System

Abstract

An object of the system according to the embodiment is to digitize the skills of the artisan and the know-how of the skilled worker and provide the digitized know-how to the user in an interactive manner.SOLUTION: A system according to an embodiment includes a digitization unit, a speech generation unit, and an interface unit. The digitizing unit digitizes the skills of the artisan and the know-how of the skilled worker using the generated AI. The conversation generation unit generates an answer to the question from the user in a conversation format on the basis of the skills of the artisan and the know-how of the skilled worker digitized by the digitization unit. The interface unit provides the answer generated by the speech generation unit to the user.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] With conventional technology, it was difficult to efficiently digitize the skills and know-how of skilled craftsmen and pass them on to successors.

[0005] The system according to the embodiment aims to digitize the skills and know-how of skilled craftsmen and provide them to users in an interactive format. [Means for solving the problem]

[0006] The system according to the embodiment includes a digitization unit, a conversation generation unit, and an interface unit. The digitization unit digitizes the skills of artisans and the know-how of skilled workers using a generation AI. The conversation generation unit generates answers in a conversational format to questions from users based on the skills of artisans and the know-how of skilled workers digitized by the digitization unit. The interface unit provides the answers generated by the conversation generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can digitize the skills and know-how of skilled craftsmen and provide them to users in an interactive format. [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 technology transfer system according to an embodiment of the present invention is a system that uses a generation AI to digitize the skills and know-how of skilled craftsmen and provides useful information in a conversational format through the generation AI. As a result, the technology transfer system solves the problem of a lack of successors and enables even elderly people with low IT literacy to pass on their skills and know-how.

[0029] A technology transfer system according to an embodiment includes a digitization unit, a conversation generation unit, and an interface unit. The digitization unit digitizes the skills and know-how of skilled craftsmen using a generation AI. For example, the generation AI analyzes information in various formats, such as handwritten notes, old books, and digital files, and converts it into unified digital data. The generation AI can also digitize the skills and know-how of skilled craftsmen as 3D models or animations to make them easier to understand visually. The generation AI can also automatically classify the skills and know-how of skilled craftsmen and link them to related technologies and methods, thereby building a systematic database. The conversation generation unit generates answers in a conversational format to questions from users based on the skills and know-how of skilled craftsmen digitized by the digitization unit. For example, the generation AI generates appropriate answers to user questions. The generation AI can also provide answers to user questions as specific procedures or step-by-step guides. The generation AI can also learn the user's past question history and provide individually customized answers. The interface unit provides the user with the answers generated by the conversation generation unit. For example, the interface unit may provide an interface using voice input and voice output, allowing users to easily input questions and receive answers. The interface unit may also develop a simple and intuitive touchscreen interface with visually easy-to-understand icons and buttons. The interface unit may also use an emotion estimation function to analyze the user's emotional state and provide an interface design that reduces stress. This allows the technology transfer system according to the embodiment to resolve the issue of a lack of successors and enable even elderly people with low IT literacy to transfer technology and know-how. For example, the output unit may provide answers generated by the generation AI as voice output or text display, making them easy for users to understand. The voice output uses speech synthesis technology to output the answers generated by the generation AI as voice. The text display displays the answers generated by the generation AI as text, allowing users to visually confirm them.

[0030] The digitization department can analyze handwritten notes, old books, digital files, and other information in various formats and import it into a unified database. For example, the digitization department scans handwritten notes and saves them as image data. It then converts the image data into text data using OCR technology. The digitization department can also scan old books, save them as image data, and then convert them into text data using OCR technology. The digitization department can also directly read digital files and import them into a unified database. This allows information in various formats to be imported into a unified database, making it possible to centrally manage the information.

[0031] The conversation generation unit can generate appropriate answers to questions from users. For example, if a user asks, "Please tell me how to process this piece of wood," the generation AI generates an appropriate answer on how to process wood. The conversation generation unit can also provide answers to user questions as specific procedures or step-by-step guides. For example, if a user asks, "Please tell me how to paint pottery," the generation AI can explain the painting procedure step by step. The conversation generation unit can also learn the user's past question history and provide individually customized answers. For example, if a user has previously asked a question about woodworking techniques, related new technical information can be provided. This allows the generation AI to generate appropriate answers to user questions and resolve the user's concerns.

[0032] The interface unit provides an interface using voice input and voice output, allowing users to easily input questions and receive answers. The interface unit allows users to input questions using voice input, for example. For example, a user can input by voice, "Please tell me how to process this piece of wood." The interface unit also uses voice output to provide answers generated by the generation AI via voice. For example, the generation AI outputs answers regarding how to process wood via voice. The interface unit can also develop a simple and intuitive touchscreen interface and arrange visually easy-to-understand icons and buttons. For example, icons and buttons can be arranged for asking questions about woodworking techniques. By providing an interface using voice input and voice output, even elderly people with low IT literacy can easily use the device.

[0033] The digitization department digitizes the skills and know-how of artisans and skilled workers as 3D models and animations, making them easier to understand visually. For example, the digitization department uses generative AI to digitize the skills and know-how of artisans and skilled workers as 3D models. For example, the hand movements of a woodworker and the way they use their tools can be reproduced in 3D models to make them easier to understand visually. The digitization department also digitizes the skills and know-how of artisans and skilled workers as animations. For example, animations can be used to show how a potter uses a potter's wheel and how they paint, making it easier to learn visually. The digitization department also uses generative AI to digitize the skills and know-how of artisans and skilled workers as VR content. For example, a construction worker's work site can be reproduced in VR, providing an experience that makes it seem as if the user is actually there. In this way, the skills and know-how of artisans and skilled workers can be digitized as 3D models and animations to make them easier to understand visually.

[0034] The digitization department can automatically classify the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, the digitization department uses generative AI to automatically classify the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, woodworking techniques are classified into categories such as "processing," "finishing," and "repair," and related technologies are linked. The digitization department also uses generative AI to automatically tag the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, pottery techniques are classified with tags such as "molding," "firing," and "painting," and related technologies are linked. The digitization department also uses generative AI to automatically hierarchize the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, construction techniques are classified into hierarchies such as "foundation work," "structural work," and "finishing work," and related technologies are linked. This makes it possible to automatically classify the skills of artisans and the know-how of skilled workers and link them to related technologies and methods, thereby building a systematic database.

[0035] The digitization unit can also digitize the skills of artisans and the know-how of skilled workers in audio and video formats to build a multimedia database. For example, the digitization unit uses a generation AI to digitize the skills of artisans and the know-how of skilled workers in audio format. For example, the work procedures of a craftsman can be recorded in audio and stored in an audio database. The digitization unit also uses a generation AI to digitize the skills of artisans and the know-how of skilled workers in video format. For example, a video of the craftsman working can be taken and stored in a video database. The digitization unit also uses a generation AI to digitize the skills of artisans and the know-how of skilled workers in both audio and video to build a multimedia database. For example, the work procedures of a craftsman can be recorded in audio and video and stored in an integrated database. In this way, the skills of artisans and the know-how of skilled workers can be digitized in audio and video formats to build a multimedia database that is easier to understand visually and aurally.

[0036] The Digitalization Department can integrate the skills and know-how of artisans and skilled workers from different fields to create new technologies as crossover technologies. For example, the Digitalization Department integrates the skills and know-how of artisans and skilled workers from different fields to create new technologies as crossover technologies. For example, it develops new products that combine woodworking and metal processing technologies. The Digitalization Department also integrates the skills and know-how of artisans and skilled workers from different fields using generation AI to create new technologies as crossover technologies. For example, it creates new art pieces that combine pottery and glass art techniques. The Digitalization Department also integrates the skills and know-how of artisans and skilled workers from different fields to create new technologies as crossover technologies. For example, it designs new buildings that combine architectural technology and interior design technology. In this way, new technologies can be created by integrating the skills and know-how of artisans and skilled workers from different fields.

[0037] The conversation generation unit can provide answers to user questions as specific procedures or step-by-step guides. For example, the conversation generation unit uses a generation AI to provide answers to user questions as specific procedures. For example, in response to a question such as "Please tell me how to process wood," the conversation generation unit provides step-by-step explanations of processing procedures. Furthermore, the conversation generation unit uses a generation AI to provide answers to user questions as step-by-step guides. For example, in response to a question such as "Please tell me how to paint pottery," the conversation generation unit provides detailed explanations of painting procedures. Furthermore, the conversation generation unit uses a generation AI to provide answers to user questions as specific procedures. For example, in response to a question such as "Please tell me how to lay the foundation for a building," the conversation generation unit provides step-by-step explanations of the steps of foundation work. In this way, by providing answers to user questions as specific procedures or step-by-step guides, the user's understanding is deepened.

[0038] The conversation generation unit can learn the user's question history and provide individually customized answers. In the conversation generation unit, for example, the generation AI learns the user's past question history and provides individually customized answers. For example, a user who has previously asked a question about woodworking techniques is provided with related new technical information. In addition, the conversation generation unit learns the user's past question history and provides individually customized answers. For example, a user who has previously asked a question about pottery techniques is provided with related new technical information. In addition, the conversation generation unit learns the user's past question history and provides individually customized answers. For example, a user who has previously asked a question about building techniques is provided with related new technical information. In this way, by learning the user's past question history and providing individually customized answers, user satisfaction is improved.

[0039] The conversation generation unit can provide answers to user questions not only in text format but also in audio or video format. The conversation generation unit, for example, uses a generation AI to provide answers to user questions not only in text format but also in audio format. For example, an explanation of woodworking techniques is provided as an audio guide. The conversation generation unit also uses a generation AI to provide answers to user questions in video format. For example, an explanation of pottery techniques is visually shown in a video. The conversation generation unit also uses a generation AI to provide answers to user questions in multiple formats, including text, audio, and video. For example, an explanation of construction techniques is provided in text, audio, and video, allowing the user to select. In this way, answers to user questions can be provided not only in text format but also in audio and video format, thereby deepening the user's understanding.

[0040] The conversation generation unit can have an automatic translation function so that it can respond to questions in different languages. For example, the generation AI in the conversation generation unit has an automatic translation function so that it can respond to questions in different languages. For example, it translates a question in Japanese into English and provides an answer in English. Furthermore, when a user asks a question in a different language, the conversation generation unit has the generation AI translate the question using the automatic translation function and provide an answer in the appropriate language. For example, it translates a question in French into Japanese and provides an answer in Japanese. Furthermore, the conversation generation unit has the generation AI have an automatic translation function so that it can respond to questions in different languages. For example, it translates a question in Chinese into English and provides an answer in English. Thus, by having an automatic translation function so that it can respond to questions in different languages, global use becomes possible.

[0041] The interface unit can use voice recognition technology to provide an interface that allows the user to ask questions in natural language. The interface unit, for example, uses voice recognition technology to provide an interface that allows the user to ask questions in natural language. For example, it allows the user to input a question by voice, such as, "Please tell me how to process this piece of wood." The interface unit also provides a voice recognition interface that allows the user to ask questions in natural language. For example, it allows the user to input a question by voice, such as, "Please tell me how to paint pottery." The interface unit also uses voice recognition technology to provide an interface that allows the user to ask questions in natural language. For example, it allows the user to input a question by voice, such as, "Please tell me how to do foundation work for building." In this way, by using voice recognition technology to provide an interface that allows the user to ask questions in natural language, even elderly people with low IT literacy can easily use it.

[0042] The interface unit can develop a simple and intuitive touchscreen interface and arrange visually easy-to-understand icons and buttons. The interface unit, for example, develops a simple and intuitive touchscreen interface and arranges visually easy-to-understand icons and buttons. For example, icons and buttons for asking questions about woodworking techniques are arranged. The interface unit also develops a simple touchscreen interface with visually easy-to-understand icons and buttons. For example, icons and buttons for asking questions about pottery techniques are arranged. The interface unit also develops a simple and intuitive touchscreen interface and arranges visually easy-to-understand icons and buttons. For example, icons and buttons for asking questions about building techniques are arranged. In this way, by developing a simple and intuitive touchscreen interface and arranging visually easy-to-understand icons and buttons, even elderly people with low IT literacy can easily use the interface.

[0043] The interface unit may be equipped with a voice assistant function, allowing the user to operate the device by voice. The interface unit may be equipped with a voice assistant function, allowing the user to operate the device by voice. For example, a question such as "Please tell me how to process this piece of wood" may be input by voice, and the answer may be received by voice. The interface unit may also be equipped with a voice assistant function, allowing the user to operate the device by voice. For example, a question such as "Please tell me how to paint pottery" may be input by voice, and the answer may be received by voice. The interface unit may also be equipped with a voice assistant function, allowing the user to operate the device by voice. For example, a question such as "Please tell me how to do foundation work for a building" may be input by voice, and the answer may be received by voice. By equipping the device with a voice assistant function and allowing the user to operate the device by voice, even elderly people with low IT literacy can easily use the device.

[0044] The interface unit can provide a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. The interface unit, for example, provides a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. For example, a training mode for asking questions about woodworking techniques is provided. The interface unit also provides a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. For example, a training mode for asking questions about pottery techniques is provided. The interface unit also provides a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. For example, a training mode for asking questions about building techniques is provided. In this way, by providing a special training mode for the elderly and allowing them to learn how to use the interface in a step-by-step manner, even elderly people with low IT literacy can easily use the device.

[0045] The conversation generation unit can build a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress. The conversation generation unit, for example, uses a generation AI to build a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress. For example, a system that tracks the learning progress of woodworking techniques is provided. The conversation generation unit also builds a system that uses a generation AI to provide the successor with the skills and know-how to learn in stages and tracks the learning progress. For example, a system that tracks the learning progress of pottery techniques is provided. The conversation generation unit also uses a generation AI to build a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress. For example, a system that tracks the learning progress of architectural techniques is provided. In this way, by building a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress, the learning effectiveness of the successor is improved.

[0046] The conversation generation unit can evaluate the skill level of the successor and provide a customized learning plan accordingly. In the conversation generation unit, for example, the generation AI evaluates the skill level of the successor and provides a customized learning plan accordingly. For example, a learning plan is provided according to the skill level of woodworking. In addition, the conversation generation unit evaluates the skill level of the successor and provides a customized learning plan accordingly. For example, a learning plan is provided according to the skill level of pottery. In addition, the conversation generation unit evaluates the skill level of the successor and provides a customized learning plan accordingly. For example, a learning plan is provided according to the skill level of construction. In this way, by evaluating the skill level of the successor and providing a customized learning plan accordingly, the learning effectiveness of the successor is improved.

[0047] The conversation generation unit can provide the skills and know-how that the successor should learn in the form of an online course or workshop. For example, the conversation generation unit uses a generation AI to provide the skills and know-how that the successor should learn in the form of an online course. For example, an online course on woodworking techniques is provided. Furthermore, the conversation generation unit uses a generation AI to provide the skills and know-how that the successor should learn in the form of a workshop. For example, a workshop on pottery techniques is provided. Furthermore, the conversation generation unit uses a generation AI to provide the skills and know-how that the successor should learn in the form of an online course or workshop. For example, an online course or workshop on architectural techniques is provided. In this way, by providing the skills and know-how that the successor should learn in the form of an online course or workshop, the learning effectiveness of the successor is improved.

[0048] The conversation generation unit provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. The conversation generation unit, for example, provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. For example, an online forum is provided where successors of woodworking techniques and pottery techniques can interact. The conversation generation unit also provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. For example, an online forum is provided where successors of architectural techniques and interior design techniques can interact. The conversation generation unit also provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. For example, an online forum is provided where successors of metal processing techniques and glass art techniques can interact. This provides a platform where successors of different fields can interact with each other, promoting the sharing of techniques and know-how, and is expected to lead to the creation of new technologies.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The skill transfer system can further include a progress management unit that tracks the user's learning progress. The progress management unit records the progress of the user's learned skills and know-how and visualizes the learning progress. For example, the learning progress of woodworking skills can be displayed in graphs and charts, allowing the user to see at a glance how much skill the user has mastered. The progress management unit can also suggest the next skill or know-how to learn based on the user's learning progress. For example, for a user who has mastered basic woodworking skills, it can suggest the next applied skill to advance to. Furthermore, the progress management unit can provide an individually customized learning plan based on the user's learning progress. This improves the user's learning effectiveness and makes skill transfer more efficient.

[0051] The technology transfer system can also be equipped with a crossover section that integrates technologies and know-how from different fields to create new technologies. The crossover section combines technologies and know-how from different fields to create new technologies. For example, woodworking and metal processing technologies can be combined to develop a new product. Or, ceramics and glass art can be combined to create a new art piece. Furthermore, architecture and interior design technologies can be combined to design a new building. In this way, by integrating technologies and know-how from different fields, new technologies can be created and the scope of technology transfer can be expanded.

[0052] The technology transfer system can further include a skill evaluation unit that evaluates the user's skill level and provides a customized learning plan accordingly. The skill evaluation unit evaluates the user's skill level and provides an individually customized learning plan based on the evaluation. For example, a learning plan according to the skill level of woodworking technology can be provided. A learning plan according to the skill level of pottery technology can also be provided. Furthermore, a learning plan according to the skill level of architectural technology can be provided. In this way, by providing a customized learning plan according to the user's skill level, learning effectiveness can be improved and technology transfer can be made more effective.

[0053] The technology transfer system can also provide a voice recognition interface that allows users to ask questions in natural language. A voice recognition interface allows users to input questions in natural language. For example, questions such as "Please tell me how to process this piece of wood" can be input by voice. It is also possible to input questions such as "Please tell me how to paint pottery" by voice. It is also possible to input questions such as "Please tell me how to do foundation work for a building" by voice. In this way, by using voice recognition technology to provide an interface that allows users to ask questions in natural language, even elderly people with low IT literacy can easily use the system.

[0054] The technology transfer system can also be equipped with an automatic translation function that can handle questions in different languages. When a user asks a question in a different language, the automatic translation function translates the question into the appropriate language and provides an answer. For example, a question in Japanese can be translated into English and an answer can be provided in English. A question in French can also be translated into Japanese and an answer can be provided in Japanese. Furthermore, a question in Chinese can be translated into English and an answer can be provided in English. Thus, by having an automatic translation function that can handle questions in different languages, global use becomes possible.

[0055] The technology transfer system can also provide the technology and know-how that users need to learn in the form of online courses or workshops. Online courses are designed so that users can learn from the comfort of their own homes. For example, an online course on woodworking techniques can be provided. In addition, the workshop format allows users to learn the technology while actually experiencing it. For example, a workshop on pottery techniques can be provided. Furthermore, online courses and workshops on architectural techniques can also be provided. In this way, by providing the technology and know-how that users need to learn in the form of online courses or workshops, learning effectiveness can be improved and technology transfer can be made more effective.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The digitization department uses generative AI to digitize the skills and know-how of skilled craftsmen. For example, it analyzes information in various formats, such as handwritten notes, old books, and digital files, and converts them into unified digital data. It can also digitize the skills and know-how of skilled craftsmen as 3D models and animations to make them easier to understand visually. Furthermore, it can automatically classify the skills and know-how of skilled craftsmen and link them to related technologies and methods, creating a systematic database. Step 2: The conversation generation unit generates conversational answers to user questions based on the craftsmanship and expert know-how digitized by the digitization unit. For example, the generation AI can generate appropriate answers to user questions and provide them as specific procedures or step-by-step guides. It can also learn from the user's past question history and provide individually customized answers. Step 3: The interface unit provides the user with the answer generated by the conversation generation unit. For example, an interface using voice input and voice output can be provided, allowing the user to easily enter questions and receive answers. A simple and intuitive touchscreen interface can also be developed, with visually easy-to-understand icons and buttons. Furthermore, an emotion estimation function can be used to analyze the user's emotional state and provide an interface design to reduce stress.

[0058] (Example 2) The technology transfer system according to an embodiment of the present invention is a system that uses a generation AI to digitize the skills and know-how of skilled craftsmen and provides useful information in a conversational format through the generation AI. As a result, the technology transfer system solves the problem of a lack of successors and enables even elderly people with low IT literacy to pass on their skills and know-how.

[0059] A technology transfer system according to an embodiment includes a digitization unit, a conversation generation unit, and an interface unit. The digitization unit digitizes the skills and know-how of skilled craftsmen using a generation AI. For example, the generation AI analyzes information in various formats, such as handwritten notes, old books, and digital files, and converts it into unified digital data. The generation AI can also digitize the skills and know-how of skilled craftsmen as 3D models or animations to make them easier to understand visually. The generation AI can also automatically classify the skills and know-how of skilled craftsmen and link them to related technologies and methods, thereby building a systematic database. The conversation generation unit generates answers in a conversational format to questions from users based on the skills and know-how of skilled craftsmen digitized by the digitization unit. For example, the generation AI generates appropriate answers to user questions. The generation AI can also provide answers to user questions as specific procedures or step-by-step guides. The generation AI can also learn the user's past question history and provide individually customized answers. The interface unit provides the user with the answers generated by the conversation generation unit. For example, the interface unit may provide an interface using voice input and voice output, allowing users to easily input questions and receive answers. The interface unit may also develop a simple and intuitive touchscreen interface with visually easy-to-understand icons and buttons. The interface unit may also use an emotion estimation function to analyze the user's emotional state and provide an interface design that reduces stress. This allows the technology transfer system according to the embodiment to resolve the issue of a lack of successors and enable even elderly people with low IT literacy to transfer technology and know-how. For example, the output unit may provide answers generated by the generation AI as voice output or text display, making them easy for users to understand. The voice output uses speech synthesis technology to output the answers generated by the generation AI as voice. The text display displays the answers generated by the generation AI as text, allowing users to visually confirm them.

[0060] The digitization department can analyze handwritten notes, old books, digital files, and other information in various formats and import it into a unified database. For example, the digitization department scans handwritten notes and saves them as image data. It then converts the image data into text data using OCR technology. The digitization department can also scan old books, save them as image data, and then convert them into text data using OCR technology. The digitization department can also directly read digital files and import them into a unified database. This allows information in various formats to be imported into a unified database, making it possible to centrally manage the information.

[0061] The conversation generation unit can generate appropriate answers to questions from users. For example, if a user asks, "Please tell me how to process this piece of wood," the generation AI generates an appropriate answer on how to process wood. The conversation generation unit can also provide answers to user questions as specific procedures or step-by-step guides. For example, if a user asks, "Please tell me how to paint pottery," the generation AI can explain the painting procedure step by step. The conversation generation unit can also learn the user's past question history and provide individually customized answers. For example, if a user has previously asked a question about woodworking techniques, related new technical information can be provided. This allows the generation AI to generate appropriate answers to user questions and resolve the user's concerns.

[0062] The interface unit provides an interface using voice input and voice output, allowing users to easily input questions and receive answers. The interface unit allows users to input questions using voice input, for example. For example, a user can input by voice, "Please tell me how to process this piece of wood." The interface unit also uses voice output to provide answers generated by the generation AI via voice. For example, the generation AI outputs answers regarding how to process wood via voice. The interface unit can also develop a simple and intuitive touchscreen interface and arrange visually easy-to-understand icons and buttons. For example, icons and buttons can be arranged for asking questions about woodworking techniques. By providing an interface using voice input and voice output, even elderly people with low IT literacy can easily use the device.

[0063] The digitization department digitizes the skills and know-how of artisans and skilled workers as 3D models and animations, making them easier to understand visually. For example, the digitization department uses generative AI to digitize the skills and know-how of artisans and skilled workers as 3D models. For example, the hand movements of a woodworker and the way they use their tools can be reproduced in 3D models to make them easier to understand visually. The digitization department also digitizes the skills and know-how of artisans and skilled workers as animations. For example, animations can be used to show how a potter uses a potter's wheel and how they paint, making it easier to learn visually. The digitization department also uses generative AI to digitize the skills and know-how of artisans and skilled workers as VR content. For example, a construction worker's work site can be reproduced in VR, providing an experience that makes it seem as if the user is actually there. In this way, the skills and know-how of artisans and skilled workers can be digitized as 3D models and animations to make them easier to understand visually.

[0064] The digitization department can automatically classify the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, the digitization department uses generative AI to automatically classify the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, woodworking techniques are classified into categories such as "processing," "finishing," and "repair," and related technologies are linked. The digitization department also uses generative AI to automatically tag the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, pottery techniques are classified with tags such as "molding," "firing," and "painting," and related technologies are linked. The digitization department also uses generative AI to automatically hierarchize the skills of artisans and the know-how of skilled workers and link them to related technologies and methods. For example, construction techniques are classified into hierarchies such as "foundation work," "structural work," and "finishing work," and related technologies are linked. This makes it possible to automatically classify the skills of artisans and the know-how of skilled workers and link them to related technologies and methods, thereby building a systematic database.

[0065] The digitization unit can use the emotion estimation function to analyze the emotions of a craftsman or skilled worker when explaining a technique and generate digital data that reflects those emotions. For example, the digitization unit uses the emotion estimation function to analyze the emotions of a craftsman or skilled worker when explaining a technique and generate digital data that reflects those emotions. For example, the digitization unit can emphasize the enthusiasm with which the craftsman explains a technique. The digitization unit can also analyze the emotions of a craftsman or skilled worker when explaining a technique and generate interactive digital data that reflects those emotions. For example, the digitization unit can create an animation that reflects the joy or surprise of the craftsman. The digitization unit can also use the emotion estimation function to analyze the emotions of a craftsman or skilled worker when explaining a technique and generate audio data that reflects those emotions. For example, the digitization unit can create an audio guide that reflects the tone and strength of the craftsman's voice. In this way, by generating digital data that reflects the emotions of a craftsman or skilled worker when explaining a technique, the transfer of skills can be made more emotional.

[0066] The digitization unit can also digitize the skills of artisans and the know-how of skilled workers in audio and video formats to build a multimedia database. For example, the digitization unit uses a generation AI to digitize the skills of artisans and the know-how of skilled workers in audio format. For example, the work procedures of a craftsman can be recorded in audio and stored in an audio database. The digitization unit also uses a generation AI to digitize the skills of artisans and the know-how of skilled workers in video format. For example, a video of the craftsman working can be taken and stored in a video database. The digitization unit also uses a generation AI to digitize the skills of artisans and the know-how of skilled workers in both audio and video to build a multimedia database. For example, the work procedures of a craftsman can be recorded in audio and video and stored in an integrated database. In this way, the skills of artisans and the know-how of skilled workers can be digitized in audio and video formats to build a multimedia database that is easier to understand visually and aurally.

[0067] The Digitalization Department can integrate the skills and know-how of artisans and skilled workers from different fields to create new technologies as crossover technologies. For example, the Digitalization Department integrates the skills and know-how of artisans and skilled workers from different fields to create new technologies as crossover technologies. For example, it develops new products that combine woodworking and metal processing technologies. The Digitalization Department also integrates the skills and know-how of artisans and skilled workers from different fields using generation AI to create new technologies as crossover technologies. For example, it creates new art pieces that combine pottery and glass art techniques. The Digitalization Department also integrates the skills and know-how of artisans and skilled workers from different fields to create new technologies as crossover technologies. For example, it designs new buildings that combine architectural technology and interior design technology. In this way, new technologies can be created by integrating the skills and know-how of artisans and skilled workers from different fields.

[0068] The digitization unit can use the emotion estimation function to analyze the emotions of artisans and skilled workers when they explain their skills and generate interactive learning content that reflects those emotions. For example, the digitization unit uses the emotion estimation function to analyze the emotions of artisans and skilled workers when they explain their skills and generate interactive learning content that reflects those emotions. For example, the digitization unit creates quiz-style learning content that reflects the emotions of the artisan. The digitization unit also analyzes the emotions of artisans and skilled workers when they explain their skills and generates simulation learning content that reflects those emotions. For example, the digitization unit provides a simulation of a virtual workshop that reflects the emotions of the artisan. The digitization unit also uses the emotion estimation function to analyze the emotions of artisans and skilled workers when they explain their skills and generate game-style learning content that reflects those emotions. For example, a skill acquisition game that reflects the emotions of the artisan is created. In this way, interactive learning content that reflects the emotions of artisans and skilled workers when they explain their skills is generated, thereby deepening the learner's understanding.

[0069] The conversation generation unit can provide answers to user questions as specific procedures or step-by-step guides. For example, the conversation generation unit uses a generation AI to provide answers to user questions as specific procedures. For example, in response to a question such as "Please tell me how to process wood," the conversation generation unit provides step-by-step explanations of processing procedures. Furthermore, the conversation generation unit uses a generation AI to provide answers to user questions as step-by-step guides. For example, in response to a question such as "Please tell me how to paint pottery," the conversation generation unit provides detailed explanations of painting procedures. Furthermore, the conversation generation unit uses a generation AI to provide answers to user questions as specific procedures. For example, in response to a question such as "Please tell me how to lay the foundation for a building," the conversation generation unit provides step-by-step explanations of the steps of foundation work. In this way, by providing answers to user questions as specific procedures or step-by-step guides, the user's understanding is deepened.

[0070] The conversation generation unit can learn the user's question history and provide individually customized answers. In the conversation generation unit, for example, the generation AI learns the user's past question history and provides individually customized answers. For example, a user who has previously asked a question about woodworking techniques is provided with related new technical information. In addition, the conversation generation unit learns the user's past question history and provides individually customized answers. For example, a user who has previously asked a question about pottery techniques is provided with related new technical information. In addition, the conversation generation unit learns the user's past question history and provides individually customized answers. For example, a user who has previously asked a question about building techniques is provided with related new technical information. In this way, by learning the user's past question history and providing individually customized answers, user satisfaction is improved.

[0071] The conversation generation unit can use the emotion estimation function to analyze the emotion of the user when asking a question and generate an answer corresponding to that emotion. For example, the conversation generation unit uses the emotion estimation function to analyze the emotion of the user when asking a question and generate an answer corresponding to that emotion. For example, if the user is feeling anxious, an answer that gives a sense of security is provided. The conversation generation unit also analyzes the emotion of the user when asking a question using the emotion estimation function and generates an answer corresponding to that emotion. For example, if the user is excited, an answer that shares the user's excitement is provided. The conversation generation unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and generate an answer corresponding to that emotion. For example, if the user is in doubt, a detailed explanation is provided. In this way, by analyzing the emotion of the user when asking a question and generating an answer corresponding to that emotion, user satisfaction is improved.

[0072] The conversation generation unit can provide answers to user questions not only in text format but also in audio or video format. The conversation generation unit, for example, uses a generation AI to provide answers to user questions not only in text format but also in audio format. For example, an explanation of woodworking techniques is provided as an audio guide. The conversation generation unit also uses a generation AI to provide answers to user questions in video format. For example, an explanation of pottery techniques is visually shown in a video. The conversation generation unit also uses a generation AI to provide answers to user questions in multiple formats, including text, audio, and video. For example, an explanation of construction techniques is provided in text, audio, and video, allowing the user to select. In this way, answers to user questions can be provided not only in text format but also in audio and video format, thereby deepening the user's understanding.

[0073] The conversation generation unit can have an automatic translation function so that it can respond to questions in different languages. For example, the generation AI in the conversation generation unit has an automatic translation function so that it can respond to questions in different languages. For example, it translates a question in Japanese into English and provides an answer in English. Furthermore, when a user asks a question in a different language, the conversation generation unit has the generation AI translate the question using the automatic translation function and provide an answer in the appropriate language. For example, it translates a question in French into Japanese and provides an answer in Japanese. Furthermore, the conversation generation unit has the generation AI have an automatic translation function so that it can respond to questions in different languages. For example, it translates a question in Chinese into English and provides an answer in English. Thus, by having an automatic translation function so that it can respond to questions in different languages, global use becomes possible.

[0074] The conversation generation unit can use the emotion estimation function to analyze the emotion of the user when asking a question and provide interactive feedback according to that emotion. The conversation generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when asking a question and provide interactive feedback according to that emotion. For example, if the user is feeling anxious, feedback that gives a sense of security is provided. The conversation generation unit also analyzes the emotion of the user when asking a question using the emotion estimation function and provides interactive feedback according to that emotion. For example, if the user is excited, feedback that shares the excitement is provided. The conversation generation unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and provide interactive feedback according to that emotion. For example, if the user is in doubt, a detailed explanation is provided. In this way, by analyzing the emotion of the user when asking a question and providing interactive feedback according to that emotion, user satisfaction is improved.

[0075] The interface unit can use voice recognition technology to provide an interface that allows the user to ask questions in natural language. The interface unit, for example, uses voice recognition technology to provide an interface that allows the user to ask questions in natural language. For example, it allows the user to input a question by voice, such as, "Please tell me how to process this piece of wood." The interface unit also provides a voice recognition interface that allows the user to ask questions in natural language. For example, it allows the user to input a question by voice, such as, "Please tell me how to paint pottery." The interface unit also uses voice recognition technology to provide an interface that allows the user to ask questions in natural language. For example, it allows the user to input a question by voice, such as, "Please tell me how to do foundation work for building." In this way, by using voice recognition technology to provide an interface that allows the user to ask questions in natural language, even elderly people with low IT literacy can easily use it.

[0076] The interface unit can develop a simple and intuitive touchscreen interface and arrange visually easy-to-understand icons and buttons. The interface unit, for example, develops a simple and intuitive touchscreen interface and arranges visually easy-to-understand icons and buttons. For example, icons and buttons for asking questions about woodworking techniques are arranged. The interface unit also develops a simple touchscreen interface with visually easy-to-understand icons and buttons. For example, icons and buttons for asking questions about pottery techniques are arranged. The interface unit also develops a simple and intuitive touchscreen interface and arranges visually easy-to-understand icons and buttons. For example, icons and buttons for asking questions about building techniques are arranged. In this way, by developing a simple and intuitive touchscreen interface and arranging visually easy-to-understand icons and buttons, even elderly people with low IT literacy can easily use the interface.

[0077] The interface unit can use the emotion estimation function to analyze the emotional state of the user and provide an interface design for reducing stress. The interface unit, for example, uses the emotion estimation function to analyze the emotional state of the user and provide an interface design for reducing stress. For example, if the user is feeling anxious, a design with calm colors is provided. The interface unit also analyzes the emotional state of the user with the emotion estimation function and provides an interface design for reducing stress. For example, if the user is excited, a design that helps the user to relax is provided. The interface unit also uses the emotion estimation function to analyze the emotional state of the user and provide an interface design for reducing stress. For example, if the user is feeling doubtful, a design that gives a sense of security is provided. In this way, by using the emotion estimation function to analyze the emotional state of the user and provide an interface design for reducing stress, user satisfaction is improved.

[0078] The interface unit may be equipped with a voice assistant function, allowing the user to operate the device by voice. The interface unit may be equipped with a voice assistant function, allowing the user to operate the device by voice. For example, a question such as "Please tell me how to process this piece of wood" may be input by voice, and the answer may be received by voice. The interface unit may also be equipped with a voice assistant function, allowing the user to operate the device by voice. For example, a question such as "Please tell me how to paint pottery" may be input by voice, and the answer may be received by voice. The interface unit may also be equipped with a voice assistant function, allowing the user to operate the device by voice. For example, a question such as "Please tell me how to do foundation work for a building" may be input by voice, and the answer may be received by voice. By equipping the device with a voice assistant function and allowing the user to operate the device by voice, even elderly people with low IT literacy can easily use the device.

[0079] The interface unit can provide a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. The interface unit, for example, provides a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. For example, a training mode for asking questions about woodworking techniques is provided. The interface unit also provides a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. For example, a training mode for asking questions about pottery techniques is provided. The interface unit also provides a special training mode for the elderly, allowing them to learn how to use the interface in a step-by-step manner. For example, a training mode for asking questions about building techniques is provided. In this way, by providing a special training mode for the elderly and allowing them to learn how to use the interface in a step-by-step manner, even elderly people with low IT literacy can easily use the device.

[0080] The interface unit can use the emotion estimation function to analyze the emotional state of the user and customize the interface according to that emotion. The interface unit, for example, uses the emotion estimation function to analyze the emotional state of the user and customize the interface according to that emotion. For example, if the user is feeling anxious, it provides an interface with calm colors. The interface unit also analyzes the emotional state of the user with the emotion estimation function and customizes the interface according to that emotion. For example, if the user is excited, it provides an interface that allows the user to relax. The interface unit also uses the emotion estimation function to analyze the emotional state of the user and customize the interface according to that emotion. For example, if the user is feeling doubtful, it provides an interface that gives a sense of security. In this way, by using the emotion estimation function to analyze the emotional state of the user and customizing the interface according to that emotion, user satisfaction is improved.

[0081] The conversation generation unit can build a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress. The conversation generation unit, for example, uses a generation AI to build a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress. For example, a system that tracks the learning progress of woodworking techniques is provided. The conversation generation unit also builds a system that uses a generation AI to provide the successor with the skills and know-how to learn in stages and tracks the learning progress. For example, a system that tracks the learning progress of pottery techniques is provided. The conversation generation unit also uses a generation AI to build a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress. For example, a system that tracks the learning progress of architectural techniques is provided. In this way, by building a system that provides the successor with the skills and know-how to learn in stages and tracks the learning progress, the learning effectiveness of the successor is improved.

[0082] The conversation generation unit can evaluate the skill level of the successor and provide a customized learning plan accordingly. In the conversation generation unit, for example, the generation AI evaluates the skill level of the successor and provides a customized learning plan accordingly. For example, a learning plan is provided according to the skill level of woodworking. In addition, the conversation generation unit evaluates the skill level of the successor and provides a customized learning plan accordingly. For example, a learning plan is provided according to the skill level of pottery. In addition, the conversation generation unit evaluates the skill level of the successor and provides a customized learning plan accordingly. For example, a learning plan is provided according to the skill level of construction. In this way, by evaluating the skill level of the successor and providing a customized learning plan accordingly, the learning effectiveness of the successor is improved.

[0083] The conversation generation unit can use the emotion estimation function to analyze the emotions of the successor when studying and provide feedback to maintain motivation. The conversation generation unit, for example, uses the emotion estimation function to analyze the emotions of the successor when studying and provide feedback to maintain motivation. For example, if the successor is feeling anxious, it provides an encouraging message. The conversation generation unit also analyzes the emotions of the successor when studying with the emotion estimation function and provides feedback to maintain motivation. For example, if the successor is excited, it provides positive feedback. The conversation generation unit also uses the emotion estimation function to analyze the emotions of the successor when studying and provide feedback to maintain motivation. For example, if the successor has doubts, it provides a detailed explanation. In this way, by analyzing the emotions of the successor when studying and providing feedback to maintain motivation, the learning effect of the successor is improved.

[0084] The conversation generation unit can provide the skills and know-how that the successor should learn in the form of an online course or workshop. For example, the conversation generation unit uses a generation AI to provide the skills and know-how that the successor should learn in the form of an online course. For example, an online course on woodworking techniques is provided. Furthermore, the conversation generation unit uses a generation AI to provide the skills and know-how that the successor should learn in the form of a workshop. For example, a workshop on pottery techniques is provided. Furthermore, the conversation generation unit uses a generation AI to provide the skills and know-how that the successor should learn in the form of an online course or workshop. For example, an online course or workshop on architectural techniques is provided. In this way, by providing the skills and know-how that the successor should learn in the form of an online course or workshop, the learning effectiveness of the successor is improved.

[0085] The conversation generation unit provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. The conversation generation unit, for example, provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. For example, an online forum is provided where successors of woodworking techniques and pottery techniques can interact. The conversation generation unit also provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. For example, an online forum is provided where successors of architectural techniques and interior design techniques can interact. The conversation generation unit also provides a platform where successors of different fields can interact with each other, thereby promoting the sharing of techniques and know-how. For example, an online forum is provided where successors of metal processing techniques and glass art techniques can interact. This provides a platform where successors of different fields can interact with each other, promoting the sharing of techniques and know-how, and is expected to lead to the creation of new technologies.

[0086] The conversation generation unit can use the emotion estimation function to analyze the emotions of the successor when studying and provide learning content that corresponds to those emotions. The conversation generation unit, for example, uses the emotion estimation function to analyze the emotions of the successor when studying and provides learning content that corresponds to those emotions. For example, if the successor is feeling anxious, learning content that gives a sense of security is provided. The conversation generation unit also analyzes the emotions of the successor when studying with the emotion estimation function and provides learning content that corresponds to those emotions. For example, if the successor is excited, learning content that shares the excitement is provided. The conversation generation unit also uses the emotion estimation function to analyze the emotions of the successor when studying and provides learning content that corresponds to those emotions. For example, if the successor has doubts, learning content that provides a detailed explanation is provided. In this way, by analyzing the emotions of the successor when studying and providing learning content that corresponds to those emotions, the successor's learning effectiveness is improved.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The skill transfer system can further include a progress management unit that tracks the user's learning progress. The progress management unit records the progress of the user's learned skills and know-how and visualizes the learning progress. For example, the learning progress of woodworking skills can be displayed in graphs and charts, allowing the user to see at a glance how much skill the user has mastered. The progress management unit can also suggest the next skill or know-how to learn based on the user's learning progress. For example, for a user who has mastered basic woodworking skills, it can suggest the next applied skill to advance to. Furthermore, the progress management unit can provide an individually customized learning plan based on the user's learning progress. This improves the user's learning effectiveness and makes skill transfer more efficient.

[0089] The skill transfer system can further include an emotion response unit that estimates the user's emotions and provides learning content based on the estimated emotions. The emotion response unit analyzes the emotions felt by the user while studying and provides learning content according to those emotions. For example, if the user feels anxious while studying, it can provide content that gives the user a sense of security. Also, if the user is excited, it can provide content that shares that excitement. Furthermore, the emotion response unit can adjust the progress of the study according to the user's emotions. For example, if the user is tired, it can adjust the pace of the study to slow down. In this way, by providing learning content according to the user's emotions, the learning effect can be improved and skill transfer can be made more effective.

[0090] The technology transfer system can also be equipped with a crossover section that integrates technologies and know-how from different fields to create new technologies. The crossover section combines technologies and know-how from different fields to create new technologies. For example, woodworking and metal processing technologies can be combined to develop a new product. Or, ceramics and glass art can be combined to create a new art piece. Furthermore, architecture and interior design technologies can be combined to design a new building. In this way, by integrating technologies and know-how from different fields, new technologies can be created and the scope of technology transfer can be expanded.

[0091] The technology transfer system can further include a skill evaluation unit that evaluates the user's skill level and provides a customized learning plan accordingly. The skill evaluation unit evaluates the user's skill level and provides an individually customized learning plan based on the evaluation. For example, a learning plan according to the skill level of woodworking technology can be provided. A learning plan according to the skill level of pottery technology can also be provided. Furthermore, a learning plan according to the skill level of architectural technology can be provided. In this way, by providing a customized learning plan according to the user's skill level, learning effectiveness can be improved and technology transfer can be made more effective.

[0092] The technology transfer system can further include an emotion-responsive interface unit that estimates the user's emotions and customizes the interface based on the estimated emotions. The emotion-responsive interface unit analyzes the user's emotional state and provides an interface design that corresponds to the emotion. For example, if the user is feeling anxious, it can provide an interface with calm colors. Also, if the user is excited, it can provide an interface that allows the user to relax. Furthermore, if the user is feeling doubtful, it can provide an interface that gives a sense of security. In this way, by providing an interface that corresponds to the user's emotions, user satisfaction can be improved and technology transfer can be made more effective.

[0093] The technology transfer system can also provide a voice recognition interface that allows users to ask questions in natural language. A voice recognition interface allows users to input questions in natural language. For example, questions such as "Please tell me how to process this piece of wood" can be input by voice. It is also possible to input questions such as "Please tell me how to paint pottery" by voice. It is also possible to input questions such as "Please tell me how to do foundation work for a building" by voice. In this way, by using voice recognition technology to provide an interface that allows users to ask questions in natural language, even elderly people with low IT literacy can easily use the system.

[0094] The skill transfer system can further include an emotion-responsive learning unit that estimates the user's emotions and adjusts the progress of learning based on the estimated emotions. The emotion-responsive learning unit analyzes the user's emotions during learning and adjusts the progress of learning according to those emotions. For example, if the user is tired, the learning pace can be adjusted to slow down. Also, if the user is excited, the learning pace can be adjusted to speed up. Furthermore, if the user is feeling anxious, learning content that gives a sense of security can be provided. In this way, adjusting the progress of learning according to the user's emotions improves the learning effect and makes skill transfer more effective.

[0095] The technology transfer system can also be equipped with an automatic translation function that can handle questions in different languages. When a user asks a question in a different language, the automatic translation function translates the question into the appropriate language and provides an answer. For example, a question in Japanese can be translated into English and an answer can be provided in English. A question in French can also be translated into Japanese and an answer can be provided in Japanese. Furthermore, a question in Chinese can be translated into English and an answer can be provided in English. Thus, by having an automatic translation function that can handle questions in different languages, global use becomes possible.

[0096] The skill transfer system can further include an emotion-responsive feedback unit that estimates the user's emotions and provides feedback to maintain motivation based on the estimated emotions. The emotion-responsive feedback unit analyzes the user's emotions during learning and provides feedback according to those emotions. For example, if the user is feeling anxious, it can provide an encouraging message. Also, if the user is excited, it can provide positive feedback. Furthermore, if the user has doubts, it can provide a detailed explanation. In this way, by providing feedback according to the user's emotions, motivation is maintained and learning effectiveness is improved.

[0097] The technology transfer system can also provide the technology and know-how that users need to learn in the form of online courses or workshops. Online courses are designed so that users can learn from the comfort of their own homes. For example, an online course on woodworking techniques can be provided. In addition, the workshop format allows users to learn the technology while actually experiencing it. For example, a workshop on pottery techniques can be provided. Furthermore, online courses and workshops on architectural techniques can also be provided. In this way, by providing the technology and know-how that users need to learn in the form of online courses or workshops, learning effectiveness can be improved and technology transfer can be made more effective.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The digitization department uses generative AI to digitize the skills and know-how of skilled craftsmen. For example, it analyzes information in various formats, such as handwritten notes, old books, and digital files, and converts them into unified digital data. It can also digitize the skills and know-how of skilled craftsmen as 3D models and animations to make them easier to understand visually. Furthermore, it can automatically classify the skills and know-how of skilled craftsmen and link them to related technologies and methods, creating a systematic database. Step 2: The conversation generation unit generates conversational answers to user questions based on the craftsmanship and expert know-how digitized by the digitization unit. For example, the generation AI can generate appropriate answers to user questions and provide them as specific procedures or step-by-step guides. It can also learn from the user's past question history and provide individually customized answers. Step 3: The interface unit provides the user with the answer generated by the conversation generation unit. For example, an interface using voice input and voice output can be provided, allowing the user to easily enter questions and receive answers. A simple and intuitive touchscreen interface can also be developed, with visually easy-to-understand icons and buttons. Furthermore, an emotion estimation function can be used to analyze the user's emotional state and provide an interface design to reduce stress.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. The Digitalization Department uses generative AI to digitize the skills and know-how of skilled craftsmen. a conversation generation unit that generates answers in a conversational format to questions from a user based on the craftsmanship and know-how of the skilled worker digitized by the digitization unit; an interface unit that provides the user with the answer generated by the conversation generation unit; A system characterized by:

2. The digitizing unit Analyze handwritten notes, old books, digital files, and information in various formats into a unified database 2. The system of claim 1.

3. The conversation generation unit Generate an appropriate answer to the question from the user 2. The system of claim 1.

4. The interface unit A voice input and output interface is provided, allowing the user to easily input the question and receive the answer.

2. The system of claim 1.

5. The digitizing unit The craftsmanship and know-how of the skilled workers are digitized as 3D models and animations, making them easier to understand visually.

2. The system of claim 1.

Images