System

Abstract

An object of the system according to the embodiment is to accumulate craftsman skills as digital data and support inheritance, digitization, and proposal of new products.SOLUTION: A system according to an embodiment includes a technology accumulation unit, an education support unit, a digitization unit, and a new product suggestion unit. The skill accumulation unit accumulates the craftsman skill as digital AI by using the generation date. An education support part supports follow-up education on the basis of the craftsman technique accumulated by the technique accumulation part. The digitalization section digitalizes a part of the work on the basis of the craftsman technology accumulated by the technology accumulation section. A new commodity proposal part proposes a new commodity on the basis of the craftsman technique accumulated by the technique accumulation part.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 adequately preserve artisan skills or digitize them, so there is room for improvement.

[0005] The system of the embodiment aims to accumulate artisan skills as digital data and support their inheritance, digitalization, and new product proposals. [Means for solving the problem]

[0006] The system according to the embodiment comprises a technology accumulation unit, an education support unit, a digitization unit, and a new product proposal unit. The technology accumulation unit accumulates artisan skills as digital data using generative AI. The education support unit supports the education of successors based on the artisan skills accumulated by the technology accumulation unit. The digitization unit digitizes part of the work based on the artisan skills accumulated by the technology accumulation unit. The new product proposal unit proposes new products based on the artisan skills accumulated by the technology accumulation unit. [Effects of the Invention]

[0007] The system according to the embodiment stores artisan skills as digital data and can support the succession, digitization, and proposal of new products. [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) The support system according to an embodiment of the present invention is a system that accumulates artisan skills in a generating AI and provides work support. This support system accumulates artisan skills, supports the education of successors, digitalizes work, and proposes new products. This allows the support system to efficiently pass on artisan skills, keep factories open, and propose new products.

[0029] The support system according to the embodiment includes a technology accumulation unit, an education support unit, a digitization unit, and a new product proposal unit. The technology accumulation unit accumulates artisan skills as digital data using a generation AI. For example, the work performed by artisans is recorded using a camera or sensor, and the generation AI analyzes the data to extract key points of the technology. The technology accumulation unit also extracts key points of the technology based on video and sensor data recording the artisans' work. The education support unit supports successor training based on the artisan skills accumulated by the technology accumulation unit. For example, the generation AI generates videos and simulations explaining artisan skills and provides them to young artisans. The education support unit can also generate simulations using the generation AI to allow young artisans to practice their skills in a virtual environment. The digitization unit digitizes part of the work based on the artisan skills accumulated by the technology accumulation unit. For example, the generation AI generates automated work programs based on the artisan skills and executes them on machines or robots. The digitization unit can also generate quality control programs based on the artisan skills and automatically check product quality. The new product proposal unit proposes new products based on the artisan skills accumulated by the technology accumulation unit. For example, the generation AI analyzes artisan skills and market trend data to generate new product ideas. The new product proposal unit can also analyze artisan skills and consumer preference data to generate new product ideas that are popular with consumers. As a result, the support system according to the embodiment can efficiently accumulate artisan skills, support successor training, digitize work, and propose new products.

[0030] The technology accumulation unit uses high-precision sensors to record the subtle movements of craftsmen while they work, and the generation AI analyzes the data to extract subtle differences in their movements. For example, the technology accumulation unit uses high-precision sensors to record the hand movements and force applied when a craftsman carves wood, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in carving speed and angle are saved as data. The technology accumulation unit also uses high-precision sensors to record the hand movements of a potter when he turns a potter's wheel, and the generation AI analyzes the data to extract subtle differences in their movements. For example, changes in hand position and pressure are saved as data. The technology accumulation unit also uses high-precision sensors to record the hammer movements of a swordsmith when he forges a sword, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in striking force and angle are saved as data. This allows the craftsman's subtle movements to be recorded and analyzed with high precision.

[0031] The technology accumulation unit uses sensors to record the craftsman's work environment (temperature, humidity, sound, etc.), and the generation AI can integrate and analyze this environmental data with the work data. For example, the technology accumulation unit uses sensors to record the temperature and humidity of a woodworker's work environment, and the generation AI analyzes this data and integrates it with the work data. For example, it analyzes the impact of changes in temperature and humidity on the work. The technology accumulation unit also uses sensors to record the sounds of a potter's work environment, and the generation AI analyzes this data and integrates it with the work data. For example, it analyzes the impact of the sound of a potter's wheel turning and the sounds made during work on the craft. The technology accumulation unit also uses sensors to record the temperature and sound of a swordsmith's work environment, and the generation AI analyzes this data and integrates it with the work data. For example, it analyzes the impact of the temperature in the forge and the sound of hammer strikes on work. This allows the work environment and work data to be integrated and analyzed.

[0032] The technology accumulation unit can apply the accumulated artisanal techniques to techniques in different professions (e.g., cooking, painting, music, etc.), and the generative AI can analyze and accumulate each technique. For example, to accumulate a chef's techniques in the generative AI, the technology accumulation unit records the cooking process with cameras and sensors and analyzes the data to extract the key points of the technique. For example, how to use a knife and how to adjust the heat are saved as data. To accumulate painting techniques in the generative AI, the technology accumulation unit records the painter's brush movements with cameras and sensors and analyzes the data to extract the key points of the technique. For example, it saves the patterns of pen pressure and brush movement as data. To accumulate musical techniques in the generative AI, the technology accumulation unit records the performer's movements with cameras and sensors and analyzes the data to extract the key points of the technique. For example, it saves the finger movements and the rhythm of the performance as data. This allows the accumulation of techniques from different professions.

[0033] The technology accumulation unit can apply the accumulated artisanal techniques to techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), allowing the generative AI to analyze and accumulate each technique. For example, to accumulate traditional Japanese craft techniques in the generative AI, the technology accumulation unit records the work of artisans with cameras and sensors, analyzes the data, and extracts the key points of the technique. For example, lacquering and pottery techniques are saved as data. To accumulate Western craft techniques in the generative AI, the technology accumulation unit records the work of artisans with cameras and sensors, analyzes the data, and extracts the key points of the technique. For example, glassblowing and woodworking techniques are saved as data. To accumulate techniques from different cultural spheres in the generative AI, the technology accumulation unit records the work of artisans with cameras and sensors, analyzes the data, and extracts the key points of the technique. For example, African carving techniques and Indian dyeing techniques are saved as data. This allows technologies from different cultural spheres to be accumulated.

[0034] The education support department can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, the education support department uses a generation AI to monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it analyzes video of work and specifically points out areas for improvement. The education support department also builds a system in which the generation AI monitors the progress of young artisans in acquiring skills in real time and provides individually optimized feedback. For example, it provides feedback based on data collected by sensors. The education support department also builds a system in which the generation AI monitors the progress of young artisans in acquiring skills in real time and provides individually optimized feedback. For example, it detects mistakes made during learning and immediately suggests ways to correct them. This makes it possible to monitor the progress of young artisans in acquiring skills in real time and provide optimal feedback.

[0035] The education support department can analyze common mistakes made by young craftsmen when they acquire skills and generate training programs to prevent those mistakes. For example, the education support department uses a generation AI to analyze common mistakes made by young craftsmen when they acquire skills and generates training programs to prevent those mistakes. For example, it identifies patterns of mistakes based on past data and proposes countermeasures. The education support department also builds a system in which a generation AI analyzes common mistakes made by young craftsmen when they acquire skills and generates training programs to prevent those mistakes. For example, it provides training using simulations. The education support department also uses a generation AI to analyze common mistakes made by young craftsmen when they acquire skills and generates training programs to prevent those mistakes. For example, it identifies the causes of mistakes and proposes specific ways to improve them. This makes it possible to generate training programs to prevent common mistakes.

[0036] The Education Support Department can provide support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, the Education Support Department provides support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, it provides technical explanation videos and simulations online. The Education Support Department also builds a system that enables young craftsmen to acquire skills even from remote locations through the online platform. For example, it enables real-time feedback and Q&A. The Education Support Department also provides support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, it provides a technical practice environment using virtual reality. This allows young craftsmen in remote locations to receive support for successor training.

[0037] The Education Support Department can apply support for successor education to education in different professions (e.g., cooking, painting, music, etc.), with the generative AI generating videos and simulations that explain each technique. For example, the Education Support Department could use the generative AI to generate videos and simulations that explain cooking techniques, allowing young chefs to watch the videos and learn the techniques. For example, it could explain how to use a knife or cooking steps. The Education Support Department could also use the generative AI to generate videos and simulations that explain painting techniques, allowing young painters to watch the videos and learn the techniques. For example, it could explain how to use a brush or how to mix colors. The Education Support Department could also use the generative AI to generate videos and simulations that explain musical techniques, allowing young performers to watch the videos and learn the techniques. For example, it could explain how to play an instrument or how to keep rhythm. This allows the system to be applied to education in different professions.

[0038] The digitization unit generates automated work programs based on artisan skills, and can build a system in which multiple robots work in cooperation. For example, the digitization unit uses a generation AI to generate automated work programs based on artisan skills and build a system in which multiple robots work in cooperation. For example, multiple robots share the work of carving wood. The digitization unit also develops a system in which a generation AI generates automated work programs based on artisan skills and builds a system in which multiple robots work in cooperation. For example, multiple robots work together to mold pottery. The digitization unit also develops a system in which a generation AI generates automated work programs based on artisan skills and builds a system in which multiple robots work in cooperation. For example, multiple robots work together to forge swords. This makes it possible to build a system in which multiple robots work in cooperation.

[0039] The digitization unit can generate automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, the digitization unit uses a generation AI to generate automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, if an abnormality occurs during wood carving work, it is automatically corrected. The digitization unit can also build a system in which a generation AI generates automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, if an abnormality occurs during pottery molding work, it is automatically corrected. The digitization unit can also build a system in which a generation AI generates automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, if an abnormality occurs during a sword forging work, it is automatically corrected. This can add a function to detect abnormalities during work and automatically correct them.

[0040] The digitization unit can apply the digitization of work to tasks in different occupations (for example, cooking, painting, music, etc.), and the generative AI can generate programs to automate each task. For example, the digitization unit generates a program to automate cooking tasks using the generative AI, performing automated cooking tasks based on the skills of a chef. For example, it automates how to use a knife and how to adjust the heat. The digitization unit also generates a program to automate painting tasks using the generative AI, performing automated drawing tasks based on the skills of a painter. For example, it automates brush movements and how to mix colors. The digitization unit also generates a program to automate music tasks using the generative AI, performing automated performance tasks based on the skills of a performer. For example, it automates finger movements and how to keep rhythm. This makes it possible to automate tasks in different occupations.

[0041] The digitization department can apply the digitization of work to tasks involving techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), and the generative AI can generate programs to automate each task. For example, the digitization department could use the generative AI to generate programs to automate tasks in traditional Japanese crafts, performing automated tasks based on the skills of artisans. For example, automating lacquering or pottery work. The digitization department could also use the generative AI to generate programs to automate Western craft techniques, performing automated tasks based on the skills of artisans. For example, automating glassblowing or woodworking. The digitization department could also use the generative AI to generate programs to automate techniques from different cultural spheres, performing automated tasks based on the skills of artisans. For example, automating African carving techniques or Indian dyeing techniques. This makes it possible to automate techniques from different cultural spheres.

[0042] The new product proposal department can propose environmentally friendly materials and manufacturing methods when analyzing artisan techniques and market trend data and generating new product ideas. For example, the new product proposal department proposes environmentally friendly materials and manufacturing methods when the generation AI analyzes artisan techniques and market trend data and generates new product ideas. For example, it proposes renewable materials and eco-friendly manufacturing methods. The new product proposal department also builds a system where the generation AI analyzes artisan techniques and market trend data and generates new product ideas and proposes environmentally friendly materials and manufacturing methods. For example, it proposes low-energy consumption manufacturing methods. The new product proposal department also builds a system where the generation AI analyzes artisan techniques and market trend data and generates new product ideas and proposes environmentally friendly materials and manufacturing methods. For example, it proposes recycled materials and sustainable manufacturing methods. This makes it possible to propose environmentally friendly materials and manufacturing methods.

[0043] The new product proposal department can apply new product proposals to skills in different professions (e.g., cooking, painting, music, etc.), and the generative AI can generate new product ideas based on each skill. For example, the new product proposal department may generate new product ideas based on cooking skills and propose new dishes and foods that make use of a chef's skills. For example, it may propose new recipes and cooking methods. The new product proposal department may also generate new product ideas based on painting skills and propose new artworks and designs that make use of a painter's skills. For example, it may propose new painting styles and designs. The new product proposal department may also generate new product ideas based on music skills and propose new instruments and musical pieces that make use of a performer's skills. For example, it may propose new instrument designs and musical arrangements. This makes it possible to generate new product ideas based on skills in different professions.

[0044] The New Product Proposal Department can apply new product proposals to techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), and the generative AI can generate new product ideas based on each of these technologies. For example, the New Product Proposal Department can generate new product ideas based on traditional Japanese crafts techniques and propose new crafts that utilize the skills of artisans. For example, it can propose new designs that apply lacquering or pottery techniques. The New Product Proposal Department can also generate new product ideas based on Western crafts techniques and propose new crafts that utilize the skills of artisans. For example, it can propose new designs that apply glassblowing or woodworking techniques. The New Product Proposal Department can also generate new product ideas based on techniques from different cultural spheres and propose new crafts that utilize the skills of artisans. For example, it can propose new designs that apply African carving techniques or Indian dyeing techniques. This makes it possible to generate new product ideas based on techniques from different cultural spheres.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The technology accumulation unit uses high-precision sensors to record the subtle movements of craftsmen while they work, and the generation AI analyzes the data to extract subtle differences in their movements. For example, a high-precision sensor records the hand movements and force applied when a craftsman carves wood, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in carving speed and angle are saved as data. The technology accumulation unit also uses high-precision sensors to record the hand movements of a potter when he turns a potter's wheel, and the generation AI analyzes the data to extract subtle differences in their movements. For example, changes in hand position and pressure are saved as data. The technology accumulation unit also uses high-precision sensors to record the hammer movements of a swordsmith when he forges a sword, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in striking force and angle are saved as data. This allows the craftsman's subtle movements to be recorded and analyzed with high precision.

[0047] The technology accumulation unit uses sensors to record the craftsman's work environment (temperature, humidity, sound, etc.), and the generation AI can integrate and analyze this environmental data with the work data. For example, sensors can record the temperature and humidity of a woodworker's work environment, and the generation AI can analyze this data and integrate it with the work data. For example, it can analyze the impact of changes in temperature and humidity on the work. The technology accumulation unit also uses sensors to record the sounds in a potter's work environment, and the generation AI can analyze this data and integrate it with the work data. For example, it can analyze the impact of the sound of a potter's wheel turning and the sounds made during work on the craft. The technology accumulation unit also uses sensors to record the temperature and sounds in a swordsmith's work environment, and the generation AI can analyze this data and integrate it with the work data. For example, it can analyze the impact of the temperature in the forge and the sound of hammer strikes on work. This allows the work environment and work data to be integrated and analyzed.

[0048] The technology accumulation unit can apply the accumulated skills of artisans to skills in different professions (e.g., cooking, painting, music, etc.), and the generative AI can analyze and accumulate each skill. For example, to accumulate a chef's skills in the generative AI, the cooking process is recorded with a camera or sensor, and the data is analyzed to extract the key points of the skill. For example, how to use a knife or adjust the heat is saved as data. To accumulate painting skills in the generative AI, the technology accumulation unit records the painter's brush movements with a camera or sensor, and analyzes the data to extract the key points of the skill. For example, the pressure of the brush and the pattern of the brush movement are saved as data. To accumulate musical skills in the generative AI, the technology accumulation unit records the musician's movements with a camera or sensor, and analyzes the data to extract the key points of the skill. For example, finger movements and the rhythm of the performance are saved as data. This allows the accumulation of skills from different professions.

[0049] The technology accumulation unit can apply the accumulation of artisan techniques to techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), allowing the generative AI to analyze and accumulate each technique. For example, to accumulate traditional Japanese craft techniques in the generative AI, the artisans' work is recorded with cameras and sensors, and the data is analyzed to extract the key points of the technique. For example, lacquering and pottery techniques are saved as data. To accumulate Western craft techniques in the generative AI, the technology accumulation unit records the artisans' work with cameras and sensors, and analyzes the data to extract the key points of the technique. For example, glassblowing and woodworking techniques are saved as data. To accumulate techniques from different cultural spheres in the generative AI, the technology accumulation unit records the artisans' work with cameras and sensors, and analyzes the data to extract the key points of the technique. For example, African carving techniques and Indian dyeing techniques are saved as data. This allows technologies from different cultural spheres to be accumulated.

[0050] The Education Support Department can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, the generation AI can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it can analyze video of work and specifically point out areas for improvement. The Education Support Department also builds a system in which the generation AI can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it can provide feedback based on data collected by sensors. The Education Support Department also builds a system in which the generation AI can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it can detect mistakes made during learning and immediately suggest ways to correct them. This makes it possible to monitor the progress of young artisans in acquiring skills in real time and provide optimal feedback.

[0051] The Education Support Department can analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, the generation AI can analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, it can identify patterns of mistakes based on past data and propose countermeasures. The Education Support Department also builds a system in which the generation AI can analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, it can provide training using simulations. The Education Support Department can also analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, it can identify the causes of mistakes and propose specific ways to improve them. This makes it possible to generate training programs to prevent common mistakes.

[0052] The Education Support Department can provide support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, support for successor training can be provided on an online platform, making it available to young craftsmen in remote locations. For example, technical explanation videos and simulations can be provided online. The Education Support Department can also build a system that enables young craftsmen to acquire skills from remote locations through the online platform. For example, it can enable real-time feedback and Q&A. The Education Support Department can also provide support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, it can provide a technical practice environment using virtual reality. This allows young craftsmen in remote locations to receive support for successor training.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The technology accumulation unit uses generative AI to accumulate artisan skills as digital data. For example, the work performed by the artisan is recorded using cameras and sensors, and the generative AI analyzes the data to extract the key points of the technology. The technology accumulation unit also uses generative AI to extract the key points of the technology based on video footage and sensor data recording the work of the artisan. Step 2: The Education Support Department supports the training of successors based on the artisan skills accumulated by the Technology Accumulation Department. For example, the Generative AI generates videos and simulations that explain artisan skills and provides them to young artisans. The Education Support Department can also use the Generative AI to generate simulations so that young artisans can practice their skills in a virtual environment. Step 3: The digitization department digitizes part of the work based on the artisan skills accumulated by the technology accumulation department. For example, the generation AI generates an automated work program based on the artisan skills and has it executed by a machine or robot. The digitization department can also use the generation AI to generate a quality control program based on the artisan skills, automatically checking the quality of the product. Step 4: The New Product Proposal Department proposes new products based on the artisanal techniques accumulated by the Technology Accumulation Department. For example, the Generative AI analyzes artisanal techniques and market trend data to generate new product ideas. The New Product Proposal Department can also generate ideas for new products that are popular with consumers by analyzing artisanal techniques and consumer preference data.

[0055] (Example 2) The support system according to an embodiment of the present invention is a system that accumulates artisan skills in a generating AI and provides work support. This support system accumulates artisan skills, supports the education of successors, digitalizes work, and proposes new products. This allows the support system to efficiently pass on artisan skills, keep factories open, and propose new products.

[0056] The support system according to the embodiment includes a technology accumulation unit, an education support unit, a digitization unit, and a new product proposal unit. The technology accumulation unit accumulates artisan skills as digital data using a generation AI. For example, the work performed by artisans is recorded using a camera or sensor, and the generation AI analyzes the data to extract key points of the technology. The technology accumulation unit also extracts key points of the technology based on video and sensor data recording the artisans' work. The education support unit supports successor training based on the artisan skills accumulated by the technology accumulation unit. For example, the generation AI generates videos and simulations explaining artisan skills and provides them to young artisans. The education support unit can also generate simulations using the generation AI to allow young artisans to practice their skills in a virtual environment. The digitization unit digitizes part of the work based on the artisan skills accumulated by the technology accumulation unit. For example, the generation AI generates automated work programs based on the artisan skills and executes them on machines or robots. The digitization unit can also generate quality control programs based on the artisan skills and automatically check product quality. The new product proposal unit proposes new products based on the artisan skills accumulated by the technology accumulation unit. For example, the generation AI analyzes artisan skills and market trend data to generate new product ideas. The new product proposal unit can also analyze artisan skills and consumer preference data to generate new product ideas that are popular with consumers. As a result, the support system according to the embodiment can efficiently accumulate artisan skills, support successor training, digitize work, and propose new products.

[0057] The technology accumulation unit uses high-precision sensors to record the subtle movements of craftsmen while they work, and the generation AI analyzes the data to extract subtle differences in their movements. For example, the technology accumulation unit uses high-precision sensors to record the hand movements and force applied when a craftsman carves wood, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in carving speed and angle are saved as data. The technology accumulation unit also uses high-precision sensors to record the hand movements of a potter when he turns a potter's wheel, and the generation AI analyzes the data to extract subtle differences in their movements. For example, changes in hand position and pressure are saved as data. The technology accumulation unit also uses high-precision sensors to record the hammer movements of a swordsmith when he forges a sword, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in striking force and angle are saved as data. This allows the craftsman's subtle movements to be recorded and analyzed with high precision.

[0058] The technology accumulation unit uses sensors to record the craftsman's work environment (temperature, humidity, sound, etc.), and the generation AI can integrate and analyze this environmental data with the work data. For example, the technology accumulation unit uses sensors to record the temperature and humidity of a woodworker's work environment, and the generation AI analyzes this data and integrates it with the work data. For example, it analyzes the impact of changes in temperature and humidity on the work. The technology accumulation unit also uses sensors to record the sounds of a potter's work environment, and the generation AI analyzes this data and integrates it with the work data. For example, it analyzes the impact of the sound of a potter's wheel turning and the sounds made during work on the craft. The technology accumulation unit also uses sensors to record the temperature and sound of a swordsmith's work environment, and the generation AI analyzes this data and integrates it with the work data. For example, it analyzes the impact of the temperature in the forge and the sound of hammer strikes on work. This allows the work environment and work data to be integrated and analyzed.

[0059] The technology accumulation unit can use the emotion estimation function to analyze the stress and concentration felt by the craftsman while working, and store the emotion data together with the technical data. For example, the technology accumulation unit analyzes the stress felt by the craftsman while working using the emotion estimation function, and stores the data together with the technical data. For example, it measures the stress level based on changes in heart rate and facial expression. The technology accumulation unit also analyzes the concentration level felt by the craftsman while working using the emotion estimation function, and stores the data together with the technical data. For example, it measures the concentration level based on changes in pupil dilation and brain waves. The technology accumulation unit also analyzes the emotions felt by the craftsman while working using the emotion estimation function, and stores the data together with the technical data. For example, it measures the emotional state based on facial expressions and tone of voice. In this way, the craftsman's emotion data can be stored together with the technical data.

[0060] The technology accumulation unit can apply the accumulated artisanal techniques to techniques in different professions (e.g., cooking, painting, music, etc.), and the generative AI can analyze and accumulate each technique. For example, to accumulate a chef's techniques in the generative AI, the technology accumulation unit records the cooking process with cameras and sensors and analyzes the data to extract the key points of the technique. For example, how to use a knife and how to adjust the heat are saved as data. To accumulate painting techniques in the generative AI, the technology accumulation unit records the painter's brush movements with cameras and sensors and analyzes the data to extract the key points of the technique. For example, it saves the patterns of pen pressure and brush movement as data. To accumulate musical techniques in the generative AI, the technology accumulation unit records the performer's movements with cameras and sensors and analyzes the data to extract the key points of the technique. For example, it saves the finger movements and the rhythm of the performance as data. This allows the accumulation of techniques from different professions.

[0061] The technology accumulation unit can apply the accumulated artisanal techniques to techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), allowing the generative AI to analyze and accumulate each technique. For example, to accumulate traditional Japanese craft techniques in the generative AI, the technology accumulation unit records the work of artisans with cameras and sensors, analyzes the data, and extracts the key points of the technique. For example, lacquering and pottery techniques are saved as data. To accumulate Western craft techniques in the generative AI, the technology accumulation unit records the work of artisans with cameras and sensors, analyzes the data, and extracts the key points of the technique. For example, glassblowing and woodworking techniques are saved as data. To accumulate techniques from different cultural spheres in the generative AI, the technology accumulation unit records the work of artisans with cameras and sensors, analyzes the data, and extracts the key points of the technique. For example, African carving techniques and Indian dyeing techniques are saved as data. This allows technologies from different cultural spheres to be accumulated.

[0062] The technology accumulation unit uses the emotion estimation function to analyze emotional data when a craftsman learns a new skill, and can optimize the learning process based on that data. For example, the technology accumulation unit uses the emotion estimation function to analyze emotional data when a craftsman learns a new skill, and optimizes the learning process based on that data. For example, it measures stress levels and concentration during learning and suggests optimal learning methods. The technology accumulation unit also analyzes emotional data when a craftsman learns a new skill, and builds a system that optimizes the learning process based on that data. For example, it monitors emotional changes during learning in real time and provides appropriate feedback. The technology accumulation unit also uses the emotion estimation function to analyze emotional data when a craftsman learns a new skill, and optimizes the learning process based on that data. For example, it provides a learning environment that strengthens positive emotions. This makes it possible to optimize the learning process based on emotional data when a craftsman learns a new skill.

[0063] The education support department can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, the education support department uses a generation AI to monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it analyzes video of work and specifically points out areas for improvement. The education support department also builds a system in which the generation AI monitors the progress of young artisans in acquiring skills in real time and provides individually optimized feedback. For example, it provides feedback based on data collected by sensors. The education support department also builds a system in which the generation AI monitors the progress of young artisans in acquiring skills in real time and provides individually optimized feedback. For example, it detects mistakes made during learning and immediately suggests ways to correct them. This makes it possible to monitor the progress of young artisans in acquiring skills in real time and provide optimal feedback.

[0064] The education support department can analyze common mistakes made by young craftsmen when they acquire skills and generate training programs to prevent those mistakes. For example, the education support department uses a generation AI to analyze common mistakes made by young craftsmen when they acquire skills and generates training programs to prevent those mistakes. For example, it identifies patterns of mistakes based on past data and proposes countermeasures. The education support department also builds a system in which a generation AI analyzes common mistakes made by young craftsmen when they acquire skills and generates training programs to prevent those mistakes. For example, it provides training using simulations. The education support department also uses a generation AI to analyze common mistakes made by young craftsmen when they acquire skills and generates training programs to prevent those mistakes. For example, it identifies the causes of mistakes and proposes specific ways to improve them. This makes it possible to generate training programs to prevent common mistakes.

[0065] The education support unit uses the emotion estimation function to analyze the frustrations and changes in motivation felt by young artisans while they are learning skills, and can adjust the support content based on that emotional data. For example, the education support unit uses the emotion estimation function to analyze the frustrations and changes in motivation felt by young artisans while they are learning skills, and adjusts the support content based on that emotional data. For example, it provides an encouraging message when there is a strong sense of frustration. The education support unit also uses the emotion estimation function to analyze the emotional data felt by young artisans while they are learning skills, and builds a system that adjusts the support content based on that data. For example, it provides appropriate feedback when motivation drops. The education support unit also uses the emotion estimation function to analyze the frustrations and changes in motivation felt by young artisans while they are learning skills, and adjusts the support content based on that emotional data. For example, it provides advice to bring out positive emotions. This makes it possible to adjust the support content based on the emotional data of young artisans.

[0066] The Education Support Department can provide support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, the Education Support Department provides support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, it provides technical explanation videos and simulations online. The Education Support Department also builds a system that enables young craftsmen to acquire skills even from remote locations through the online platform. For example, it enables real-time feedback and Q&A. The Education Support Department also provides support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, it provides a technical practice environment using virtual reality. This allows young craftsmen in remote locations to receive support for successor training.

[0067] The Education Support Department can apply support for successor education to education in different professions (e.g., cooking, painting, music, etc.), with the generative AI generating videos and simulations that explain each technique. For example, the Education Support Department could use the generative AI to generate videos and simulations that explain cooking techniques, allowing young chefs to watch the videos and learn the techniques. For example, it could explain how to use a knife or cooking steps. The Education Support Department could also use the generative AI to generate videos and simulations that explain painting techniques, allowing young painters to watch the videos and learn the techniques. For example, it could explain how to use a brush or how to mix colors. The Education Support Department could also use the generative AI to generate videos and simulations that explain musical techniques, allowing young performers to watch the videos and learn the techniques. For example, it could explain how to play an instrument or how to keep rhythm. This allows the system to be applied to education in different professions.

[0068] The education support unit can use the emotion estimation function to generate a motivational program to enhance the positive emotions felt by young artisans while they are acquiring skills. The education support unit, for example, uses the emotion estimation function to generate a motivational program to enhance the positive emotions felt by young artisans while they are acquiring skills. For example, feedback that emphasizes successful experiences is provided. The education support unit also analyzes emotional data of young artisans while they are acquiring skills, and builds a system to generate a motivational program to enhance the positive emotions. For example, tasks that give them a sense of accomplishment are provided. The education support unit also uses the emotion estimation function to generate a motivational program to enhance the positive emotions felt by young artisans while they are acquiring skills. For example, words of praise or encouraging messages are provided. This makes it possible to generate a motivational program to enhance the positive emotions felt by young artisans.

[0069] The digitization unit generates automated work programs based on artisan skills, and can build a system in which multiple robots work in cooperation. For example, the digitization unit uses a generation AI to generate automated work programs based on artisan skills and build a system in which multiple robots work in cooperation. For example, multiple robots share the work of carving wood. The digitization unit also develops a system in which a generation AI generates automated work programs based on artisan skills and builds a system in which multiple robots work in cooperation. For example, multiple robots work together to mold pottery. The digitization unit also develops a system in which a generation AI generates automated work programs based on artisan skills and builds a system in which multiple robots work in cooperation. For example, multiple robots work together to forge swords. This makes it possible to build a system in which multiple robots work in cooperation.

[0070] The digitization unit can generate automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, the digitization unit uses a generation AI to generate automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, if an abnormality occurs during wood carving work, it is automatically corrected. The digitization unit can also build a system in which a generation AI generates automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, if an abnormality occurs during pottery molding work, it is automatically corrected. The digitization unit can also build a system in which a generation AI generates automated work programs based on artisan skills and add a function to detect abnormalities during work and automatically correct them. For example, if an abnormality occurs during a sword forging work, it is automatically corrected. This can add a function to detect abnormalities during work and automatically correct them.

[0071] The digitization unit uses the emotion estimation function to analyze the anxiety and resistance felt by the craftsman toward digitized work, and can optimize the digitization process based on the emotion data. For example, the digitization unit uses the emotion estimation function to analyze the anxiety and resistance felt by the craftsman toward digitized work, and optimizes the digitization process based on the emotion data. For example, appropriate support is provided when anxiety is strong. The digitization unit also uses the emotion estimation function to analyze the emotion data felt by the craftsman toward digitized work, and builds a system that optimizes the digitization process based on the data. For example, a phased introduction is performed when resistance is strong. The digitization unit also uses the emotion estimation function to analyze the anxiety and resistance felt by the craftsman toward digitized work, and optimizes the digitization process based on the emotion data. For example, advice is provided to elicit positive emotions. This allows the digitization process to be optimized by analyzing the anxiety and resistance felt by the craftsman.

[0072] The digitization unit can apply the digitization of work to tasks in different occupations (for example, cooking, painting, music, etc.), and the generative AI can generate programs to automate each task. For example, the digitization unit generates a program to automate cooking tasks using the generative AI, performing automated cooking tasks based on the skills of a chef. For example, it automates how to use a knife and how to adjust the heat. The digitization unit also generates a program to automate painting tasks using the generative AI, performing automated drawing tasks based on the skills of a painter. For example, it automates brush movements and how to mix colors. The digitization unit also generates a program to automate music tasks using the generative AI, performing automated performance tasks based on the skills of a performer. For example, it automates finger movements and how to keep rhythm. This makes it possible to automate tasks in different occupations.

[0073] The digitization department can apply the digitization of work to tasks involving techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), and the generative AI can generate programs to automate each task. For example, the digitization department could use the generative AI to generate programs to automate tasks in traditional Japanese crafts, performing automated tasks based on the skills of artisans. For example, automating lacquering or pottery work. The digitization department could also use the generative AI to generate programs to automate Western craft techniques, performing automated tasks based on the skills of artisans. For example, automating glassblowing or woodworking. The digitization department could also use the generative AI to generate programs to automate techniques from different cultural spheres, performing automated tasks based on the skills of artisans. For example, automating African carving techniques or Indian dyeing techniques. This makes it possible to automate techniques from different cultural spheres.

[0074] The digitization unit can use the emotion estimation function to build a feedback system for reinforcing the positive emotions felt by the craftsman toward the digitized work. For example, the digitization unit uses the emotion estimation function to build a feedback system for reinforcing the positive emotions felt by the craftsman toward the digitized work. For example, feedback that emphasizes a successful experience is provided. The digitization unit also analyzes emotion data about the digitized work of the craftsman using the emotion estimation function, and builds a feedback system for reinforcing the positive emotions based on the data. For example, tasks that give the craftsman a sense of accomplishment are provided. The digitization unit also uses the emotion estimation function to build a feedback system for reinforcing the positive emotions felt by the craftsman toward the digitized work. For example, words of praise or messages of encouragement are provided. In this way, a feedback system for reinforcing the positive emotions of the craftsman can be built.

[0075] The new product proposal department can take consumer emotional data into consideration when analyzing artisan skills and market trend data to generate new product ideas. For example, when the generation AI analyzes artisan skills and market trend data to generate new product ideas, the new product proposal department can take consumer emotional data into consideration when making proposals. For example, it can propose designs based on consumer preferences and emotional responses. The new product proposal department can also build a system that takes consumer emotional data into consideration when the generation AI analyzes artisan skills and market trend data to generate new product ideas. For example, it can propose optimal products based on emotional scores. The new product proposal department can also take consumer emotional data into consideration when the generation AI analyzes artisan skills and market trend data to generate new product ideas. For example, it can propose designs that evoke positive emotions. This makes it possible to propose new products by taking consumer emotional data into consideration.

[0076] The new product proposal department can propose environmentally friendly materials and manufacturing methods when analyzing artisan techniques and market trend data and generating new product ideas. For example, the new product proposal department proposes environmentally friendly materials and manufacturing methods when the generation AI analyzes artisan techniques and market trend data and generates new product ideas. For example, it proposes renewable materials and eco-friendly manufacturing methods. The new product proposal department also builds a system where the generation AI analyzes artisan techniques and market trend data and generates new product ideas and proposes environmentally friendly materials and manufacturing methods. For example, it proposes low-energy consumption manufacturing methods. The new product proposal department also builds a system where the generation AI analyzes artisan techniques and market trend data and generates new product ideas and proposes environmentally friendly materials and manufacturing methods. For example, it proposes recycled materials and sustainable manufacturing methods. This makes it possible to propose environmentally friendly materials and manufacturing methods.

[0077] The new product proposal unit can use the emotion estimation function to analyze the expectations and excitement that consumers feel toward a new product and optimize the design of the new product based on the emotion data. For example, the new product proposal unit can use the emotion estimation function to analyze the expectations and excitement that consumers feel toward a new product and optimize the design of the new product based on the emotion data. For example, the design can be adjusted based on the emotion score. The new product proposal unit can also use the emotion estimation function to analyze the emotion data that consumers feel toward a new product and build a system that optimizes the design of the new product based on the data. For example, the new product proposal unit can propose a design that elicits positive emotions. The new product proposal unit can also use the emotion estimation function to analyze the expectations and excitement that consumers feel toward a new product and optimize the design of the new product based on the emotion data. For example, the new product proposal unit can propose a design that enhances expectations. In this way, consumer expectations and excitement can be analyzed and the design of the new product can be optimized.

[0078] The new product proposal department can apply new product proposals to skills in different professions (e.g., cooking, painting, music, etc.), and the generative AI can generate new product ideas based on each skill. For example, the new product proposal department may generate new product ideas based on cooking skills and propose new dishes and foods that make use of a chef's skills. For example, it may propose new recipes and cooking methods. The new product proposal department may also generate new product ideas based on painting skills and propose new artworks and designs that make use of a painter's skills. For example, it may propose new painting styles and designs. The new product proposal department may also generate new product ideas based on music skills and propose new instruments and musical pieces that make use of a performer's skills. For example, it may propose new instrument designs and musical arrangements. This makes it possible to generate new product ideas based on skills in different professions.

[0079] The New Product Proposal Department can apply new product proposals to techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), and the generative AI can generate new product ideas based on each of these technologies. For example, the New Product Proposal Department can generate new product ideas based on traditional Japanese crafts techniques and propose new crafts that utilize the skills of artisans. For example, it can propose new designs that apply lacquering or pottery techniques. The New Product Proposal Department can also generate new product ideas based on Western crafts techniques and propose new crafts that utilize the skills of artisans. For example, it can propose new designs that apply glassblowing or woodworking techniques. The New Product Proposal Department can also generate new product ideas based on techniques from different cultural spheres and propose new crafts that utilize the skills of artisans. For example, it can propose new designs that apply African carving techniques or Indian dyeing techniques. This makes it possible to generate new product ideas based on techniques from different cultural spheres.

[0080] The new product proposal unit can use the emotion estimation function to generate a marketing strategy for enhancing the positive emotions that consumers feel toward a new product. The new product proposal unit, for example, uses the emotion estimation function to generate a marketing strategy for enhancing the positive emotions that consumers feel toward a new product. For example, it adjusts advertisements and promotions based on the emotion scores. The new product proposal unit also uses the emotion estimation function to analyze emotional data about consumers toward a new product, and builds a system for generating a marketing strategy for enhancing positive emotions based on the data. For example, it proposes advertisements that elicit positive emotions. The new product proposal unit also uses the emotion estimation function to generate a marketing strategy for enhancing the positive emotions that consumers feel toward a new product. For example, it proposes promotions that increase anticipation. In this way, a marketing strategy for enhancing positive emotions in consumers can be generated.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The technology accumulation unit uses high-precision sensors to record the subtle movements of craftsmen while they work, and the generation AI analyzes the data to extract subtle differences in their movements. For example, a high-precision sensor records the hand movements and force applied when a craftsman carves wood, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in carving speed and angle are saved as data. The technology accumulation unit also uses high-precision sensors to record the hand movements of a potter when he turns a potter's wheel, and the generation AI analyzes the data to extract subtle differences in their movements. For example, changes in hand position and pressure are saved as data. The technology accumulation unit also uses high-precision sensors to record the hammer movements of a swordsmith when he forges a sword, and the generation AI analyzes the data to extract subtle differences in their movements. For example, subtle differences in striking force and angle are saved as data. This allows the craftsman's subtle movements to be recorded and analyzed with high precision.

[0083] The technology accumulation unit uses sensors to record the craftsman's work environment (temperature, humidity, sound, etc.), and the generation AI can integrate and analyze this environmental data with the work data. For example, sensors can record the temperature and humidity of a woodworker's work environment, and the generation AI can analyze this data and integrate it with the work data. For example, it can analyze the impact of changes in temperature and humidity on the work. The technology accumulation unit also uses sensors to record the sounds in a potter's work environment, and the generation AI can analyze this data and integrate it with the work data. For example, it can analyze the impact of the sound of a potter's wheel turning and the sounds made during work on the craft. The technology accumulation unit also uses sensors to record the temperature and sounds in a swordsmith's work environment, and the generation AI can analyze this data and integrate it with the work data. For example, it can analyze the impact of the temperature in the forge and the sound of hammer strikes on work. This allows the work environment and work data to be integrated and analyzed.

[0084] The technology accumulation unit can use the emotion estimation function to analyze the stress and concentration felt by the craftsman while working, and store this emotion data together with the technology data. For example, the emotion estimation function can analyze the stress felt by the craftsman while working, and store this data together with the technology data. For example, the stress level can be measured based on changes in heart rate and facial expression. The technology accumulation unit can also use the emotion estimation function to analyze the concentration level felt by the craftsman while working, and store this data together with the technology data. For example, the concentration level can be measured based on changes in pupil dilation and brain waves. The technology accumulation unit can also use the emotion estimation function to analyze the emotions felt by the craftsman while working, and store this data together with the technology data. For example, the emotional state can be measured based on facial expressions and voice tone. In this way, the craftsman's emotion data can be stored together with the technology data.

[0085] The technology accumulation unit can apply the accumulated skills of artisans to skills in different professions (e.g., cooking, painting, music, etc.), and the generative AI can analyze and accumulate each skill. For example, to accumulate a chef's skills in the generative AI, the cooking process is recorded with a camera or sensor, and the data is analyzed to extract the key points of the skill. For example, how to use a knife or adjust the heat is saved as data. To accumulate painting skills in the generative AI, the technology accumulation unit records the painter's brush movements with a camera or sensor, and analyzes the data to extract the key points of the skill. For example, the pressure of the brush and the pattern of the brush movement are saved as data. To accumulate musical skills in the generative AI, the technology accumulation unit records the musician's movements with a camera or sensor, and analyzes the data to extract the key points of the skill. For example, finger movements and the rhythm of the performance are saved as data. This allows the accumulation of skills from different professions.

[0086] The technology accumulation unit can apply the accumulation of artisan techniques to techniques from different cultural spheres (for example, traditional Japanese crafts and Western crafts), allowing the generative AI to analyze and accumulate each technique. For example, to accumulate traditional Japanese craft techniques in the generative AI, the artisans' work is recorded with cameras and sensors, and the data is analyzed to extract the key points of the technique. For example, lacquering and pottery techniques are saved as data. To accumulate Western craft techniques in the generative AI, the technology accumulation unit records the artisans' work with cameras and sensors, and analyzes the data to extract the key points of the technique. For example, glassblowing and woodworking techniques are saved as data. To accumulate techniques from different cultural spheres in the generative AI, the technology accumulation unit records the artisans' work with cameras and sensors, and analyzes the data to extract the key points of the technique. For example, African carving techniques and Indian dyeing techniques are saved as data. This allows technologies from different cultural spheres to be accumulated.

[0087] The technology accumulation unit uses the emotion estimation function to analyze emotional data when a craftsman learns a new skill, and can optimize the learning process based on that data. For example, the emotion estimation function can analyze emotional data when a craftsman learns a new skill, and optimize the learning process based on that data. For example, it can measure stress levels and concentration during learning and suggest optimal learning methods. The technology accumulation unit can also analyze emotional data when a craftsman learns a new skill, and build a system that optimizes the learning process based on that data. For example, it can monitor emotional changes during learning in real time and provide appropriate feedback. The technology accumulation unit can also use the emotion estimation function to analyze emotional data when a craftsman learns a new skill, and optimize the learning process based on that data. For example, it can provide a learning environment that strengthens positive emotions. This makes it possible to optimize the learning process based on emotional data when a craftsman learns a new skill.

[0088] The Education Support Department can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, the generation AI can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it can analyze video of work and specifically point out areas for improvement. The Education Support Department also builds a system in which the generation AI can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it can provide feedback based on data collected by sensors. The Education Support Department also builds a system in which the generation AI can monitor the progress of young artisans in acquiring skills in real time and provide individually optimized feedback. For example, it can detect mistakes made during learning and immediately suggest ways to correct them. This makes it possible to monitor the progress of young artisans in acquiring skills in real time and provide optimal feedback.

[0089] The Education Support Department can analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, the generation AI can analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, it can identify patterns of mistakes based on past data and propose countermeasures. The Education Support Department also builds a system in which the generation AI can analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, it can provide training using simulations. The Education Support Department can also analyze common mistakes made by young craftsmen when they are learning skills and generate training programs to prevent those mistakes. For example, it can identify the causes of mistakes and propose specific ways to improve them. This makes it possible to generate training programs to prevent common mistakes.

[0090] The education support department can use the emotion estimation function to analyze the setbacks and changes in motivation that young artisans feel while acquiring skills, and adjust the support content based on that emotion data. For example, the emotion estimation function can be used to analyze the setbacks and changes in motivation that young artisans feel while acquiring skills, and adjust the support content based on that emotion data. For example, an encouraging message can be provided if the sense of setback is strong. The education support department can also use the emotion estimation function to analyze the emotional data of young artisans while acquiring skills, and build a system that adjusts the support content based on that data. For example, appropriate feedback can be provided when motivation drops. The education support department can also use the emotion estimation function to analyze the setbacks and changes in motivation that young artisans feel while acquiring skills, and adjust the support content based on that emotion data. For example, advice can be provided to bring out positive emotions. This makes it possible to adjust the support content based on the emotional data of young artisans.

[0091] The Education Support Department can provide support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, support for successor training can be provided on an online platform, making it available to young craftsmen in remote locations. For example, technical explanation videos and simulations can be provided online. The Education Support Department can also build a system that enables young craftsmen to acquire skills from remote locations through the online platform. For example, it can enable real-time feedback and Q&A. The Education Support Department can also provide support for successor training on an online platform, making it available to young craftsmen in remote locations. For example, it can provide a technical practice environment using virtual reality. This allows young craftsmen in remote locations to receive support for successor training.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The technology accumulation unit uses generative AI to accumulate artisan skills as digital data. For example, the work performed by the artisan is recorded using cameras and sensors, and the generative AI analyzes the data to extract the key points of the technology. The technology accumulation unit also uses generative AI to extract the key points of the technology based on video footage and sensor data recording the work of the artisan. Step 2: The Education Support Department supports the training of successors based on the artisan skills accumulated by the Technology Accumulation Department. For example, the Generative AI generates videos and simulations that explain artisan skills and provides them to young artisans. The Education Support Department can also use the Generative AI to generate simulations so that young artisans can practice their skills in a virtual environment. Step 3: The digitization department digitizes part of the work based on the artisan skills accumulated by the technology accumulation department. For example, the generation AI generates an automated work program based on the artisan skills and has it executed by a machine or robot. The digitization department can also use the generation AI to generate a quality control program based on the artisan skills, automatically checking the quality of the product. Step 4: The New Product Proposal Department proposes new products based on the artisanal techniques accumulated by the Technology Accumulation Department. For example, the Generative AI analyzes artisanal techniques and market trend data to generate new product ideas. The New Product Proposal Department can also generate ideas for new products that are popular with consumers by analyzing artisanal techniques and consumer preference data.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the robot 414, 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 robot 414 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.

[0139] 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.

[0140] 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.

[0141] 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 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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. [Explanation of symbols]

[0161] 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. A technology accumulation department that uses generative AI to accumulate artisan skills as digital data; an education support department that supports successor education based on the artisan skills accumulated by the technology accumulation department; a digitization unit that digitizes a part of work based on the artisan skills accumulated by the technology accumulation unit; a new product proposal unit that proposes new products based on the artisan skills accumulated by the skill accumulation unit. A system characterized by:

2. The technology accumulation department High-precision sensors record the subtle movements of craftsmen while they work, and the generative AI analyzes the data to extract subtle differences in their movements.

2. The system of claim 1.

3. The technology accumulation department The craftsman's working environment (temperature, humidity, sound, etc.) is recorded by sensors, and the generative AI integrates and analyzes the environmental data and work data.

2. The system of claim 1.

4. The technology accumulation department Analyzing the stress and concentration felt by craftsmen while they are working, and storing this emotional data along with technical data 2. The system of claim 1.

5. The technology accumulation department The accumulated skills of artisans will be applied to skills in different occupations (e.g., cooking, painting, music, etc.), and the generation AI will analyze and accumulate each skill.

2. The system of claim 1.

6. The technology accumulation department The accumulated skills of artisans will be applied to skills from different cultural spheres (for example, traditional Japanese crafts and Western crafts), and the generative AI will analyze and accumulate each skill.

2. The system of claim 1.

Images