System

Abstract

An object of the system according to the embodiment is to accurately record an operation or a procedure of a traditional technique and to transmit the operation or the procedure to a subsequent generation.SOLUTION: A system includes an operation recording part, an operation analysis part, and a teaching material creation part. An operation recording part records the operation or procedure of the traditional technique by using the generation AI. The motion analysis unit analyzes the motion recorded by the motion recording unit and extracts an important point or action. The teaching material creation unit creates a teaching material on the basis of the points and the actions extracted by the motion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately record the movements and procedures of traditional techniques and pass them on to future generations.

[0005] The system according to the embodiment aims to accurately record the movements and procedures of traditional techniques and pass them on to future generations. [Means for solving the problem]

[0006] The system according to the embodiment includes a motion recording unit, a motion analysis unit, and a teaching material creation unit. The motion recording unit uses a generation AI to record the motions or steps of traditional techniques. The motion analysis unit analyzes the motions recorded by the motion recording unit and extracts important points and gestures. The teaching material creation unit creates teaching materials based on the points and gestures extracted by the motion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately record the movements and procedures of traditional techniques and pass them on to future generations. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 traditional technique inheritance system according to an embodiment of the present invention is a system for memorizing the movements and key points of traditional Japanese techniques and passing them on to future generations. This system uses generative AI to record the movements and procedures of traditional techniques and pass them on to future generations. As a result, the traditional technique inheritance system records and analyzes the movements and procedures of traditional techniques and creates educational materials, making it possible to preserve and disseminate the techniques.

[0029] A traditional technique inheritance system according to an embodiment includes a motion recording unit, a motion analysis unit, and a teaching material creation unit. The motion recording unit uses a generation AI to record the motions or steps of a traditional technique. For example, the generation AI collects tea ceremony motions using a camera or sensor and records the motions. The generation AI can also record motions such as those used in calligraphy and the production process of a craft. For example, the generation AI records a series of tea ceremony motions and analyzes the movements and key points in detail. The motion analysis unit analyzes the motions recorded by the motion recording unit and extracts important points and movements. For example, the generation AI extracts important movements from tea ceremony motions, such as how to hold a tea bowl and how to prepare tea, and stores them as data to be passed on to future generations. The generation AI can also analyze brush movements and changes in writing pressure in calligraphy movements to extract important points. For example, the generation AI analyzes calligraphy motions and extracts important points based on brush movements and changes in writing pressure. The teaching material creation unit creates teaching materials based on the points and gestures extracted by the motion analysis unit. For example, the generation AI creates text and video that explain in detail the movements of the tea ceremony and provides them as teaching materials to be passed on to future generations. The generation AI can also create text and video that explain in detail the movements of calligraphy and provide them as teaching materials to be passed on to future generations. For example, the generation AI can create text and video that explain in detail the movements of calligraphy and provide them as teaching materials to be passed on to future generations. In this way, the traditional technique inheritance system can record and analyze the movements and procedures of traditional techniques and create teaching materials, thereby enabling the preservation and dissemination of techniques.

[0030] The motion recording unit can analyze minute movements and changes in force that occur during motion recording in real time and collect detailed motion data. For example, the motion recording unit uses generative AI to analyze minute hand movements and changes in force that occur during motion recording in tea ceremony in real time and collect detailed motion data. For example, it analyzes the position of the fingers and the amount of force used when holding a tea bowl. The motion recording unit also analyzes changes in brush movement and pressure during calligraphy motion recording in real time and collects detailed motion data. For example, it analyzes the angle of the brush and the strength of pressure to evaluate the beauty of the calligraphy. The motion recording unit also analyzes the movement of tools and the amount of force used during the craft production process in real time and collects detailed motion data. For example, it analyzes the movement of a carving knife and the amount of force used to evaluate the accuracy of the carving. This allows minute movements and changes in force during motion recording to be analyzed in real time and collect detailed motion data.

[0031] The motion recording unit simultaneously collects cultural and historical information behind the motion, deepening understanding of the motion. For example, while recording tea ceremony movements, the generation AI simultaneously collects information about the history and cultural background of tea ceremony to deepen understanding of the motion. For example, it analyzes the origins of tea ceremony and the differences between schools. Similarly, while recording calligraphy movements, the generation AI simultaneously collects information about the history and cultural background of calligraphy to deepen understanding of the motion. For example, it analyzes the development of calligraphy and the works of famous calligraphers. Similarly, during the process of creating a craft, the generation AI simultaneously collects information about the history and cultural background of the craft to deepen understanding of the motion. For example, it analyzes the craft's production techniques and regional characteristics. This allows the generation AI to simultaneously collect cultural and historical information behind the motion, deepening understanding of the motion.

[0032] The action recording unit simultaneously records sensory information such as sound or smell, allowing for the collection of more multifaceted data. For example, while recording the actions of a tea ceremony, the action recording unit simultaneously records sensory information such as sound and smell, allowing for the collection of more multifaceted data. For example, it records the sound of making tea and the aroma of the tea. Furthermore, while recording the actions of calligraphy, the action recording unit simultaneously records sensory information such as sound and smell, allowing for the collection of more multifaceted data. For example, it records the sound of the brush touching the paper and the scent of the ink. Furthermore, during the process of creating a craft, the action recording unit simultaneously records sensory information such as sound and smell, allowing for the collection of more multifaceted data. For example, it records the sound of tools being used during carving and the scent of wood. This allows for the simultaneous recording of sensory information such as sound and smell, allowing for the collection of more multifaceted data.

[0033] The motion recording unit simultaneously records traditional techniques from different regions or schools and performs comparative analysis, thereby revealing the diversity of techniques. For example, while recording the movements of a tea ceremony, the generation AI simultaneously records tea ceremonies from different regions or schools and performs comparative analysis. For example, the difference between the movements of Omotesenke and Urasenke is analyzed. Similarly, while recording the movements of calligraphy, the generation AI simultaneously records calligraphy from different regions or schools and performs comparative analysis. For example, the difference between the movements of kanji calligraphy and kana calligraphy is analyzed. Similarly, during the craft production process, the generation AI simultaneously records crafts from different regions or schools and performs comparative analysis. For example, the difference between the production processes of Kyoto lacquerware and Kanazawa lacquerware is analyzed. In this way, the diversity of techniques can be revealed by simultaneously recording traditional techniques from different regions or schools and performing comparative analysis.

[0034] The motion analysis unit can evaluate the efficiency and beauty of movements and extract optimal movement patterns. For example, during tea ceremony movement analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for how to hold a tea bowl and how to make tea. Also, during calligraphy movement analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for brush movement and pressure. Also, during the process of creating a craft, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for carving knife movement and force adjustment. This allows the generation AI to evaluate the efficiency and beauty of movements and extract optimal movement patterns.

[0035] The motion analysis unit can also collect examples of failed movements and provide data for learning from failure. For example, during motion analysis of tea ceremony, the motion analysis unit collects examples of failed movements by the generative AI and provides data for learning from failure. For example, it analyzes incorrect movements when holding a tea bowl or incorrect ways of making tea. Furthermore, during motion analysis of calligraphy, the motion analysis unit collects examples of failed movements by the generative AI and provides data for learning from failure. For example, it analyzes incorrect patterns of brush movement and pressure. Furthermore, during the process of creating a craft, the motion analysis unit collects examples of failed movements by the generative AI and provides data for learning from failure. For example, it analyzes incorrect patterns of carving knife movement and pressure application. In this way, it is possible to collect examples of failed movements and provide data for learning from failure.

[0036] The motion analysis unit can perform automatic translation and cultural adaptation to adapt the results of the motion analysis to different languages ​​and cultural spheres. For example, the motion analysis unit uses a generation AI to automatically translate the results of tea ceremony motion analysis to adapt it to different languages ​​and cultural spheres. For example, the motion analysis unit translates the results of tea ceremony motion analysis into English or French, and explains the cultural background of tea ceremony as well. The motion analysis unit also uses a generation AI to automatically translate the results of calligraphy motion analysis to adapt it to different languages ​​and cultural spheres. For example, the motion analysis unit translates the results of calligraphy motion analysis into Chinese or Spanish, and explains the history and techniques of calligraphy as well. The motion analysis unit also uses a generation AI to automatically translate the results of motion analysis of the craft production process to adapt it to different languages ​​and cultural spheres. For example, the motion analysis unit translates the results of craft production into German or Italian, and explains the craft production techniques and cultural background as well. In this way, automatic translation and cultural adaptation can be performed to adapt the results of the motion analysis to different languages ​​and cultural spheres.

[0037] The motion analysis unit can apply the results of the motion analysis to other fields to improve skills in those fields. For example, the motion analysis unit can apply the results of the motion analysis of tea ceremony to the field of sports to improve skills. For example, the concentration and relaxation techniques used in tea ceremony can be applied to mental training for athletes. The motion analysis unit can also apply the results of the motion analysis of calligraphy to the field of medicine to improve skills. For example, calligraphy pen pressure and hand movements can be applied to rehabilitation. The motion analysis unit can also apply the results of the motion analysis of the process of creating a craft to other fields to improve skills. For example, the precise manual work required for crafts can be applied to robotics. In this way, the results of the motion analysis can be applied to other fields to improve skills in those fields.

[0038] The teaching material creation unit can monitor a user's learning progress in real time and provide individually optimized teaching materials. For example, the generation AI in the teaching material creation unit monitors the user's tea ceremony learning progress in real time and provides individually optimized teaching materials. For example, detailed instruction on how to hold a tea bowl and how to prepare tea is provided according to the user's level of proficiency. The teaching material creation unit can also monitor the calligraphy learning progress in real time and provide individually optimized teaching materials. For example, appropriate practice methods are suggested according to the user's brush movements and pressure. The teaching material creation unit can also monitor the user's learning progress in the craft production process in real time and provide individually optimized teaching materials. For example, specific points for improvement are suggested according to the user's use of tools and the amount of force used. In this way, the user's learning progress can be monitored in real time and individually optimized teaching materials can be provided.

[0039] The teaching material creation unit generates 3D models of movements and can provide an environment in which users can learn interactively. For example, the generation AI in the teaching material creation unit generates 3D models of movements in the tea ceremony and provides an environment in which users can learn interactively. For example, the way to hold a tea bowl and how to prepare tea are reproduced in 3D models, allowing users to actually operate them. The teaching material creation unit also generates 3D models of calligraphy movements and provides an environment in which users can learn interactively. For example, the movement and pressure of a brush are reproduced in 3D models, allowing users to practice actually moving the brush. The teaching material creation unit also generates 3D models of the process of making a craft and provides an environment in which users can learn interactively. For example, the use of tools and the amount of force to be applied are reproduced in 3D models, allowing users to actually operate them. In this way, 3D models of movements can be generated and an environment in which users can learn interactively can be provided.

[0040] The teaching material creation unit can adapt the teaching materials to different devices, making them usable in multiple learning environments. For example, the teaching material creation unit uses a generation AI to adapt tea ceremony teaching materials to a VR headset, allowing users to learn tea ceremony movements in a virtual space. For example, the teaching material creation unit allows users to experience how to hold a tea bowl and how to prepare tea in a VR environment. The teaching material creation unit also uses a generation AI to adapt calligraphy teaching materials to smartphones, allowing users to easily learn calligraphy movements. For example, the teaching material creation unit allows users to practice brush movements and pressure on a smartphone screen. The teaching material creation unit also uses a generation AI to adapt teaching materials on the process of craft production to tablets, allowing users to learn interactively. For example, the teaching material creation unit allows users to practice how to use tools and how much pressure to apply on a tablet screen. This allows the teaching materials to be adapted to different devices, making them usable in a variety of learning environments.

[0041] The teaching material creation unit customizes teaching materials according to different age groups or learning levels, making it possible to accommodate a wide range of users. For example, the generation AI customizes tea ceremony teaching materials according to different age groups and learning levels, making it possible to accommodate a wide range of users. For example, it provides simple explanations for children and detailed explanations for adults. The teaching material creation unit also customizes calligraphy teaching materials according to different age groups and learning levels, making it possible to accommodate a wide range of users. For example, it provides basic practice methods for beginners and advanced techniques for advanced users. The teaching material creation unit also customizes teaching materials for the craft production process according to different age groups and learning levels, making it possible to accommodate a wide range of users. For example, it provides simple crafts for children and professional production methods for adults. This makes it possible to customize teaching materials according to different age groups and learning levels, making it possible to accommodate a wide range of users.

[0042] The action recording unit can store the recorded actions and procedures in a database, providing a system for passing them on to future generations. For example, the action recording unit can provide a system in which a generation AI stores the actions and procedures of the tea ceremony in a database and passes them on to future generations. For example, it can store detailed data on how to hold a tea bowl and how to make tea. The action recording unit can also provide a system in which a generation AI stores the actions and procedures of calligraphy in a database and passes them on to future generations. For example, it can store detailed data on brush movements and pressure. The action recording unit can also provide a system in which a generation AI stores the actions and procedures of the craft production process in a database and passes them on to future generations. For example, it can store detailed data on how to use tools and the amount of force used. This makes it possible to provide a system in which a generation AI stores the recorded actions and procedures in a database and passes them on to future generations.

[0043] The action recording unit can create new teaching materials based on the stored data and support the spread of traditional techniques. For example, the generation AI can create new teaching materials based on the stored tea ceremony data to support the spread of traditional techniques. For example, the generation AI can create teaching materials including detailed explanations on how to hold a tea bowl and how to prepare tea. The action recording unit can also create new teaching materials based on the stored calligraphy data to support the spread of traditional techniques. For example, the generation AI can create teaching materials including detailed explanations on brush movements and pressure. The action recording unit can also create new teaching materials based on the stored data on the production process of crafts to support the spread of traditional techniques. For example, the generation AI can create teaching materials including detailed explanations on how to use tools and how much force to apply. In this way, new teaching materials can be created based on the stored data to support the spread of traditional techniques.

[0044] The action recording unit can publish the saved data online and disseminate it widely. For example, the action recording unit publishes the tea ceremony data saved by the generative AI online and disseminates it widely. For example, it creates a website including detailed explanations on how to hold a tea bowl and how to make tea. The action recording unit also publishes the calligraphy data saved by the generative AI online and disseminates it widely. For example, it creates a website including detailed explanations on brush movements and pressure. The action recording unit also publishes the craft production process data saved by the generative AI online and disseminates it widely. For example, it creates a website including detailed explanations on how to use tools and how much force to use. In this way, the saved data can be published online and disseminated widely.

[0045] The action recording unit can create teaching materials adapted to different regions or cultural spheres based on the stored data, thereby promoting international dissemination. For example, the generation AI can create teaching materials adapted to different regions or cultural spheres based on the stored tea ceremony data, thereby promoting international dissemination. For example, the generation AI can translate the data into English or French, including explaining the cultural background of the tea ceremony. The generation AI can also create teaching materials adapted to different regions or cultural spheres based on the stored calligraphy data, thereby promoting international dissemination. For example, the generation AI can translate the data into Chinese or Spanish, including explaining the history and techniques of calligraphy. The generation AI can also create teaching materials adapted to different regions or cultural spheres based on the stored data of the craft production process, thereby promoting international dissemination. For example, the generation AI can translate the data into German or Italian, including explaining the craft production techniques and cultural background. This allows the generation AI to create teaching materials adapted to different regions or cultural spheres based on the stored data, thereby promoting international dissemination.

[0046] The action recording unit can create teaching materials for different age groups or learning levels based on the stored data, thereby catering to a wide range of users. For example, the generation AI can create teaching materials for different age groups or learning levels based on the stored tea ceremony data, thereby catering to a wide range of users. For example, simple explanations for children and detailed explanations for adults can be provided. The generation AI can also create teaching materials for different age groups or learning levels based on the stored calligraphy data, thereby catering to a wide range of users. For example, basic practice methods for beginners and advanced techniques for advanced users can be provided. The generation AI can also create teaching materials for different age groups or learning levels based on the stored data of the craft production process, thereby catering to a wide range of users. For example, simple crafts for children and more advanced production methods for adults can be provided. This allows the generation AI to create teaching materials for different age groups or learning levels based on the stored data, thereby catering to a wide range of users.

[0047] The action recording unit can record the actions of a master and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can record the actions of a tea ceremony master and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can create teaching materials including detailed explanations of how to hold a tea bowl and how to make tea. The action recording unit can also record the actions of a calligraphy master and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can create teaching materials including detailed explanations of brush movements and pressure. The action recording unit can also record the actions of a master during the process of creating a craft and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can create teaching materials including detailed explanations of how to use tools and how much force to use. In this way, the master's actions can be recorded and provided as teaching materials to be passed on to apprentices.

[0048] The motion recording unit can analyze the movements of a master and compare them with the movements of a disciple to provide feedback. For example, the generation AI in the motion recording unit can analyze the movements of a tea ceremony master and compare them with the movements of the disciple to provide feedback. For example, it can point out differences in how the master holds the tea bowl or how the tea is made, and suggest areas for improvement. The motion recording unit can also analyze the movements of a calligraphy master and compare them with the movements of the disciple to provide feedback. For example, it can point out differences in brush movement and pressure, and suggest areas for improvement. The motion recording unit can also analyze the movements of a master in the process of creating a craft, and compare them with the movements of the disciple to provide feedback. For example, it can point out differences in how the tool is used or the amount of force applied, and suggest areas for improvement. In this way, the generation AI can analyze the movements of a master and compare them with the movements of the disciple to provide feedback.

[0049] The action recording unit can record the actions of a master and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can record the actions of a tea ceremony master and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can translate the actions into English or French and explain the cultural background of the tea ceremony. The action recording unit can also record the actions of a calligraphy master and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can translate the actions into Chinese or Spanish and explain the history and techniques of calligraphy. The generation AI can also record the actions of a master during the process of creating a craft item and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can translate the actions into German or Italian and explain the craft item's production techniques and cultural background. This allows the generation AI to record the actions of a master and provide them as teaching materials adapted to different regions or cultural spheres.

[0050] The action recording unit can record the actions of a master and provide them as teaching materials for different age groups or learning levels. For example, the generation AI can record the actions of a tea ceremony master and provide them as teaching materials for different age groups or learning levels. For example, it can provide simple explanations for children and detailed explanations for adults. The action recording unit can also record the actions of a calligraphy master and provide them as teaching materials for different age groups or learning levels. For example, it can provide basic practice methods for beginners and advanced techniques for advanced masters. The action recording unit can also record the actions of a master during the process of making a craft and provide them as teaching materials for different age groups or learning levels. For example, it can provide simple crafts for children and more advanced production methods for adults. This allows the generation AI to record the actions of a master and provide them as teaching materials for different age groups or learning levels.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The motion recording unit simultaneously collects cultural and historical information behind the motion, deepening understanding of the motion. For example, while recording tea ceremony movements, the generation AI simultaneously collects information about the history and cultural background of tea ceremony to deepen understanding of the motion. For example, it analyzes the origins of tea ceremony and the differences between schools. Similarly, while recording calligraphy movements, the generation AI simultaneously collects information about the history and cultural background of calligraphy to deepen understanding of the motion. For example, it analyzes the development of calligraphy and the works of famous calligraphers. Similarly, during the process of creating a craft, the generation AI simultaneously collects information about the history and cultural background of the craft to deepen understanding of the motion. For example, it analyzes the craft's production techniques and regional characteristics. This allows the generation AI to simultaneously collect cultural and historical information behind the motion, deepening understanding of the motion.

[0053] The action recording unit simultaneously records sensory information such as sound or smell, allowing for the collection of more multifaceted data. For example, while recording the movements of a tea ceremony, the generation AI simultaneously records sensory information such as sound and smell to collect more multifaceted data. For example, it records the sound of making tea and the aroma of the tea. Similarly, while recording the movements of calligraphy, the action recording unit simultaneously records sensory information such as sound and smell to collect more multifaceted data. For example, it records the sound of the brush touching the paper and the scent of the ink. Similarly, during the process of creating a craft, the generation AI simultaneously records sensory information such as sound and smell to collect more multifaceted data. For example, it records the sound of tools being used during carving and the scent of wood. This allows for the simultaneous recording of sensory information such as sound and smell to collect more multifaceted data.

[0054] The motion recording unit simultaneously records traditional techniques from different regions or schools and performs comparative analysis, thereby revealing the diversity of techniques. For example, while recording the movements of a tea ceremony, the generation AI simultaneously records tea ceremonies from different regions or schools and performs comparative analysis. For example, it analyzes the differences in movements between Omotesenke and Urasenke. Similarly, while recording calligraphy movements, the generation AI simultaneously records calligraphy from different regions or schools and performs comparative analysis. For example, it analyzes the differences in movements between kanji calligraphy and kana calligraphy. Similarly, during the craft production process, the generation AI simultaneously records crafts from different regions or schools and performs comparative analysis. For example, it analyzes the differences in the production processes of lacquerware from Kyoto and Kanazawa. This allows the traditional techniques of different regions and schools to be simultaneously recorded and comparative analysis, thereby revealing the diversity of techniques.

[0055] The motion analysis unit can evaluate the efficiency and beauty of movements and extract optimal movement patterns. For example, during a tea ceremony motion analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for how to hold a tea bowl and how to make tea. Similarly, during a calligraphy motion analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for brush movement and pressure. Similarly, during the process of creating a craft, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for carving knife movement and force application. This allows the generation AI to evaluate the efficiency and beauty of movements and extract optimal movement patterns.

[0056] The motion analysis unit can also collect examples of failed movements and provide data for learning from failure. For example, during motion analysis of tea ceremony, the generative AI can collect examples of failed movements and provide data for learning from failure. For example, it can analyze incorrect movements when holding a tea bowl or incorrect ways of making tea. Similarly, during motion analysis of calligraphy, the motion analysis unit can collect examples of failed movements and provide data for learning from failure. For example, it can analyze incorrect patterns of brush movement and pressure. Similarly, during the process of creating a craft, the generative AI can collect examples of failed movements and provide data for learning from failure. For example, it can analyze incorrect patterns of carving knife movement and pressure. This allows the generative AI to collect examples of failed movements and provide data for learning from failure.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The action recording unit uses the generation AI to record the actions or procedures of traditional techniques. For example, the generation AI collects tea ceremony actions using cameras and sensors and records the actions. The generation AI can also record actions such as calligraphy and the process of creating crafts. Step 2: The motion analysis unit analyzes the movements recorded by the motion recording unit and extracts important points and gestures. For example, in the movements of the tea ceremony, the generation AI extracts important gestures, such as how to hold a tea bowl and how to prepare tea, and saves them as data to be passed on to future generations. The generation AI can also analyze the movements of the brush and changes in writing pressure in calligraphy movements and extract important points. Step 3: The teaching material creation unit creates teaching materials based on the points and gestures extracted by the motion analysis unit. For example, the generation AI creates text and video that explain in detail the movements of the tea ceremony and provides them as teaching materials to be passed on to future generations. The generation AI can also create text and video that explain in detail the movements of calligraphy and provide them as teaching materials to be passed on to future generations.

[0059] (Example 2) The traditional technique inheritance system according to an embodiment of the present invention is a system for memorizing the movements and key points of traditional Japanese techniques and passing them on to future generations. This system uses generative AI to record the movements and procedures of traditional techniques and pass them on to future generations. As a result, the traditional technique inheritance system records and analyzes the movements and procedures of traditional techniques and creates educational materials, making it possible to preserve and disseminate the techniques.

[0060] A traditional technique inheritance system according to an embodiment includes a motion recording unit, a motion analysis unit, and a teaching material creation unit. The motion recording unit uses a generation AI to record the motions or steps of a traditional technique. For example, the generation AI collects tea ceremony motions using a camera or sensor and records the motions. The generation AI can also record motions such as those used in calligraphy and the production process of a craft. For example, the generation AI records a series of tea ceremony motions and analyzes the movements and key points in detail. The motion analysis unit analyzes the motions recorded by the motion recording unit and extracts important points and movements. For example, the generation AI extracts important movements from tea ceremony motions, such as how to hold a tea bowl and how to prepare tea, and stores them as data to be passed on to future generations. The generation AI can also analyze brush movements and changes in writing pressure in calligraphy movements to extract important points. For example, the generation AI analyzes calligraphy motions and extracts important points based on brush movements and changes in writing pressure. The teaching material creation unit creates teaching materials based on the points and gestures extracted by the motion analysis unit. For example, the generation AI creates text and video that explain in detail the movements of the tea ceremony and provides them as teaching materials to be passed on to future generations. The generation AI can also create text and video that explain in detail the movements of calligraphy and provide them as teaching materials to be passed on to future generations. For example, the generation AI can create text and video that explain in detail the movements of calligraphy and provide them as teaching materials to be passed on to future generations. In this way, the traditional technique inheritance system can record and analyze the movements and procedures of traditional techniques and create teaching materials, thereby enabling the preservation and dissemination of techniques.

[0061] The motion recording unit can analyze minute movements and changes in force that occur during motion recording in real time and collect detailed motion data. For example, the motion recording unit uses generative AI to analyze minute hand movements and changes in force that occur during motion recording in tea ceremony in real time and collect detailed motion data. For example, it analyzes the position of the fingers and the amount of force used when holding a tea bowl. The motion recording unit also analyzes changes in brush movement and pressure during calligraphy motion recording in real time and collects detailed motion data. For example, it analyzes the angle of the brush and the strength of pressure to evaluate the beauty of the calligraphy. The motion recording unit also analyzes the movement of tools and the amount of force used during the craft production process in real time and collects detailed motion data. For example, it analyzes the movement of a carving knife and the amount of force used to evaluate the accuracy of the carving. This allows minute movements and changes in force during motion recording to be analyzed in real time and collect detailed motion data.

[0062] The motion recording unit simultaneously collects cultural and historical information behind the motion, deepening understanding of the motion. For example, while recording tea ceremony movements, the generation AI simultaneously collects information about the history and cultural background of tea ceremony to deepen understanding of the motion. For example, it analyzes the origins of tea ceremony and the differences between schools. Similarly, while recording calligraphy movements, the generation AI simultaneously collects information about the history and cultural background of calligraphy to deepen understanding of the motion. For example, it analyzes the development of calligraphy and the works of famous calligraphers. Similarly, during the process of creating a craft, the generation AI simultaneously collects information about the history and cultural background of the craft to deepen understanding of the motion. For example, it analyzes the craft's production techniques and regional characteristics. This allows the generation AI to simultaneously collect cultural and historical information behind the motion, deepening understanding of the motion.

[0063] The action recording unit can use the emotion estimation function to record the emotional state when performing an action and analyze the relationship between emotion and action. For example, while recording tea ceremony actions, the action recording unit has the generation AI use the emotion estimation function to record the emotional state when performing the tea ceremony and analyze the relationship between emotion and action. For example, it analyzes the level of concentration and relaxation when making tea. Also, while recording calligraphy actions, the action recording unit has the generation AI use the emotion estimation function to record the emotional state when performing calligraphy and analyze the relationship between emotion and action. For example, it analyzes the level of tension and satisfaction when writing calligraphy. Also, during the process of creating a craft, the generation AI uses the emotion estimation function to record the emotional state when creating a craft and analyze the relationship between emotion and action. For example, it analyzes the level of concentration and sense of accomplishment when carving. In this way, the emotion estimation function can be used to record the emotional state when performing an action and analyze the relationship between emotion and action.

[0064] The action recording unit simultaneously records sensory information such as sound or smell, allowing for the collection of more multifaceted data. For example, while recording the actions of a tea ceremony, the action recording unit simultaneously records sensory information such as sound and smell, allowing for the collection of more multifaceted data. For example, it records the sound of making tea and the aroma of the tea. Furthermore, while recording the actions of calligraphy, the action recording unit simultaneously records sensory information such as sound and smell, allowing for the collection of more multifaceted data. For example, it records the sound of the brush touching the paper and the scent of the ink. Furthermore, during the process of creating a craft, the action recording unit simultaneously records sensory information such as sound and smell, allowing for the collection of more multifaceted data. For example, it records the sound of tools being used during carving and the scent of wood. This allows for the simultaneous recording of sensory information such as sound and smell, allowing for the collection of more multifaceted data.

[0065] The motion recording unit simultaneously records traditional techniques from different regions or schools and performs comparative analysis, thereby revealing the diversity of techniques. For example, while recording the movements of a tea ceremony, the generation AI simultaneously records tea ceremonies from different regions or schools and performs comparative analysis. For example, the difference between the movements of Omotesenke and Urasenke is analyzed. Similarly, while recording the movements of calligraphy, the generation AI simultaneously records calligraphy from different regions or schools and performs comparative analysis. For example, the difference between the movements of kanji calligraphy and kana calligraphy is analyzed. Similarly, during the craft production process, the generation AI simultaneously records crafts from different regions or schools and performs comparative analysis. For example, the difference between the production processes of Kyoto lacquerware and Kanazawa lacquerware is analyzed. In this way, the diversity of techniques can be revealed by simultaneously recording traditional techniques from different regions or schools and performing comparative analysis.

[0066] The motion recording unit uses the emotion estimation function to monitor emotional changes during motion recording in real time and make suggestions for improving the motion according to the emotion. For example, during tea ceremony motion recording, the generation AI uses the emotion estimation function to monitor emotional changes in real time and make suggestions for improving the motion according to the emotion. For example, if the subject is nervous, the generation AI makes suggestions to relax. Furthermore, during calligraphy motion recording, the generation AI uses the emotion estimation function to monitor emotional changes in real time and make suggestions for improving the motion according to the emotion. For example, if the subject is losing concentration, the generation AI makes suggestions to improve the concentration. Furthermore, during the process of creating a craft, the generation AI uses the emotion estimation function to monitor emotional changes in real time and make suggestions for improving the motion according to the emotion. For example, if the subject is tired, the generation AI makes suggestions to take a break. In this way, the emotion estimation function can be used to monitor emotional changes during motion recording in real time and make suggestions for improving the motion according to the emotion.

[0067] The motion analysis unit can evaluate the efficiency and beauty of movements and extract optimal movement patterns. For example, during tea ceremony movement analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for how to hold a tea bowl and how to make tea. Also, during calligraphy movement analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for brush movement and pressure. Also, during the process of creating a craft, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for carving knife movement and force adjustment. This allows the generation AI to evaluate the efficiency and beauty of movements and extract optimal movement patterns.

[0068] The motion analysis unit can also collect examples of failed movements and provide data for learning from failure. For example, during motion analysis of tea ceremony, the motion analysis unit collects examples of failed movements by the generative AI and provides data for learning from failure. For example, it analyzes incorrect movements when holding a tea bowl or incorrect ways of making tea. Furthermore, during motion analysis of calligraphy, the motion analysis unit collects examples of failed movements by the generative AI and provides data for learning from failure. For example, it analyzes incorrect patterns of brush movement and pressure. Furthermore, during the process of creating a craft, the motion analysis unit collects examples of failed movements by the generative AI and provides data for learning from failure. For example, it analyzes incorrect patterns of carving knife movement and pressure application. In this way, it is possible to collect examples of failed movements and provide data for learning from failure.

[0069] The motion analysis unit can use the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, during tea ceremony motion analysis, the generation AI uses the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, the generation AI extracts improvement points for a movement to relax when the person is nervous. Furthermore, during calligraphy motion analysis, the generation AI uses the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, the generation AI extracts improvement points for a movement to increase concentration when the person is losing concentration. Furthermore, during the process of creating a craft, the generation AI uses the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, the generation AI extracts improvement points for a movement to take a break when the person is tired. In this way, the emotion estimation function can be used to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion.

[0070] The motion analysis unit can perform automatic translation and cultural adaptation to adapt the results of the motion analysis to different languages ​​and cultural spheres. For example, the motion analysis unit uses a generation AI to automatically translate the results of tea ceremony motion analysis to adapt it to different languages ​​and cultural spheres. For example, the motion analysis unit translates the results of tea ceremony motion analysis into English or French, and explains the cultural background of tea ceremony as well. The motion analysis unit also uses a generation AI to automatically translate the results of calligraphy motion analysis to adapt it to different languages ​​and cultural spheres. For example, the motion analysis unit translates the results of calligraphy motion analysis into Chinese or Spanish, and explains the history and techniques of calligraphy as well. The motion analysis unit also uses a generation AI to automatically translate the results of motion analysis of the craft production process to adapt it to different languages ​​and cultural spheres. For example, the motion analysis unit translates the results of craft production into German or Italian, and explains the craft production techniques and cultural background as well. In this way, automatic translation and cultural adaptation can be performed to adapt the results of the motion analysis to different languages ​​and cultural spheres.

[0071] The motion analysis unit can apply the results of the motion analysis to other fields to improve skills in those fields. For example, the motion analysis unit can apply the results of the motion analysis of tea ceremony to the field of sports to improve skills. For example, the concentration and relaxation techniques used in tea ceremony can be applied to mental training for athletes. The motion analysis unit can also apply the results of the motion analysis of calligraphy to the field of medicine to improve skills. For example, calligraphy pen pressure and hand movements can be applied to rehabilitation. The motion analysis unit can also apply the results of the motion analysis of the process of creating a craft to other fields to improve skills. For example, the precise manual work required for crafts can be applied to robotics. In this way, the results of the motion analysis can be applied to other fields to improve skills in those fields.

[0072] The motion analysis unit can use the emotion estimation function to collect the user's emotional reactions to the motion analysis results and provide emotion-based feedback. The motion analysis unit can, for example, collect the user's emotional reactions to the motion analysis results of tea ceremony and provide emotion-based feedback. For example, it can make suggestions for motion improvement that will help the user relax. The motion analysis unit can also collect the user's emotional reactions to the motion analysis results of calligraphy and provide emotion-based feedback. For example, it can make suggestions for motion improvement that will help the user increase concentration. The motion analysis unit can also collect the user's emotional reactions to the motion analysis results of the craft production process and provide emotion-based feedback. For example, it can make suggestions for motion improvement that will give the user a sense of accomplishment. In this way, the emotion estimation function can be used to collect the user's emotional reactions to the motion analysis results and provide emotion-based feedback.

[0073] The teaching material creation unit can monitor a user's learning progress in real time and provide individually optimized teaching materials. For example, the generation AI in the teaching material creation unit monitors the user's tea ceremony learning progress in real time and provides individually optimized teaching materials. For example, detailed instruction on how to hold a tea bowl and how to prepare tea is provided according to the user's level of proficiency. The teaching material creation unit can also monitor the calligraphy learning progress in real time and provide individually optimized teaching materials. For example, appropriate practice methods are suggested according to the user's brush movements and pressure. The teaching material creation unit can also monitor the user's learning progress in the craft production process in real time and provide individually optimized teaching materials. For example, specific points for improvement are suggested according to the user's use of tools and the amount of force used. In this way, the user's learning progress can be monitored in real time and individually optimized teaching materials can be provided.

[0074] The teaching material creation unit generates 3D models of movements and can provide an environment in which users can learn interactively. For example, the generation AI in the teaching material creation unit generates 3D models of movements in the tea ceremony and provides an environment in which users can learn interactively. For example, the way to hold a tea bowl and how to prepare tea are reproduced in 3D models, allowing users to actually operate them. The teaching material creation unit also generates 3D models of calligraphy movements and provides an environment in which users can learn interactively. For example, the movement and pressure of a brush are reproduced in 3D models, allowing users to practice actually moving the brush. The teaching material creation unit also generates 3D models of the process of making a craft and provides an environment in which users can learn interactively. For example, the use of tools and the amount of force to be applied are reproduced in 3D models, allowing users to actually operate them. In this way, 3D models of movements can be generated and an environment in which users can learn interactively can be provided.

[0075] The teaching material creation unit can use the emotion estimation function to analyze the user's emotional state during learning and provide learning support according to the emotion. For example, the teaching material creation unit uses the emotion estimation function to analyze the user's emotional state while the generation AI is learning tea ceremony and provide learning support according to the emotion. For example, if the user is nervous, the teaching material creation unit provides advice on how to relax. The teaching material creation unit can also use the emotion estimation function to analyze the user's emotional state during calligraphy learning and provide learning support according to the emotion. For example, if the user is lacking concentration, the teaching material creation unit provides advice on how to improve concentration. The teaching material creation unit can also use the emotion estimation function to analyze the user's emotional state during learning the process of making a craft and provide learning support according to the emotion. For example, if the user is tired, the teaching material creation unit provides advice on how to take a break. In this way, the emotion estimation function can be used to analyze the user's emotional state during learning and provide learning support according to the emotion.

[0076] The teaching material creation unit can adapt the teaching materials to different devices, making them usable in multiple learning environments. For example, the teaching material creation unit uses a generation AI to adapt tea ceremony teaching materials to a VR headset, allowing users to learn tea ceremony movements in a virtual space. For example, the teaching material creation unit allows users to experience how to hold a tea bowl and how to prepare tea in a VR environment. The teaching material creation unit also uses a generation AI to adapt calligraphy teaching materials to smartphones, allowing users to easily learn calligraphy movements. For example, the teaching material creation unit allows users to practice brush movements and pressure on a smartphone screen. The teaching material creation unit also uses a generation AI to adapt teaching materials on the process of craft production to tablets, allowing users to learn interactively. For example, the teaching material creation unit allows users to practice how to use tools and how much pressure to apply on a tablet screen. This allows the teaching materials to be adapted to different devices, making them usable in a variety of learning environments.

[0077] The teaching material creation unit customizes teaching materials according to different age groups or learning levels, making it possible to accommodate a wide range of users. For example, the generation AI customizes tea ceremony teaching materials according to different age groups and learning levels, making it possible to accommodate a wide range of users. For example, it provides simple explanations for children and detailed explanations for adults. The teaching material creation unit also customizes calligraphy teaching materials according to different age groups and learning levels, making it possible to accommodate a wide range of users. For example, it provides basic practice methods for beginners and advanced techniques for advanced users. The teaching material creation unit also customizes teaching materials for the craft production process according to different age groups and learning levels, making it possible to accommodate a wide range of users. For example, it provides simple crafts for children and professional production methods for adults. This makes it possible to customize teaching materials according to different age groups and learning levels, making it possible to accommodate a wide range of users.

[0078] The teaching material creation unit can use the emotion estimation function to collect the user's emotional reactions to the teaching materials and improve the teaching materials based on the emotions. For example, the generation AI in the teaching material creation unit collects the user's emotional reactions to tea ceremony teaching materials and improves the teaching materials based on the emotions. For example, the content of the teaching materials is changed to one that helps the user relax. The teaching material creation unit can also use the generation AI to collect the user's emotional reactions to calligraphy teaching materials and improve the teaching materials based on the emotions. For example, the content of the teaching materials is changed to one that helps the user increase concentration. The teaching material creation unit can also use the generation AI to collect the user's emotional reactions to teaching materials on the process of making a craft and improve the teaching materials based on the emotions. For example, the content of the teaching materials is changed to one that gives the user a sense of accomplishment. In this way, the emotion estimation function can be used to collect the user's emotional reactions to the teaching materials and improve the teaching materials based on the emotions.

[0079] The action recording unit can store the recorded actions and procedures in a database, providing a system for passing them on to future generations. For example, the action recording unit can provide a system in which a generation AI stores the actions and procedures of the tea ceremony in a database and passes them on to future generations. For example, it can store detailed data on how to hold a tea bowl and how to make tea. The action recording unit can also provide a system in which a generation AI stores the actions and procedures of calligraphy in a database and passes them on to future generations. For example, it can store detailed data on brush movements and pressure. The action recording unit can also provide a system in which a generation AI stores the actions and procedures of the craft production process in a database and passes them on to future generations. For example, it can store detailed data on how to use tools and the amount of force used. This makes it possible to provide a system in which a generation AI stores the recorded actions and procedures in a database and passes them on to future generations.

[0080] The action recording unit can create new teaching materials based on the stored data and support the spread of traditional techniques. For example, the generation AI can create new teaching materials based on the stored tea ceremony data to support the spread of traditional techniques. For example, the generation AI can create teaching materials including detailed explanations on how to hold a tea bowl and how to prepare tea. The action recording unit can also create new teaching materials based on the stored calligraphy data to support the spread of traditional techniques. For example, the generation AI can create teaching materials including detailed explanations on brush movements and pressure. The action recording unit can also create new teaching materials based on the stored data on the production process of crafts to support the spread of traditional techniques. For example, the generation AI can create teaching materials including detailed explanations on how to use tools and how much force to apply. In this way, new teaching materials can be created based on the stored data to support the spread of traditional techniques.

[0081] The action recording unit can publish the saved data online and disseminate it widely. For example, the action recording unit publishes the tea ceremony data saved by the generative AI online and disseminates it widely. For example, it creates a website including detailed explanations on how to hold a tea bowl and how to make tea. The action recording unit also publishes the calligraphy data saved by the generative AI online and disseminates it widely. For example, it creates a website including detailed explanations on brush movements and pressure. The action recording unit also publishes the craft production process data saved by the generative AI online and disseminates it widely. For example, it creates a website including detailed explanations on how to use tools and how much force to use. In this way, the saved data can be published online and disseminated widely.

[0082] The action recording unit can create teaching materials adapted to different regions or cultural spheres based on the stored data, thereby promoting international dissemination. For example, the generation AI can create teaching materials adapted to different regions or cultural spheres based on the stored tea ceremony data, thereby promoting international dissemination. For example, the generation AI can translate the data into English or French, including explaining the cultural background of the tea ceremony. The generation AI can also create teaching materials adapted to different regions or cultural spheres based on the stored calligraphy data, thereby promoting international dissemination. For example, the generation AI can translate the data into Chinese or Spanish, including explaining the history and techniques of calligraphy. The generation AI can also create teaching materials adapted to different regions or cultural spheres based on the stored data of the craft production process, thereby promoting international dissemination. For example, the generation AI can translate the data into German or Italian, including explaining the craft production techniques and cultural background. This allows the generation AI to create teaching materials adapted to different regions or cultural spheres based on the stored data, thereby promoting international dissemination.

[0083] The action recording unit can create teaching materials for different age groups or learning levels based on the stored data, thereby catering to a wide range of users. For example, the generation AI can create teaching materials for different age groups or learning levels based on the stored tea ceremony data, thereby catering to a wide range of users. For example, simple explanations for children and detailed explanations for adults can be provided. The generation AI can also create teaching materials for different age groups or learning levels based on the stored calligraphy data, thereby catering to a wide range of users. For example, basic practice methods for beginners and advanced techniques for advanced users can be provided. The generation AI can also create teaching materials for different age groups or learning levels based on the stored data of the craft production process, thereby catering to a wide range of users. For example, simple crafts for children and more advanced production methods for adults can be provided. This allows the generation AI to create teaching materials for different age groups or learning levels based on the stored data, thereby catering to a wide range of users.

[0084] The action recording unit uses the emotion estimation function to collect the user's emotional reactions to the stored data and improve the teaching materials based on the emotions. For example, the action recording unit collects the user's emotional reactions to the tea ceremony data stored by the generation AI and improves the teaching materials based on the emotions. For example, the content of the teaching materials is changed to one that helps the user relax. The action recording unit also collects the user's emotional reactions to the calligraphy data stored by the generation AI and improves the teaching materials based on the emotions. For example, the content of the teaching materials is changed to one that helps the user increase concentration. The action recording unit also collects the user's emotional reactions to the craft production process data stored by the generation AI and improves the teaching materials based on the emotions. For example, the content of the teaching materials is changed to one that gives the user a sense of accomplishment. In this way, the emotion estimation function can be used to collect the user's emotional reactions to the stored data and improve the teaching materials based on the emotions.

[0085] The action recording unit can record the actions of a master and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can record the actions of a tea ceremony master and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can create teaching materials including detailed explanations of how to hold a tea bowl and how to make tea. The action recording unit can also record the actions of a calligraphy master and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can create teaching materials including detailed explanations of brush movements and pressure. The action recording unit can also record the actions of a master during the process of creating a craft and provide them as teaching materials to be passed on to apprentices. For example, the generation AI can create teaching materials including detailed explanations of how to use tools and how much force to use. In this way, the master's actions can be recorded and provided as teaching materials to be passed on to apprentices.

[0086] The motion recording unit can analyze the movements of a master and compare them with the movements of a disciple to provide feedback. For example, the generation AI in the motion recording unit can analyze the movements of a tea ceremony master and compare them with the movements of the disciple to provide feedback. For example, it can point out differences in how the master holds the tea bowl or how the tea is made, and suggest areas for improvement. The motion recording unit can also analyze the movements of a calligraphy master and compare them with the movements of the disciple to provide feedback. For example, it can point out differences in brush movement and pressure, and suggest areas for improvement. The motion recording unit can also analyze the movements of a master in the process of creating a craft, and compare them with the movements of the disciple to provide feedback. For example, it can point out differences in how the tool is used or the amount of force applied, and suggest areas for improvement. In this way, the generation AI can analyze the movements of a master and compare them with the movements of the disciple to provide feedback.

[0087] The action recording unit can use the emotion estimation function to support the transfer of skills between a master and a disciple. For example, in order for the generation AI to support the transfer of skills between a tea ceremony master and a disciple, the action recording unit uses the emotion estimation function to analyze the master's emotional state and provide appropriate feedback to the disciple. For example, if the master is relaxed, the action recording unit provides advice to the disciple on how to relax. In addition, in order for the generation AI to support the transfer of skills between a calligraphy master and a disciple, the action recording unit uses the emotion estimation function to analyze the master's emotional state and provide appropriate feedback to the disciple. For example, if the master is concentrating, the action recording unit provides advice to the disciple on how to improve their concentration. In addition, in order for the generation AI to support the transfer of skills between a master and a disciple in the process of creating a craft, the action recording unit uses the emotion estimation function to analyze the master's emotional state and provide appropriate feedback to the disciple. For example, if the master is feeling a sense of accomplishment, the action recording unit provides advice to the disciple on how to feel a sense of accomplishment. In this way, the emotion estimation function can be used to support the transfer of skills between a master and a disciple.

[0088] The action recording unit can record the actions of a master and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can record the actions of a tea ceremony master and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can translate the actions into English or French and explain the cultural background of the tea ceremony. The action recording unit can also record the actions of a calligraphy master and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can translate the actions into Chinese or Spanish and explain the history and techniques of calligraphy. The generation AI can also record the actions of a master during the process of creating a craft item and provide them as teaching materials adapted to different regions or cultural spheres. For example, the generation AI can translate the actions into German or Italian and explain the craft item's production techniques and cultural background. This allows the generation AI to record the actions of a master and provide them as teaching materials adapted to different regions or cultural spheres.

[0089] The action recording unit can record the actions of a master and provide them as teaching materials for different age groups or learning levels. For example, the generation AI can record the actions of a tea ceremony master and provide them as teaching materials for different age groups or learning levels. For example, it can provide simple explanations for children and detailed explanations for adults. The action recording unit can also record the actions of a calligraphy master and provide them as teaching materials for different age groups or learning levels. For example, it can provide basic practice methods for beginners and advanced techniques for advanced masters. The action recording unit can also record the actions of a master during the process of making a craft and provide them as teaching materials for different age groups or learning levels. For example, it can provide simple crafts for children and more advanced production methods for adults. This allows the generation AI to record the actions of a master and provide them as teaching materials for different age groups or learning levels.

[0090] The action recording unit can use the emotion estimation function to support the transfer of skills between a master and a disciple. For example, in order for the generation AI to support the transfer of skills between a tea ceremony master and a disciple, the action recording unit uses the emotion estimation function to analyze the master's emotional state and provide appropriate feedback to the disciple. For example, if the master is relaxed, the action recording unit provides advice to the disciple on how to relax. In addition, in order for the generation AI to support the transfer of skills between a calligraphy master and a disciple, the action recording unit uses the emotion estimation function to analyze the master's emotional state and provide appropriate feedback to the disciple. For example, if the master is concentrating, the action recording unit provides advice to the disciple on how to improve their concentration. In addition, in order for the generation AI to support the transfer of skills between a master and a disciple in the process of creating a craft, the action recording unit uses the emotion estimation function to analyze the master's emotional state and provide appropriate feedback to the disciple. For example, if the master is feeling a sense of accomplishment, the action recording unit provides advice to the disciple on how to feel a sense of accomplishment. In this way, the emotion estimation function can be used to support the transfer of skills between a master and a disciple.

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

[0092] The motion recording unit simultaneously collects cultural and historical information behind the motion, deepening understanding of the motion. For example, while recording tea ceremony movements, the generation AI simultaneously collects information about the history and cultural background of tea ceremony to deepen understanding of the motion. For example, it analyzes the origins of tea ceremony and the differences between schools. Similarly, while recording calligraphy movements, the generation AI simultaneously collects information about the history and cultural background of calligraphy to deepen understanding of the motion. For example, it analyzes the development of calligraphy and the works of famous calligraphers. Similarly, during the process of creating a craft, the generation AI simultaneously collects information about the history and cultural background of the craft to deepen understanding of the motion. For example, it analyzes the craft's production techniques and regional characteristics. This allows the generation AI to simultaneously collect cultural and historical information behind the motion, deepening understanding of the motion.

[0093] The action recording unit simultaneously records sensory information such as sound or smell, allowing for the collection of more multifaceted data. For example, while recording the movements of a tea ceremony, the generation AI simultaneously records sensory information such as sound and smell to collect more multifaceted data. For example, it records the sound of making tea and the aroma of the tea. Similarly, while recording the movements of calligraphy, the action recording unit simultaneously records sensory information such as sound and smell to collect more multifaceted data. For example, it records the sound of the brush touching the paper and the scent of the ink. Similarly, during the process of creating a craft, the generation AI simultaneously records sensory information such as sound and smell to collect more multifaceted data. For example, it records the sound of tools being used during carving and the scent of wood. This allows for the simultaneous recording of sensory information such as sound and smell to collect more multifaceted data.

[0094] The motion recording unit simultaneously records traditional techniques from different regions or schools and performs comparative analysis, thereby revealing the diversity of techniques. For example, while recording the movements of a tea ceremony, the generation AI simultaneously records tea ceremonies from different regions or schools and performs comparative analysis. For example, it analyzes the differences in movements between Omotesenke and Urasenke. Similarly, while recording calligraphy movements, the generation AI simultaneously records calligraphy from different regions or schools and performs comparative analysis. For example, it analyzes the differences in movements between kanji calligraphy and kana calligraphy. Similarly, during the craft production process, the generation AI simultaneously records crafts from different regions or schools and performs comparative analysis. For example, it analyzes the differences in the production processes of lacquerware from Kyoto and Kanazawa. This allows the traditional techniques of different regions and schools to be simultaneously recorded and comparative analysis, thereby revealing the diversity of techniques.

[0095] The motion analysis unit can evaluate the efficiency and beauty of movements and extract optimal movement patterns. For example, during a tea ceremony motion analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for how to hold a tea bowl and how to make tea. Similarly, during a calligraphy motion analysis, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for brush movement and pressure. Similarly, during the process of creating a craft, the generation AI evaluates the efficiency and beauty of movements and extracts optimal movement patterns. For example, it analyzes the optimal patterns for carving knife movement and force application. This allows the generation AI to evaluate the efficiency and beauty of movements and extract optimal movement patterns.

[0096] The motion analysis unit can also collect examples of failed movements and provide data for learning from failure. For example, during motion analysis of tea ceremony, the generative AI can collect examples of failed movements and provide data for learning from failure. For example, it can analyze incorrect movements when holding a tea bowl or incorrect ways of making tea. Similarly, during motion analysis of calligraphy, the motion analysis unit can collect examples of failed movements and provide data for learning from failure. For example, it can analyze incorrect patterns of brush movement and pressure. Similarly, during the process of creating a craft, the generative AI can collect examples of failed movements and provide data for learning from failure. For example, it can analyze incorrect patterns of carving knife movement and pressure. This allows the generative AI to collect examples of failed movements and provide data for learning from failure.

[0097] The action recording unit can use the emotion estimation function to record the emotional state when performing an action and analyze the relationship between emotion and action. For example, while recording tea ceremony actions, the generation AI uses the emotion estimation function to record the emotional state when performing the tea ceremony and analyze the relationship between emotion and action. For example, it analyzes the level of concentration and relaxation when making tea. Furthermore, while recording calligraphy actions, the action recording unit can use the emotion estimation function to record the emotional state when performing calligraphy and analyze the relationship between emotion and action. For example, it analyzes the level of tension and satisfaction when writing calligraphy. Furthermore, during the process of creating a craft, the generation AI uses the emotion estimation function to record the emotional state when creating the craft and analyze the relationship between emotion and action. For example, it analyzes the level of concentration and sense of accomplishment when carving. In this way, the emotion estimation function can be used to record the emotional state when performing an action and analyze the relationship between emotion and action.

[0098] The motion recording unit uses the emotion estimation function to monitor emotional changes during motion recording in real time and make suggestions for motion improvement according to the emotion. For example, while motions are being recorded for a tea ceremony, the generation AI uses the emotion estimation function to monitor emotional changes in real time and make suggestions for motion improvement according to the emotion. For example, if the subject is nervous, it makes suggestions to relax. Furthermore, while motions are being recorded for calligraphy, the generation AI uses the emotion estimation function to monitor emotional changes in real time and make suggestions for motion improvement according to the emotion. For example, if the subject is losing concentration, it makes suggestions to improve concentration. Furthermore, during the process of creating a craft, the generation AI uses the emotion estimation function to monitor emotional changes in real time and make suggestions for motion improvement according to the emotion. For example, if the subject is tired, it makes a suggestion to take a break. In this way, the emotion estimation function can be used to monitor emotional changes during motion recording in real time and make suggestions for motion improvement according to the emotion.

[0099] The motion analysis unit can use the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, during a tea ceremony movement analysis, the generation AI uses the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, when the user is nervous, the generation AI uses the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, when the user is tense, the generation AI extracts improvement points for the movement based on the emotion. For example, when the user's concentration is low, the generation AI extracts improvement points for the movement to increase concentration. During the craft production process, the generation AI uses the emotion estimation function to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion. For example, when the user is tired, the generation AI extracts improvement points for the movement based on the emotion. In this way, the emotion estimation function can be used to analyze emotional changes during a movement and extract improvement points for the movement based on the emotion.

[0100] The motion analysis unit can use the emotion estimation function to collect the user's emotional reactions to the motion analysis results and provide emotion-based feedback. For example, the emotion estimation function can collect the user's emotional reactions to the motion analysis results of tea ceremony and provide emotion-based feedback. For example, the emotion estimation function can make suggestions for improving the user's movements to help them relax. The motion analysis unit can also collect the user's emotional reactions to the motion analysis results of calligraphy and provide emotion-based feedback. For example, the emotion estimation function can make suggestions for improving the user's movements to help them increase their concentration. The emotion estimation function can also collect the user's emotional reactions to the motion analysis results of the craft production process and provide emotion-based feedback. For example, the emotion estimation function can make suggestions for improving the user's movements to help them feel a sense of accomplishment.

[0101] The teaching material creation unit can use the emotion estimation function to analyze the user's emotional state during learning and provide learning support according to the emotion. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state during learning tea ceremony and provide learning support according to the emotion. For example, if the user is nervous, advice on how to relax can be provided. The teaching material creation unit can also use the emotion estimation function to analyze the user's emotional state during learning calligraphy and provide learning support according to the emotion. For example, if the user is lacking concentration, advice on how to improve concentration can be provided. The teaching material creation unit can also use the emotion estimation function to analyze the user's emotional state during learning the process of making a craft and provide learning support according to the emotion. For example, advice on taking a break can be provided if the user is tired. This allows the emotion estimation function to analyze the user's emotional state during learning and provide learning support according to the emotion.

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

[0103] Step 1: The action recording unit uses the generation AI to record the actions or procedures of traditional techniques. For example, the generation AI collects tea ceremony actions using cameras and sensors and records the actions. The generation AI can also record actions such as calligraphy and the process of creating crafts. Step 2: The motion analysis unit analyzes the movements recorded by the motion recording unit and extracts important points and gestures. For example, in the movements of the tea ceremony, the generation AI extracts important gestures, such as how to hold a tea bowl and how to prepare tea, and saves them as data to be passed on to future generations. The generation AI can also analyze the movements of the brush and changes in writing pressure in calligraphy movements and extract important points. Step 3: The teaching material creation unit creates teaching materials based on the points and gestures extracted by the motion analysis unit. For example, the generation AI creates text and video that explain in detail the movements of the tea ceremony and provides them as teaching materials to be passed on to future generations. The generation AI can also create text and video that explain in detail the movements of calligraphy and provide them as teaching materials to be passed on to future generations.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

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

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an action recording unit that records the actions or procedures of traditional techniques using a generation AI; a motion analysis unit that analyzes the motions recorded by the motion recording unit and extracts important points and actions; a teaching material creation unit that creates teaching materials based on the points and actions extracted by the motion analysis unit. A system characterized by:

2. The operation recording unit Detailed movement data is collected by analyzing minute movements and changes in force in real time while recording the movement.

2. The system of claim 1.

3. The motion analysis unit Evaluate the efficiency and beauty of movements and extract the optimal movement pattern 2. The system of claim 1.

4. The teaching material creation unit Monitor users' learning progress in real time and provide individually optimized learning materials 2. The system of claim 1.

5. The operation recording unit Provide a system for storing recorded actions and procedures in a database and passing them on to future generations 2. The system of claim 1.

6. The operation recording unit Recording emotional states when performing movements and analyzing the relationship between emotions and movements 2. The system of claim 1.

7. The motion analysis unit Analyzing emotional changes during movement and identifying areas for improvement in emotion-based movements 2. The system of claim 1.

8. The teaching material creation unit Analyzes the user's emotional state during learning and provides learning support according to their emotions 2. The system of claim 1.

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