feedback unit provides concise, concise feedback if the craftsman is tired. This enables effective feedback by providing feedback of appropriate length according to the craftsman's emotions. 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 the craftsman's emotional data into an AI, which can then suggest the optimal length of feedback.

[0080] The feedback unit can prioritize feedback based on the product's production stage. For example, the feedback unit will provide priority feedback to products with approaching deadlines. The feedback unit uses AI to evaluate the product's production stage and provide appropriate feedback priorities. For example, the feedback unit will provide priority feedback to products with approaching deadlines. The feedback unit can also provide basic feedback to products that have just begun production. For example, the feedback unit will provide basic feedback to products that have just begun production. Furthermore, the feedback unit can provide detailed feedback to completed products and indicate areas for improvement for the next time. For example, the feedback unit will provide detailed feedback to completed products and indicate areas for improvement for the next time. This enables efficient improvement by providing feedback priorities according to the product's production stage. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input product production stage data into AI, and the AI ​​can propose the optimal feedback priority.

[0081] The feedback unit can adjust the order of feedback based on the relevance of the products during the feedback process. For example, the feedback unit can provide common feedback to similar products to encourage efficient improvement. The feedback unit uses AI to evaluate the relevance of products and provide an appropriate order of feedback. For example, the feedback unit can provide common feedback to similar products to encourage efficient improvement. The feedback unit can also provide individual feedback to products for specific customers to improve customer satisfaction. For example, the feedback unit can provide individual feedback to products for specific customers to improve customer satisfaction. Furthermore, the feedback unit can provide detailed feedback to new products to improve quality. For example, the feedback unit can provide detailed feedback to new products to improve quality. This enables efficient improvement by providing an order of feedback according to the relevance of the products. 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 product relevance data into the AI, and the AI ​​can suggest the optimal order of feedback.

[0082] The adjustment unit can estimate the emotions of the craftsman and change the adjustment method of the manufacturing process based on the estimated emotions of the craftsman. For example, if the craftsman is stressed, the adjustment unit will prioritize automated processes to reduce the workload. The adjustment unit estimates the emotions of the craftsman using an emotion estimation function, such as an emotion engine or generative AI. For example, the adjustment unit estimates emotions using the craftsman's facial expressions and voice data as input and prioritizes automated processes if the craftsman is stressed. The adjustment unit can also increase manual processes to stimulate creativity if the craftsman is relaxed. For example, based on the craftsman's emotional data, the adjustment unit increases manual processes if the craftsman is relaxed. The adjustment unit can also provide tools to streamline work if the craftsman is tired. For example, based on the craftsman's emotional data, the adjustment unit provides tools to streamline work if the craftsman is tired. This enables efficient manufacturing by providing an adjustment method of the manufacturing process that corresponds to the emotions of the craftsman. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the emotional data of the craftsman into the AI, which can then suggest the optimal adjustment method for the manufacturing process.

[0083] The adjustment unit can select the optimal adjustment method by referring to past manufacturing data during adjustment. For example, the adjustment unit proposes the optimal adjustment method based on past successful manufacturing processes. The adjustment unit uses AI to analyze past manufacturing data and select the optimal adjustment method. For example, the adjustment unit proposes the optimal adjustment method based on past successful manufacturing processes. The adjustment unit can also analyze past failed manufacturing processes and propose areas for improvement. For example, the adjustment unit proposes areas for improvement based on past failed manufacturing processes. Furthermore, the adjustment unit can propose adjustment methods suitable for specific materials or technologies based on past manufacturing data. For example, the adjustment unit proposes adjustment methods suitable for specific materials or technologies based on past manufacturing data. In this way, the optimal adjustment method can be provided by utilizing past manufacturing data. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input past manufacturing data into AI, and the AI ​​can propose the optimal adjustment method.

[0084] The adjustment unit can apply different adjustment algorithms depending on the product category during adjustment. For example, for woodworking products, the adjustment unit applies an adjustment algorithm based on the characteristics of the wood. The adjustment unit uses AI to analyze the product category and apply an appropriate adjustment algorithm. For example, the adjustment unit applies an adjustment algorithm based on the characteristics of the wood to woodworking products. The adjustment unit can also apply an adjustment algorithm based on the firing process to ceramic products. For example, the adjustment unit applies an adjustment algorithm based on the firing process to ceramic products. Furthermore, the adjustment unit can apply an adjustment algorithm based on the weaving method and dyeing technique to textile products. For example, the adjustment unit applies an adjustment algorithm based on the weaving method and dyeing technique to textile products. This enables appropriate adjustment by providing an adjustment algorithm according to the product category. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input product category data into AI, and the AI ​​can propose the optimal adjustment algorithm.

[0085] The adjustment unit can estimate the emotions of the craftsman and determine the priority of adjustments based on the estimated emotions. For example, if the craftsman is feeling stressed, the adjustment unit will prioritize the most important adjustment tasks. The adjustment unit estimates the emotions of the craftsman using an emotion estimation function, such as an emotion engine or generative AI. For example, the adjustment unit estimates emotions using the craftsman's facial expressions and voice data as input, and if the craftsman is feeling stressed, it will prioritize the most important adjustment tasks. The adjustment unit can also prioritize creative adjustment tasks if the craftsman is relaxed. For example, based on the craftsman's emotional data, the adjustment unit will prioritize creative adjustment tasks if the craftsman is relaxed. The adjustment unit can also prioritize simple adjustment tasks if the craftsman is tired. For example, based on the craftsman's emotional data, the adjustment unit will prioritize simple adjustment tasks if the craftsman is tired. This enables efficient adjustments by providing adjustment priorities according to the craftsman's emotions. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the craftsman's emotional data into the AI, which can then suggest the optimal adjustment priorities.

[0086] The adjustment unit can determine the priority of adjustments based on the product's manufacturing date during the adjustment process. For example, the adjustment unit prioritizes adjustments for products with approaching deadlines. The adjustment unit uses AI to evaluate the product's manufacturing date and provide appropriate adjustment priorities. For example, the adjustment unit prioritizes adjustments for products with approaching deadlines. The adjustment unit can also perform basic adjustments for products that have just begun production. For example, the adjustment unit performs basic adjustments for products that have just begun production. Furthermore, the adjustment unit can perform detailed adjustments for completed products and suggest areas for improvement for the next production cycle. For example, the adjustment unit performs detailed adjustments for completed products and suggests areas for improvement for the next production cycle. This enables efficient adjustments by providing adjustment priorities according to the product's manufacturing date. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input product manufacturing date data into the AI, which can then suggest the optimal adjustment priorities.

[0087] The adjustment unit can adjust the order of adjustments based on the relevance of the products during the adjustment process. For example, the adjustment unit can perform common adjustments on similar products to promote efficient improvement. The adjustment unit uses AI to evaluate the relevance of products and provide an appropriate order of adjustments. For example, the adjustment unit can perform common adjustments on similar products to promote efficient improvement. The adjustment unit can also perform individual adjustments on products for specific customers to improve customer satisfaction. For example, the adjustment unit can perform individual adjustments on products for specific customers to improve customer satisfaction. Furthermore, the adjustment unit can perform detailed adjustments on new products to improve quality. For example, the adjustment unit can perform detailed adjustments on new products to improve quality. This enables efficient adjustments by providing an order of adjustments according to the relevance of the products. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input product relevance data into the AI, and the AI ​​can propose the optimal order of adjustments.

[0088] The digitalization unit can estimate the emotions of the craftsman and adjust the digitalization method based on the estimated emotions. For example, if the craftsman is stressed, the digitalization unit can provide a simple digitalization tool to reduce the workload. The digitalization unit estimates the craftsman's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the digitalization unit estimates emotions using the craftsman's facial expressions and voice data as input and provides a simple digitalization tool if the craftsman is stressed. The digitalization unit can also provide detailed digitalization options to stimulate creativity if the craftsman is relaxed. For example, based on the craftsman's emotional data, the digitalization unit provides detailed digitalization options if the craftsman is relaxed. The digitalization unit can also prioritize providing automated tools to streamline the work if the craftsman is tired. For example, based on the craftsman's emotional data, the digitalization unit provides automated tools if the craftsman is tired. This enables efficient digitalization by providing a digitalization method that is tailored to the craftsman's emotions. Some or all of the above processing in the digitalization unit may be performed using AI or not. For example, the digitalization department can input the emotional data of craftsmen into an AI, which can then propose the optimal digitalization method.

[0089] The Digitalization Department can select the optimal digitalization method by referring to past technical data during the digitalization process. For example, the Digitalization Department can propose the optimal digitalization method based on past successful digitalization projects. The Digitalization Department can analyze past technical data using AI and select the optimal digitalization method. For example, the Digitalization Department can propose the optimal digitalization method based on past successful digitalization projects. The Digitalization Department can also analyze past unsuccessful digitalization projects and propose areas for improvement. For example, the Digitalization Department can propose areas for improvement based on past unsuccessful digitalization projects. Furthermore, the Digitalization Department can propose digitalization methods suitable for specific materials or technologies based on past technical data. For example, the Digitalization Department can propose digitalization methods suitable for specific materials or technologies based on past technical data. In this way, the Digitalization Department can provide the optimal digitalization method by utilizing past technical data. Some or all of the above processes in the Digitalization Department may be performed using AI or not. For example, the Digitalization Department can input past technical data into AI, and the AI ​​can propose the optimal digitalization method.

[0090] The digitization unit can estimate the emotions of craftsmen and determine the priority of digitization based on the estimated emotions. For example, if a craftsman is feeling stressed, the digitization unit will prioritize the most important digitization tasks. The digitization unit estimates the emotions of craftsmen using an emotion estimation function, such as an emotion engine or generative AI. For example, the digitization unit estimates emotions using the craftsman's facial expressions and voice data as input, and if the craftsman is feeling stressed, it will prioritize the most important digitization tasks. The digitization unit can also prioritize creative digitization tasks if the craftsman is relaxed. For example, based on the craftsman's emotional data, the digitization unit will prioritize creative digitization tasks if the craftsman is relaxed. The digitization unit can also prioritize simple digitization tasks if the craftsman is tired. For example, based on the craftsman's emotional data, the digitization unit will prioritize simple digitization tasks if the craftsman is tired. This enables efficient digitization by providing digitization priorities according to the craftsman's emotions. Some or all of the above processing in the digitization unit may be performed using AI or not. For example, the digitalization department can input the emotional data of craftsmen into an AI, which can then suggest the optimal priorities for digitalization.

[0091] The digitalization department can adjust the order of digitalization based on the relevance of technologies during the digitalization process. For example, the digitalization department can perform common digitalization on similar technologies to promote efficient improvement. The digitalization department uses AI to evaluate the relevance of technologies and provide an appropriate order of digitalization. For example, the digitalization department can perform common digitalization on similar technologies to promote efficient improvement. The digitalization department can also perform individual digitalization on technologies for specific customers to improve customer satisfaction. For example, the digitalization department can perform individual digitalization on technologies for specific customers to improve customer satisfaction. Furthermore, the digitalization department can perform detailed digitalization on new technologies to improve quality. For example, the digitalization department can perform detailed digitalization on new technologies to improve quality. This enables efficient digitalization by providing an order of digitalization according to the relevance of technologies. Some or all of the above processes in the digitalization department may be performed using AI or not. For example, the digitalization department can input technology relevance data into AI, and the AI ​​can propose the optimal order of digitalization.

[0092] The customization unit can estimate the emotions of the craftsman and adjust the customization method based on the estimated emotions. For example, if the craftsman is stressed, the customization unit can provide a simple customization tool to reduce the workload. The customization unit estimates the craftsman's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the customization unit estimates emotions using the craftsman's facial expressions and voice data as input, and provides a simple customization tool if the craftsman is stressed. The customization unit can also provide detailed customization options to stimulate creativity if the craftsman is relaxed. For example, based on the craftsman's emotional data, the customization unit provides detailed customization options if the craftsman is relaxed. The customization unit can also prioritize providing automated tools to streamline the work if the craftsman is tired. For example, based on the craftsman's emotional data, the customization unit provides automated tools if the craftsman is tired. This enables efficient customization by providing a customization method that is appropriate to the craftsman's emotions. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the craftsman's emotional data into AI, and the AI ​​can suggest the optimal customization method.

[0093] The customization department can select the optimal customization method by referring to past customization data during the customization process. For example, the customization department can propose the optimal customization method based on past successful customization projects. The customization department can analyze past customization data using AI to select the optimal customization method. For example, the customization department can propose the optimal customization method based on past successful customization projects. The customization department can also analyze past unsuccessful customization projects and propose areas for improvement. For example, the customization department can propose areas for improvement based on past unsuccessful customization projects. Furthermore, the customization department can propose customization methods suitable for specific materials or technologies based on past customization data. For example, the customization department can propose customization methods suitable for specific materials or technologies based on past customization data. In this way, the optimal customization method can be provided by utilizing past customization data. Some or all of the above processes in the customization department may be performed using AI or not. For example, the customization department can input past customization data into AI, and the AI ​​can propose the optimal customization method.

[0094] The customization unit can estimate the emotions of the craftsman and determine the priority of customization based on the estimated emotions. For example, if the craftsman is stressed, the customization unit will prioritize the most important customization tasks. The customization unit estimates the craftsman's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the customization unit estimates emotions using the craftsman's facial expressions and voice data as input, and if the craftsman is stressed, it will prioritize the most important customization tasks. The customization unit can also prioritize creative customization tasks if the craftsman is relaxed. For example, based on the craftsman's emotional data, the customization unit will prioritize creative customization tasks if the craftsman is relaxed. The customization unit can also prioritize simple customization tasks if the craftsman is tired. For example, based on the craftsman's emotional data, the customization unit will prioritize simple customization tasks if the craftsman is tired. This enables efficient customization by providing customization priorities according to the craftsman's emotions. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization department can input the emotional data of craftsmen into an AI, which can then suggest the optimal customization priorities.

[0095] The customization unit can adjust the order of customization based on the relevance of products during the customization process. For example, the customization unit can perform common customizations on similar products to promote efficient improvement. The customization unit uses AI to evaluate the relevance of products and provide an appropriate customization order. For example, the customization unit can perform common customizations on similar products to promote efficient improvement. The customization unit can also perform individual customizations on products for specific customers to improve customer satisfaction. For example, the customization unit can perform individual customizations on products for specific customers to improve customer satisfaction. Furthermore, the customization unit can perform detailed customizations on new products to improve quality. For example, the customization unit can perform detailed customizations on new products to improve quality. This enables efficient customization by providing a customization order according to the relevance of products. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input product relevance data into the AI, and the AI ​​can propose the optimal customization order.

[0096] The Ministry of Education can estimate the emotions of artisans and adjust the content of educational programs based on those estimated emotions. For example, if an artisan is stressed, the Ministry of Education can provide a simple educational program to reduce the learning burden. The Ministry of Education uses emotion estimation functions, such as an emotion engine or generative AI, to estimate the emotions of artisans. For example, the Ministry of Education can estimate emotions using the artisan's facial expressions and voice data as input and provide a simple educational program if they are stressed. The Ministry of Education can also provide detailed educational options to stimulate learning motivation if the artisan is relaxed. For example, based on the artisan's emotional data, the Ministry of Education can provide detailed educational options if they are relaxed. The Ministry of Education can also prioritize providing automated tools to streamline learning if the artisan is tired. For example, based on the artisan's emotional data, the Ministry of Education can provide automated tools if they are tired. This allows for efficient learning by providing educational program content tailored to the artisan's emotions. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input the artisan's emotional data into an AI, which can then suggest the optimal educational program content.

[0097] The Ministry of Education can select the optimal teaching method by referring to past educational data during the teaching process. For example, the Ministry of Education can propose the optimal teaching method based on past successful educational programs. The Ministry of Education can use AI to analyze past educational data and select the optimal teaching method. For example, the Ministry of Education can propose the optimal teaching method based on past successful educational programs. The Ministry of Education can also analyze past unsuccessful educational programs and propose areas for improvement. For example, the Ministry of Education can propose areas for improvement based on past unsuccessful educational programs. Furthermore, the Ministry of Education can propose teaching methods suitable for specific materials or technologies based on past educational data. For example, the Ministry of Education can propose teaching methods suitable for specific materials or technologies based on past educational data. In this way, the Ministry of Education can provide the optimal teaching method by utilizing past educational data. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input past educational data into AI, and the AI ​​can propose the optimal teaching method.

[0098] The Ministry of Education can estimate the emotions of artisans and prioritize educational programs based on those estimated emotions. For example, if an artisan is stressed, the Ministry of Education will prioritize the most important educational tasks. The Ministry of Education estimates the emotions of artisans using an emotion estimation function, such as an emotion engine or generative AI. For example, the Ministry of Education estimates emotions using the artisan's facial expressions and voice data as input, and prioritizes the most important educational tasks if the artisan is stressed. The Ministry of Education can also prioritize creative educational tasks if the artisan is relaxed. For example, based on the artisan's emotional data, the Ministry of Education will prioritize creative educational tasks if the artisan is relaxed. The Ministry of Education can also prioritize easy educational tasks if the artisan is tired. For example, based on the artisan's emotional data, the Ministry of Education will prioritize easy educational tasks if the artisan is tired. This allows for efficient learning by providing educational program priorities that correspond to the artisan's emotions. Some or all of the above processing in the Ministry of Education may be performed using AI or not. For example, the Ministry of Education could input emotional data of artisans into an AI, which could then suggest the optimal priorities for educational programs.

[0099] The Ministry of Education can adjust the order of educational programs based on the relevance of technologies during education. For example, the Ministry of Education can implement common educational programs for similar technologies to promote efficient learning. The Ministry of Education can use AI to evaluate the relevance of technologies and provide an appropriate order of educational programs. For example, the Ministry of Education can implement common educational programs for similar technologies to promote efficient learning. The Ministry of Education can also implement individualized educational programs for technologies aimed at specific customers to improve customer satisfaction. For example, the Ministry of Education can implement individualized educational programs for technologies aimed at specific customers to improve customer satisfaction. Furthermore, the Ministry of Education can implement detailed educational programs for new technologies to improve quality. For example, the Ministry of Education can implement detailed educational programs for new technologies to improve quality. This enables efficient learning by providing an order of educational programs according to the relevance of technologies. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input technology relevance data into AI, and the AI ​​can propose the optimal order of educational programs.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The design support department can provide different support methods depending on the skill level of the craftsman. For example, beginner craftsmen can be provided with basic design guidelines and step-by-step instructions. Intermediate craftsmen can be provided with advanced design options and customizable tools. Advanced craftsmen can be provided with a highly flexible design environment and advanced analysis tools. This allows for efficient design support by providing support methods tailored to the skill level of the craftsman.

[0102] The digitalization department can estimate the emotions of craftsmen and adjust the digitalization method based on those estimates. For example, if a craftsman is stressed, a simple digitalization tool can be provided to reduce their workload. If a craftsman is relaxed, a detailed digitalization option can be provided to unleash their creativity. If a craftsman is tired, automated tools to streamline the work can be prioritized. This allows for efficient digitalization by providing a digitalization method tailored to the emotions of the craftsmen.

[0103] The customization department can select the optimal customization method by referring to past customization data. For example, it can propose the best customization method based on past successful customization projects. It can also analyze past unsuccessful customization projects and propose improvements. It can also propose customization methods suitable for specific materials or technologies. In this way, by utilizing past customization data, it can provide the optimal customization method.

[0104] The Ministry of Education can estimate the emotions of artisans and adjust the content of educational programs based on those estimates. For example, if an artisan is stressed, a simple educational program can be provided to reduce the learning burden. If an artisan is relaxed, detailed educational options can be provided to stimulate their motivation to learn. If an artisan is tired, automated tools to streamline learning can be prioritized. This allows for more efficient learning by providing educational programs tailored to the emotions of the artisans.

[0105] The feedback system can adjust the level of detail in feedback based on the importance of the product. For example, high-value products can receive detailed feedback to improve quality. General products can receive standard feedback to facilitate efficient improvement. Prototypes can receive concise feedback to support rapid improvement. This allows for efficient improvement by providing feedback with a level of detail appropriate to the importance of the product.

[0106] The adjustment unit can estimate the emotions of the craftsman and change the manufacturing process adjustments based on the estimated emotions. For example, if the craftsman is stressed, automated processes to reduce the workload can be prioritized. If the craftsman is relaxed, manual processes can be increased to stimulate creativity. If the craftsman is tired, tools to streamline the work can be provided. In this way, by providing a manufacturing process adjustment method that responds to the emotions of the craftsman, efficient manufacturing becomes possible.

[0107] The design support department can prioritize providing highly relevant design patterns based on the geographical location of craftsmen. For example, if a craftsman is in a specific region, it can suggest traditional design patterns from that region. If a craftsman moves to a different region, it can suggest design patterns based on the culture and customs of that region. If a craftsman is targeting an international market, it can suggest global design trends. In this way, providing design patterns based on the geographical location of craftsmen enables designs that are appropriate for the region.

[0108] The feedback system can estimate the worker's emotions and adjust the way feedback is presented based on those emotions. For example, if a worker is stressed, positive feedback can be prioritized. If a worker is relaxed, detailed feedback can be provided, specifically outlining areas for improvement. If a worker is tired, concise and to-the-point feedback can be provided. This allows for more effective feedback by providing feedback tailored to the worker's emotions.

[0109] The adjustment unit can select the optimal adjustment method by referring to past manufacturing data. For example, it can propose the optimal adjustment method based on past successful manufacturing processes. It can also analyze past failed manufacturing processes and propose improvements. It can also propose adjustment methods suitable for specific materials or technologies. In this way, by utilizing past manufacturing data, the optimal adjustment method can be provided.

[0110] The customization section can estimate the craftsman's emotions and adjust the customization method based on those emotions. For example, if the craftsman is stressed, a simple customization tool can be provided to reduce their workload. If the craftsman is relaxed, detailed customization options can be provided to unleash their creativity. If the craftsman is tired, automated tools to streamline the work can be prioritized. This allows for efficient customization by providing a customization method that is tailored to the craftsman's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The Design Support Department provides data-driven design support tools. When craftsmen design products, the Design Support Department proposes optimal designs based on past design data. Using AI, it can analyze design data and suggest design improvements to craftsmen. For example, it can propose new designs based on past successful design patterns, and analyze unsuccessful design patterns to suggest improvements. It can also propose design patterns suitable for specific materials or technologies. Step 2: The Feedback Department provides real-time feedback based on the design support provided by the Design Support Department. The Feedback Department provides real-time feedback as the craftsman proceeds with the manufacturing process. It can use AI to monitor the manufacturing process and suggest areas for improvement to the craftsman. For example, it can monitor the progress of the manufacturing process in real time and suggest areas for improvement. It can also analyze manufacturing process data and suggest ways to optimize the manufacturing process. Step 3: The adjustment unit automatically adjusts the manufacturing process based on the feedback provided by the feedback unit. The adjustment unit automatically makes adjustments at each stage of the manufacturing process. AI can be used to monitor the manufacturing process and automatically adjust it. For example, it can monitor the progress of the manufacturing process in real time and automatically adjust it. It can also analyze manufacturing process data and optimize the manufacturing process.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the design support unit, feedback unit, adjustment unit, digitalization unit, customization unit, and education unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the design support unit is implemented by the control unit 46A of the smart device 14 and proposes an optimal design based on past design data. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents improvements to the manufacturing process in real time. The adjustment unit is implemented by, for example, the control unit 46A of the smart device 14 and performs automatic adjustment of the manufacturing process. The digitalization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and saves the craftsman's work as a digital record. The customization unit is implemented by, for example, the control unit 46A of the smart device 14 and proposes a product design that meets customer requirements. The education unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides training programs to craftsmen. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the design support unit, feedback unit, adjustment unit, digitalization unit, customization unit, and education unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the design support unit is implemented by the control unit 46A of the smart glasses 214 and proposes an optimal design based on past design data. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and presents improvements to the manufacturing process in real time. The adjustment unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs automatic adjustment of the manufacturing process. The digitalization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and saves the craftsman's work as a digital record. The customization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes a product design that meets customer requirements. The education unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides training programs to craftsmen. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 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.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the 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.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 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.

[0148] Each of the multiple elements described above, including the design support unit, feedback unit, adjustment unit, digitalization unit, customization unit, and education unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the design support unit is implemented by the control unit 46A of the headset terminal 314 and proposes an optimal design based on past design data. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents improvements to the manufacturing process in real time. The adjustment unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs automatic adjustment of the manufacturing process. The digitalization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and saves the craftsman's work as a digital record. The customization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes a product design that meets customer requirements. The education unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides training programs to craftsmen. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the design support unit, feedback unit, adjustment unit, digitalization unit, customization unit, and education unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the design support unit is implemented by the control unit 46A of the robot 414 and proposes an optimal design based on past design data. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents improvements to the manufacturing process in real time. The adjustment unit is implemented by, for example, the control unit 46A of the robot 414 and performs automatic adjustment of the manufacturing process. The digitalization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and saves the worker's work as a digital record. The customization unit is implemented by, for example, the control unit 46A of the robot 414 and proposes a product design that meets customer requirements. The education unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides training programs to workers. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) The Design Support Department provides data-driven design support tools, A feedback unit that provides real-time feedback based on the design support provided by the aforementioned design support unit, The system includes an adjustment unit that automatically adjusts the manufacturing process based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The company has a digitalization department that digitizes the skills of craftsmen and promotes knowledge sharing. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a customization section that provides customization and personalization capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The Department of Education is dedicated to digitizing education and training programs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned design support unit, We estimate the emotions of the craftsmen and adjust the design support approach based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned design support unit, We analyze past design data and propose the optimal design pattern. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned design support unit, During design support, we provide different support methods depending on the skill level of the craftsman. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned design support unit, The system estimates the emotions of the craftsmen and prioritizes design support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned design support unit, We prioritize providing highly relevant design patterns based on the geographical location information of the craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned design support unit, Analyze the social media activities of artisans and provide relevant design patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned feedback unit is The system estimates the emotions of the craftsman and adjusts the way feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback unit is The system estimates the craftsman's emotions and adjusts the length of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback unit is When providing feedback, we prioritize the feedback based on the product's production date. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is When providing feedback, we adjust the order of feedback based on its relevance to the product. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, Estimate the emotions of the craftsmen and change how the manufacturing process is adjusted based on the estimated emotions of the craftsmen. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, During adjustment, the optimal adjustment method is selected by referring to past manufacturing data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, During adjustment, different adjustment algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, The system estimates the emotions of the craftsmen and determines the priority of adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, During adjustments, the priority of adjustments is determined based on the product's manufacturing date. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, During adjustment, adjust the order of adjustments based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned digitization unit, The system estimates the emotions of the craftsmen and adjusts the digitization method based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned digitization unit, During the digitization process, the optimal digitization method is selected by referring to past technical data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned digitization unit, The system estimates the emotions of the craftsmen and determines the priority of digitalization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned digitization unit, When digitizing, adjust the order of digitization based on the relevance of the technologies. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned customization unit is It estimates the emotions of the craftsman and adjusts the customization method based on the estimated emotions of the craftsman. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned customization unit is During customization, past customization data is referenced to select the optimal customization method. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned customization unit is The system estimates the emotions of the craftsmen and determines the priority of customization based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned customization unit is During customization, the order of customizations is adjusted based on the relevance of the products. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned Ministry of Education, The program estimates the emotions of the craftsmen and adjusts the content of the training program based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned Ministry of Education, During education, the optimal teaching method is selected by referring to past educational data. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned Ministry of Education, The system estimates the emotions of the craftsmen and prioritizes educational programs based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned Ministry of Education, During education, the order of educational programs is adjusted based on the relevance of the technology. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Design Support Department provides data-driven design support tools, A feedback unit that provides real-time feedback based on the design support provided by the aforementioned design support unit, The system includes an adjustment unit that automatically adjusts the manufacturing process based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features.

2. The company has a digitalization department that digitizes the skills of craftsmen and promotes knowledge sharing. The system according to feature 1.

3. It features a customization section that provides customization and personalization capabilities. The system according to feature 1.

4. The Department of Education is dedicated to digitizing education and training programs. The system according to feature 1.

5. The aforementioned design support unit, We estimate the emotions of the craftsmen and adjust the design support approach based on those estimated emotions. The system according to feature 1.

6. The aforementioned design support unit, We analyze past design data and propose the optimal design pattern. The system according to feature 1.

7. The aforementioned design support unit, During design support, we provide different support methods depending on the skill level of the craftsman. The system according to feature 1.

8. The aforementioned design support unit, The system estimates the emotions of the craftsmen and determines the priority of design support based on those estimated emotions. The system according to feature 1.

9. The aforementioned design support unit, We prioritize providing highly relevant design patterns based on the geographical location information of the craftsmen. The system according to feature 1.

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