System

Abstract

The purpose of the system according to the embodiment is to accurately record gestures and points of traditional techniques and to pass them on to the next generation.SOLUTION: A system according to an embodiment includes a recording unit, an analysis unit, and a generation unit. The recording unit records the actions and points of the traditional technique. The analysis unit analyzes the data recorded by the recording unit. The generation unit generates a teaching material based on the gesture and the point extracted by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately record the movements and key points of traditional techniques and pass them on to future generations, so there is room for improvement.

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

[0006] The system according to the embodiment includes a recording unit, an analysis unit, and a generation unit. The recording unit records the gestures and key points of traditional techniques. The analysis unit analyzes the data recorded by the recording unit. The generation unit generates teaching materials based on the gestures and key points extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately record the movements and key points 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention records the gestures and key points of traditional Japanese techniques and passes them on to future generations. This system records the gestures and key points of traditional techniques using video and text, analyzes them using AI, extracts the gestures and key points, and generates educational materials for future generations. For example, the system may film videos of gestures in tea ceremony, calligraphy, martial arts, and other arts, and add text commentary. This data is input into AI. The system then analyzes the input data using AI to extract the gestures and key points. For example, AI may extract hand movements and postures in tea ceremony gestures and calligraphy brush strokes. Furthermore, the system generates educational materials for future generations based on the extracted gestures and key points. For example, AI may organize the extracted information, edit videos to emphasize key points of the gestures, and add detailed text commentary. This system facilitates the inheritance of traditional techniques and ensures accurate transmission to future generations. This system promotes the preservation and dissemination of traditional techniques. For example, someone who wants to learn tea ceremony etiquette can use this system to learn the correct gestures. It can also be used as a teaching material to pass on calligraphy techniques to future generations.

[0029] A traditional technique inheritance system according to an embodiment includes a recording unit, an analysis unit, and a generation unit. The recording unit records the gestures and key points of traditional techniques. For example, the recording unit can record videos of gestures in tea ceremony, calligraphy, martial arts, and the like and add text commentary. The recording unit can also input the video and text data into AI. The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit uses AI to understand the content of the video and text and extract gestures and key points. For example, the analysis unit can extract hand movements and posture in tea ceremony gestures and the way a calligraphy brush is moved. The generation unit generates teaching materials based on the gestures and key points extracted by the analysis unit. For example, the generation unit can organize the extracted information using AI, edit videos to emphasize key gestures, and add detailed text commentary. This enables the traditional technique inheritance system according to an embodiment to record, analyze, and generate teaching materials for the gestures and key points of traditional techniques.

[0030] The recording unit can record the gestures and key points of traditional techniques using video and text. The recording unit can record, for example, gestures in tea ceremony, calligraphy, martial arts, etc. using video and add text commentary. The video can include, for example, but is not limited to, resolution, frame rate, file format, etc. The text can include, for example, but is not limited to, number of characters, format, language, etc. This allows the gestures and key points of traditional techniques to be recorded using video and text.

[0031] The analysis unit can analyze data using video analysis and natural language processing to extract gestures and key points. The analysis unit can, for example, use AI to understand the content of the video or text and extract gestures and key points. Examples of video analysis include, but are not limited to, motion recognition, object detection, and frame analysis. Examples of natural language processing include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. This allows data to be analyzed using video analysis and natural language processing to extract gestures and key points.

[0032] The generation unit can perform video editing and text generation based on the extracted information to create teaching materials. The generation unit can, for example, use AI to organize the extracted information, edit the video to emphasize key points of the behavior, and add detailed explanations in text. Examples of video editing include, but are not limited to, cut editing, adding effects, and inserting subtitles. Examples of text generation include, but are not limited to, template-based generation and machine learning-based generation. This allows video editing and text generation based on the extracted information to create teaching materials.

[0033] The recording unit can record the details of the gestures of traditional techniques in high definition during recording. For example, the recording unit records the hand movements and tool handling in tea ceremony gestures in high resolution. The recording unit can also record the way a calligraphy brush is moved and changes in writing pressure in high resolution. The recording unit can also record the fine movements of martial arts techniques in high resolution. This allows for detailed recording by recording the details of the gestures of traditional techniques in high resolution. High definition includes, but is not limited to, for example, resolution, image quality, and frame rate.

[0034] The recording unit can record gestures from multiple angles using multiple camera angles during recording. For example, the recording unit can simultaneously record gestures in a tea ceremony from the front, side, and above. The recording unit can also record the way a calligraphy brush is moved from both the hand and the overall movement. The recording unit can also record martial arts techniques from different angles, allowing for a complete picture of the movement. This allows for more detailed recording by recording gestures from multiple angles using multiple camera angles. Examples of multiple camera angles include, but are not limited to, the front, side, and above.

[0035] The recording unit can automatically convert the commentary into text using voice recognition during recording. For example, the recording unit can convert audio explaining tea ceremony manners into text in real time. The recording unit can also automatically convert audio explaining calligraphy techniques into text. The recording unit can also convert commentary on martial arts techniques into text using voice recognition, making it editable later. This enables efficient recording by automatically converting commentary into text using voice recognition. Voice recognition includes, for example, a voice recognition engine, a method for preprocessing voice data, and the like, but is not limited to these examples.

[0036] During recording, the recording unit can prioritize recording regional gestures by taking into account the user's geographical location information. For example, when recording tea ceremony gestures in Kyoto, the recording unit can prioritize recording tools and procedures that are regionally specific. Furthermore, when recording martial arts techniques in Okinawa, the recording unit can also prioritize recording regional techniques. Furthermore, when recording calligraphy techniques in Tokyo, the recording unit can prioritize recording regional styles. Thus, by prioritizing recording regional gestures by taking into account the user's geographical location information, regional techniques can be accurately recorded. Examples of geographical location information include, but are not limited to, GPS data, location information services, and the like.

[0037] The recording unit can analyze the user's social media activity and record related behaviors during recording. For example, if the user posts about tea ceremony on social media, the recording unit can focus on recording those behaviors. Also, if the user posts about calligraphy, the recording unit can prioritize recording those techniques. Also, if the user posts about martial arts, the recording unit can prioritize recording those techniques. In this way, by analyzing the user's social media activity and recording related behaviors, it is possible to record according to the user's interests. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples.

[0038] The recording unit can customize the recording method based on the user's past feedback when recording. For example, the recording unit adjusts the recording method by reflecting points previously pointed out by the user. The recording unit can also change the order of actions to be recorded based on the user's past feedback. The recording unit can also adjust the camera angle during recording by referring to the user's past feedback. In this way, customizing the recording method by reflecting the user's past feedback enables optimal recording for the user. Feedback includes, for example, survey results, user reviews, comments, etc., but is not limited to these examples.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the gesture. For example, the analysis unit performs a detailed analysis of important gestures. The analysis unit can also perform a concise analysis of basic gestures. The analysis unit can also perform a detailed analysis of gestures in which the user is particularly interested. This allows for optimal analysis by adjusting the level of detail of the analysis based on the importance of the gesture. The importance includes, for example, frequency, impact, expert evaluation, etc., but is not limited to these examples.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of gesture. For example, the analysis unit applies an analysis algorithm specialized in hand movements and tool handling to gestures in the tea ceremony. The analysis unit can also apply an analysis algorithm specialized in brush strokes and changes in pen pressure to calligraphy techniques. The analysis unit can also apply an analysis algorithm specialized in the speed of movement and the amount of force to martial arts techniques. In this way, optimal analysis can be performed by applying different analysis algorithms depending on the category of gesture. Categories include, for example, types of movement and fields of technique, but are not limited to these examples.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on data of the user's behavior that has been analyzed in the past. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. This enables more accurate analysis by improving the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past data sets, analysis reports, etc., but are not limited to these examples.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time when the gesture was recorded. For example, the analysis unit prioritizes analysis of recently recorded gestures. The analysis unit can also prioritize analysis of gestures recorded during a period in which the user is particularly interested. The analysis unit can also prioritize analysis of gestures recorded during important events or occasions. In this way, determining the priority of analysis based on the time when the gesture was recorded enables optimal analysis. The time when the gesture was recorded includes, but is not limited to, for example, the date and time, the season, and the timing of the event.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the gestures. For example, the analysis unit analyzes related gestures together. The analysis unit can also adjust the order of analysis according to the flow of the gestures. The analysis unit can also prioritize analysis of gestures in which the user is particularly interested. This allows for optimal analysis by adjusting the order of analysis based on the relevance of the gestures. Relevance includes, for example, a common theme, technical relevance, etc., but is not limited to such examples.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit can avoid technical terms and perform an easy-to-understand analysis for beginners. For intermediate users, the analysis unit can use technical terms appropriately. For advanced users, the analysis unit can use a lot of technical terms and perform a detailed analysis. This allows for optimal analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginners, intermediate users, and advanced users.

[0045] The generation unit can adjust the level of detail of the teaching material based on the importance of the gesture during generation. For example, the generation unit generates teaching material including detailed explanations for important gestures. The generation unit can also generate teaching material including concise explanations for basic gestures. The generation unit can also generate teaching material including detailed explanations for gestures in which the user is particularly interested. In this way, by adjusting the level of detail of the teaching material based on the importance of the gesture, optimal teaching material can be provided. The importance includes, for example, frequency, impact, expert evaluation, etc., but is not limited to these examples.

[0046] The generation unit can apply different generation algorithms depending on the category of gestures during generation. For example, the generation unit applies a generation algorithm specialized for hand movements and tool handling to gestures in tea ceremony. The generation unit can also apply a generation algorithm specialized for brush strokes and changes in pen pressure to calligraphy techniques. The generation unit can also apply a generation algorithm specialized for movement speed and strength to martial arts techniques. In this way, optimal teaching materials can be provided by applying different generation algorithms depending on the category of gestures. Categories include, for example, types of movements and fields of technique, but are not limited to these examples.

[0047] The generation unit can improve the accuracy of the teaching materials based on the user's past generation results during generation. The generation unit improves the accuracy of the teaching materials, for example, based on data of teaching materials previously generated by the user. The generation unit can also adjust the generation algorithm by referring to the user's past generation results. The generation unit can also adjust the level of detail of the teaching materials based on the user's past generation results. In this way, more accurate teaching materials can be provided by improving the accuracy of the teaching materials by referring to the user's past generation results. Past generation results include, for example, past teaching materials, generation reports, etc., but are not limited to these examples.

[0048] The generation unit can determine the priority of teaching materials based on the time when the gestures were recorded during generation. For example, the generation unit can prioritize the most recently recorded gestures as teaching materials. The generation unit can also prioritize the gestures recorded during a period in which the user is particularly interested as teaching materials. The generation unit can also prioritize the gestures recorded during important events or ceremonies as teaching materials. In this way, by determining the priority of teaching materials based on the time when the gestures were recorded, optimal teaching materials can be provided. The recording time includes, for example, date and time, season, timing of an event, etc., but is not limited to these examples.

[0049] The generation unit can adjust the order of the teaching materials based on the relevance of the gestures during generation. For example, the generation unit groups related gestures together to create teaching materials. The generation unit can also adjust the order of the teaching materials according to the flow of the gestures. The generation unit can also prioritize the creation of teaching materials for gestures in which the user is particularly interested. In this way, optimal teaching materials can be provided by adjusting the order of the teaching materials based on the relevance of the gestures. Relevance includes, for example, a common theme, technical relevance, etc., but is not limited to such examples.

[0050] The generation unit can adjust the use of technical terms in the learning materials based on the user's level of expertise during generation. For example, the generation unit can avoid technical terms and generate easy-to-understand learning materials for beginners. The generation unit can also generate learning materials using appropriate technical terms for intermediate learners. The generation unit can also generate detailed learning materials using a lot of technical terms for advanced learners. In this way, optimal learning materials can be provided by adjusting the use of technical terms in the learning materials according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced, for example.

[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 recording unit can acquire the user's biometric information and adjust the accuracy of the recording based on the acquired biometric information. For example, it can monitor the user's heart rate and skin temperature, and if it is determined that the user is highly focused, it can record the details of the user's movements with higher resolution. Also, if the user's stress level is high, it can reduce the frequency of recording and record again in a relaxed state. Furthermore, if the user is highly fatigued, it can record with breaks in between. This allows optimal recording by adjusting the accuracy of the recording based on the user's biometric information.

[0053] The analysis unit can refer to the user's past learning history and customize the analysis results. For example, it can adjust the level of detail in the analysis based on data on gestures the user has learned in the past. It can also perform a more detailed analysis of gestures that the user is particularly interested in. It can also focus on analyzing gestures that the user has difficulty with and suggest areas for improvement. This allows for more effective learning by customizing the analysis results with reference to the user's past learning history.

[0054] The generation unit can adjust the format of the learning materials according to the user's learning style. For example, for a user who prefers visual learning, the generation unit can generate learning materials that make extensive use of videos and illustrations. For a user who prefers auditory learning, the generation unit can also generate learning materials that mainly include audio commentary. Furthermore, for a user who prefers practical learning, the generation unit can generate learning materials that include interactive exercises. In this way, by adjusting the format of the learning materials according to the user's learning style, the optimal learning experience can be provided.

[0055] The recording unit can analyze the user's environmental sounds and adjust the timing of recording. For example, if the surroundings are quiet, it can record the details of the user's movements. If the surroundings are noisy, it can pause recording and record again in a quiet environment. Furthermore, if a specific environmental sound affects the user's movements, it can filter out and record that sound. This allows for optimal recording by analyzing the user's environmental sounds and adjusting the timing of recording.

[0056] The recording unit can monitor the user's learning progress and adjust the frequency of recording according to the progress. For example, if the user is making good progress in their studies, the frequency of recording can be increased to collect detailed data. If the user is struggling with their studies, the frequency of recording can be reduced to allow time for review. Furthermore, if the user shows particular interest in a particular behavior, the frequency of recording that behavior can be increased. In this way, optimal learning support can be provided by adjusting the frequency of recording according to the user's learning progress.

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

[0058] Step 1: The recording unit records the movements and key points of traditional techniques. For example, the recording unit can record videos of movements in tea ceremony, calligraphy, martial arts, etc., and add text commentary. The recording unit can also input the video and text data into the AI. Step 2: The analysis unit analyzes the data recorded by the recording unit. For example, it uses AI to understand the content of the video or text and extracts gestures and key points. Specifically, it can extract hand movements and posture in tea ceremony gestures, or the way a calligraphy brush is held. Step 3: The generator generates teaching materials based on the gestures and key points extracted by the analyzer. For example, it can use AI to organize the extracted information, edit the video to emphasize key gesture points, and add detailed explanations in text.

[0059] (Example 2) A system according to an embodiment of the present invention records the gestures and key points of traditional Japanese techniques and passes them on to future generations. This system records the gestures and key points of traditional techniques using video and text, analyzes them using AI, extracts the gestures and key points, and generates educational materials for future generations. For example, the system may film videos of gestures in tea ceremony, calligraphy, martial arts, and other arts, and add text commentary. This data is input into AI. The system then analyzes the input data using AI to extract the gestures and key points. For example, AI may extract hand movements and postures in tea ceremony gestures and calligraphy brush strokes. Furthermore, the system generates educational materials for future generations based on the extracted gestures and key points. For example, AI may organize the extracted information, edit videos to emphasize key points of the gestures, and add detailed text commentary. This system facilitates the inheritance of traditional techniques and ensures accurate transmission to future generations. This system promotes the preservation and dissemination of traditional techniques. For example, someone who wants to learn tea ceremony etiquette can use this system to learn the correct gestures. It can also be used as a teaching material to pass on calligraphy techniques to future generations.

[0060] A traditional technique inheritance system according to an embodiment includes a recording unit, an analysis unit, and a generation unit. The recording unit records the gestures and key points of traditional techniques. For example, the recording unit can record videos of gestures in tea ceremony, calligraphy, martial arts, and the like and add text commentary. The recording unit can also input the video and text data into AI. The analysis unit analyzes the data recorded by the recording unit. For example, the analysis unit uses AI to understand the content of the video and text and extract gestures and key points. For example, the analysis unit can extract hand movements and posture in tea ceremony gestures and the way a calligraphy brush is moved. The generation unit generates teaching materials based on the gestures and key points extracted by the analysis unit. For example, the generation unit can organize the extracted information using AI, edit videos to emphasize key gestures, and add detailed text commentary. This enables the traditional technique inheritance system according to an embodiment to record, analyze, and generate teaching materials for the gestures and key points of traditional techniques.

[0061] The recording unit can record the gestures and key points of traditional techniques using video and text. The recording unit can record, for example, gestures in tea ceremony, calligraphy, martial arts, etc. using video and add text commentary. The video can include, for example, but is not limited to, resolution, frame rate, file format, etc. The text can include, for example, but is not limited to, number of characters, format, language, etc. This allows the gestures and key points of traditional techniques to be recorded using video and text.

[0062] The analysis unit can analyze data using video analysis and natural language processing to extract gestures and key points. The analysis unit can, for example, use AI to understand the content of the video or text and extract gestures and key points. Examples of video analysis include, but are not limited to, motion recognition, object detection, and frame analysis. Examples of natural language processing include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. This allows data to be analyzed using video analysis and natural language processing to extract gestures and key points.

[0063] The generation unit can perform video editing and text generation based on the extracted information to create teaching materials. The generation unit can, for example, use AI to organize the extracted information, edit the video to emphasize key points of the behavior, and add detailed explanations in text. Examples of video editing include, but are not limited to, cut editing, adding effects, and inserting subtitles. Examples of text generation include, but are not limited to, template-based generation and machine learning-based generation. This allows video editing and text generation based on the extracted information to create teaching materials.

[0064] The recording unit can estimate the user's emotions and adjust the timing of recording based on the estimated user emotions. For example, if the user is concentrating, the recording unit optimizes the timing of recording important points of the user's actions. Furthermore, if the user is tired, the recording unit can record while taking breaks. Furthermore, if the user is excited, the recording unit can quickly record the user's actions and add details later. This allows for optimal recording by adjusting the timing of recording based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The recording unit can record the details of the gestures of traditional techniques in high definition during recording. For example, the recording unit records the hand movements and tool handling in tea ceremony gestures in high resolution. The recording unit can also record the way a calligraphy brush is moved and changes in writing pressure in high resolution. The recording unit can also record the fine movements of martial arts techniques in high resolution. This allows for detailed recording by recording the details of the gestures of traditional techniques in high resolution. High definition includes, but is not limited to, for example, resolution, image quality, and frame rate.

[0066] The recording unit can record gestures from multiple angles using multiple camera angles during recording. For example, the recording unit can simultaneously record gestures in a tea ceremony from the front, side, and above. The recording unit can also record the way a calligraphy brush is moved from both the hand and the overall movement. The recording unit can also record martial arts techniques from different angles, allowing for a complete picture of the movement. This allows for more detailed recording by recording gestures from multiple angles using multiple camera angles. Examples of multiple camera angles include, but are not limited to, the front, side, and above.

[0067] The recording unit can automatically convert the commentary into text using voice recognition during recording. For example, the recording unit can convert audio explaining tea ceremony manners into text in real time. The recording unit can also automatically convert audio explaining calligraphy techniques into text. The recording unit can also convert commentary on martial arts techniques into text using voice recognition, making it editable later. This enables efficient recording by automatically converting commentary into text using voice recognition. Voice recognition includes, for example, a voice recognition engine, a method for preprocessing voice data, and the like, but is not limited to these examples.

[0068] The recording unit can estimate the user's emotions and determine the priority of actions to be recorded based on the estimated user emotions. For example, the recording unit prioritizes recording actions that the user is interested in. The recording unit can also focus on recording actions that the user is not good at. If the user is relaxed, the recording unit can record actions in order starting with basic actions. This enables optimal recording by determining the priority of actions to be recorded based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] During recording, the recording unit can prioritize recording regional gestures by taking into account the user's geographical location information. For example, when recording tea ceremony gestures in Kyoto, the recording unit can prioritize recording tools and procedures that are regionally specific. Furthermore, when recording martial arts techniques in Okinawa, the recording unit can also prioritize recording regional techniques. Furthermore, when recording calligraphy techniques in Tokyo, the recording unit can prioritize recording regional styles. Thus, by prioritizing recording regional gestures by taking into account the user's geographical location information, regional techniques can be accurately recorded. Examples of geographical location information include, but are not limited to, GPS data, location information services, and the like.

[0070] The recording unit can analyze the user's social media activity and record related behaviors during recording. For example, if the user posts about tea ceremony on social media, the recording unit can focus on recording those behaviors. Also, if the user posts about calligraphy, the recording unit can prioritize recording those techniques. Also, if the user posts about martial arts, the recording unit can prioritize recording those techniques. In this way, by analyzing the user's social media activity and recording related behaviors, it is possible to record according to the user's interests. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples.

[0071] The recording unit can customize the recording method based on the user's past feedback when recording. For example, the recording unit adjusts the recording method by reflecting points previously pointed out by the user. The recording unit can also change the order of actions to be recorded based on the user's past feedback. The recording unit can also adjust the camera angle during recording by referring to the user's past feedback. In this way, customizing the recording method by reflecting the user's past feedback enables optimal recording for the user. Feedback includes, for example, survey results, user reviews, comments, etc., but is not limited to these examples.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide visually appealing analysis results. By adjusting the way the analysis is presented based on the user's emotions, optimal analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the gesture. For example, the analysis unit performs a detailed analysis of important gestures. The analysis unit can also perform a concise analysis of basic gestures. The analysis unit can also perform a detailed analysis of gestures in which the user is particularly interested. This allows for optimal analysis by adjusting the level of detail of the analysis based on the importance of the gesture. The importance includes, for example, frequency, impact, expert evaluation, etc., but is not limited to these examples.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of gesture. For example, the analysis unit applies an analysis algorithm specialized in hand movements and tool handling to gestures in the tea ceremony. The analysis unit can also apply an analysis algorithm specialized in brush strokes and changes in pen pressure to calligraphy techniques. The analysis unit can also apply an analysis algorithm specialized in the speed of movement and the amount of force to martial arts techniques. In this way, optimal analysis can be performed by applying different analysis algorithms depending on the category of gesture. Categories include, for example, types of movement and fields of technique, but are not limited to these examples.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on data of the user's behavior that has been analyzed in the past. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. This enables more accurate analysis by improving the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past data sets, analysis reports, etc., but are not limited to these examples.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is excited, the analysis unit can also perform a visually appealing analysis. This allows for optimal analysis by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time when the gesture was recorded. For example, the analysis unit prioritizes analysis of recently recorded gestures. The analysis unit can also prioritize analysis of gestures recorded during a period in which the user is particularly interested. The analysis unit can also prioritize analysis of gestures recorded during important events or occasions. In this way, determining the priority of analysis based on the time when the gesture was recorded enables optimal analysis. The time when the gesture was recorded includes, but is not limited to, for example, the date and time, the season, and the timing of the event.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the gestures. For example, the analysis unit analyzes related gestures together. The analysis unit can also adjust the order of analysis according to the flow of the gestures. The analysis unit can also prioritize analysis of gestures in which the user is particularly interested. This allows for optimal analysis by adjusting the order of analysis based on the relevance of the gestures. Relevance includes, for example, a common theme, technical relevance, etc., but is not limited to such examples.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. For example, the analysis unit can avoid technical terms and perform an easy-to-understand analysis for beginners. For intermediate users, the analysis unit can use technical terms appropriately. For advanced users, the analysis unit can use a lot of technical terms and perform a detailed analysis. This allows for optimal analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginners, intermediate users, and advanced users.

[0080] The generation unit can estimate the user's emotions and adjust the expression method of the generated teaching materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates teaching materials that include detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate concise teaching materials that focus on the main points. Furthermore, if the user is excited, the generation unit can generate visually appealing teaching materials. In this way, optimal teaching materials can be provided by adjusting the expression method of the generated teaching materials based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The generation unit can adjust the level of detail of the teaching material based on the importance of the gesture during generation. For example, the generation unit generates teaching material including detailed explanations for important gestures. The generation unit can also generate teaching material including concise explanations for basic gestures. The generation unit can also generate teaching material including detailed explanations for gestures in which the user is particularly interested. In this way, by adjusting the level of detail of the teaching material based on the importance of the gesture, optimal teaching material can be provided. The importance includes, for example, frequency, impact, expert evaluation, etc., but is not limited to these examples.

[0082] The generation unit can apply different generation algorithms depending on the category of gestures during generation. For example, the generation unit applies a generation algorithm specialized for hand movements and tool handling to gestures in tea ceremony. The generation unit can also apply a generation algorithm specialized for brush strokes and changes in pen pressure to calligraphy techniques. The generation unit can also apply a generation algorithm specialized for movement speed and strength to martial arts techniques. In this way, optimal teaching materials can be provided by applying different generation algorithms depending on the category of gestures. Categories include, for example, types of movements and fields of technique, but are not limited to these examples.

[0083] The generation unit can improve the accuracy of the teaching materials based on the user's past generation results during generation. The generation unit improves the accuracy of the teaching materials, for example, based on data of teaching materials previously generated by the user. The generation unit can also adjust the generation algorithm by referring to the user's past generation results. The generation unit can also adjust the level of detail of the teaching materials based on the user's past generation results. In this way, more accurate teaching materials can be provided by improving the accuracy of the teaching materials by referring to the user's past generation results. Past generation results include, for example, past teaching materials, generation reports, etc., but are not limited to these examples.

[0084] The generation unit can estimate the user's emotions and adjust the length of the generated learning material based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point learning material. If the user is relaxed, the generation unit can also generate longer learning material with detailed explanations. If the user is excited, the generation unit can also generate learning material with visually stimulating effects. In this way, optimal learning material can be provided by adjusting the length of the learning material based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The generation unit can determine the priority of teaching materials based on the time when the gestures were recorded during generation. For example, the generation unit can prioritize the most recently recorded gestures as teaching materials. The generation unit can also prioritize the gestures recorded during a period in which the user is particularly interested as teaching materials. The generation unit can also prioritize the gestures recorded during important events or ceremonies as teaching materials. In this way, by determining the priority of teaching materials based on the time when the gestures were recorded, optimal teaching materials can be provided. The recording time includes, for example, date and time, season, timing of an event, etc., but is not limited to these examples.

[0086] The generation unit can adjust the order of the teaching materials based on the relevance of the gestures during generation. For example, the generation unit groups related gestures together to create teaching materials. The generation unit can also adjust the order of the teaching materials according to the flow of the gestures. The generation unit can also prioritize the creation of teaching materials for gestures in which the user is particularly interested. In this way, optimal teaching materials can be provided by adjusting the order of the teaching materials based on the relevance of the gestures. Relevance includes, for example, a common theme, technical relevance, etc., but is not limited to such examples.

[0087] The generation unit can adjust the use of technical terms in the learning materials based on the user's level of expertise during generation. For example, the generation unit can avoid technical terms and generate easy-to-understand learning materials for beginners. The generation unit can also generate learning materials using appropriate technical terms for intermediate learners. The generation unit can also generate detailed learning materials using a lot of technical terms for advanced learners. In this way, optimal learning materials can be provided by adjusting the use of technical terms in the learning materials according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced, for example. === Hard Collateral 1-1 === Each of the multiple elements including the recording unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recording unit can use the camera 42 or microphone 38B of the smart device 14 to capture video of the movements of traditional techniques and add text commentary. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to understand the content of the video and text and extract movements and key points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and can organize the extracted information, edit the video to emphasize key points of the movements, and add detailed text commentary. === Hard Collateral 1-2 === Each of the multiple elements, including the recording unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recording unit can use the camera 42 and microphone 238 of the smart glasses 214 to capture video of the movements of traditional techniques and add text commentary. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, uses AI to understand the content of the video and text and extract movements and key points. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, can organize the extracted information, edit the video to emphasize key points of the movements, and add detailed text commentary. === Hard Collateral 1-3 === Each of the multiple elements including the recording unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the recording unit can use the camera 42 and microphone 238 of the headset-type terminal 314 to capture video of the movements of traditional techniques and add text commentary. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to understand the content of the video and text and extract movements and key points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and can organize the extracted information, edit the video to emphasize key points of the movements, and add detailed text commentary. === Hard Collateral 1-4 === Each of the multiple elements including the recording unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit can use the camera 42 and microphone 238 of the robot 414 to capture video of the movements of traditional techniques and add text commentary. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to understand the content of the video and text and extract movements and key points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and can organize the extracted information, edit the video to emphasize key points of the movements, and add detailed text commentary.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The recording unit can acquire the user's biometric information and adjust the accuracy of the recording based on the acquired biometric information. For example, it can monitor the user's heart rate and skin temperature, and if it is determined that the user is highly focused, it can record the details of the user's movements with higher resolution. Also, if the user's stress level is high, it can reduce the frequency of recording and record again in a relaxed state. Furthermore, if the user is highly fatigued, it can record with breaks in between. This allows optimal recording by adjusting the accuracy of the recording based on the user's biometric information.

[0090] The analysis unit can refer to the user's past learning history and customize the analysis results. For example, it can adjust the level of detail in the analysis based on data on gestures the user has learned in the past. It can also perform a more detailed analysis of gestures that the user is particularly interested in. It can also focus on analyzing gestures that the user has difficulty with and suggest areas for improvement. This allows for more effective learning by customizing the analysis results with reference to the user's past learning history.

[0091] The generation unit can adjust the format of the learning materials according to the user's learning style. For example, for a user who prefers visual learning, the generation unit can generate learning materials that make extensive use of videos and illustrations. For a user who prefers auditory learning, the generation unit can also generate learning materials that mainly include audio commentary. Furthermore, for a user who prefers practical learning, the generation unit can generate learning materials that include interactive exercises. In this way, by adjusting the format of the learning materials according to the user's learning style, the optimal learning experience can be provided.

[0092] The recording unit can analyze the user's environmental sounds and adjust the timing of recording. For example, if the surroundings are quiet, it can record the details of the user's movements. If the surroundings are noisy, it can pause recording and record again in a quiet environment. Furthermore, if a specific environmental sound affects the user's movements, it can filter out and record that sound. This allows for optimal recording by analyzing the user's environmental sounds and adjusting the timing of recording.

[0093] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide analysis results using detailed graphs and charts. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can provide analysis results using visually appealing animations. In this way, optimal analysis results can be provided by adjusting the visual presentation of the analysis based on the user's emotions.

[0094] The generation unit can estimate the user's emotions and adjust the difficulty of the generated learning materials based on the estimated user's emotions. For example, if the user is relaxed, it can generate learning materials with a high level of difficulty. Also, if the user is tired, it can generate learning materials with a low level of difficulty. Furthermore, if the user is excited, it can generate learning materials that include challenging tasks. In this way, optimal learning can be provided by adjusting the difficulty of the generated learning materials based on the user's emotions.

[0095] The recording unit can estimate the user's emotions and adjust the order of actions to be recorded based on the estimated user emotions. For example, if the user is relaxed, the recording unit can record actions in order starting from the most basic actions. Also, if the user is excited, the recording unit can prioritize more difficult actions. Furthermore, if the user is tired, the recording unit can take breaks in between. This allows optimal recording by adjusting the order of actions to be recorded based on the user's emotions.

[0096] The analysis unit can estimate the user's emotions and adjust the analysis feedback based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback that focuses on the main points can be provided. Furthermore, if the user is excited, visually appealing feedback can be provided. In this way, optimal feedback can be provided by adjusting the analysis feedback based on the user's emotions.

[0097] The generation unit can estimate the user's emotions and adjust the interactivity of the generated learning materials based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate learning materials that include many interactive exercises. If the user is in a hurry, the generation unit can generate simple learning materials with less interactivity. Furthermore, if the user is excited, the generation unit can generate learning materials that incorporate game elements. In this way, the generation unit can provide an optimal learning experience by adjusting the interactivity of the generated learning materials based on the user's emotions.

[0098] The recording unit can monitor the user's learning progress and adjust the frequency of recording according to the progress. For example, if the user is making good progress in their studies, the frequency of recording can be increased to collect detailed data. If the user is struggling with their studies, the frequency of recording can be reduced to allow time for review. Furthermore, if the user shows particular interest in a particular behavior, the frequency of recording that behavior can be increased. In this way, optimal learning support can be provided by adjusting the frequency of recording according to the user's learning progress.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The recording unit records the movements and key points of traditional techniques. For example, the recording unit can record videos of movements in tea ceremony, calligraphy, martial arts, etc., and add text commentary. The recording unit can also input the video and text data into the AI. Step 2: The analysis unit analyzes the data recorded by the recording unit. For example, it uses AI to understand the content of the video or text and extracts gestures and key points. Specifically, it can extract hand movements and posture in tea ceremony gestures, or the way a calligraphy brush is held. Step 3: The generator generates teaching materials based on the gestures and key points extracted by the analyzer. For example, it can use AI to organize the extracted information, edit the video to emphasize key gesture points, and add detailed explanations in text.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recording section that records the movements and key points of traditional techniques, an analysis unit that analyzes the data recorded by the recording unit; a generation unit that generates teaching materials based on the actions and points extracted by the analysis unit; Equipped with A system characterized by:

2. The recording unit Recording the movements and key points of traditional techniques through video and text 2. The system of claim 1.

3. The analysis unit Analyze data using video analysis and natural language processing to extract gestures and key points 2. The system of claim 1.

4. The generation unit Edit videos and generate text based on the extracted information to create teaching materials 2. The system of claim 1.

5. The recording unit Estimate the user's emotions and adjust the timing of recording based on the estimated user emotions.

2. The system of claim 1.

6. The recording unit When recording, the details of the traditional techniques are recorded in high definition.

2. The system of claim 1.

7. The recording unit When recording, use multiple camera angles to capture your movements from multiple angles 2. The system of claim 1.

8. The recording unit During recording, voice recognition is used to automatically transcribe commentary into text.

2. The system of claim 1.

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