System

Abstract

An object of a system according to an embodiment is to efficiently learn craftsmanship and create a new technique.SOLUTION: A system includes an input unit, an analysis unit, and an output unit. The input unit inputs a moving image of a craftsmanship. The analysis unit analyzes the moving image input by the input unit and learns the details of the technique. The output unit performs an original output based on the technique learned by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not efficiently pass on craftsmanship or create new technologies, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently learn craftsmanship and create new technology. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and an output unit. The input unit inputs a video of a craftsman's skill. The analysis unit analyzes the video input by the input unit and learns the details of the skill. The output unit produces original output based on the skill learned by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently learn craftsmanship and create new techniques. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A robot artisan system according to an embodiment of the present invention automatically inputs videos of artisanship, analyzes them using a generation AI, learns the techniques, and produces original output. The robot artisan system inputs videos of artisanship, analyzes them, and learns the details of the techniques to produce original output. For example, the robot artisan system inputs videos of artisanship. For example, the robot artisan system inputs videos of artisans with Wajima lacquerware skills at work. This video is analyzed by a generation AI. The robot artisan system then uses the generation AI to analyze the input videos and learn the details of the techniques. The generation AI analyzes the movements and procedures in the videos to understand the details of the techniques. For example, the generation AI learns how to apply Wajima lacquerware and the drying procedure. The robot artisan system then produces original output based on the techniques learned by the generation AI. For example, the Wajima lacquerware techniques learned by the generation AI can be used to create lacquerware with a new design. This allows the robot artisan system to preserve artisanship and generate new ideas and designs. This allows the robot artisan system to pass on traditional techniques without letting them die out. For example, even if a craftsman is affected by a major earthquake or other disaster, the robot craftsman can inherit his skills and preserve traditional techniques. Furthermore, the robot craftsman system can generate new ideas and designs, contributing to the development of traditional techniques.

[0029] The robot craftsman system according to the embodiment includes an input unit, an analysis unit, and an output unit. The input unit inputs videos of craftsmanship. Examples of videos of craftsmanship include, but are not limited to, woodworking, pottery, and cooking. The input unit, for example, inputs videos of craftsmanship to a generation AI. The input unit can also efficiently input videos of craftsmanship using the generation AI. For example, the input unit causes the generation AI to analyze the content of the video and input it in an appropriate format. The analysis unit uses the generation AI to analyze the video input by the input unit and learn the details of the technique. Examples of the details of the technique include, but are not limited to, steps, tools used, and technical tips. For example, the analysis unit causes the generation AI to analyze the actions and steps in the video and understand the details of the technique. The analysis unit can also efficiently learn the details of the technique by using the generation AI to analyze the actions and steps in the video. For example, the analysis unit causes the generation AI to analyze the actions and steps in the video and understand the details of the technique. The output unit generates original output based on the technique learned by the analysis unit. Original output includes, but is not limited to, new designs, improved techniques, and the like. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. The output unit can also use the generation AI to generate original output based on the learned techniques. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. In this way, the robot artisan system according to the embodiment can analyze videos of artisanal techniques, learn techniques, and generate original output.

[0030] The input unit can input videos of craftsmanship using a generation AI. The generation AI includes, for example, a specific algorithm or model, but is not limited to such examples. The input unit efficiently inputs videos of craftsmanship using, for example, a generation AI. For example, the input unit uses a generation AI to analyze the content of the video and input it in an appropriate format. The input unit can also automatically input videos of craftsmanship using a generation AI. For example, the input unit uses a generation AI to analyze the content of the video and input it in an appropriate format. In this way, videos of craftsmanship can be efficiently input using a generation AI. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs videos of craftsmanship using a generation AI and analyzes the content of the video.

[0031] The analysis unit can analyze the video using a generative AI to learn the details of the technology. The generative AI includes, for example, a specific algorithm or model, but is not limited to such examples. The analysis unit can analyze the video using a generative AI to efficiently learn the details of the technology. For example, the analysis unit causes the generative AI to analyze the actions and procedures in the video to understand the details of the technology. The analysis unit can also analyze the actions and procedures in the video to efficiently learn the details of the technology. For example, the analysis unit causes the generative AI to analyze the actions and procedures in the video to understand the details of the technology. In this way, the generative AI can be used to efficiently learn the details of the technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit uses the generative AI to analyze the video to learn the details of the technology.

[0032] The analysis unit can analyze actions or procedures in a video to understand the details of a technique. Actions or procedures include, but are not limited to, types of actions and steps of a procedure. The analysis unit can also analyze actions and procedures in a video to understand the details of a technique. For example, the analysis unit can analyze actions and procedures in a video to understand the details of a technique. In this way, by analyzing actions and procedures in a video, the details of a technique can be deeply understood. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze actions and procedures in a video to understand the details of a technique.

[0033] The output unit can generate original output based on the learned technology. Examples of original output include, but are not limited to, new designs and improved technology. For example, the output unit creates lacquerware with a new design based on the technology learned by the generative AI. The output unit can also generate original output based on the learned technology using the generative AI. For example, the output unit creates lacquerware with a new design based on the technology learned by the generative AI. In this way, new designs and ideas can be created by generating original output based on the learned technology. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit generates original output based on the technology learned using the generative AI.

[0034] The output unit can create lacquerware with a new design using the learned techniques. Examples of lacquerware with a new design include, but are not limited to, shapes, patterns, and colors. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. The output unit can also create lacquerware with a new design based on the learned techniques using the generation AI. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. In this way, lacquerware with a new design can be created using the learned techniques. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI.

[0035] The input unit can analyze the user's past video input history and select an appropriate input method. Suitable input methods include, but are not limited to, voice input and text input. For example, the input unit can analyze the trends of videos the user has watched in the past and preferentially input related videos. The input unit can also input videos of a similar genre based on the craftsmanship genre that the user has previously preferred to watch. For example, the input unit selects an optimal video length based on the length of videos the user has previously watched. This allows the optimal input method to be selected by analyzing the past video input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's past video input history using a generation AI and select an appropriate input method.

[0036] When inputting videos, the input unit can filter them based on the user's current projects and areas of interest. Examples of current projects and areas of interest include, but are not limited to, project management tools and survey results. For example, the input unit preferentially inputs videos of craftsmanship related to a project the user is currently working on. The input unit can also filter and input videos related to the user's areas of interest. For example, the input unit inputs related videos based on keywords searched by the user in the past. This allows highly relevant videos to be input by filtering videos based on the user's current projects and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit filters videos based on the user's current projects and areas of interest using generation AI.

[0037] When inputting video, the input unit can select an appropriate input means according to the user's input method. Input methods include, but are not limited to, for example, voice, text, and image. For example, when a user issues instructions by voice, the input unit inputs video by prioritizing voice input. Furthermore, when a user issues instructions by text, the input unit can also input video by prioritizing text input. For example, when a user issues instructions using images, the input unit inputs video by prioritizing image input. This enables efficient video input by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit selects an appropriate input means according to the user's input method using a generation AI.

[0038] When inputting videos, the input unit can prioritize inputting highly relevant videos based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific region, the input unit prioritizes inputting videos related to craftsmanship in that region. Furthermore, when the user is traveling, the input unit can prioritize inputting videos related to craftsmanship at the travel destination. For example, when the user is at home, the input unit prioritizes inputting videos of craftsmanship that can be practiced at home. In this way, highly relevant videos can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit uses generation AI to prioritize inputting highly relevant videos by taking the user's geographical location information into consideration.

[0039] When a video is input, the input unit can analyze the user's social media activity and input related videos. Social media activity includes, for example, the content of posts and reactions from followers, but is not limited to these examples. The input unit, for example, inputs videos of craftsmanship related to videos shared by the user on social media. The input unit can also input videos of craftsmanship that the user's social media followers are interested in. For example, the input unit analyzes the content of the user's social media posts and inputs videos of related craftsmanship. In this way, related videos can be efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit analyzes the user's social media activity using generation AI and inputs related videos.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting a video. Past feedback includes, but is not limited to, survey results, user comments, and the like. For example, the input unit analyzes trends in videos that users have previously rated highly and inputs related videos. The input unit can also analyze trends in videos that users have previously rated poorly and exclude similar videos. For example, the input unit suggests an optimal input method based on the user's past feedback. In this way, the optimal input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit customizes the input method by reflecting the user's past feedback using a generation AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the technology. The importance of a technology includes, but is not limited to, the scope of application and the influence of the technology. For example, the analysis unit provides detailed analysis results for technologies with high importance. The analysis unit can also provide concise analysis results for technologies with low importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the technology. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the technology using a generation AI.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the technique. Technique categories include, but are not limited to, woodworking, pottery, cooking, etc. For example, in the case of woodworking techniques, the analysis unit applies an analysis algorithm based on the characteristics of wood. Furthermore, in the case of metal processing techniques, the analysis unit can also apply an analysis algorithm based on the characteristics of metal. For example, in the case of pottery techniques, the analysis unit applies an analysis algorithm based on the characteristics of clay. In this way, applying different analysis algorithms depending on the category of the technique enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit applies different analysis algorithms depending on the category of the technique using generation AI.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, databases, etc. For example, the analysis unit can improve the analysis accuracy of similar techniques based on the user's past analysis results. The analysis unit can also identify points prone to error from the user's past analysis results and improve the analysis accuracy. For example, the analysis unit analyzes the user's past analysis results and selects the optimal analysis algorithm. This allows the analysis accuracy to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit improves the analysis accuracy by referring to the user's past analysis results using a generation AI.

[0044] During analysis, the analysis unit can determine analysis priorities based on the time when the video was shot. The time when the video was shot includes, but is not limited to, metadata, timestamps, etc. The analysis unit, for example, prioritizes analyzing the most recent video to learn the latest techniques. The analysis unit can also prioritize analyzing older videos to learn traditional techniques. For example, the analysis unit dynamically adjusts the analysis priorities based on the time when the video was shot. This enables efficient analysis by determining the analysis priorities based on the time when the video was shot. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit determines the analysis priorities based on the time when the video was shot using a generation AI.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the technologies. The relevance of the technologies includes, but is not limited to, for example, the range of application and influence of the technologies. For example, the analysis unit prioritizes analysis of highly relevant technologies to efficiently learn. The analysis unit can also postpone analysis of less relevant technologies. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the technologies. This enables efficient analysis by adjusting the order of analysis based on the relevance of the technologies. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit adjusts the order of analysis based on the relevance of the technologies using a generation AI.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, qualifications and years of experience. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results that use technical terminology appropriately. For example, if the user is an advanced user, the analysis unit can provide detailed analysis results that use a lot of technical terminology. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise using a generation AI.

[0047] The output unit can adjust the level of detail of the output based on the importance of the learned technology at the time of output. The importance of a technology includes, but is not limited to, the scope of application and influence of the technology. For example, the output unit provides detailed output for technologies with high importance. The output unit can also provide concise output for technologies with low importance. For example, the output unit dynamically adjusts the level of detail of the output according to the importance of the technology. This enables efficient output by adjusting the level of detail of the output based on the importance of the learned technology. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit adjusts the level of detail of the output based on the importance of the technology learned using the generation AI.

[0048] The output unit can apply different output methods depending on the skill category at the time of output. Skill categories include, but are not limited to, woodworking, pottery, cooking, etc. For example, in the case of woodworking, the output unit can apply an output method based on the characteristics of wood. Furthermore, in the case of metal processing, the output unit can also apply an output method based on the characteristics of metal. For example, in the case of pottery, the output unit can apply an output method based on the characteristics of clay. In this way, applying different output methods depending on the skill category enables more accurate output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit applies different output methods depending on the skill category using generative AI.

[0049] The output unit can improve the accuracy of the output by referring to the user's past output results. Past output results include, but are not limited to, output history, databases, etc. For example, the output unit can improve the output accuracy of similar techniques based on the user's past output results. The output unit can also identify error-prone points from the user's past output results and improve the output accuracy. For example, the output unit can analyze the user's past output results and select the optimal output method. This can improve the output accuracy by referring to the user's past output results. Some or all of the above-described processing in the output unit can be performed, for example, using AI or without AI. For example, the output unit can improve the output accuracy by referring to the user's past output results using a generation AI.

[0050] At the time of output, the output unit can select the optimal output method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the output unit performs output according to the characteristics of that area. Furthermore, if the user is traveling, the output unit can also perform output according to the characteristics of the travel destination. For example, if the user is at home, the output unit performs output that can be practiced at home. In this way, the optimal output method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit uses a generation AI to select the optimal output method by taking into account the user's geographical location information.

[0051] At the time of output, the output unit can analyze the user's social media activity and generate related output. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, the output unit generates output related to the content shared by the user on social media. The output unit can also generate output related to the content in which the user's social media followers are interested. For example, the output unit analyzes the content posted by the user on social media and generates related output. This allows the user's social media activity to be analyzed, thereby efficiently generating related output. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit analyzes the user's social media activity using a generation AI and generates related output.

[0052] The output unit can customize the output method by reflecting the user's past feedback at the time of output. Past feedback includes, but is not limited to, survey results, user comments, etc. For example, the output unit analyzes the trend of outputs that the user has previously rated highly and performs related output. The output unit can also analyze the trend of outputs that the user has previously rated poorly and exclude similar outputs. For example, the output unit suggests an optimal output method based on the user's past feedback. In this way, the optimal output method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit customizes the output method by reflecting the user's past feedback using a generation AI.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The robot craftsman system can further include a cloud collaboration unit. The cloud collaboration unit collaborates with databases and analysis resources on the cloud to improve system performance. For example, the cloud collaboration unit retrieves technical details from a large database on the cloud and provides them to the analysis unit. The cloud collaboration unit can also improve the analysis unit's processing speed by utilizing high-performance analysis resources on the cloud. This allows the system to efficiently analyze more data and generate high-quality output. Furthermore, the cloud collaboration unit can adjust the use of cloud resources based on the user's emotions. For example, when the user is concentrating, cloud resources can be maximized, and when the user is relaxed, resource use can be reduced.

[0055] The robot craftsman system can further include a security unit. The security unit protects data and communications within the system and ensures user privacy. For example, the security unit encrypts data and performs access control to prevent unauthorized access from outside. The security unit can also protect users' personal information and ensure privacy. This allows users to use the system with peace of mind. Furthermore, the security unit can adjust the security level based on the user's emotions. For example, if the user feels anxious, the security level can be increased, and if the user feels relaxed, the security level can be kept moderate.

[0056] The robot craftsman system can further include an energy management unit. The energy management unit optimizes the energy consumption of the entire system to achieve efficient operation. For example, the energy management unit monitors the operating status of the analysis unit and output unit and optimizes energy consumption. The energy management unit can also adjust the energy supply according to the operating status of the system. This allows the system to improve energy efficiency and achieve sustainable operation. Furthermore, the energy management unit can adjust the energy consumption optimization method based on the user's emotions. For example, when the user is concentrating, energy consumption can be minimized, and when the user is relaxed, energy consumption can be kept moderate.

[0057] The robot craftsman system can further include a maintenance unit. The maintenance unit monitors the status of each part of the system and performs maintenance as necessary. For example, the maintenance unit monitors the operating status of the analysis unit and output unit and issues an alert if an abnormality is detected. The maintenance unit can also manage regular maintenance schedules to ensure stable operation of the system. This ensures that the system is always operating in optimal condition and can provide high-quality output. Furthermore, the maintenance unit can adjust the timing of maintenance based on the user's emotions. For example, if the user is concentrating, maintenance can be postponed, and if the user is relaxed, maintenance can be performed as a priority.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The input unit inputs a video of a craftsmanship. The video of a craftsmanship may include, but is not limited to, woodworking, pottery, cooking, etc. The input unit can efficiently input the video of the craftsmanship using a generative AI. For example, the generative AI analyzes the content of the video and inputs it in an appropriate format. Step 2: The analysis unit uses the generation AI to analyze the video input by the input unit and learn the details of the technique. The details of the technique include, but are not limited to, the steps, tools used, and technical tips. The analysis unit uses the generation AI to analyze the actions and steps in the video and understand the details of the technique. Step 3: The output unit generates original output based on the techniques learned by the analysis unit. Original output includes, but is not limited to, new designs and improved techniques. For example, a new lacquerware design may be created based on the techniques learned by the generative AI.

[0060] (Example 2) A robot artisan system according to an embodiment of the present invention automatically inputs videos of artisanship, analyzes them using a generation AI, learns the techniques, and produces original output. The robot artisan system inputs videos of artisanship, analyzes them, and learns the details of the techniques to produce original output. For example, the robot artisan system inputs videos of artisanship. For example, the robot artisan system inputs videos of artisans with Wajima lacquerware skills at work. This video is analyzed by a generation AI. The robot artisan system then uses the generation AI to analyze the input videos and learn the details of the techniques. The generation AI analyzes the movements and procedures in the videos to understand the details of the techniques. For example, the generation AI learns how to apply Wajima lacquerware and the drying procedure. The robot artisan system then produces original output based on the techniques learned by the generation AI. For example, the Wajima lacquerware techniques learned by the generation AI can be used to create lacquerware with a new design. This allows the robot artisan system to preserve artisanship and generate new ideas and designs. This allows the robot artisan system to pass on traditional techniques without letting them die out. For example, even if a craftsman is affected by a major earthquake or other disaster, the robot craftsman can inherit his skills and preserve traditional techniques. Furthermore, the robot craftsman system can generate new ideas and designs, contributing to the development of traditional techniques.

[0061] The robot craftsman system according to the embodiment includes an input unit, an analysis unit, and an output unit. The input unit inputs videos of craftsmanship. Examples of videos of craftsmanship include, but are not limited to, woodworking, pottery, and cooking. The input unit, for example, inputs videos of craftsmanship to a generation AI. The input unit can also efficiently input videos of craftsmanship using the generation AI. For example, the input unit causes the generation AI to analyze the content of the video and input it in an appropriate format. The analysis unit uses the generation AI to analyze the video input by the input unit and learn the details of the technique. Examples of the details of the technique include, but are not limited to, steps, tools used, and technical tips. For example, the analysis unit causes the generation AI to analyze the actions and steps in the video and understand the details of the technique. The analysis unit can also efficiently learn the details of the technique by using the generation AI to analyze the actions and steps in the video. For example, the analysis unit causes the generation AI to analyze the actions and steps in the video and understand the details of the technique. The output unit generates original output based on the technique learned by the analysis unit. Original output includes, but is not limited to, new designs, improved techniques, and the like. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. The output unit can also use the generation AI to generate original output based on the learned techniques. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. In this way, the robot artisan system according to the embodiment can analyze videos of artisanal techniques, learn techniques, and generate original output.

[0062] The input unit can input videos of craftsmanship using a generation AI. The generation AI includes, for example, a specific algorithm or model, but is not limited to such examples. The input unit efficiently inputs videos of craftsmanship using, for example, a generation AI. For example, the input unit uses a generation AI to analyze the content of the video and input it in an appropriate format. The input unit can also automatically input videos of craftsmanship using a generation AI. For example, the input unit uses a generation AI to analyze the content of the video and input it in an appropriate format. In this way, videos of craftsmanship can be efficiently input using a generation AI. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs videos of craftsmanship using a generation AI and analyzes the content of the video.

[0063] The analysis unit can analyze the video using a generative AI to learn the details of the technology. The generative AI includes, for example, a specific algorithm or model, but is not limited to such examples. The analysis unit can analyze the video using a generative AI to efficiently learn the details of the technology. For example, the analysis unit causes the generative AI to analyze the actions and procedures in the video to understand the details of the technology. The analysis unit can also analyze the actions and procedures in the video to efficiently learn the details of the technology. For example, the analysis unit causes the generative AI to analyze the actions and procedures in the video to understand the details of the technology. In this way, the generative AI can be used to efficiently learn the details of the technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit uses the generative AI to analyze the video to learn the details of the technology.

[0064] The analysis unit can analyze actions or procedures in a video to understand the details of a technique. Actions or procedures include, but are not limited to, types of actions and steps of a procedure. The analysis unit can also analyze actions and procedures in a video to understand the details of a technique. For example, the analysis unit can analyze actions and procedures in a video to understand the details of a technique. In this way, by analyzing actions and procedures in a video, the details of a technique can be deeply understood. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze actions and procedures in a video to understand the details of a technique.

[0065] The output unit can generate original output based on the learned technology. Examples of original output include, but are not limited to, new designs and improved technology. For example, the output unit creates lacquerware with a new design based on the technology learned by the generative AI. The output unit can also generate original output based on the learned technology using the generative AI. For example, the output unit creates lacquerware with a new design based on the technology learned by the generative AI. In this way, new designs and ideas can be created by generating original output based on the learned technology. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit generates original output based on the technology learned using the generative AI.

[0066] The output unit can create lacquerware with a new design using the learned techniques. Examples of lacquerware with a new design include, but are not limited to, shapes, patterns, and colors. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. The output unit can also create lacquerware with a new design based on the learned techniques using the generation AI. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI. In this way, lacquerware with a new design can be created using the learned techniques. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit creates lacquerware with a new design based on the techniques learned by the generation AI.

[0067] The input unit can estimate the user's emotions and adjust the timing of video input based on the estimated user emotions. Examples of user emotions include, but are not limited to, concentration, fatigue, and excitement. For example, when the user is concentrating, the input unit continuously inputs multiple videos of craftsmanship. Furthermore, when the user is tired, the input unit can input videos with short breaks in between. For example, when the user is excited, the input unit preferentially inputs videos that attract the user's attention. This enables more effective learning by adjusting the timing of video input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or without AI. For example, the input unit estimates the user's emotions using a generation AI and adjusts the timing of video input based on the estimated emotions.

[0068] The input unit can analyze the user's past video input history and select an appropriate input method. Suitable input methods include, but are not limited to, voice input and text input. For example, the input unit can analyze the trends of videos the user has watched in the past and preferentially input related videos. The input unit can also input videos of a similar genre based on the craftsmanship genre that the user has previously preferred to watch. For example, the input unit selects an optimal video length based on the length of videos the user has previously watched. This allows the optimal input method to be selected by analyzing the past video input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can analyze the user's past video input history using a generation AI and select an appropriate input method.

[0069] When inputting videos, the input unit can filter them based on the user's current projects and areas of interest. Examples of current projects and areas of interest include, but are not limited to, project management tools and survey results. For example, the input unit preferentially inputs videos of craftsmanship related to a project the user is currently working on. The input unit can also filter and input videos related to the user's areas of interest. For example, the input unit inputs related videos based on keywords searched by the user in the past. This allows highly relevant videos to be input by filtering videos based on the user's current projects and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit filters videos based on the user's current projects and areas of interest using generation AI.

[0070] When inputting video, the input unit can select an appropriate input means according to the user's input method. Input methods include, but are not limited to, for example, voice, text, and image. For example, when a user issues instructions by voice, the input unit inputs video by prioritizing voice input. Furthermore, when a user issues instructions by text, the input unit can also input video by prioritizing text input. For example, when a user issues instructions using images, the input unit inputs video by prioritizing image input. This enables efficient video input by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit selects an appropriate input means according to the user's input method using a generation AI.

[0071] The input unit can estimate the user's emotions and determine the priority of videos to be input based on the estimated user's emotions. Examples of user emotions include, but are not limited to, relaxation, concentration, and excitement. For example, when the user is relaxed, the input unit prioritizes input of videos that have a relaxing effect. Furthermore, when the user is concentrating, the input unit can prioritize input of videos that enhance concentration. For example, when the user is excited, the input unit prioritizes input of videos that maintain excitement. This enables more effective learning by determining the priority of videos based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit estimates the user's emotions using a generation AI and determines the priority of videos based on the estimated emotions.

[0072] When inputting videos, the input unit can prioritize inputting highly relevant videos based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific region, the input unit prioritizes inputting videos related to craftsmanship in that region. Furthermore, when the user is traveling, the input unit can prioritize inputting videos related to craftsmanship at the travel destination. For example, when the user is at home, the input unit prioritizes inputting videos of craftsmanship that can be practiced at home. In this way, highly relevant videos can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit uses generation AI to prioritize inputting highly relevant videos by taking the user's geographical location information into consideration.

[0073] When a video is input, the input unit can analyze the user's social media activity and input related videos. Social media activity includes, for example, the content of posts and reactions from followers, but is not limited to these examples. The input unit, for example, inputs videos of craftsmanship related to videos shared by the user on social media. The input unit can also input videos of craftsmanship that the user's social media followers are interested in. For example, the input unit analyzes the content of the user's social media posts and inputs videos of related craftsmanship. In this way, related videos can be efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit analyzes the user's social media activity using generation AI and inputs related videos.

[0074] The input unit can customize the input method by reflecting the user's past feedback when inputting a video. Past feedback includes, but is not limited to, survey results, user comments, and the like. For example, the input unit analyzes trends in videos that users have previously rated highly and inputs related videos. The input unit can also analyze trends in videos that users have previously rated poorly and exclude similar videos. For example, the input unit suggests an optimal input method based on the user's past feedback. In this way, the optimal input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit customizes the input method by reflecting the user's past feedback using a generation AI.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. Examples of user emotions include, but are not limited to, relaxation, hurry, and excitement. For example, if the user is relaxed, the analysis unit displays the analysis results at a leisurely pace. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results that focus on the main points. For example, if the user is excited, the analysis unit displays the analysis results with a visually stimulating effect. This allows for adjusting the presentation of the analysis based on the user's emotions, thereby providing more effective analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit estimates the user's emotions using a generation AI and adjusts the presentation of the analysis based on the estimated emotions.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the technology. The importance of a technology includes, but is not limited to, the scope of application and the influence of the technology. For example, the analysis unit provides detailed analysis results for technologies with high importance. The analysis unit can also provide concise analysis results for technologies with low importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis according to the importance of the technology. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the technology using a generation AI.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the technique. Technique categories include, but are not limited to, woodworking, pottery, cooking, etc. For example, in the case of woodworking techniques, the analysis unit applies an analysis algorithm based on the characteristics of wood. Furthermore, in the case of metal processing techniques, the analysis unit can also apply an analysis algorithm based on the characteristics of metal. For example, in the case of pottery techniques, the analysis unit applies an analysis algorithm based on the characteristics of clay. In this way, applying different analysis algorithms depending on the category of the technique enables more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit applies different analysis algorithms depending on the category of the technique using generation AI.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis history, databases, etc. For example, the analysis unit can improve the analysis accuracy of similar techniques based on the user's past analysis results. The analysis unit can also identify points prone to error from the user's past analysis results and improve the analysis accuracy. For example, the analysis unit analyzes the user's past analysis results and selects the optimal analysis algorithm. This allows the analysis accuracy to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit improves the analysis accuracy by referring to the user's past analysis results using a generation AI.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Examples of user emotions include, but are not limited to, hurry, relaxation, and excitement. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. For example, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for adjusting the length of the analysis based on the user's emotions to provide more effective analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions using a generation AI and adjust the length of the analysis based on the estimated emotions.

[0080] During analysis, the analysis unit can determine analysis priorities based on the time when the video was shot. The time when the video was shot includes, but is not limited to, metadata, timestamps, etc. The analysis unit, for example, prioritizes analyzing the most recent video to learn the latest techniques. The analysis unit can also prioritize analyzing older videos to learn traditional techniques. For example, the analysis unit dynamically adjusts the analysis priorities based on the time when the video was shot. This enables efficient analysis by determining the analysis priorities based on the time when the video was shot. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit determines the analysis priorities based on the time when the video was shot using a generation AI.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the technologies. The relevance of the technologies includes, but is not limited to, for example, the range of application and influence of the technologies. For example, the analysis unit prioritizes analysis of highly relevant technologies to efficiently learn. The analysis unit can also postpone analysis of less relevant technologies. For example, the analysis unit dynamically adjusts the order of analysis based on the relevance of the technologies. This enables efficient analysis by adjusting the order of analysis based on the relevance of the technologies. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit adjusts the order of analysis based on the relevance of the technologies using a generation AI.

[0082] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, qualifications and years of experience. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the user is an intermediate user, the analysis unit can provide analysis results that use technical terminology appropriately. For example, if the user is an advanced user, the analysis unit can provide detailed analysis results that use a lot of technical terminology. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without AI. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise using a generation AI.

[0083] The output unit can estimate the user's emotions and adjust the output method based on the estimated user emotions. Examples of user emotions include, but are not limited to, relaxation, hurry, and excitement. For example, if the user is relaxed, the output unit can perform output at a leisurely pace. Also, if the user is in a hurry, the output unit can perform output quickly. For example, if the user is excited, the output unit can perform output with a visually stimulating effect. This enables more effective output by adjusting the output method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the output unit can be performed using, for example, AI, or without AI. For example, the output unit can estimate the user's emotions using a generation AI and adjust the output method based on the estimated emotions.

[0084] The output unit can adjust the level of detail of the output based on the importance of the learned technology at the time of output. The importance of a technology includes, but is not limited to, the scope of application and influence of the technology. For example, the output unit provides detailed output for technologies with high importance. The output unit can also provide concise output for technologies with low importance. For example, the output unit dynamically adjusts the level of detail of the output according to the importance of the technology. This enables efficient output by adjusting the level of detail of the output based on the importance of the learned technology. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit adjusts the level of detail of the output based on the importance of the technology learned using the generation AI.

[0085] The output unit can apply different output methods depending on the skill category at the time of output. Skill categories include, but are not limited to, woodworking, pottery, cooking, etc. For example, in the case of woodworking, the output unit can apply an output method based on the characteristics of wood. Furthermore, in the case of metal processing, the output unit can also apply an output method based on the characteristics of metal. For example, in the case of pottery, the output unit can apply an output method based on the characteristics of clay. In this way, applying different output methods depending on the skill category enables more accurate output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit applies different output methods depending on the skill category using generative AI.

[0086] The output unit can improve the accuracy of the output by referring to the user's past output results. Past output results include, but are not limited to, output history, databases, etc. For example, the output unit can improve the output accuracy of similar techniques based on the user's past output results. The output unit can also identify error-prone points from the user's past output results and improve the output accuracy. For example, the output unit can analyze the user's past output results and select the optimal output method. This can improve the output accuracy by referring to the user's past output results. Some or all of the above-described processing in the output unit can be performed, for example, using AI or without AI. For example, the output unit can improve the output accuracy by referring to the user's past output results using a generation AI.

[0087] The output unit can estimate the user's emotions and determine output priorities based on the estimated user emotions. Examples of user emotions include, but are not limited to, relaxation, concentration, and excitement. For example, when the user is relaxed, the output unit prioritizes output that has a relaxing effect. Furthermore, when the user is concentrating, the output unit can prioritize output that enhances concentration. For example, when the user is excited, the output unit prioritizes output that maintains excitement. This enables more effective output by determining output priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or without AI. For example, the output unit estimates the user's emotions using a generation AI and determines output priorities based on the estimated emotions.

[0088] At the time of output, the output unit can select the optimal output method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the output unit performs output according to the characteristics of that area. Furthermore, if the user is traveling, the output unit can also perform output according to the characteristics of the travel destination. For example, if the user is at home, the output unit performs output that can be practiced at home. In this way, the optimal output method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit uses a generation AI to select the optimal output method by taking into account the user's geographical location information.

[0089] At the time of output, the output unit can analyze the user's social media activity and generate related output. Social media activity includes, but is not limited to, for example, the content of posts and the reactions of followers. For example, the output unit generates output related to the content shared by the user on social media. The output unit can also generate output related to the content in which the user's social media followers are interested. For example, the output unit analyzes the content posted by the user on social media and generates related output. This allows the user's social media activity to be analyzed, thereby efficiently generating related output. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit analyzes the user's social media activity using a generation AI and generates related output.

[0090] The output unit can customize the output method by reflecting the user's past feedback at the time of output. Past feedback includes, but is not limited to, survey results, user comments, etc. For example, the output unit analyzes the trend of outputs that the user has previously rated highly and performs related output. The output unit can also analyze the trend of outputs that the user has previously rated poorly and exclude similar outputs. For example, the output unit suggests an optimal output method based on the user's past feedback. In this way, the optimal output method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit customizes the output method by reflecting the user's past feedback using a generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, and output unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can capture a video of the craftsman's technique using the camera 42 of the smart device 14 and send it to the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the input video using a generative AI and learns the details of the technique. The output unit, realized, for example, by the control unit 46A of the smart device 14, creates lacquerware with a new design based on the learned technique. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, and output unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can capture a video of the craftsmanship using the camera 42 of the smart glasses 214 and transmit it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input video using a generative AI to learn the details of the technique. The output unit is realized, for example, by the control unit 46A of the smart glasses 214, and creates lacquerware with a new design based on the learned technique. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, and output unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit can use the camera 42 of the headset terminal 314 to capture a video of the craftsman's skills and send it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input video using a generative AI to learn the details of the technique. The output unit is realized, for example, by the control unit 46A of the headset terminal 314, and creates lacquerware with a new design based on the learned technique. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, analysis unit, and output unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can use the camera 42 of the robot 414 to capture a video of the craftsmanship and send it to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input video using a generative AI to learn the details of the technique. The output unit is realized, for example, by the control unit 46A of the robot 414, and creates lacquerware with a new design based on the learned technique.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The robot artisan system may further include a feedback collection unit. The feedback collection unit collects feedback from users and provides it to the analysis unit. For example, when a user rates a generated output, the feedback collection unit collects the rating. The feedback collection unit may also collect user comments and suggestions for improvement and provide them to the analysis unit. This allows the analysis unit to improve technical learning based on the collected feedback and generate higher quality output. Furthermore, the feedback collection unit may estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, detailed feedback may be requested, and if the user is in a hurry, brief feedback may be requested.

[0093] The robot artisan system may further include a database unit. The database unit stores details of the techniques learned by the analysis unit and a history of output. For example, details of Wajima lacquerware techniques learned by the analysis unit are stored in the database unit. The database unit may also store improvements to designs and techniques of previously generated output. This allows the analysis unit to learn new techniques while referring to past data, and the output unit to generate new output based on past designs and techniques. Furthermore, the database unit may adjust how data is presented based on the user's emotions. For example, if the user is excited, visually appealing data may be presented, and if the user is relaxed, detailed data may be presented.

[0094] The robot artisan system can further include a prediction unit. The prediction unit predicts future technology trends and demands based on the technologies learned by the analysis unit. For example, it predicts future trends in lacquerware designs based on the Wajima lacquerware technologies learned by the analysis unit. The prediction unit can also predict the technologies and designs that a user will next desire based on the user's past output history. This allows the output unit to generate new output based on the predicted trends and demands. Furthermore, the prediction unit can adjust the way in which the prediction results are presented based on the user's emotions. For example, it can provide detailed prediction results when the user is concentrating, and provide concise prediction results when the user is relaxed.

[0095] The robot craftsman system may further include a customization unit. The customization unit customizes the content of the output based on the user's preferences and requests. For example, if the user prefers a particular design or technology, the customization unit generates the output based on that preference. The customization unit may also adjust the content of the output based on the user's past feedback. This makes it possible to provide output that meets the user's individual requests. Furthermore, the customization unit may also adjust the customization method based on the user's emotions. For example, if the user is excited, a visually stimulating design may be provided, and if the user is relaxed, a calming design may be provided.

[0096] The robot craftsman system may further include an interaction unit. The interaction unit learns skills and generates output through dialogue with the user. For example, when the user asks a question, the interaction unit responds based on the learning results of the analysis unit. The interaction unit can also adjust the content of the output according to the user's instructions. This allows the user to learn skills and generate output while interacting with the system. Furthermore, the interaction unit can adjust the method of interaction based on the user's emotions. For example, if the user is relaxed, the interaction can be conducted at a leisurely pace, and if the user is in a hurry, the interaction can be conducted quickly.

[0097] The robot craftsman system can further include a cloud collaboration unit. The cloud collaboration unit collaborates with databases and analysis resources on the cloud to improve system performance. For example, the cloud collaboration unit retrieves technical details from a large database on the cloud and provides them to the analysis unit. The cloud collaboration unit can also improve the analysis unit's processing speed by utilizing high-performance analysis resources on the cloud. This allows the system to efficiently analyze more data and generate high-quality output. Furthermore, the cloud collaboration unit can adjust the use of cloud resources based on the user's emotions. For example, when the user is concentrating, cloud resources can be maximized, and when the user is relaxed, resource use can be reduced.

[0098] The robot craftsman system can further include a security unit. The security unit protects data and communications within the system and ensures user privacy. For example, the security unit encrypts data and performs access control to prevent unauthorized access from outside. The security unit can also protect users' personal information and ensure privacy. This allows users to use the system with peace of mind. Furthermore, the security unit can adjust the security level based on the user's emotions. For example, if the user feels anxious, the security level can be increased, and if the user feels relaxed, the security level can be kept moderate.

[0099] The robot craftsman system can further include an energy management unit. The energy management unit optimizes the energy consumption of the entire system to achieve efficient operation. For example, the energy management unit monitors the operating status of the analysis unit and output unit and optimizes energy consumption. The energy management unit can also adjust the energy supply according to the operating status of the system. This allows the system to improve energy efficiency and achieve sustainable operation. Furthermore, the energy management unit can adjust the energy consumption optimization method based on the user's emotions. For example, when the user is concentrating, energy consumption can be minimized, and when the user is relaxed, energy consumption can be kept moderate.

[0100] The robot craftsman system can further include a maintenance unit. The maintenance unit monitors the status of each part of the system and performs maintenance as necessary. For example, the maintenance unit monitors the operating status of the analysis unit and output unit and issues an alert if an abnormality is detected. The maintenance unit can also manage regular maintenance schedules to ensure stable operation of the system. This ensures that the system is always operating in optimal condition and can provide high-quality output. Furthermore, the maintenance unit can adjust the timing of maintenance based on the user's emotions. For example, if the user is concentrating, maintenance can be postponed, and if the user is relaxed, maintenance can be performed as a priority.

[0101] The robot craftsman system may further include a notification unit. The notification unit notifies the user of the system status, analysis results, and output progress. For example, the notification unit notifies the user when the analysis unit completes learning a technique. The notification unit may also notify the user when the output unit generates a new design. This allows the user to understand the system status and progress in real time. Furthermore, the notification unit may adjust the notification method based on the user's emotions. For example, if the user is relaxed, a gentle notification sound may be used, and if the user is concentrating, a visual notification may be prioritized.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The input unit inputs a video of a craftsmanship. The video of a craftsmanship may include, but is not limited to, woodworking, pottery, cooking, etc. The input unit can efficiently input the video of the craftsmanship using a generative AI. For example, the generative AI analyzes the content of the video and inputs it in an appropriate format. Step 2: The analysis unit uses the generation AI to analyze the video input by the input unit and learn the details of the technique. The details of the technique include, but are not limited to, the steps, tools used, and technical tips. The analysis unit uses the generation AI to analyze the actions and steps in the video and understand the details of the technique. Step 3: The output unit generates original output based on the techniques learned by the analysis unit. Original output includes, but is not limited to, new designs and improved techniques. For example, a new lacquerware design may be created based on the techniques learned by the generative AI.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a 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.

[0142] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting videos of craftsmanship; an analysis unit that analyzes the video input by the input unit and learns details of the technology; an output unit that generates an original output based on the technology learned by the analysis unit. A system characterized by:

2. The input unit Inputting craftsmanship videos using generative AI 2. The system of claim 1.

3. The analysis unit Using generative AI to analyze videos and learn technical details 2. The system of claim 1.

4. The analysis unit Analyze actions or steps in the video to understand technical details 2. The system of claim 1.

5. The output unit Create original output based on the skills learned 2. The system of claim 1.

6. The output unit Create new lacquerware designs using the techniques learned 2. The system of claim 1.

7. The input unit Estimate the user's emotions and adjust the timing of video input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past video input history and select the appropriate input method 2. The system of claim 1.

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