System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting video, audio, and text data of a technology from a craftsman; means for analyzing the collected data and performing video segmentation and image recognition; means for collecting information on user preferences and strengths; means for generating optimal learning content based on the analyzed data and user information; and means for providing the generated learning content to the user and checking the comprehension level.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] There is a social problem in fields that require advanced skills, with a lack of successors making it difficult to pass on skills. This problem is particularly serious in the fields of traditional crafts and specialized techniques, and there is a need for effective ways to pass on skills to the next generation. Traditional methods are time-consuming and labor-intensive, requiring long-term training to master, so it is essential to introduce new methods that improve efficiency while maintaining quality. [Means for solving the problem]

[0005] The present invention is a system that collects video, audio, and text data of skills from craftsmen, analyzes the collected data, and performs video segmentation and image recognition. It also includes a means for collecting information on user preferences and strengths and weaknesses, and generating optimal learning content based on the analysis data and user information. This system can provide the generated learning content to users and check their level of comprehension. Furthermore, by including a function that generates slow-motion videos showing detailed movements and audio guides emphasizing important points based on the analysis data, the efficiency of skill acquisition can be improved. Furthermore, by including interactive quizzes and checkpoints in the generated learning content, the user's level of comprehension can be improved. These means enable the efficient transfer of difficult skills and the development of successors.

[0006] An "artisan" is a skilled worker or professional technician who has mastered and practices a particular skill or art to a high level.

[0007] "Technical footage" refers to video clips or video files that record the techniques and work performed by craftsmen.

[0008] "Audio data" refers to audio recordings of craftsmen explaining techniques and procedures.

[0009] "Text data" refers to written information such as technical procedures and background information.

[0010] "Analyzing the data" refers to the process of processing collected video, audio, and text data to extract or classify key information.

[0011] "Video segmentation" is a technique for dividing recorded video into individual actions or steps.

[0012] "Image recognition" is a technology that identifies objects and actions within video data and analyzes their characteristics.

[0013] "User" refers to a learner or trainee who wishes to acquire a skill.

[0014] "User information" refers to information including personal learning characteristics such as user preferences, strengths and weaknesses.

[0015] "Learning content" refers to educational materials for acquiring skills that are generated based on analytical data and user information.

[0016] "Checking comprehension" refers to the process of assessing how well a user has understood the learning content.

[0017] "Slow motion video" refers to video that is played back slower than normal playback speed in order to observe action in detail.

[0018] "Audio guide" refers to a function that provides audio explanations of important points and precautions when acquiring skills.

[0019] "Interactive quiz" refers to a learning activity that includes interactive questions to test a user's understanding.

[0020] A "checkpoint" refers to a point or milestone set up to check learning progress and level of understanding. [Brief explanation of the drawings]

[0021] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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, a 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), and an APU (Accelerated Processing Unit).

[0025] 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.

[0026] 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.

[0027] 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), Bluetooth (registered trademark), etc.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0033] 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.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] An embodiment of the present invention is described below. The entire system consists of three main components: a server, a terminal, and a user. The server is a central processing unit that collects data, analyzes the data, and generates learning content. The terminal is the device that the user uses as an interface, and can be a PC, tablet, or smartphone. The user is a learner who wants to acquire skills.

[0043] First, the server collects video, audio, and text data of the craftsman's technique. The collection is done as follows:

[0044] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0045] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0046] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0047] The server then analyzes the collected data.

[0048] 1. The server uses a video data analysis module to identify the action segments of the technology (e.g., kneading clay, shaping, baking, etc.).

[0049] 2. The server uses image recognition technology to highlight important points in the video (hand position, tool usage, etc.).

[0050] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0051] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0052] The terminal is responsible for collecting information about the user's preferences and strengths and weaknesses.

[0053] 1. The device displays questions to the user in the form of a questionnaire (e.g., preferred learning style, level of experience and understanding of technology).

[0054] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[0055] 3. The information collected by the device is sent to the server.

[0056] The server then generates the learning content.

[0057] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[0058] 2. The server annotates the video, inserting text and arrows to indicate important action.

[0059] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[0060] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[0061] Finally, the terminal provides the generated learning content to the user.

[0062] 1. The device displays customized images, text, and audio guides to support users as they learn.

[0063] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[0064] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0065] As a concrete example, consider a user who wants to learn pottery. The server collects and analyzes video and audio-text data from skilled potters. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user then learns each step of the technique, progressing smoothly while checking their understanding through interactive quizzes.

[0066] This allows you to efficiently learn even highly difficult techniques.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[0070] Step 2:

[0071] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technique (e.g., kneading clay, shaping, baking, etc.). It uses image recognition technology to highlight important points in the video (hand position and tool usage). It transcribes the audio data and extracts keywords and important phrases. It analyzes the text data and extracts and structures important steps and points to note.

[0072] Step 3:

[0073] The device collects information about the user's preferences, strengths, and weaknesses. It displays questions to the user in the form of a questionnaire to collect information about preferred learning styles and their experience and understanding of technology. The user answers the questionnaire and enters their preferences, strengths, and weaknesses. The information collected by the device is sent to the server.

[0074] Step 4:

[0075] The server generates learning content. It determines the optimal learning format based on the user's preferences and characteristics. For users who prefer video, it selects slow-motion video that shows the action in detail and adds annotations to the video. It inserts text and arrows to highlight important actions. It uses audio data to generate audio guides that emphasize important points. It uses text data to create interactive quizzes and instruction manuals with checkpoints.

[0076] Step 5:

[0077] The device provides the generated learning content to the user. It displays customized video, text, and audio guides to support the user's learning. The user uses the device to progress through the learning process and answer the provided interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0078] Example 1

[0079] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0080] In conventional education systems, data collection and analysis of technology acquisition and optimization of individual learning are not adequately performed. As a result, learners are unable to access content that is best suited to them, making it difficult for them to acquire skills efficiently.

[0081] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0082] In this invention, the server includes a means for collecting video, audio, and text data of techniques from craftsmen, a means for analyzing the collected data, performing video segmentation and image recognition, and a means for adding annotations to the learning content, thereby enabling the provision of optimal learning content tailored to the needs of individual learners and enabling deeper understanding.

[0083] An "artisan" is someone who has expertise and skill in a particular skill or craft and who actually demonstrates that skill.

[0084] "Technique" refers to the skill and knowledge in a particular area of ​​expertise, as demonstrated by a craftsman.

[0085] "Video" refers to moving image data recorded using a video camera or other imaging device.

[0086] "Audio" refers to sound data recorded using a recording device such as a microphone.

[0087] "Text data" is data expressed as text information, and includes procedures and background information.

[0088] "Means for collecting" refers to devices and methods for acquiring video, audio, and text data of techniques from craftsmen and storing them in a database.

[0089] "Means of analysis" refers to technology that analyzes collected data and performs video segmentation and image recognition.

[0090] "Segmentation" is the process of analyzing video data and dividing it into specific actions or scenes.

[0091] "Image recognition" is a technology that analyzes video data to detect specific objects and actions.

[0092] "User information" is data that includes information about a user's preferences and strengths and weaknesses.

[0093] An "annotation means" is a method or device for adding auxiliary information (for example, text, arrows, etc.) to video or text data.

[0094] "Learning content" refers to materials for users to learn from, and includes information such as video, text, and audio guides.

[0095] "Interactive quizzes" are questions that users can answer as they learn, to assess their understanding.

[0096] A "checkpoint" is an interaction point during the learning process where a user can check their progress and understanding.

[0097] Detailed description of the embodiment of the present invention: The invention is implemented based on a system consisting of three main components: a server, a terminal, and a user.

[0098] First, the server collects video, audio, and text data of the craftsman's techniques from the artisan. To do this, it uses hardware such as a video camera, microphone, and scanner. For example, a video camera is used to film the artisan creating pottery, and the video is saved as digital data. The artisan's explanations are also recorded with a microphone and saved as audio data. Furthermore, the procedure manuals and background information provided by the artisan are digitized with a scanner and saved as text data.

[0099] Next, the server analyzes the collected data. This step involves the use of advanced video analysis software and voice recognition technology. For example, a video analysis module such as OpenCV is used to analyze the video frame by frame and identify each movement segment of the technique. Furthermore, image recognition technology is used to highlight important points in the video (hand position, tools used, etc.). At the same time, the Google Speech-to-Text API is used to transcribe the audio data and extract important keywords and phrases. Additionally, a text analysis algorithm is used to structure and organize the key parts of the instruction manual.

[0100] The device is used to collect information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs. For example, if a user wishes to learn pottery and responds that they prefer video-based learning, that information is immediately reflected in the server.

[0101] The server generates learning content optimized for each individual user based on the collected user information and analysis data. This process involves the use of video editing software and voice synthesis technology. For example, Final Cut Pro is used to add annotations and slow-motion effects to the video. Amazon Polly is also used to generate audio guides to emphasize important points. For example, for a user wanting to learn pottery, the process of kneading clay is played back in slow motion, with arrows indicating the hand positions and tools used. The audio guide also explains key points of the steps.

[0102] Finally, the device provides the generated learning content to the user. The user can view the video, text, and audio guides on the device, and progress through interactive quizzes and checkpoints. For example, while watching a video, the user can answer questions such as, "What's the next step in kneading the clay?" to check their level of understanding. The device sends the quiz results and learning progress to the server, which uses them to evaluate the user's level of understanding and provide additional learning content as needed.

[0103] An example prompt for a generative AI model might look like this:

[0104] "Identify key actions from a video demonstrating pottery techniques and generate annotated slow-motion footage. Then create an associated quiz so learners can test their understanding."

[0105] The above is a detailed description of the embodiments for carrying out the present invention.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1: Data collection

[0108] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the server uses a video camera to film the craftsmen's demonstrations of their techniques and records them as video data. The server also uses a microphone to record explanations and saves them as audio data. Furthermore, the server uses a scanner to digitize the procedures and background information provided by the craftsmen and saves them as text data.

[0109] Input: Video, audio, and instructions for technical demonstrations

[0110] Output: Digital video data, audio data, text data

[0111] Step 2: Data analysis

[0112] The server analyzes the collected data. Using a video data analysis module, the server identifies segments of the technology's actions (e.g., kneading clay, shaping, baking, etc.). It also uses image recognition technology to highlight important points in the video. At the same time, it transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data to structure important steps and important points.

[0113] Input: Digital video data, audio data, text data

[0114] Output: Analyzed action segments, key point annotations, transcripts, and structured instructions

[0115] Step 3: Collect user information

[0116] The device collects information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs.

[0117] Input: Survey Question

[0118] Output: User preferences and strengths and weaknesses

[0119] Step 4: Creating learning content

[0120] The server generates optimal learning content based on the analysis data and user information. It uses video editing software to add annotations and slow-motion effects to the video. It also uses speech synthesis technology to generate audio guides and emphasize important points. Furthermore, it creates interactive quizzes and instruction manuals with checkpoints based on the analysis data.

[0121] Input: Analysis data, user information

[0122] Output: Annotated slow-motion video, audio guide, interactive quiz, instructions with checkpoints

[0123] Step 5: Provide learning content

[0124] The device provides the generated learning content to the user. Specifically, the device displays customized videos, text, and audio guides for the user to review. The user plays the videos and answers interactive quizzes and checkpoints. The results and learning progress are sent to the server, which uses this information to evaluate the user's level of understanding and provide additional learning content as needed.

[0125] Input: Customized learning content

[0126] Output: User comprehension assessment data, learning progress data

[0127] (Application example 1)

[0128] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] There are limited means for efficiently learning traditional advanced technologies and specialized knowledge, and there is a lack of effective content for learning knowledge related to the operation and maintenance of factory robots. This makes it difficult for beginners to acquire skills efficiently and reliably. The present invention aims to solve this problem and provide an effective and efficient learning system for learning robotics technology.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0131] In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to users and checking their level of understanding, means for collecting and analyzing technical demonstration data related to factory robot operation and maintenance, and means for generating educational content for learning factory robot operation procedures and maintenance methods based on the collected data, thereby enabling users to efficiently acquire advanced factory robot operation and maintenance techniques.

[0132] A "craftsman" is a technician who has advanced skills and specialized knowledge and can demonstrate those skills.

[0133] "Technical footage" is video data that records a craftsman demonstrating technical work.

[0134] "Audio" refers to audio data that records the craftsman's commentary and explanations during the technical demonstration.

[0135] "Text data" refers to written information provided by craftsmen, such as technical procedures and background information, stored in digital format.

[0136] A "server" is a central processing unit that collects data, analyzes data, and generates learning content.

[0137] "Video segmentation" is the process of identifying technology operation segments (e.g., part installation, adjustment, inspection, etc.) from video data.

[0138] "Image recognition" is a technique for highlighting important points in a video (such as the robot's operating position or how to use a tool).

[0139] "Information on user preferences and strengths and weaknesses" refers to information on the learner's preferred learning format and their experience and understanding of technology.

[0140] "Learning content" refers to educational resources (composite data such as video, audio, and text) that are generated based on collected and analyzed technical data and provided to learners.

[0141] "Means to check comprehension" refers to methods that use interactive quizzes and checkpoints to assess learners' understanding of the technology.

[0142] A "factory robot" is an automated mechanical device used in a factory that performs specific tasks automatically.

[0143] "Operational procedures" refer to the procedures and methods for operating factory robots safely and efficiently.

[0144] "Maintenance methods" refer to the inspection and repair techniques required for the operation and maintenance of factory robots.

[0145] "Educational content" is a collection of information that can be viewed, heard, and read to learn the operating procedures and maintenance methods of factory robots.

[0146] The embodiment of the present invention will be described in detail below: This system mainly comprises three components: a server, a terminal, and a user.

[0147] First, the server is the central processing unit that collects and analyzes video, audio, and text data. Specifically, the server uses the following hardware and software:

[0148] Hardware: A PC or server with a powerful GPU

[0149] Software: OpenCV, PyDub, Flask, speech recognition APIs, and machine learning frameworks (e.g., TensorFlow)

[0150] The server collects video, audio, and text data of techniques from craftsmen. Specifically, it uses a video camera to film technical demonstrations and saves them as video data. It also records explanations with a microphone and records audio data. It also saves technical procedures and related information as text data. These data are stored in the server's database.

[0151] The server then analyzes this data. Using a video data analysis module, the video is segmented to identify action segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video. Meanwhile, audio data is transcribed using a speech recognition API to extract keywords and important phrases. Text data is analyzed using natural language processing technology to extract and structure important procedures and precautions.

[0152] The terminal functions as the user's interface. The terminal can be a device such as a smartphone, tablet, PC, or head-mounted display (HMD). The terminal collects information about the user's preferences and strengths and weaknesses in the form of a questionnaire. For example, the questionnaire asks whether the user prefers images or detailed text explanations. These response data are sent to the server.

[0153] The server generates optimal learning content based on the collected user information. Depending on the user's preferences and characteristics, the server generates slow-motion video, annotated video, and audio guides that emphasize important points. Furthermore, based on the text data, the server also creates interactive quizzes and instruction manuals with checkpoints. This allows users to learn skills effectively.

[0154] The device presents learning content provided by the server to the user. The user uses the provided content to progress through their studies and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0155] For example, if a user wants to learn how to maintain a factory robot, the server collects and analyzes technical demonstration video, audio commentary, and text data from expert technicians. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user can then use the learning content to study and check their understanding through interactive quizzes.

[0156] Example prompt sentence:

[0157] "Please provide easy-to-understand step-by-step instructions with video and audio for beginners who are replacing bearings on a robot arm for the first time."

[0158] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0159] Step 1:

[0160] The server collects video, audio, and text data of technical demonstrations from craftsmen. The craftsmen's technical demonstrations are filmed with a video camera and the video data is saved. The craftsmen's explanations of the techniques are also recorded with a microphone and saved as audio data. Furthermore, technical procedures and background information are saved as text data in the database. [Input]: Video, audio, and text data of technical demonstrations. [Output]: Technical data saved on the server.

[0161] Step 2:

[0162] The server analyzes the collected video data. The video data analysis module is used to identify the technology's operating segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video (e.g., robot operating position, tool usage method, etc.). [Input]: Collected video data. [Output]: Operating segments and highlighted important points.

[0163] Step 3:

[0164] The server analyzes the collected voice data. It uses a speech recognition API to transcribe the voice and extract keywords and important phrases. [Input]: Collected voice data. [Output]: Transcribed text data and extracted keywords.

[0165] Step 4:

[0166] The server analyzes the collected text data. Natural language processing technology is used to extract and structure important procedures and precautions. [Input]: Collected text data. [Output]: Extracted and structured procedures and precautions.

[0167] Step 5:

[0168] The device collects information about the user's preferences and strengths and weaknesses. Questions are displayed to the user in the form of a questionnaire, and the user answers the questions. The survey results are sent to the server. [Input]: User's survey responses. [Output]: User's preferences and strengths and weaknesses.

[0169] Step 6:

[0170] The server generates optimal learning content based on analysis data and user information. It generates slow-motion video, annotated video, and audio guides that emphasize important points according to the user's preferences and characteristics. It also creates interactive quizzes and instruction manuals with checkpoints. [Input]: Analysis data and user information. [Output]: Customized learning content.

[0171] Step 7:

[0172] The device provides the generated learning content to the user. The user uses the device to progress through the learning process and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which evaluates the user's level of understanding. [Input]: Customized learning content and user's learning progress information. [Output]: Evaluation of the user's level of understanding.

[0173] As a specific example of operation, if a user wants to learn how to replace bearings on a robot arm, the server will collect and analyze the technician's demonstration video and commentary audio using a video camera and microphone. The video data analysis module will identify the motion segments of parts installation and adjustment and highlight important points using image recognition. The device will collect the user's preferences and strengths and weaknesses through a questionnaire and send this information to the server. Based on this, the server will generate slow-motion video, annotated video, and interactive quizzes and provide them to the user. The user can learn through this content and have their understanding tested through quizzes.

[0174] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0175] An embodiment of the present invention is described below: The system comprises three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[0176] Data collection and analysis

[0177] First, the server collects video, audio, and text data of the craftsman's skills. Specifically, this is done as follows:

[0178] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0179] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0180] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0181] The server then analyzes the collected data.

[0182] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[0183] 2. The server uses image recognition technology to highlight important points in the video (e.g., hand position or tool usage).

[0184] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0185] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0186] Collection of User Information

[0187] The terminal collects information about the user's preferences and strengths and weaknesses.

[0188] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[0189] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[0190] 3. The device sends the collected information to the server.

[0191] Generating learning content

[0192] The server generates the learning content.

[0193] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[0194] 2. The server annotates the video, inserting text and arrows to indicate important action.

[0195] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[0196] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[0197] Emotion engine integration

[0198] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[0199] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine.

[0200] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results.

[0201] 3. When the server determines that the user is having difficulty understanding or is feeling stressed, it will lower the difficulty level of the content or provide a refresher guide.

[0202] Learning support

[0203] The terminal provides the generated learning content to the user.

[0204] 1. The device displays customized images, text, and audio guides to support users as they learn.

[0205] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[0206] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0207] Specific examples

[0208] For example, for a user wanting to learn pottery, the following might work:

[0209] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[0210] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[0211] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[0212] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[0213] The above process makes it possible to acquire skills more efficiently and effectively. The dynamic adjustment function based on the user's emotions improves the quality of learning and ensures smooth transfer of difficult skills.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[0217] Step 2:

[0218] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technology. For example, it recognizes specific actions such as kneading clay, shaping, and baking. It then uses image recognition technology to highlight important points in the video, such as the position of hands and how tools are used. It transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data in a similar way, extracting and structuring important steps and points to note.

[0219] Step 3:

[0220] The device collects information about the user's preferences and strengths and weaknesses. This information is collected using questionnaire-style questions. The user inputs their preferences, such as whether they like video or find text easier to understand, as well as information about their own skill level. The device then sends the collected information to the server.

[0221] Step 4:

[0222] The server generates optimal learning content based on user information. It determines the appropriate format of learning content based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the actions in detail, annotates the video, and inserts text and arrows to indicate important actions. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[0223] Step 5:

[0224] While the user is studying, the device uses an emotion engine to monitor the user's facial expressions and tone of voice in real time. The emotion engine analyzes whether the user is concentrating or feeling stressed. The emotion data is sent to the server, which dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the user is having difficulty understanding the content, it may lower the difficulty level or provide a refresher guide.

[0225] Step 6:

[0226] The device provides the generated learning content to the user. The user uses the device to progress through the learning process using videos, audio guides, and text explanations. The user's level of understanding is confirmed by answering the provided interactive quizzes and checkpoints. The device then sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0227] Example 2

[0228] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0229] Current technical education systems lack support functions for efficiently and effectively acquiring craftsmanship skills. Furthermore, it is difficult to dynamically adjust learning content according to the user's learning style and emotional state. This results in insufficient improvement in user understanding and a poor quality learning experience.

[0230] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to the user and checking their level of understanding, and means for analyzing the user's emotional data in real time and dynamically adjusting the learning content and presentation method. This enables efficient and effective skill acquisition and enables dynamic adjustments based on the user's emotional state.

[0231] A "craftsman" is a specialist who possesses specific skills and techniques and is responsible for teaching those skills to others.

[0232] "Technical footage" is video data recorded to visualize the techniques and work procedures of craftsmen.

[0233] "Audio data" refers to recorded data of craftsmen giving oral explanations about techniques and work procedures.

[0234] "Text data" refers to data documenting technical procedures and background information provided by craftsmen.

[0235] "Means of collection" refers to the devices and technologies used to capture video, audio, and text data.

[0236] "Means of analysis" refers to the techniques and devices used to analyze collected data and extract meaningful information.

[0237] "Video segmentation" is the process of dividing video data into meaningful segments.

[0238] "Image recognition" is a technology that identifies specific objects or actions within a video.

[0239] "Information about user preferences and strengths and weaknesses" is data about the user's learning style and level of understanding of technology.

[0240] "Means for generating optimal learning content" refers to technologies and devices that provide learning information in the most optimal form for users based on collected and analyzed data.

[0241] "Means for checking comprehension" is a process for assessing how well a user has understood the learning content.

[0242] "Emotion data" refers to data relating to the user's emotional state based on facial expressions, tone of voice, and the like.

[0243] "Dynamic adjustment means" refers to technology that changes the learning content and presentation method in response to the user's changing situation in real time.

[0244] An embodiment of the present invention is described below: The system is composed of three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[0245] Data collection and analysis

[0246] First, the server collects video, audio, and text data of the craftsman's technique from the craftsman.

[0247] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0248] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0249] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0250] The server then analyzes the collected data.

[0251] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[0252] 2. The server uses image recognition technology to highlight important action points in the video (e.g., hand position or tool usage).

[0253] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0254] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0255] Collection of User Information

[0256] The terminal collects information about the user's preferences and strengths and weaknesses.

[0257] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[0258] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses into the device.

[0259] 3. The device sends the collected information to the server.

[0260] Generating learning content

[0261] The server generates learning content based on the user information.

[0262] 1. The server determines the optimal learning format based on the user's preferences and characteristics (e.g., for a user who prefers video, slow-motion video that shows the movements in detail is selected).

[0263] 2. The server adds annotations to the video data, inserting text and arrows at important action points (e.g., adding annotations such as "Turn your hand 45 degrees here" at action points).

[0264] 3. The server uses the audio data to generate audio guidance that emphasizes important points (e.g., adding guidance such as "Keep the soil moist until this step is complete").

[0265] 4. The server uses the text data to create instructions including interactive quizzes and checkpoints (e.g., a checkpoint such as "Please check the dampness checkpoint before proceeding to the next step").

[0266] Emotion engine integration

[0267] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[0268] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[0269] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user expresses surprise, it adds a detailed explanation).

[0270] 3. If the server determines that the user is having difficulty understanding, it will lower the difficulty of the learning content or display a refresher guide (e.g., "It's time to take a short break").

[0271] Learning support

[0272] The terminal provides the generated learning content to the user.

[0273] 1. The device displays customized images, text, and audio guides to help users progress through their learning.

[0274] 2. The user uses the device to answer the provided interactive quizzes and checkpoints (e.g., answering a quiz such as "What will the condition of the soil be after the next step?").

[0275] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0276] Examples of specific examples and prompts

[0277] Specific examples

[0278] For users who wish to learn pottery techniques:

[0279] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[0280] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[0281] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[0282] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[0283] Prompt Sentence Examples

[0284] "Generate specific slow-motion footage and annotated explanations for users who want to learn the pottery techniques of master artisans. Also include the ability to analyze the user's emotional state in real time while learning and dynamically adjust the learning content."

[0285] As described above, this system allows for efficient and effective skill acquisition, and improves the quality of learning by dynamically adjusting according to the user's emotional state.

[0286] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0287] Step 1: Data collection

[0288] The server collects video, audio, and text data of the craftsman's technique from the craftsman.

[0289] Input: Craftsman demonstrations, explanations, and technical procedures

[0290] Specific behavior:

[0291] The server uses a video camera to capture the craftsman's demonstration of his skills and records it as video data.

[0292] The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0293] After completing the work, the user scans the handwritten technical instructions and background information and uploads them to the server, which stores them as text data.

[0294] Output: Video data, audio data, text data

[0295] Step 2: Data analysis

[0296] The server analyzes the collected data.

[0297] Input: Output data from Step 1 (video data, audio data, text data)

[0298] Specific behavior:

[0299] The server invokes a video data analysis module to identify the motion segments of the technique.

[0300] The server uses image recognition technology to identify and highlight important points in the video (e.g., hand position or tool usage).

[0301] The server transcribes the audio data and uses an analysis engine to extract keywords and important phrases (e.g., important phrases such as "soil moisture is important").

[0302] The server analyzes the text data, extracts important procedures and points to note, and structures them in a tree-like structure.

[0303] Output: Segmented video data, enhanced image points, text phrases, structured procedural data

[0304] Step 3: Collect user information

[0305] The terminal collects information about the user's preferences and strengths and weaknesses.

[0306] Input: User learning preferences, experience and comprehension information

[0307] Specific behavior:

[0308] The device displays a questionnaire form on the screen, asking about the user's learning style preferences and experience with technology.

[0309] Users answer surveys and enter information into forms.

[0310] The device transmits the collected information to the server in real time.

[0311] Output: Information about the user's learning preferences, experience, and comprehension

[0312] Step 4: Generate learning content

[0313] The server generates learning content based on the user information.

[0314] Input: Output data from Step 2, user information from Step 3

[0315] Specific behavior:

[0316] The server plans the optimal learning format based on the user's preferences and characteristics. For example, for a user who prioritizes visuals, it uses a lot of slow-motion footage to show important movements in detail.

[0317] The server adds annotations to the video data, overlaying text and arrows on important action scenes (e.g., annotations such as "Turn your hand 45 degrees here").

[0318] The server uses the audio data to generate audio instructions that emphasize important points (e.g., "Keep the soil moist until this step is complete").

[0319] The server uses the text data to generate instructions that include interactive quizzes and checkpoints (e.g., "Please check the dampness checkpoint before proceeding to the next step").

[0320] Output: customized video, text, audio guide, interactive quizzes, checkpoints

[0321] Step 5: Integrating the Emotion Engine

[0322] The device is equipped with an emotion engine that recognizes the user's emotions.

[0323] Input: Facial expressions and tone of voice of the user during training

[0324] Specific behavior:

[0325] During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[0326] The device sends emotional data to the server, and the server dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user reacts with surprise, it adds a detailed explanation).

[0327] If the server determines that the user is having difficulty understanding the content, it will lower the difficulty level of the learning content or display a refresher guide (for example, displaying "It's time to take a short break").

[0328] Output: Dynamically adjusted learning

[0329] Step 6: Learning support

[0330] The terminal provides the generated learning content to the user.

[0331] Input: customized video, text, audio guide, interactive quizzes, checkpoints

[0332] Specific behavior:

[0333] The device displays customized images and text guides on the screen to help users progress through their studies.

[0334] Users use the terminal to answer the provided interactive quizzes and checkpoints (e.g., answering questions such as "What will the condition of the soil be after the next step?").

[0335] The device sends the user's answers and progress information to the server, and the server evaluates the user's level of understanding based on this information.

[0336] Output: User's learning progress, understanding assessment

[0337] (Application example 2)

[0338] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0339] In recent years, there has been a demand for systems that can efficiently transfer skills and knowledge. However, conventional systems have had difficulty generating dynamic learning content that reflects the user's emotions and level of understanding in real time. Furthermore, they have not been able to provide personalized content based on the learner's preferences and characteristics, which has led to issues such as reduced learning effectiveness.

[0340] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, and means for collecting information on user preferences and strengths and weaknesses. This makes it possible to generate optimal learning content based on the analysis data and user information. Furthermore, by including means for analyzing the user's facial expressions and voice in real time and collecting emotional data, it is possible to dynamically adjust the learning content based on the emotional data, providing an optimized learning experience for each individual user.

[0341] A "craftsman" is a specialist with a particular skill or craftsmanship.

[0342] "Technical footage" is video data that records the specific actions and procedures performed by craftsmen.

[0343] "Audio" is a recording of the sounds made when a craftsman provides commentary on a technique or procedure.

[0344] "Text data" refers to textual information such as technical procedures and background information.

[0345] The "means for collecting data" is a system for capturing video, audio, and text data of the technology into a server.

[0346] The "means for analyzing data" refers to a system that uses collected video, audio, and text data to perform technical action segmentation and image recognition.

[0347] "Video segmentation" is the process of dividing video data into meaningful segments.

[0348] "Image recognition" is a technology that highlights and identifies important points within a video.

[0349] "Information on user preferences and strengths and weaknesses" is data on what types of learning formats the user prefers and in what areas the user has strengths and weaknesses.

[0350] The "means for generating optimal learning content" is a system that generates learning content in a form that is most suitable for the user based on analytical data and collected user information.

[0351] The "means for providing learning content and checking comprehension" is a system that presents the generated learning content to the user and evaluates the user's level of comprehension.

[0352] "Means for analyzing facial expressions and voice in real time and collecting emotional data" refers to a system that monitors the user's facial expressions and tone of voice, analyzes their emotions, and digitizes them.

[0353] The "means for dynamically adjusting learning content based on emotional data" is a system that adaptively changes the difficulty level and presentation method of learning content for users based on emotional data obtained in real time.

[0354] The embodiment of the present invention will be specifically described below. This system mainly consists of three main components: a server, a terminal, and a user. It is also characterized by including an emotion engine that recognizes the user's emotions.

[0355] Data collection and analysis

[0356] First, the server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, the craftsman's explanation of the technique is recorded with a microphone and saved as audio data on the server. Furthermore, the technical procedure manual and background information provided by the craftsman are collected as text data.

[0357] The server then analyzes the collected data. It uses a video data analysis module to identify technology operation segments and image recognition technology to highlight important points in the video. It also transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data and structures important steps and points to note.

[0358] Collection of User Information

[0359] The device collects information about the user's preferences, strengths, and weaknesses, presents questions in the form of a questionnaire, and collects information entered by the user. This information is then sent to the server.

[0360] Generating learning content

[0361] The server determines the optimal learning format based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the action in detail and adds annotations to the video to highlight important movements. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[0362] Emotion engine integration

[0363] While the device is learning, it monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine. The emotion data is sent to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the device determines that the user is struggling to understand or feeling stressed, it will lower the difficulty of the content or provide a refresher guide. This makes the user's learning experience more effective and comfortable.

[0364] Learning support

[0365] The device provides the generated learning content to the user and checks their level of understanding. The user can check their level of understanding by answering interactive quizzes and checkpoints. The device also sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0366] Specific examples

[0367] For example, when providing training support for new staff at a flagship store, the system works as follows: The server collects and analyzes data on customer service skills and product knowledge from experienced staff, and generates slow-motion video and annotated explanations. The user begins learning on the device, learning customer service skills through video and audio guidance. The device's emotion engine monitors the user's concentration and stress levels, and adjusts the learning content as necessary. Interactive quizzes are also provided as the user progresses to check their level of understanding.

[0368] Example of prompt input to a generative AI model

[0369] "Analyze the customer service scenario in this video and highlight the important points (e.g., how to converse with customers, explaining product features)."

[0370] In this way, the system provides a learning experience optimized for each user and supports effective skill acquisition.

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] Collecting user information. The device presents questions in a questionnaire format and collects information entered by the user about their preferences, strengths, and weaknesses. This information is sent to the server and used to generate learning content later.

[0374] Input: Survey results (preferences, weaknesses, experience level)

[0375] Output: User information data

[0376] Specific operation: The user answers questions displayed on the device screen, and the device collects this as data and sends it to the server.

[0377] Step 2:

[0378] Technical data is collected. The server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, explanations of the technique are recorded with a microphone and saved as text data.

[0379] Input: Video camera footage, microphone audio, text data

[0380] Output: Technical video data, technical audio data, technical text data

[0381] Specific operation: A technical demonstration is recorded with a video camera and the video is sent to a server. Commentary audio is also recorded with a microphone and saved on the server.

[0382] Step 3:

[0383] Analyzes technical data. The server uses a video data analysis module to segment technical actions and uses image recognition technology to highlight important points in the video. It also transcribes audio data and extracts keywords and important phrases. It analyzes text data and structures important procedures and points to note.

[0384] Input: Technical video data, technical audio data, technical text data

[0385] Output: Analysis result data (segmented video data, highlighted points, transcribed text, structured procedural data)

[0386] Specific operations: The server runs a video analysis module to identify the operation segments of the technology. Image recognition technology highlights important points. Audio analysis software transcribes the audio and extracts keywords and important phrases. Text analysis software structures important steps and important points.

[0387] Step 4:

[0388] The server generates learning content. Based on the user's preferences and characteristics, the server determines the optimal learning format. If video is preferred, it generates slow-motion video and adds annotations with text and arrows. It generates audio guides based on audio data to emphasize important points, and creates interactive quizzes and instruction manuals with checkpoints.

[0389] Input: User information data, analysis result data

[0390] Output: Learning content (annotated videos, audio guides, interactive instructions)

[0391] Specific operations: The server selects the optimal learning format based on user information, adds annotations and generates audio guides using video editing software, and adds interactive quizzes and checkpoints using text editing software.

[0392] Step 5:

[0393] Provides learning content. The device presents the generated learning content to the user and checks their level of understanding. The user solidifies the learning content by answering interactive quizzes and checkpoints. The device sends the user's answers to the server and evaluates their level of understanding.

[0394] Input: Learning content, user answers

[0395] Output: User progress and understanding assessment data

[0396] How it works: The device displays video, audio, and text, and the user answers interactive quizzes. The device then sends the answers to a server, which then runs an algorithm to assess comprehension.

[0397] Step 6:

[0398] The device monitors the user's emotions. The device analyzes the user's facial expressions and tone of voice in real time while they are learning, and collects emotional data using an emotion engine. The collected emotional data is sent to a server, and the difficulty level and presentation method of the content are dynamically adjusted based on the analysis results.

[0399] Input: User's facial expression data, voice data

[0400] Output: Sentiment analysis data, dynamically adjusted learning content

[0401] Specific operation: The device collects the user's facial expressions and voice using the camera and microphone, analyzes them with the emotion analysis engine, sends the emotion data to the server, and executes the content adjustment algorithm.

[0402] Through each step, it has been demonstrated that the system can provide a personalized learning experience for each user.

[0403] 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.

[0404] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0405] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0406] [Second embodiment]

[0407] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0408] 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.

[0409] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0410] 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.

[0411] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0412] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0413] 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.

[0414] 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.

[0415] 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 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.

[0416] 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.

[0417] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0418] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0419] An embodiment of the present invention is described below. The entire system consists of three main components: a server, a terminal, and a user. The server is a central processing unit that collects data, analyzes the data, and generates learning content. The terminal is the device that the user uses as an interface, and can be a PC, tablet, or smartphone. The user is a learner who wants to acquire skills.

[0420] First, the server collects video, audio, and text data of the craftsman's technique. The collection is done as follows:

[0421] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0422] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0423] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0424] The server then analyzes the collected data.

[0425] 1. The server uses a video data analysis module to identify the action segments of the technology (e.g., kneading clay, shaping, baking, etc.).

[0426] 2. The server uses image recognition technology to highlight important points in the video (hand position, tool usage, etc.).

[0427] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0428] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0429] The terminal is responsible for collecting information about the user's preferences and strengths and weaknesses.

[0430] 1. The device displays questions to the user in the form of a questionnaire (e.g., preferred learning style, level of experience and understanding of technology).

[0431] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[0432] 3. The information collected by the device is sent to the server.

[0433] The server then generates the learning content.

[0434] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[0435] 2. The server annotates the video, inserting text and arrows to indicate important action.

[0436] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[0437] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[0438] Finally, the terminal provides the generated learning content to the user.

[0439] 1. The device displays customized images, text, and audio guides to support users as they learn.

[0440] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[0441] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0442] As a concrete example, consider a user who wants to learn pottery. The server collects and analyzes video and audio-text data from skilled potters. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user then learns each step of the technique, progressing smoothly while checking their understanding through interactive quizzes.

[0443] This allows you to efficiently learn even highly difficult techniques.

[0444] The processing flow will be explained below.

[0445] Step 1:

[0446] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[0447] Step 2:

[0448] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technique (e.g., kneading clay, shaping, baking, etc.). It uses image recognition technology to highlight important points in the video (hand position and tool usage). It transcribes the audio data and extracts keywords and important phrases. It analyzes the text data and extracts and structures important steps and points to note.

[0449] Step 3:

[0450] The device collects information about the user's preferences, strengths, and weaknesses. It displays questions to the user in the form of a questionnaire to collect information about preferred learning styles and their experience and understanding of technology. The user answers the questionnaire and enters their preferences, strengths, and weaknesses. The information collected by the device is sent to the server.

[0451] Step 4:

[0452] The server generates learning content. It determines the optimal learning format based on the user's preferences and characteristics. For users who prefer video, it selects slow-motion video that shows the action in detail and adds annotations to the video. It inserts text and arrows to highlight important actions. It uses audio data to generate audio guides that emphasize important points. It uses text data to create interactive quizzes and instruction manuals with checkpoints.

[0453] Step 5:

[0454] The device provides the generated learning content to the user. It displays customized video, text, and audio guides to support the user's learning. The user uses the device to progress through the learning process and answer the provided interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0455] Example 1

[0456] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0457] In conventional education systems, data collection and analysis of technology acquisition and optimization of individual learning are not adequately performed. As a result, learners are unable to access content that is best suited to them, making it difficult for them to acquire skills efficiently.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0459] In this invention, the server includes a means for collecting video, audio, and text data of techniques from craftsmen, a means for analyzing the collected data, performing video segmentation and image recognition, and a means for adding annotations to the learning content, thereby enabling the provision of optimal learning content tailored to the needs of individual learners and enabling deeper understanding.

[0460] An "artisan" is someone who has expertise and skill in a particular skill or craft and who actually demonstrates that skill.

[0461] "Technique" refers to the skill and knowledge in a particular area of ​​expertise, as demonstrated by a craftsman.

[0462] "Video" refers to moving image data recorded using a video camera or other imaging device.

[0463] "Audio" refers to sound data recorded using a recording device such as a microphone.

[0464] "Text data" is data expressed as text information, and includes procedures and background information.

[0465] "Means for collecting" refers to devices and methods for acquiring video, audio, and text data of techniques from craftsmen and storing them in a database.

[0466] "Means of analysis" refers to technology that analyzes collected data and performs video segmentation and image recognition.

[0467] "Segmentation" is the process of analyzing video data and dividing it into specific actions or scenes.

[0468] "Image recognition" is a technology that analyzes video data to detect specific objects and actions.

[0469] "User information" is data that includes information about a user's preferences and strengths and weaknesses.

[0470] An "annotation means" is a method or device for adding auxiliary information (for example, text, arrows, etc.) to video or text data.

[0471] "Learning content" refers to materials for users to learn from, and includes information such as video, text, and audio guides.

[0472] "Interactive quizzes" are questions that users can answer as they learn, to assess their understanding.

[0473] A "checkpoint" is an interaction point during the learning process where a user can check their progress and understanding.

[0474] Detailed description of the embodiment of the present invention: The invention is implemented based on a system consisting of three main components: a server, a terminal, and a user.

[0475] First, the server collects video, audio, and text data of the craftsman's techniques from the artisan. To do this, it uses hardware such as a video camera, microphone, and scanner. For example, a video camera is used to film the artisan creating pottery, and the video is saved as digital data. The artisan's explanations are also recorded with a microphone and saved as audio data. Furthermore, the procedure manuals and background information provided by the artisan are digitized with a scanner and saved as text data.

[0476] Next, the server analyzes the collected data. This step involves the use of advanced video analysis software and voice recognition technology. For example, a video analysis module such as OpenCV is used to analyze the video frame by frame and identify each movement segment of the technique. Furthermore, image recognition technology is used to highlight important points in the video (hand position, tools used, etc.). At the same time, the Google Speech-to-Text API is used to transcribe the audio data and extract important keywords and phrases. Additionally, a text analysis algorithm is used to structure and organize the key parts of the instruction manual.

[0477] The device is used to collect information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs. For example, if a user wishes to learn pottery and responds that they prefer video-based learning, that information is immediately reflected in the server.

[0478] The server generates learning content optimized for each individual user based on the collected user information and analysis data. This process involves the use of video editing software and voice synthesis technology. For example, Final Cut Pro is used to add annotations and slow-motion effects to the video. Amazon Polly is also used to generate audio guides to emphasize important points. For example, for a user wanting to learn pottery, the process of kneading clay is played back in slow motion, with arrows indicating the hand positions and tools used. The audio guide also explains key points of the steps.

[0479] Finally, the device provides the generated learning content to the user. The user can view the video, text, and audio guides on the device, and progress through interactive quizzes and checkpoints. For example, while watching a video, the user can answer questions such as, "What's the next step in kneading the clay?" to check their level of understanding. The device sends the quiz results and learning progress to the server, which uses them to evaluate the user's level of understanding and provide additional learning content as needed.

[0480] An example prompt for a generative AI model might look like this:

[0481] "Identify key actions from a video demonstrating pottery techniques and generate annotated slow-motion footage. Then create an associated quiz so learners can test their understanding."

[0482] The above is a detailed description of the embodiments for carrying out the present invention.

[0483] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0484] Step 1: Data collection

[0485] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the server uses a video camera to film the craftsmen's demonstrations of their techniques and records them as video data. The server also uses a microphone to record explanations and saves them as audio data. Furthermore, the server uses a scanner to digitize the procedures and background information provided by the craftsmen and saves them as text data.

[0486] Input: Video, audio, and instructions for technical demonstrations

[0487] Output: Digital video data, audio data, text data

[0488] Step 2: Data analysis

[0489] The server analyzes the collected data. Using a video data analysis module, the server identifies segments of the technology's actions (e.g., kneading clay, shaping, baking, etc.). It also uses image recognition technology to highlight important points in the video. At the same time, it transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data to structure important steps and important points.

[0490] Input: Digital video data, audio data, text data

[0491] Output: Analyzed action segments, key point annotations, transcripts, and structured instructions

[0492] Step 3: Collect user information

[0493] The device collects information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs.

[0494] Input: Survey Question

[0495] Output: User preferences and strengths and weaknesses

[0496] Step 4: Creating learning content

[0497] The server generates optimal learning content based on the analysis data and user information. It uses video editing software to add annotations and slow-motion effects to the video. It also uses speech synthesis technology to generate audio guides and emphasize important points. Furthermore, it creates interactive quizzes and instruction manuals with checkpoints based on the analysis data.

[0498] Input: Analysis data, user information

[0499] Output: Annotated slow-motion video, audio guide, interactive quiz, instructions with checkpoints

[0500] Step 5: Provide learning content

[0501] The device provides the generated learning content to the user. Specifically, the device displays customized videos, text, and audio guides for the user to review. The user plays the videos and answers interactive quizzes and checkpoints. The results and learning progress are sent to the server, which uses this information to evaluate the user's level of understanding and provide additional learning content as needed.

[0502] Input: Customized learning content

[0503] Output: User comprehension assessment data, learning progress data

[0504] (Application example 1)

[0505] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0506] There are limited means for efficiently learning traditional advanced technologies and specialized knowledge, and there is a lack of effective content for learning knowledge related to the operation and maintenance of factory robots. This makes it difficult for beginners to acquire skills efficiently and reliably. The present invention aims to solve this problem and provide an effective and efficient learning system for learning robotics technology.

[0507] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0508] In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to users and checking their level of understanding, means for collecting and analyzing technical demonstration data related to factory robot operation and maintenance, and means for generating educational content for learning factory robot operation procedures and maintenance methods based on the collected data, thereby enabling users to efficiently acquire advanced factory robot operation and maintenance techniques.

[0509] A "craftsman" is a technician who has advanced skills and specialized knowledge and can demonstrate those skills.

[0510] "Technical footage" is video data that records a craftsman demonstrating technical work.

[0511] "Audio" refers to audio data that records the craftsman's commentary and explanations during the technical demonstration.

[0512] "Text data" refers to written information provided by craftsmen, such as technical procedures and background information, stored in digital format.

[0513] A "server" is a central processing unit that collects data, analyzes data, and generates learning content.

[0514] "Video segmentation" is the process of identifying technology operation segments (e.g., part installation, adjustment, inspection, etc.) from video data.

[0515] "Image recognition" is a technique for highlighting important points in a video (such as the robot's operating position or how to use a tool).

[0516] "Information on user preferences and strengths and weaknesses" refers to information on the learner's preferred learning format and their experience and understanding of technology.

[0517] "Learning content" refers to educational resources (composite data such as video, audio, and text) that are generated based on collected and analyzed technical data and provided to learners.

[0518] "Means to check comprehension" refers to methods that use interactive quizzes and checkpoints to assess learners' understanding of the technology.

[0519] A "factory robot" is an automated mechanical device used in a factory that performs specific tasks automatically.

[0520] "Operational procedures" refer to the procedures and methods for operating factory robots safely and efficiently.

[0521] "Maintenance methods" refer to the inspection and repair techniques required for the operation and maintenance of factory robots.

[0522] "Educational content" is a collection of information that can be viewed, heard, and read to learn the operating procedures and maintenance methods of factory robots.

[0523] The embodiment of the present invention will be described in detail below: This system mainly comprises three components: a server, a terminal, and a user.

[0524] First, the server is the central processing unit that collects and analyzes video, audio, and text data. Specifically, the server uses the following hardware and software:

[0525] Hardware: A PC or server with a powerful GPU

[0526] Software: OpenCV, PyDub, Flask, speech recognition APIs, and machine learning frameworks (e.g., TensorFlow)

[0527] The server collects video, audio, and text data of techniques from craftsmen. Specifically, it uses a video camera to film technical demonstrations and saves them as video data. It also records explanations with a microphone and records audio data. It also saves technical procedures and related information as text data. These data are stored in the server's database.

[0528] The server then analyzes this data. Using a video data analysis module, the video is segmented to identify action segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video. Meanwhile, audio data is transcribed using a speech recognition API to extract keywords and important phrases. Text data is analyzed using natural language processing technology to extract and structure important procedures and precautions.

[0529] The terminal functions as the user's interface. The terminal can be a device such as a smartphone, tablet, PC, or head-mounted display (HMD). The terminal collects information about the user's preferences and strengths and weaknesses in the form of a questionnaire. For example, the questionnaire asks whether the user prefers images or detailed text explanations. These response data are sent to the server.

[0530] The server generates optimal learning content based on the collected user information. Depending on the user's preferences and characteristics, the server generates slow-motion video, annotated video, and audio guides that emphasize important points. Furthermore, based on the text data, the server also creates interactive quizzes and instruction manuals with checkpoints. This allows users to learn skills effectively.

[0531] The device presents learning content provided by the server to the user. The user uses the provided content to progress through their studies and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0532] For example, if a user wants to learn how to maintain a factory robot, the server collects and analyzes technical demonstration video, audio commentary, and text data from expert technicians. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user can then use the learning content to study and check their understanding through interactive quizzes.

[0533] Example prompt sentence:

[0534] "Please provide easy-to-understand step-by-step instructions with video and audio for beginners who are replacing bearings on a robot arm for the first time."

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1:

[0537] The server collects video, audio, and text data of technical demonstrations from craftsmen. The craftsmen's technical demonstrations are filmed with a video camera and the video data is saved. The craftsmen's explanations of the techniques are also recorded with a microphone and saved as audio data. Furthermore, technical procedures and background information are saved as text data in the database. [Input]: Video, audio, and text data of technical demonstrations. [Output]: Technical data saved on the server.

[0538] Step 2:

[0539] The server analyzes the collected video data. The video data analysis module is used to identify the technology's operating segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video (e.g., robot operating position, tool usage method, etc.). [Input]: Collected video data. [Output]: Operating segments and highlighted important points.

[0540] Step 3:

[0541] The server analyzes the collected voice data. It uses a speech recognition API to transcribe the voice and extract keywords and important phrases. [Input]: Collected voice data. [Output]: Transcribed text data and extracted keywords.

[0542] Step 4:

[0543] The server analyzes the collected text data. Natural language processing technology is used to extract and structure important procedures and precautions. [Input]: Collected text data. [Output]: Extracted and structured procedures and precautions.

[0544] Step 5:

[0545] The device collects information about the user's preferences and strengths and weaknesses. Questions are displayed to the user in the form of a questionnaire, and the user answers the questions. The survey results are sent to the server. [Input]: User's survey responses. [Output]: User's preferences and strengths and weaknesses.

[0546] Step 6:

[0547] The server generates optimal learning content based on analysis data and user information. It generates slow-motion video, annotated video, and audio guides that emphasize important points according to the user's preferences and characteristics. It also creates interactive quizzes and instruction manuals with checkpoints. [Input]: Analysis data and user information. [Output]: Customized learning content.

[0548] Step 7:

[0549] The device provides the generated learning content to the user. The user uses the device to progress through the learning process and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which evaluates the user's level of understanding. [Input]: Customized learning content and user's learning progress information. [Output]: Evaluation of the user's level of understanding.

[0550] As a specific example of operation, if a user wants to learn how to replace bearings on a robot arm, the server will collect and analyze the technician's demonstration video and commentary audio using a video camera and microphone. The video data analysis module will identify the motion segments of parts installation and adjustment and highlight important points using image recognition. The device will collect the user's preferences and strengths and weaknesses through a questionnaire and send this information to the server. Based on this, the server will generate slow-motion video, annotated video, and interactive quizzes and provide them to the user. The user can learn through this content and have their understanding tested through quizzes.

[0551] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0552] An embodiment of the present invention is described below: The system comprises three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[0553] Data collection and analysis

[0554] First, the server collects video, audio, and text data of the craftsman's skills. Specifically, this is done as follows:

[0555] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0556] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0557] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0558] The server then analyzes the collected data.

[0559] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[0560] 2. The server uses image recognition technology to highlight important points in the video (e.g., hand position or tool usage).

[0561] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0562] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0563] Collection of User Information

[0564] The terminal collects information about the user's preferences and strengths and weaknesses.

[0565] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[0566] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[0567] 3. The device sends the collected information to the server.

[0568] Generating learning content

[0569] The server generates the learning content.

[0570] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[0571] 2. The server annotates the video, inserting text and arrows to indicate important action.

[0572] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[0573] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[0574] Emotion engine integration

[0575] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[0576] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine.

[0577] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results.

[0578] 3. When the server determines that the user is having difficulty understanding or is feeling stressed, it will lower the difficulty level of the content or provide a refresher guide.

[0579] Learning support

[0580] The terminal provides the generated learning content to the user.

[0581] 1. The device displays customized images, text, and audio guides to support users as they learn.

[0582] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[0583] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0584] Specific examples

[0585] For example, for a user wanting to learn pottery, the following might work:

[0586] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[0587] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[0588] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[0589] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[0590] The above process makes it possible to acquire skills more efficiently and effectively. The dynamic adjustment function based on the user's emotions improves the quality of learning and ensures smooth transfer of difficult skills.

[0591] The processing flow will be explained below.

[0592] Step 1:

[0593] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[0594] Step 2:

[0595] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technology. For example, it recognizes specific actions such as kneading clay, shaping, and baking. It then uses image recognition technology to highlight important points in the video, such as the position of hands and how tools are used. It transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data in a similar way, extracting and structuring important steps and points to note.

[0596] Step 3:

[0597] The device collects information about the user's preferences and strengths and weaknesses. This information is collected using questionnaire-style questions. The user inputs their preferences, such as whether they like video or find text easier to understand, as well as information about their own skill level. The device then sends the collected information to the server.

[0598] Step 4:

[0599] The server generates optimal learning content based on user information. It determines the appropriate format of learning content based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the actions in detail, annotates the video, and inserts text and arrows to indicate important actions. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[0600] Step 5:

[0601] While the user is studying, the device uses an emotion engine to monitor the user's facial expressions and tone of voice in real time. The emotion engine analyzes whether the user is concentrating or feeling stressed. The emotion data is sent to the server, which dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the user is having difficulty understanding the content, it may lower the difficulty level or provide a refresher guide.

[0602] Step 6:

[0603] The device provides the generated learning content to the user. The user uses the device to progress through the learning process using videos, audio guides, and text explanations. The user's level of understanding is confirmed by answering the provided interactive quizzes and checkpoints. The device then sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0604] Example 2

[0605] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0606] Current technical education systems lack support functions for efficiently and effectively acquiring craftsmanship skills. Furthermore, it is difficult to dynamically adjust learning content according to the user's learning style and emotional state. This results in insufficient improvement in user understanding and a poor quality learning experience.

[0607] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to the user and checking their level of understanding, and means for analyzing the user's emotional data in real time and dynamically adjusting the learning content and presentation method. This enables efficient and effective skill acquisition and enables dynamic adjustments based on the user's emotional state.

[0608] A "craftsman" is a specialist who possesses specific skills and techniques and is responsible for teaching those skills to others.

[0609] "Technical footage" is video data recorded to visualize the techniques and work procedures of craftsmen.

[0610] "Audio data" refers to recorded data of craftsmen giving oral explanations about techniques and work procedures.

[0611] "Text data" refers to data documenting technical procedures and background information provided by craftsmen.

[0612] "Means of collection" refers to the devices and technologies used to capture video, audio, and text data.

[0613] "Means of analysis" refers to the techniques and devices used to analyze collected data and extract meaningful information.

[0614] "Video segmentation" is the process of dividing video data into meaningful segments.

[0615] "Image recognition" is a technology that identifies specific objects or actions within a video.

[0616] "Information about user preferences and strengths and weaknesses" is data about the user's learning style and level of understanding of technology.

[0617] "Means for generating optimal learning content" refers to technologies and devices that provide learning information in the most optimal form for users based on collected and analyzed data.

[0618] "Means for checking comprehension" is a process for assessing how well a user has understood the learning content.

[0619] "Emotion data" refers to data relating to the user's emotional state based on facial expressions, tone of voice, and the like.

[0620] "Dynamic adjustment means" refers to technology that changes the learning content and presentation method in response to the user's changing situation in real time.

[0621] An embodiment of the present invention is described below: The system is composed of three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[0622] Data collection and analysis

[0623] First, the server collects video, audio, and text data of the craftsman's technique from the craftsman.

[0624] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0625] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0626] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0627] The server then analyzes the collected data.

[0628] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[0629] 2. The server uses image recognition technology to highlight important action points in the video (e.g., hand position or tool usage).

[0630] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0631] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0632] Collection of User Information

[0633] The terminal collects information about the user's preferences and strengths and weaknesses.

[0634] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[0635] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses into the device.

[0636] 3. The device sends the collected information to the server.

[0637] Generating learning content

[0638] The server generates learning content based on the user information.

[0639] 1. The server determines the optimal learning format based on the user's preferences and characteristics (e.g., for a user who prefers video, slow-motion video that shows the movements in detail is selected).

[0640] 2. The server adds annotations to the video data, inserting text and arrows at important action points (e.g., adding annotations such as "Turn your hand 45 degrees here" at action points).

[0641] 3. The server uses the audio data to generate audio guidance that emphasizes important points (e.g., adding guidance such as "Keep the soil moist until this step is complete").

[0642] 4. The server uses the text data to create instructions including interactive quizzes and checkpoints (e.g., a checkpoint such as "Please check the dampness checkpoint before proceeding to the next step").

[0643] Emotion engine integration

[0644] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[0645] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[0646] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user expresses surprise, it adds a detailed explanation).

[0647] 3. If the server determines that the user is having difficulty understanding, it will lower the difficulty of the learning content or display a refresher guide (e.g., "It's time to take a short break").

[0648] Learning support

[0649] The terminal provides the generated learning content to the user.

[0650] 1. The device displays customized images, text, and audio guides to help users progress through their learning.

[0651] 2. The user uses the device to answer the provided interactive quizzes and checkpoints (e.g., answering a quiz such as "What will the condition of the soil be after the next step?").

[0652] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0653] Examples of specific examples and prompts

[0654] Specific examples

[0655] For users who wish to learn pottery techniques:

[0656] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[0657] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[0658] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[0659] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[0660] Prompt Sentence Examples

[0661] "Generate specific slow-motion footage and annotated explanations for users who want to learn the pottery techniques of master artisans. Also include the ability to analyze the user's emotional state in real time while learning and dynamically adjust the learning content."

[0662] As described above, this system allows for efficient and effective skill acquisition, and improves the quality of learning by dynamically adjusting according to the user's emotional state.

[0663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0664] Step 1: Data collection

[0665] The server collects video, audio, and text data of the craftsman's technique from the craftsman.

[0666] Input: Craftsman demonstrations, explanations, and technical procedures

[0667] Specific behavior:

[0668] The server uses a video camera to capture the craftsman's demonstration of his skills and records it as video data.

[0669] The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0670] After completing the work, the user scans the handwritten technical instructions and background information and uploads them to the server, which stores them as text data.

[0671] Output: Video data, audio data, text data

[0672] Step 2: Data analysis

[0673] The server analyzes the collected data.

[0674] Input: Output data from Step 1 (video data, audio data, text data)

[0675] Specific behavior:

[0676] The server invokes a video data analysis module to identify the motion segments of the technique.

[0677] The server uses image recognition technology to identify and highlight important points in the video (e.g., hand position or tool usage).

[0678] The server transcribes the audio data and uses an analysis engine to extract keywords and important phrases (e.g., important phrases such as "soil moisture is important").

[0679] The server analyzes the text data, extracts important procedures and points to note, and structures them in a tree-like structure.

[0680] Output: Segmented video data, enhanced image points, text phrases, structured procedural data

[0681] Step 3: Collect user information

[0682] The terminal collects information about the user's preferences and strengths and weaknesses.

[0683] Input: User learning preferences, experience and comprehension information

[0684] Specific behavior:

[0685] The device displays a questionnaire form on the screen, asking about the user's learning style preferences and experience with technology.

[0686] Users answer surveys and enter information into forms.

[0687] The device transmits the collected information to the server in real time.

[0688] Output: Information about the user's learning preferences, experience, and comprehension

[0689] Step 4: Generate learning content

[0690] The server generates learning content based on the user information.

[0691] Input: Output data from Step 2, user information from Step 3

[0692] Specific behavior:

[0693] The server plans the optimal learning format based on the user's preferences and characteristics. For example, for a user who prioritizes visuals, it uses a lot of slow-motion footage to show important movements in detail.

[0694] The server adds annotations to the video data, overlaying text and arrows on important action scenes (e.g., annotations such as "Turn your hand 45 degrees here").

[0695] The server uses the audio data to generate audio instructions that emphasize important points (e.g., "Keep the soil moist until this step is complete").

[0696] The server uses the text data to generate instructions that include interactive quizzes and checkpoints (e.g., "Please check the dampness checkpoint before proceeding to the next step").

[0697] Output: customized video, text, audio guide, interactive quizzes, checkpoints

[0698] Step 5: Integrating the Emotion Engine

[0699] The device is equipped with an emotion engine that recognizes the user's emotions.

[0700] Input: Facial expressions and tone of voice of the user during training

[0701] Specific behavior:

[0702] During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[0703] The device sends emotional data to the server, and the server dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user reacts with surprise, it adds a detailed explanation).

[0704] If the server determines that the user is having difficulty understanding the content, it will lower the difficulty level of the learning content or display a refresher guide (for example, displaying "It's time to take a short break").

[0705] Output: Dynamically adjusted learning

[0706] Step 6: Learning support

[0707] The terminal provides the generated learning content to the user.

[0708] Input: customized video, text, audio guide, interactive quizzes, checkpoints

[0709] Specific behavior:

[0710] The device displays customized images and text guides on the screen to help users progress through their studies.

[0711] Users use the terminal to answer the provided interactive quizzes and checkpoints (e.g., answering questions such as "What will the condition of the soil be after the next step?").

[0712] The device sends the user's answers and progress information to the server, and the server evaluates the user's level of understanding based on this information.

[0713] Output: User's learning progress, understanding assessment

[0714] (Application example 2)

[0715] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0716] In recent years, there has been a demand for systems that can efficiently transfer skills and knowledge. However, conventional systems have had difficulty generating dynamic learning content that reflects the user's emotions and level of understanding in real time. Furthermore, they have not been able to provide personalized content based on the learner's preferences and characteristics, which has led to issues such as reduced learning effectiveness.

[0717] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, and means for collecting information on user preferences and strengths and weaknesses. This makes it possible to generate optimal learning content based on the analysis data and user information. Furthermore, by including means for analyzing the user's facial expressions and voice in real time and collecting emotional data, it is possible to dynamically adjust the learning content based on the emotional data, providing an optimized learning experience for each individual user.

[0718] A "craftsman" is a specialist with a particular skill or craftsmanship.

[0719] "Technical footage" is video data that records the specific actions and procedures performed by craftsmen.

[0720] "Audio" is a recording of the sounds made when a craftsman provides commentary on a technique or procedure.

[0721] "Text data" refers to textual information such as technical procedures and background information.

[0722] The "means for collecting data" is a system for capturing video, audio, and text data of the technology into a server.

[0723] The "means for analyzing data" refers to a system that uses collected video, audio, and text data to perform technical action segmentation and image recognition.

[0724] "Video segmentation" is the process of dividing video data into meaningful segments.

[0725] "Image recognition" is a technology that highlights and identifies important points within a video.

[0726] "Information on user preferences and strengths and weaknesses" is data on what types of learning formats the user prefers and in what areas the user has strengths and weaknesses.

[0727] The "means for generating optimal learning content" is a system that generates learning content in a form that is most suitable for the user based on analytical data and collected user information.

[0728] The "means for providing learning content and checking comprehension" is a system that presents the generated learning content to the user and evaluates the user's level of comprehension.

[0729] "Means for analyzing facial expressions and voice in real time and collecting emotional data" refers to a system that monitors the user's facial expressions and tone of voice, analyzes their emotions, and digitizes them.

[0730] The "means for dynamically adjusting learning content based on emotional data" is a system that adaptively changes the difficulty level and presentation method of learning content for users based on emotional data obtained in real time.

[0731] The embodiment of the present invention will be specifically described below. This system mainly consists of three main components: a server, a terminal, and a user. It is also characterized by including an emotion engine that recognizes the user's emotions.

[0732] Data collection and analysis

[0733] First, the server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, the craftsman's explanation of the technique is recorded with a microphone and saved as audio data on the server. Furthermore, the technical procedure manual and background information provided by the craftsman are collected as text data.

[0734] The server then analyzes the collected data. It uses a video data analysis module to identify technology operation segments and image recognition technology to highlight important points in the video. It also transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data and structures important steps and points to note.

[0735] Collection of User Information

[0736] The device collects information about the user's preferences, strengths, and weaknesses, presents questions in the form of a questionnaire, and collects information entered by the user. This information is then sent to the server.

[0737] Generating learning content

[0738] The server determines the optimal learning format based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the action in detail and adds annotations to the video to highlight important movements. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[0739] Emotion engine integration

[0740] While the device is learning, it monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine. The emotion data is sent to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the device determines that the user is struggling to understand or feeling stressed, it will lower the difficulty of the content or provide a refresher guide. This makes the user's learning experience more effective and comfortable.

[0741] Learning support

[0742] The device provides the generated learning content to the user and checks their level of understanding. The user can check their level of understanding by answering interactive quizzes and checkpoints. The device also sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0743] Specific examples

[0744] For example, when providing training support for new staff at a flagship store, the system works as follows: The server collects and analyzes data on customer service skills and product knowledge from experienced staff, and generates slow-motion video and annotated explanations. The user begins learning on the device, learning customer service skills through video and audio guidance. The device's emotion engine monitors the user's concentration and stress levels, and adjusts the learning content as necessary. Interactive quizzes are also provided as the user progresses to check their level of understanding.

[0745] Example of prompt input to a generative AI model

[0746] "Analyze the customer service scenario in this video and highlight the important points (e.g., how to converse with customers, explaining product features)."

[0747] In this way, the system provides a learning experience optimized for each user and supports effective skill acquisition.

[0748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0749] Step 1:

[0750] Collecting user information. The device presents questions in a questionnaire format and collects information entered by the user about their preferences, strengths, and weaknesses. This information is sent to the server and used to generate learning content later.

[0751] Input: Survey results (preferences, weaknesses, experience level)

[0752] Output: User information data

[0753] Specific operation: The user answers questions displayed on the device screen, and the device collects this as data and sends it to the server.

[0754] Step 2:

[0755] Technical data is collected. The server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, explanations of the technique are recorded with a microphone and saved as text data.

[0756] Input: Video camera footage, microphone audio, text data

[0757] Output: Technical video data, technical audio data, technical text data

[0758] Specific operation: A technical demonstration is recorded with a video camera and the video is sent to a server. Commentary audio is also recorded with a microphone and saved on the server.

[0759] Step 3:

[0760] Analyzes technical data. The server uses a video data analysis module to segment technical actions and uses image recognition technology to highlight important points in the video. It also transcribes audio data and extracts keywords and important phrases. It analyzes text data and structures important procedures and points to note.

[0761] Input: Technical video data, technical audio data, technical text data

[0762] Output: Analysis result data (segmented video data, highlighted points, transcribed text, structured procedural data)

[0763] Specific operations: The server runs a video analysis module to identify the operation segments of the technology. Image recognition technology highlights important points. Audio analysis software transcribes the audio and extracts keywords and important phrases. Text analysis software structures important steps and important points.

[0764] Step 4:

[0765] The server generates learning content. Based on the user's preferences and characteristics, the server determines the optimal learning format. If video is preferred, it generates slow-motion video and adds annotations with text and arrows. It generates audio guides based on audio data to emphasize important points, and creates interactive quizzes and instruction manuals with checkpoints.

[0766] Input: User information data, analysis result data

[0767] Output: Learning content (annotated videos, audio guides, interactive instructions)

[0768] Specific operations: The server selects the optimal learning format based on user information, adds annotations and generates audio guides using video editing software, and adds interactive quizzes and checkpoints using text editing software.

[0769] Step 5:

[0770] Provides learning content. The device presents the generated learning content to the user and checks their level of understanding. The user solidifies the learning content by answering interactive quizzes and checkpoints. The device sends the user's answers to the server and evaluates their level of understanding.

[0771] Input: Learning content, user answers

[0772] Output: User progress and understanding assessment data

[0773] How it works: The device displays video, audio, and text, and the user answers interactive quizzes. The device then sends the answers to a server, which then runs an algorithm to assess comprehension.

[0774] Step 6:

[0775] The device monitors the user's emotions. The device analyzes the user's facial expressions and tone of voice in real time while they are learning, and collects emotional data using an emotion engine. The collected emotional data is sent to a server, and the difficulty level and presentation method of the content are dynamically adjusted based on the analysis results.

[0776] Input: User's facial expression data, voice data

[0777] Output: Sentiment analysis data, dynamically adjusted learning content

[0778] Specific operation: The device collects the user's facial expressions and voice using the camera and microphone, analyzes them with the emotion analysis engine, sends the emotion data to the server, and executes the content adjustment algorithm.

[0779] Through each step, it has been demonstrated that the system can provide a personalized learning experience for each user.

[0780] 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.

[0781] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0782] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0783] [Third embodiment]

[0784] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0785] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0786] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0787] 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.

[0788] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0789] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0790] 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.

[0791] 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.

[0792] 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 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.

[0793] 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.

[0794] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0795] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0796] An embodiment of the present invention is described below. The entire system consists of three main components: a server, a terminal, and a user. The server is a central processing unit that collects data, analyzes the data, and generates learning content. The terminal is the device that the user uses as an interface, and can be a PC, tablet, or smartphone. The user is a learner who wants to acquire skills.

[0797] First, the server collects video, audio, and text data of the craftsman's technique. The collection is done as follows:

[0798] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0799] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0800] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0801] The server then analyzes the collected data.

[0802] 1. The server uses a video data analysis module to identify the action segments of the technology (e.g., kneading clay, shaping, baking, etc.).

[0803] 2. The server uses image recognition technology to highlight important points in the video (hand position, tool usage, etc.).

[0804] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0805] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0806] The terminal is responsible for collecting information about the user's preferences and strengths and weaknesses.

[0807] 1. The device displays questions to the user in the form of a questionnaire (e.g., preferred learning style, level of experience and understanding of technology).

[0808] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[0809] 3. The information collected by the device is sent to the server.

[0810] The server then generates the learning content.

[0811] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[0812] 2. The server annotates the video, inserting text and arrows to indicate important action.

[0813] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[0814] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[0815] Finally, the terminal provides the generated learning content to the user.

[0816] 1. The device displays customized images, text, and audio guides to support users as they learn.

[0817] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[0818] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0819] As a concrete example, consider a user who wants to learn pottery. The server collects and analyzes video and audio-text data from skilled potters. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user then learns each step of the technique, progressing smoothly while checking their understanding through interactive quizzes.

[0820] This allows you to efficiently learn even highly difficult techniques.

[0821] The processing flow will be explained below.

[0822] Step 1:

[0823] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[0824] Step 2:

[0825] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technique (e.g., kneading clay, shaping, baking, etc.). It uses image recognition technology to highlight important points in the video (hand position and tool usage). It transcribes the audio data and extracts keywords and important phrases. It analyzes the text data and extracts and structures important steps and points to note.

[0826] Step 3:

[0827] The device collects information about the user's preferences, strengths, and weaknesses. It displays questions to the user in the form of a questionnaire to collect information about preferred learning styles and their experience and understanding of technology. The user answers the questionnaire and enters their preferences, strengths, and weaknesses. The information collected by the device is sent to the server.

[0828] Step 4:

[0829] The server generates learning content. It determines the optimal learning format based on the user's preferences and characteristics. For users who prefer video, it selects slow-motion video that shows the action in detail and adds annotations to the video. It inserts text and arrows to highlight important actions. It uses audio data to generate audio guides that emphasize important points. It uses text data to create interactive quizzes and instruction manuals with checkpoints.

[0830] Step 5:

[0831] The device provides the generated learning content to the user. It displays customized video, text, and audio guides to support the user's learning. The user uses the device to progress through the learning process and answer the provided interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0832] Example 1

[0833] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0834] In conventional education systems, data collection and analysis of technology acquisition and optimization of individual learning are not adequately performed. As a result, learners are unable to access content that is best suited to them, making it difficult for them to acquire skills efficiently.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0836] In this invention, the server includes a means for collecting video, audio, and text data of techniques from craftsmen, a means for analyzing the collected data, performing video segmentation and image recognition, and a means for adding annotations to the learning content, thereby enabling the provision of optimal learning content tailored to the needs of individual learners and enabling deeper understanding.

[0837] An "artisan" is someone who has expertise and skill in a particular skill or craft and who actually demonstrates that skill.

[0838] "Technique" refers to the skill and knowledge in a particular area of ​​expertise, as demonstrated by a craftsman.

[0839] "Video" refers to moving image data recorded using a video camera or other imaging device.

[0840] "Audio" refers to sound data recorded using a recording device such as a microphone.

[0841] "Text data" is data expressed as text information, and includes procedures and background information.

[0842] "Means for collecting" refers to devices and methods for acquiring video, audio, and text data of techniques from craftsmen and storing them in a database.

[0843] "Means of analysis" refers to technology that analyzes collected data and performs video segmentation and image recognition.

[0844] "Segmentation" is the process of analyzing video data and dividing it into specific actions or scenes.

[0845] "Image recognition" is a technology that analyzes video data to detect specific objects and actions.

[0846] "User information" is data that includes information about a user's preferences and strengths and weaknesses.

[0847] An "annotation means" is a method or device for adding auxiliary information (for example, text, arrows, etc.) to video or text data.

[0848] "Learning content" refers to materials for users to learn from, and includes information such as video, text, and audio guides.

[0849] "Interactive quizzes" are questions that users can answer as they learn, to assess their understanding.

[0850] A "checkpoint" is an interaction point during the learning process where a user can check their progress and understanding.

[0851] Detailed description of the embodiment of the present invention: The invention is implemented based on a system consisting of three main components: a server, a terminal, and a user.

[0852] First, the server collects video, audio, and text data of the craftsman's techniques from the artisan. To do this, it uses hardware such as a video camera, microphone, and scanner. For example, a video camera is used to film the artisan creating pottery, and the video is saved as digital data. The artisan's explanations are also recorded with a microphone and saved as audio data. Furthermore, the procedure manuals and background information provided by the artisan are digitized with a scanner and saved as text data.

[0853] Next, the server analyzes the collected data. This step involves the use of advanced video analysis software and voice recognition technology. For example, a video analysis module such as OpenCV is used to analyze the video frame by frame and identify each movement segment of the technique. Furthermore, image recognition technology is used to highlight important points in the video (hand position, tools used, etc.). At the same time, the Google Speech-to-Text API is used to transcribe the audio data and extract important keywords and phrases. Additionally, a text analysis algorithm is used to structure and organize the key parts of the instruction manual.

[0854] The device is used to collect information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs. For example, if a user wishes to learn pottery and responds that they prefer video-based learning, that information is immediately reflected in the server.

[0855] The server generates learning content optimized for each individual user based on the collected user information and analysis data. This process involves the use of video editing software and voice synthesis technology. For example, Final Cut Pro is used to add annotations and slow-motion effects to the video. Amazon Polly is also used to generate audio guides to emphasize important points. For example, for a user wanting to learn pottery, the process of kneading clay is played back in slow motion, with arrows indicating the hand positions and tools used. The audio guide also explains key points of the steps.

[0856] Finally, the device provides the generated learning content to the user. The user can view the video, text, and audio guides on the device, and progress through interactive quizzes and checkpoints. For example, while watching a video, the user can answer questions such as, "What's the next step in kneading the clay?" to check their level of understanding. The device sends the quiz results and learning progress to the server, which uses them to evaluate the user's level of understanding and provide additional learning content as needed.

[0857] An example prompt for a generative AI model might look like this:

[0858] "Identify key actions from a video demonstrating pottery techniques and generate annotated slow-motion footage. Then create an associated quiz so learners can test their understanding."

[0859] The above is a detailed description of the embodiments for carrying out the present invention.

[0860] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0861] Step 1: Data collection

[0862] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the server uses a video camera to film the craftsmen's demonstrations of their techniques and records them as video data. The server also uses a microphone to record explanations and saves them as audio data. Furthermore, the server uses a scanner to digitize the procedures and background information provided by the craftsmen and saves them as text data.

[0863] Input: Video, audio, and instructions for technical demonstrations

[0864] Output: Digital video data, audio data, text data

[0865] Step 2: Data analysis

[0866] The server analyzes the collected data. Using a video data analysis module, the server identifies segments of the technology's actions (e.g., kneading clay, shaping, baking, etc.). It also uses image recognition technology to highlight important points in the video. At the same time, it transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data to structure important steps and important points.

[0867] Input: Digital video data, audio data, text data

[0868] Output: Analyzed action segments, key point annotations, transcripts, and structured instructions

[0869] Step 3: Collect user information

[0870] The device collects information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs.

[0871] Input: Survey Question

[0872] Output: User preferences and strengths and weaknesses

[0873] Step 4: Creating learning content

[0874] The server generates optimal learning content based on the analysis data and user information. It uses video editing software to add annotations and slow-motion effects to the video. It also uses speech synthesis technology to generate audio guides and emphasize important points. Furthermore, it creates interactive quizzes and instruction manuals with checkpoints based on the analysis data.

[0875] Input: Analysis data, user information

[0876] Output: Annotated slow-motion video, audio guide, interactive quiz, instructions with checkpoints

[0877] Step 5: Provide learning content

[0878] The device provides the generated learning content to the user. Specifically, the device displays customized videos, text, and audio guides for the user to review. The user plays the videos and answers interactive quizzes and checkpoints. The results and learning progress are sent to the server, which uses this information to evaluate the user's level of understanding and provide additional learning content as needed.

[0879] Input: Customized learning content

[0880] Output: User comprehension assessment data, learning progress data

[0881] (Application example 1)

[0882] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] There are limited means for efficiently learning traditional advanced technologies and specialized knowledge, and there is a lack of effective content for learning knowledge related to the operation and maintenance of factory robots. This makes it difficult for beginners to acquire skills efficiently and reliably. The present invention aims to solve this problem and provide an effective and efficient learning system for learning robotics technology.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0885] In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to users and checking their level of understanding, means for collecting and analyzing technical demonstration data related to factory robot operation and maintenance, and means for generating educational content for learning factory robot operation procedures and maintenance methods based on the collected data, thereby enabling users to efficiently acquire advanced factory robot operation and maintenance techniques.

[0886] A "craftsman" is a technician who has advanced skills and specialized knowledge and can demonstrate those skills.

[0887] "Technical footage" is video data that records a craftsman demonstrating technical work.

[0888] "Audio" refers to audio data that records the craftsman's commentary and explanations during the technical demonstration.

[0889] "Text data" refers to written information provided by craftsmen, such as technical procedures and background information, stored in digital format.

[0890] A "server" is a central processing unit that collects data, analyzes data, and generates learning content.

[0891] "Video segmentation" is the process of identifying technology operation segments (e.g., part installation, adjustment, inspection, etc.) from video data.

[0892] "Image recognition" is a technique for highlighting important points in a video (such as the robot's operating position or how to use a tool).

[0893] "Information on user preferences and strengths and weaknesses" refers to information on the learner's preferred learning format and their experience and understanding of technology.

[0894] "Learning content" refers to educational resources (composite data such as video, audio, and text) that are generated based on collected and analyzed technical data and provided to learners.

[0895] "Means to check comprehension" refers to methods that use interactive quizzes and checkpoints to assess learners' understanding of the technology.

[0896] A "factory robot" is an automated mechanical device used in a factory that performs specific tasks automatically.

[0897] "Operational procedures" refer to the procedures and methods for operating factory robots safely and efficiently.

[0898] "Maintenance methods" refer to the inspection and repair techniques required for the operation and maintenance of factory robots.

[0899] "Educational content" is a collection of information that can be viewed, heard, and read to learn the operating procedures and maintenance methods of factory robots.

[0900] The embodiment of the present invention will be described in detail below: This system mainly comprises three components: a server, a terminal, and a user.

[0901] First, the server is the central processing unit that collects and analyzes video, audio, and text data. Specifically, the server uses the following hardware and software:

[0902] Hardware: A PC or server with a powerful GPU

[0903] Software: OpenCV, PyDub, Flask, speech recognition APIs, and machine learning frameworks (e.g., TensorFlow)

[0904] The server collects video, audio, and text data of techniques from craftsmen. Specifically, it uses a video camera to film technical demonstrations and saves them as video data. It also records explanations with a microphone and records audio data. It also saves technical procedures and related information as text data. These data are stored in the server's database.

[0905] The server then analyzes this data. Using a video data analysis module, the video is segmented to identify action segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video. Meanwhile, audio data is transcribed using a speech recognition API to extract keywords and important phrases. Text data is analyzed using natural language processing technology to extract and structure important procedures and precautions.

[0906] The terminal functions as the user's interface. The terminal can be a device such as a smartphone, tablet, PC, or head-mounted display (HMD). The terminal collects information about the user's preferences and strengths and weaknesses in the form of a questionnaire. For example, the questionnaire asks whether the user prefers images or detailed text explanations. These response data are sent to the server.

[0907] The server generates optimal learning content based on the collected user information. Depending on the user's preferences and characteristics, the server generates slow-motion video, annotated video, and audio guides that emphasize important points. Furthermore, based on the text data, the server also creates interactive quizzes and instruction manuals with checkpoints. This allows users to learn skills effectively.

[0908] The device presents learning content provided by the server to the user. The user uses the provided content to progress through their studies and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0909] For example, if a user wants to learn how to maintain a factory robot, the server collects and analyzes technical demonstration video, audio commentary, and text data from expert technicians. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user can then use the learning content to study and check their understanding through interactive quizzes.

[0910] Example prompt sentence:

[0911] "Please provide easy-to-understand step-by-step instructions with video and audio for beginners who are replacing bearings on a robot arm for the first time."

[0912] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0913] Step 1:

[0914] The server collects video, audio, and text data of technical demonstrations from craftsmen. The craftsmen's technical demonstrations are filmed with a video camera and the video data is saved. The craftsmen's explanations of the techniques are also recorded with a microphone and saved as audio data. Furthermore, technical procedures and background information are saved as text data in the database. [Input]: Video, audio, and text data of technical demonstrations. [Output]: Technical data saved on the server.

[0915] Step 2:

[0916] The server analyzes the collected video data. The video data analysis module is used to identify the technology's operating segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video (e.g., robot operating position, tool usage method, etc.). [Input]: Collected video data. [Output]: Operating segments and highlighted important points.

[0917] Step 3:

[0918] The server analyzes the collected voice data. It uses a speech recognition API to transcribe the voice and extract keywords and important phrases. [Input]: Collected voice data. [Output]: Transcribed text data and extracted keywords.

[0919] Step 4:

[0920] The server analyzes the collected text data. Natural language processing technology is used to extract and structure important procedures and precautions. [Input]: Collected text data. [Output]: Extracted and structured procedures and precautions.

[0921] Step 5:

[0922] The device collects information about the user's preferences and strengths and weaknesses. Questions are displayed to the user in the form of a questionnaire, and the user answers the questions. The survey results are sent to the server. [Input]: User's survey responses. [Output]: User's preferences and strengths and weaknesses.

[0923] Step 6:

[0924] The server generates optimal learning content based on analysis data and user information. It generates slow-motion video, annotated video, and audio guides that emphasize important points according to the user's preferences and characteristics. It also creates interactive quizzes and instruction manuals with checkpoints. [Input]: Analysis data and user information. [Output]: Customized learning content.

[0925] Step 7:

[0926] The device provides the generated learning content to the user. The user uses the device to progress through the learning process and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which evaluates the user's level of understanding. [Input]: Customized learning content and user's learning progress information. [Output]: Evaluation of the user's level of understanding.

[0927] As a specific example of operation, if a user wants to learn how to replace bearings on a robot arm, the server will collect and analyze the technician's demonstration video and commentary audio using a video camera and microphone. The video data analysis module will identify the motion segments of parts installation and adjustment and highlight important points using image recognition. The device will collect the user's preferences and strengths and weaknesses through a questionnaire and send this information to the server. Based on this, the server will generate slow-motion video, annotated video, and interactive quizzes and provide them to the user. The user can learn through this content and have their understanding tested through quizzes.

[0928] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0929] An embodiment of the present invention is described below: The system comprises three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[0930] Data collection and analysis

[0931] First, the server collects video, audio, and text data of the craftsman's skills. Specifically, this is done as follows:

[0932] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[0933] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[0934] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[0935] The server then analyzes the collected data.

[0936] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[0937] 2. The server uses image recognition technology to highlight important points in the video (e.g., hand position or tool usage).

[0938] 3. The server transcribes the audio data and extracts keywords and important phrases.

[0939] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[0940] Collection of User Information

[0941] The terminal collects information about the user's preferences and strengths and weaknesses.

[0942] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[0943] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[0944] 3. The device sends the collected information to the server.

[0945] Generating learning content

[0946] The server generates the learning content.

[0947] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[0948] 2. The server annotates the video, inserting text and arrows to indicate important action.

[0949] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[0950] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[0951] Emotion engine integration

[0952] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[0953] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine.

[0954] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results.

[0955] 3. When the server determines that the user is having difficulty understanding or is feeling stressed, it will lower the difficulty level of the content or provide a refresher guide.

[0956] Learning support

[0957] The terminal provides the generated learning content to the user.

[0958] 1. The device displays customized images, text, and audio guides to support users as they learn.

[0959] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[0960] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[0961] Specific examples

[0962] For example, for a user wanting to learn pottery, the following might work:

[0963] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[0964] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[0965] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[0966] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[0967] The above process makes it possible to acquire skills more efficiently and effectively. The dynamic adjustment function based on the user's emotions improves the quality of learning and ensures smooth transfer of difficult skills.

[0968] The processing flow will be explained below.

[0969] Step 1:

[0970] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[0971] Step 2:

[0972] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technology. For example, it recognizes specific actions such as kneading clay, shaping, and baking. It then uses image recognition technology to highlight important points in the video, such as the position of hands and how tools are used. It transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data in a similar way, extracting and structuring important steps and points to note.

[0973] Step 3:

[0974] The device collects information about the user's preferences and strengths and weaknesses. This information is collected using questionnaire-style questions. The user inputs their preferences, such as whether they like video or find text easier to understand, as well as information about their own skill level. The device then sends the collected information to the server.

[0975] Step 4:

[0976] The server generates optimal learning content based on user information. It determines the appropriate format of learning content based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the actions in detail, annotates the video, and inserts text and arrows to indicate important actions. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[0977] Step 5:

[0978] While the user is studying, the device uses an emotion engine to monitor the user's facial expressions and tone of voice in real time. The emotion engine analyzes whether the user is concentrating or feeling stressed. The emotion data is sent to the server, which dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the user is having difficulty understanding the content, it may lower the difficulty level or provide a refresher guide.

[0979] Step 6:

[0980] The device provides the generated learning content to the user. The user uses the device to progress through the learning process using videos, audio guides, and text explanations. The user's level of understanding is confirmed by answering the provided interactive quizzes and checkpoints. The device then sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[0981] Example 2

[0982] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0983] Current technical education systems lack support functions for efficiently and effectively acquiring craftsmanship skills. Furthermore, it is difficult to dynamically adjust learning content according to the user's learning style and emotional state. This results in insufficient improvement in user understanding and a poor quality learning experience.

[0984] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to the user and checking their level of understanding, and means for analyzing the user's emotional data in real time and dynamically adjusting the learning content and presentation method. This enables efficient and effective skill acquisition and enables dynamic adjustments based on the user's emotional state.

[0985] A "craftsman" is a specialist who possesses specific skills and techniques and is responsible for teaching those skills to others.

[0986] "Technical footage" is video data recorded to visualize the techniques and work procedures of craftsmen.

[0987] "Audio data" refers to recorded data of craftsmen giving oral explanations about techniques and work procedures.

[0988] "Text data" refers to data documenting technical procedures and background information provided by craftsmen.

[0989] "Means of collection" refers to the devices and technologies used to capture video, audio, and text data.

[0990] "Means of analysis" refers to the techniques and devices used to analyze collected data and extract meaningful information.

[0991] "Video segmentation" is the process of dividing video data into meaningful segments.

[0992] "Image recognition" is a technology that identifies specific objects or actions within a video.

[0993] "Information about user preferences and strengths and weaknesses" is data about the user's learning style and level of understanding of technology.

[0994] "Means for generating optimal learning content" refers to technologies and devices that provide learning information in the most optimal form for users based on collected and analyzed data.

[0995] "Means for checking comprehension" is a process for assessing how well a user has understood the learning content.

[0996] "Emotion data" refers to data relating to the user's emotional state based on facial expressions, tone of voice, and the like.

[0997] "Dynamic adjustment means" refers to technology that changes the learning content and presentation method in response to the user's changing situation in real time.

[0998] An embodiment of the present invention is described below: The system is composed of three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[0999] Data collection and analysis

[1000] First, the server collects video, audio, and text data of the craftsman's technique from the craftsman.

[1001] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[1002] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[1003] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[1004] The server then analyzes the collected data.

[1005] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[1006] 2. The server uses image recognition technology to highlight important action points in the video (e.g., hand position or tool usage).

[1007] 3. The server transcribes the audio data and extracts keywords and important phrases.

[1008] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[1009] Collection of User Information

[1010] The terminal collects information about the user's preferences and strengths and weaknesses.

[1011] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[1012] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses into the device.

[1013] 3. The device sends the collected information to the server.

[1014] Generating learning content

[1015] The server generates learning content based on the user information.

[1016] 1. The server determines the optimal learning format based on the user's preferences and characteristics (e.g., for a user who prefers video, slow-motion video that shows the movements in detail is selected).

[1017] 2. The server adds annotations to the video data, inserting text and arrows at important action points (e.g., adding annotations such as "Turn your hand 45 degrees here" at action points).

[1018] 3. The server uses the audio data to generate audio guidance that emphasizes important points (e.g., adding guidance such as "Keep the soil moist until this step is complete").

[1019] 4. The server uses the text data to create instructions including interactive quizzes and checkpoints (e.g., a checkpoint such as "Please check the dampness checkpoint before proceeding to the next step").

[1020] Emotion engine integration

[1021] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[1022] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[1023] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user expresses surprise, it adds a detailed explanation).

[1024] 3. If the server determines that the user is having difficulty understanding, it will lower the difficulty of the learning content or display a refresher guide (e.g., "It's time to take a short break").

[1025] Learning support

[1026] The terminal provides the generated learning content to the user.

[1027] 1. The device displays customized images, text, and audio guides to help users progress through their learning.

[1028] 2. The user uses the device to answer the provided interactive quizzes and checkpoints (e.g., answering a quiz such as "What will the condition of the soil be after the next step?").

[1029] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[1030] Examples of specific examples and prompts

[1031] Specific examples

[1032] For users who wish to learn pottery techniques:

[1033] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[1034] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[1035] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[1036] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[1037] Prompt Sentence Examples

[1038] "Generate specific slow-motion footage and annotated explanations for users who want to learn the pottery techniques of master artisans. Also include the ability to analyze the user's emotional state in real time while learning and dynamically adjust the learning content."

[1039] As described above, this system allows for efficient and effective skill acquisition, and improves the quality of learning by dynamically adjusting according to the user's emotional state.

[1040] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1041] Step 1: Data collection

[1042] The server collects video, audio, and text data of the craftsman's technique from the craftsman.

[1043] Input: Craftsman demonstrations, explanations, and technical procedures

[1044] Specific behavior:

[1045] The server uses a video camera to capture the craftsman's demonstration of his skills and records it as video data.

[1046] The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[1047] After completing the work, the user scans the handwritten technical instructions and background information and uploads them to the server, which stores them as text data.

[1048] Output: Video data, audio data, text data

[1049] Step 2: Data analysis

[1050] The server analyzes the collected data.

[1051] Input: Output data from Step 1 (video data, audio data, text data)

[1052] Specific behavior:

[1053] The server invokes a video data analysis module to identify the motion segments of the technique.

[1054] The server uses image recognition technology to identify and highlight important points in the video (e.g., hand position or tool usage).

[1055] The server transcribes the audio data and uses an analysis engine to extract keywords and important phrases (e.g., important phrases such as "soil moisture is important").

[1056] The server analyzes the text data, extracts important procedures and points to note, and structures them in a tree-like structure.

[1057] Output: Segmented video data, enhanced image points, text phrases, structured procedural data

[1058] Step 3: Collect user information

[1059] The terminal collects information about the user's preferences and strengths and weaknesses.

[1060] Input: User learning preferences, experience and comprehension information

[1061] Specific behavior:

[1062] The device displays a questionnaire form on the screen, asking about the user's learning style preferences and experience with technology.

[1063] Users answer surveys and enter information into forms.

[1064] The device transmits the collected information to the server in real time.

[1065] Output: Information about the user's learning preferences, experience, and comprehension

[1066] Step 4: Generate learning content

[1067] The server generates learning content based on the user information.

[1068] Input: Output data from Step 2, user information from Step 3

[1069] Specific behavior:

[1070] The server plans the optimal learning format based on the user's preferences and characteristics. For example, for a user who prioritizes visuals, it uses a lot of slow-motion footage to show important movements in detail.

[1071] The server adds annotations to the video data, overlaying text and arrows on important action scenes (e.g., annotations such as "Turn your hand 45 degrees here").

[1072] The server uses the audio data to generate audio instructions that emphasize important points (e.g., "Keep the soil moist until this step is complete").

[1073] The server uses the text data to generate instructions that include interactive quizzes and checkpoints (e.g., "Please check the dampness checkpoint before proceeding to the next step").

[1074] Output: customized video, text, audio guide, interactive quizzes, checkpoints

[1075] Step 5: Integrating the Emotion Engine

[1076] The device is equipped with an emotion engine that recognizes the user's emotions.

[1077] Input: Facial expressions and tone of voice of the user during training

[1078] Specific behavior:

[1079] During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[1080] The device sends emotional data to the server, and the server dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user reacts with surprise, it adds a detailed explanation).

[1081] If the server determines that the user is having difficulty understanding the content, it will lower the difficulty level of the learning content or display a refresher guide (for example, displaying "It's time to take a short break").

[1082] Output: Dynamically adjusted learning

[1083] Step 6: Learning support

[1084] The terminal provides the generated learning content to the user.

[1085] Input: customized video, text, audio guide, interactive quizzes, checkpoints

[1086] Specific behavior:

[1087] The device displays customized images and text guides on the screen to help users progress through their studies.

[1088] Users use the terminal to answer the provided interactive quizzes and checkpoints (e.g., answering questions such as "What will the condition of the soil be after the next step?").

[1089] The device sends the user's answers and progress information to the server, and the server evaluates the user's level of understanding based on this information.

[1090] Output: User's learning progress, understanding assessment

[1091] (Application example 2)

[1092] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1093] In recent years, there has been a demand for systems that can efficiently transfer skills and knowledge. However, conventional systems have had difficulty generating dynamic learning content that reflects the user's emotions and level of understanding in real time. Furthermore, they have not been able to provide personalized content based on the learner's preferences and characteristics, which has led to issues such as reduced learning effectiveness.

[1094] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, and means for collecting information on user preferences and strengths and weaknesses. This makes it possible to generate optimal learning content based on the analysis data and user information. Furthermore, by including means for analyzing the user's facial expressions and voice in real time and collecting emotional data, it is possible to dynamically adjust the learning content based on the emotional data, providing an optimized learning experience for each individual user.

[1095] A "craftsman" is a specialist with a particular skill or craftsmanship.

[1096] "Technical footage" is video data that records the specific actions and procedures performed by craftsmen.

[1097] "Audio" is a recording of the sounds made when a craftsman provides commentary on a technique or procedure.

[1098] "Text data" refers to textual information such as technical procedures and background information.

[1099] The "means for collecting data" is a system for capturing video, audio, and text data of the technology into a server.

[1100] The "means for analyzing data" refers to a system that uses collected video, audio, and text data to perform technical action segmentation and image recognition.

[1101] "Video segmentation" is the process of dividing video data into meaningful segments.

[1102] "Image recognition" is a technology that highlights and identifies important points within a video.

[1103] "Information on user preferences and strengths and weaknesses" is data on what types of learning formats the user prefers and in what areas the user has strengths and weaknesses.

[1104] The "means for generating optimal learning content" is a system that generates learning content in a form that is most suitable for the user based on analytical data and collected user information.

[1105] The "means for providing learning content and checking comprehension" is a system that presents the generated learning content to the user and evaluates the user's level of comprehension.

[1106] "Means for analyzing facial expressions and voice in real time and collecting emotional data" refers to a system that monitors the user's facial expressions and tone of voice, analyzes their emotions, and digitizes them.

[1107] The "means for dynamically adjusting learning content based on emotional data" is a system that adaptively changes the difficulty level and presentation method of learning content for users based on emotional data obtained in real time.

[1108] The embodiment of the present invention will be specifically described below. This system mainly consists of three main components: a server, a terminal, and a user. It is also characterized by including an emotion engine that recognizes the user's emotions.

[1109] Data collection and analysis

[1110] First, the server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, the craftsman's explanation of the technique is recorded with a microphone and saved as audio data on the server. Furthermore, the technical procedure manual and background information provided by the craftsman are collected as text data.

[1111] The server then analyzes the collected data. It uses a video data analysis module to identify technology operation segments and image recognition technology to highlight important points in the video. It also transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data and structures important steps and points to note.

[1112] Collection of User Information

[1113] The device collects information about the user's preferences, strengths, and weaknesses, presents questions in the form of a questionnaire, and collects information entered by the user. This information is then sent to the server.

[1114] Generating learning content

[1115] The server determines the optimal learning format based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the action in detail and adds annotations to the video to highlight important movements. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[1116] Emotion engine integration

[1117] While the device is learning, it monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine. The emotion data is sent to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the device determines that the user is struggling to understand or feeling stressed, it will lower the difficulty of the content or provide a refresher guide. This makes the user's learning experience more effective and comfortable.

[1118] Learning support

[1119] The device provides the generated learning content to the user and checks their level of understanding. The user can check their level of understanding by answering interactive quizzes and checkpoints. The device also sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[1120] Specific examples

[1121] For example, when providing training support for new staff at a flagship store, the system works as follows: The server collects and analyzes data on customer service skills and product knowledge from experienced staff, and generates slow-motion video and annotated explanations. The user begins learning on the device, learning customer service skills through video and audio guidance. The device's emotion engine monitors the user's concentration and stress levels, and adjusts the learning content as necessary. Interactive quizzes are also provided as the user progresses to check their level of understanding.

[1122] Example of prompt input to a generative AI model

[1123] "Analyze the customer service scenario in this video and highlight the important points (e.g., how to converse with customers, explaining product features)."

[1124] In this way, the system provides a learning experience optimized for each user and supports effective skill acquisition.

[1125] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1126] Step 1:

[1127] Collecting user information. The device presents questions in a questionnaire format and collects information entered by the user about their preferences, strengths, and weaknesses. This information is sent to the server and used to generate learning content later.

[1128] Input: Survey results (preferences, weaknesses, experience level)

[1129] Output: User information data

[1130] Specific operation: The user answers questions displayed on the device screen, and the device collects this as data and sends it to the server.

[1131] Step 2:

[1132] Technical data is collected. The server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, explanations of the technique are recorded with a microphone and saved as text data.

[1133] Input: Video camera footage, microphone audio, text data

[1134] Output: Technical video data, technical audio data, technical text data

[1135] Specific operation: A technical demonstration is recorded with a video camera and the video is sent to a server. Commentary audio is also recorded with a microphone and saved on the server.

[1136] Step 3:

[1137] Analyzes technical data. The server uses a video data analysis module to segment technical actions and uses image recognition technology to highlight important points in the video. It also transcribes audio data and extracts keywords and important phrases. It analyzes text data and structures important procedures and points to note.

[1138] Input: Technical video data, technical audio data, technical text data

[1139] Output: Analysis result data (segmented video data, highlighted points, transcribed text, structured procedural data)

[1140] Specific operations: The server runs a video analysis module to identify the operation segments of the technology. Image recognition technology highlights important points. Audio analysis software transcribes the audio and extracts keywords and important phrases. Text analysis software structures important steps and important points.

[1141] Step 4:

[1142] The server generates learning content. Based on the user's preferences and characteristics, the server determines the optimal learning format. If video is preferred, it generates slow-motion video and adds annotations with text and arrows. It generates audio guides based on audio data to emphasize important points, and creates interactive quizzes and instruction manuals with checkpoints.

[1143] Input: User information data, analysis result data

[1144] Output: Learning content (annotated videos, audio guides, interactive instructions)

[1145] Specific operations: The server selects the optimal learning format based on user information, adds annotations and generates audio guides using video editing software, and adds interactive quizzes and checkpoints using text editing software.

[1146] Step 5:

[1147] Provides learning content. The device presents the generated learning content to the user and checks their level of understanding. The user solidifies the learning content by answering interactive quizzes and checkpoints. The device sends the user's answers to the server and evaluates their level of understanding.

[1148] Input: Learning content, user answers

[1149] Output: User progress and understanding assessment data

[1150] How it works: The device displays video, audio, and text, and the user answers interactive quizzes. The device then sends the answers to a server, which then runs an algorithm to assess comprehension.

[1151] Step 6:

[1152] The device monitors the user's emotions. The device analyzes the user's facial expressions and tone of voice in real time while they are learning, and collects emotional data using an emotion engine. The collected emotional data is sent to a server, and the difficulty level and presentation method of the content are dynamically adjusted based on the analysis results.

[1153] Input: User's facial expression data, voice data

[1154] Output: Sentiment analysis data, dynamically adjusted learning content

[1155] Specific operation: The device collects the user's facial expressions and voice using the camera and microphone, analyzes them with the emotion analysis engine, sends the emotion data to the server, and executes the content adjustment algorithm.

[1156] Through each step, it has been demonstrated that the system can provide a personalized learning experience for each user.

[1157] 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.

[1158] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1159] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1160] [Fourth embodiment]

[1161] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1164] 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.

[1165] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1166] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1167] 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.

[1168] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1169] 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.

[1170] 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 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.

[1171] 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.

[1172] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1173] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] An embodiment of the present invention is described below. The entire system consists of three main components: a server, a terminal, and a user. The server is a central processing unit that collects data, analyzes the data, and generates learning content. The terminal is the device that the user uses as an interface, and can be a PC, tablet, or smartphone. The user is a learner who wants to acquire skills.

[1175] First, the server collects video, audio, and text data of the craftsman's technique. The collection is done as follows:

[1176] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[1177] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[1178] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[1179] The server then analyzes the collected data.

[1180] 1. The server uses a video data analysis module to identify the action segments of the technology (e.g., kneading clay, shaping, baking, etc.).

[1181] 2. The server uses image recognition technology to highlight important points in the video (hand position, tool usage, etc.).

[1182] 3. The server transcribes the audio data and extracts keywords and important phrases.

[1183] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[1184] The terminal is responsible for collecting information about the user's preferences and strengths and weaknesses.

[1185] 1. The device displays questions to the user in the form of a questionnaire (e.g., preferred learning style, level of experience and understanding of technology).

[1186] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[1187] 3. The information collected by the device is sent to the server.

[1188] The server then generates the learning content.

[1189] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[1190] 2. The server annotates the video, inserting text and arrows to indicate important action.

[1191] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[1192] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[1193] Finally, the terminal provides the generated learning content to the user.

[1194] 1. The device displays customized images, text, and audio guides to support users as they learn.

[1195] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[1196] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[1197] As a concrete example, consider a user who wants to learn pottery. The server collects and analyzes video and audio-text data from skilled potters. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user then learns each step of the technique, progressing smoothly while checking their understanding through interactive quizzes.

[1198] This allows you to efficiently learn even highly difficult techniques.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[1202] Step 2:

[1203] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technique (e.g., kneading clay, shaping, baking, etc.). It uses image recognition technology to highlight important points in the video (hand position and tool usage). It transcribes the audio data and extracts keywords and important phrases. It analyzes the text data and extracts and structures important steps and points to note.

[1204] Step 3:

[1205] The device collects information about the user's preferences, strengths, and weaknesses. It displays questions to the user in the form of a questionnaire to collect information about preferred learning styles and their experience and understanding of technology. The user answers the questionnaire and enters their preferences, strengths, and weaknesses. The information collected by the device is sent to the server.

[1206] Step 4:

[1207] The server generates learning content. It determines the optimal learning format based on the user's preferences and characteristics. For users who prefer video, it selects slow-motion video that shows the action in detail and adds annotations to the video. It inserts text and arrows to highlight important actions. It uses audio data to generate audio guides that emphasize important points. It uses text data to create interactive quizzes and instruction manuals with checkpoints.

[1208] Step 5:

[1209] The device provides the generated learning content to the user. It displays customized video, text, and audio guides to support the user's learning. The user uses the device to progress through the learning process and answer the provided interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[1210] Example 1

[1211] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1212] In conventional education systems, data collection and analysis of technology acquisition and optimization of individual learning are not adequately performed. As a result, learners are unable to access content that is best suited to them, making it difficult for them to acquire skills efficiently.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1214] In this invention, the server includes a means for collecting video, audio, and text data of techniques from craftsmen, a means for analyzing the collected data, performing video segmentation and image recognition, and a means for adding annotations to the learning content, thereby enabling the provision of optimal learning content tailored to the needs of individual learners and enabling deeper understanding.

[1215] An "artisan" is someone who has expertise and skill in a particular skill or craft and who actually demonstrates that skill.

[1216] "Technique" refers to the skill and knowledge in a particular area of ​​expertise, as demonstrated by a craftsman.

[1217] "Video" refers to moving image data recorded using a video camera or other imaging device.

[1218] "Audio" refers to sound data recorded using a recording device such as a microphone.

[1219] "Text data" is data expressed as text information, and includes procedures and background information.

[1220] "Means for collecting" refers to devices and methods for acquiring video, audio, and text data of techniques from craftsmen and storing them in a database.

[1221] "Means of analysis" refers to technology that analyzes collected data and performs video segmentation and image recognition.

[1222] "Segmentation" is the process of analyzing video data and dividing it into specific actions or scenes.

[1223] "Image recognition" is a technology that analyzes video data to detect specific objects and actions.

[1224] "User information" is data that includes information about a user's preferences and strengths and weaknesses.

[1225] An "annotation means" is a method or device for adding auxiliary information (for example, text, arrows, etc.) to video or text data.

[1226] "Learning content" refers to materials for users to learn from, and includes information such as video, text, and audio guides.

[1227] "Interactive quizzes" are questions that users can answer as they learn, to assess their understanding.

[1228] A "checkpoint" is an interaction point during the learning process where a user can check their progress and understanding.

[1229] Detailed description of the embodiment of the present invention: The invention is implemented based on a system consisting of three main components: a server, a terminal, and a user.

[1230] First, the server collects video, audio, and text data of the craftsman's techniques from the artisan. To do this, it uses hardware such as a video camera, microphone, and scanner. For example, a video camera is used to film the artisan creating pottery, and the video is saved as digital data. The artisan's explanations are also recorded with a microphone and saved as audio data. Furthermore, the procedure manuals and background information provided by the artisan are digitized with a scanner and saved as text data.

[1231] Next, the server analyzes the collected data. This step involves the use of advanced video analysis software and voice recognition technology. For example, a video analysis module such as OpenCV is used to analyze the video frame by frame and identify each movement segment of the technique. Furthermore, image recognition technology is used to highlight important points in the video (hand position, tools used, etc.). At the same time, the Google Speech-to-Text API is used to transcribe the audio data and extract important keywords and phrases. Additionally, a text analysis algorithm is used to structure and organize the key parts of the instruction manual.

[1232] The device is used to collect information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs. For example, if a user wishes to learn pottery and responds that they prefer video-based learning, that information is immediately reflected in the server.

[1233] The server generates learning content optimized for each individual user based on the collected user information and analysis data. This process involves the use of video editing software and voice synthesis technology. For example, Final Cut Pro is used to add annotations and slow-motion effects to the video. Amazon Polly is also used to generate audio guides to emphasize important points. For example, for a user wanting to learn pottery, the process of kneading clay is played back in slow motion, with arrows indicating the hand positions and tools used. The audio guide also explains key points of the steps.

[1234] Finally, the device provides the generated learning content to the user. The user can view the video, text, and audio guides on the device, and progress through interactive quizzes and checkpoints. For example, while watching a video, the user can answer questions such as, "What's the next step in kneading the clay?" to check their level of understanding. The device sends the quiz results and learning progress to the server, which uses them to evaluate the user's level of understanding and provide additional learning content as needed.

[1235] An example prompt for a generative AI model might look like this:

[1236] "Identify key actions from a video demonstrating pottery techniques and generate annotated slow-motion footage. Then create an associated quiz so learners can test their understanding."

[1237] The above is a detailed description of the embodiments for carrying out the present invention.

[1238] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1239] Step 1: Data collection

[1240] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the server uses a video camera to film the craftsmen's demonstrations of their techniques and records them as video data. The server also uses a microphone to record explanations and saves them as audio data. Furthermore, the server uses a scanner to digitize the procedures and background information provided by the craftsmen and saves them as text data.

[1241] Input: Video, audio, and instructions for technical demonstrations

[1242] Output: Digital video data, audio data, text data

[1243] Step 2: Data analysis

[1244] The server analyzes the collected data. Using a video data analysis module, the server identifies segments of the technology's actions (e.g., kneading clay, shaping, baking, etc.). It also uses image recognition technology to highlight important points in the video. At the same time, it transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data to structure important steps and important points.

[1245] Input: Digital video data, audio data, text data

[1246] Output: Analyzed action segments, key point annotations, transcripts, and structured instructions

[1247] Step 3: Collect user information

[1248] The device collects information about the user's preferences and strengths and weaknesses. Specifically, the device displays questions to the user in the form of a questionnaire, which the user answers. This information is sent to the server in real time and stored in a database of the user's learning needs.

[1249] Input: Survey Question

[1250] Output: User preferences and strengths and weaknesses

[1251] Step 4: Creating learning content

[1252] The server generates optimal learning content based on the analysis data and user information. It uses video editing software to add annotations and slow-motion effects to the video. It also uses speech synthesis technology to generate audio guides and emphasize important points. Furthermore, it creates interactive quizzes and instruction manuals with checkpoints based on the analysis data.

[1253] Input: Analysis data, user information

[1254] Output: Annotated slow-motion video, audio guide, interactive quiz, instructions with checkpoints

[1255] Step 5: Provide learning content

[1256] The device provides the generated learning content to the user. Specifically, the device displays customized videos, text, and audio guides for the user to review. The user plays the videos and answers interactive quizzes and checkpoints. The results and learning progress are sent to the server, which uses this information to evaluate the user's level of understanding and provide additional learning content as needed.

[1257] Input: Customized learning content

[1258] Output: User comprehension assessment data, learning progress data

[1259] (Application example 1)

[1260] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1261] There are limited means for efficiently learning traditional advanced technologies and specialized knowledge, and there is a lack of effective content for learning knowledge related to the operation and maintenance of factory robots. This makes it difficult for beginners to acquire skills efficiently and reliably. The present invention aims to solve this problem and provide an effective and efficient learning system for learning robotics technology.

[1262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1263] In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to users and checking their level of understanding, means for collecting and analyzing technical demonstration data related to factory robot operation and maintenance, and means for generating educational content for learning factory robot operation procedures and maintenance methods based on the collected data, thereby enabling users to efficiently acquire advanced factory robot operation and maintenance techniques.

[1264] A "craftsman" is a technician who has advanced skills and specialized knowledge and can demonstrate those skills.

[1265] "Technical footage" is video data that records a craftsman demonstrating technical work.

[1266] "Audio" refers to audio data that records the craftsman's commentary and explanations during the technical demonstration.

[1267] "Text data" refers to written information provided by craftsmen, such as technical procedures and background information, stored in digital format.

[1268] A "server" is a central processing unit that collects data, analyzes data, and generates learning content.

[1269] "Video segmentation" is the process of identifying technology operation segments (e.g., part installation, adjustment, inspection, etc.) from video data.

[1270] "Image recognition" is a technique for highlighting important points in a video (such as the robot's operating position or how to use a tool).

[1271] "Information on user preferences and strengths and weaknesses" refers to information on the learner's preferred learning format and their experience and understanding of technology.

[1272] "Learning content" refers to educational resources (composite data such as video, audio, and text) that are generated based on collected and analyzed technical data and provided to learners.

[1273] "Means to check comprehension" refers to methods that use interactive quizzes and checkpoints to assess learners' understanding of the technology.

[1274] A "factory robot" is an automated mechanical device used in a factory that performs specific tasks automatically.

[1275] "Operational procedures" refer to the procedures and methods for operating factory robots safely and efficiently.

[1276] "Maintenance methods" refer to the inspection and repair techniques required for the operation and maintenance of factory robots.

[1277] "Educational content" is a collection of information that can be viewed, heard, and read to learn the operating procedures and maintenance methods of factory robots.

[1278] The embodiment of the present invention will be described in detail below: This system mainly comprises three components: a server, a terminal, and a user.

[1279] First, the server is the central processing unit that collects and analyzes video, audio, and text data. Specifically, the server uses the following hardware and software:

[1280] Hardware: A PC or server with a powerful GPU

[1281] Software: OpenCV, PyDub, Flask, speech recognition APIs, and machine learning frameworks (e.g., TensorFlow)

[1282] The server collects video, audio, and text data of techniques from craftsmen. Specifically, it uses a video camera to film technical demonstrations and saves them as video data. It also records explanations with a microphone and records audio data. It also saves technical procedures and related information as text data. These data are stored in the server's database.

[1283] The server then analyzes this data. Using a video data analysis module, the video is segmented to identify action segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video. Meanwhile, audio data is transcribed using a speech recognition API to extract keywords and important phrases. Text data is analyzed using natural language processing technology to extract and structure important procedures and precautions.

[1284] The terminal functions as the user's interface. The terminal can be a device such as a smartphone, tablet, PC, or head-mounted display (HMD). The terminal collects information about the user's preferences and strengths and weaknesses in the form of a questionnaire. For example, the questionnaire asks whether the user prefers images or detailed text explanations. These response data are sent to the server.

[1285] The server generates optimal learning content based on the collected user information. Depending on the user's preferences and characteristics, the server generates slow-motion video, annotated video, and audio guides that emphasize important points. Furthermore, based on the text data, the server also creates interactive quizzes and instruction manuals with checkpoints. This allows users to learn skills effectively.

[1286] The device presents learning content provided by the server to the user. The user uses the provided content to progress through their studies and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[1287] For example, if a user wants to learn how to maintain a factory robot, the server collects and analyzes technical demonstration video, audio commentary, and text data from expert technicians. If the user responds in a survey that they prefer video, the server generates slow-motion and annotated video and provides it via the device. The user can then use the learning content to study and check their understanding through interactive quizzes.

[1288] Example prompt sentence:

[1289] "Please provide easy-to-understand step-by-step instructions with video and audio for beginners who are replacing bearings on a robot arm for the first time."

[1290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1291] Step 1:

[1292] The server collects video, audio, and text data of technical demonstrations from craftsmen. The craftsmen's technical demonstrations are filmed with a video camera and the video data is saved. The craftsmen's explanations of the techniques are also recorded with a microphone and saved as audio data. Furthermore, technical procedures and background information are saved as text data in the database. [Input]: Video, audio, and text data of technical demonstrations. [Output]: Technical data saved on the server.

[1293] Step 2:

[1294] The server analyzes the collected video data. The video data analysis module is used to identify the technology's operating segments (e.g., part installation, adjustment, inspection, etc.). Image recognition technology is used to highlight important points in the video (e.g., robot operating position, tool usage method, etc.). [Input]: Collected video data. [Output]: Operating segments and highlighted important points.

[1295] Step 3:

[1296] The server analyzes the collected voice data. It uses a speech recognition API to transcribe the voice and extract keywords and important phrases. [Input]: Collected voice data. [Output]: Transcribed text data and extracted keywords.

[1297] Step 4:

[1298] The server analyzes the collected text data. Natural language processing technology is used to extract and structure important procedures and precautions. [Input]: Collected text data. [Output]: Extracted and structured procedures and precautions.

[1299] Step 5:

[1300] The device collects information about the user's preferences and strengths and weaknesses. Questions are displayed to the user in the form of a questionnaire, and the user answers the questions. The survey results are sent to the server. [Input]: User's survey responses. [Output]: User's preferences and strengths and weaknesses.

[1301] Step 6:

[1302] The server generates optimal learning content based on analysis data and user information. It generates slow-motion video, annotated video, and audio guides that emphasize important points according to the user's preferences and characteristics. It also creates interactive quizzes and instruction manuals with checkpoints. [Input]: Analysis data and user information. [Output]: Customized learning content.

[1303] Step 7:

[1304] The device provides the generated learning content to the user. The user uses the device to progress through the learning process and answer interactive quizzes and checkpoints. The device sends the user's answers and progress information to the server, which evaluates the user's level of understanding. [Input]: Customized learning content and user's learning progress information. [Output]: Evaluation of the user's level of understanding.

[1305] As a specific example of operation, if a user wants to learn how to replace bearings on a robot arm, the server will collect and analyze the technician's demonstration video and commentary audio using a video camera and microphone. The video data analysis module will identify the motion segments of parts installation and adjustment and highlight important points using image recognition. The device will collect the user's preferences and strengths and weaknesses through a questionnaire and send this information to the server. Based on this, the server will generate slow-motion video, annotated video, and interactive quizzes and provide them to the user. The user can learn through this content and have their understanding tested through quizzes.

[1306] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1307] An embodiment of the present invention is described below: The system comprises three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[1308] Data collection and analysis

[1309] First, the server collects video, audio, and text data of the craftsman's skills. Specifically, this is done as follows:

[1310] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[1311] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[1312] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[1313] The server then analyzes the collected data.

[1314] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[1315] 2. The server uses image recognition technology to highlight important points in the video (e.g., hand position or tool usage).

[1316] 3. The server transcribes the audio data and extracts keywords and important phrases.

[1317] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[1318] Collection of User Information

[1319] The terminal collects information about the user's preferences and strengths and weaknesses.

[1320] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[1321] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses.

[1322] 3. The device sends the collected information to the server.

[1323] Generating learning content

[1324] The server generates the learning content.

[1325] 1. The server determines the optimal learning format based on the user's preferences and characteristics. For example, if a user prefers video, it will select slow-motion video that shows the movements in detail.

[1326] 2. The server annotates the video, inserting text and arrows to indicate important action.

[1327] 3. The server uses the audio data to generate audio guidance that emphasizes important points.

[1328] 4. The server creates a procedure manual based on the text data, including interactive quizzes and checkpoints.

[1329] Emotion engine integration

[1330] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[1331] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine.

[1332] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results.

[1333] 3. When the server determines that the user is having difficulty understanding or is feeling stressed, it will lower the difficulty level of the content or provide a refresher guide.

[1334] Learning support

[1335] The terminal provides the generated learning content to the user.

[1336] 1. The device displays customized images, text, and audio guides to support users as they learn.

[1337] 2. The user uses the device to progress through the learning process and answer the interactive quizzes and checkpoints provided.

[1338] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[1339] Specific examples

[1340] For example, for a user wanting to learn pottery, the following might work:

[1341] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[1342] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[1343] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[1344] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[1345] The above process makes it possible to acquire skills more efficiently and effectively. The dynamic adjustment function based on the user's emotions improves the quality of learning and ensures smooth transfer of difficult skills.

[1346] The processing flow will be explained below.

[1347] Step 1:

[1348] The server collects video, audio, and text data of the techniques from the craftsmen. Specifically, the craftsmen's demonstrations of their techniques are filmed with a video camera and recorded as video data. Explanations of the techniques are recorded with a microphone and recorded as audio data. Technical procedures and background information provided by the craftsmen are stored in a database as text data.

[1349] Step 2:

[1350] The server analyzes the collected data. It uses a video data analysis module to identify the action segments of the technology. For example, it recognizes specific actions such as kneading clay, shaping, and baking. It then uses image recognition technology to highlight important points in the video, such as the position of hands and how tools are used. It transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data in a similar way, extracting and structuring important steps and points to note.

[1351] Step 3:

[1352] The device collects information about the user's preferences and strengths and weaknesses. This information is collected using questionnaire-style questions. The user inputs their preferences, such as whether they like video or find text easier to understand, as well as information about their own skill level. The device then sends the collected information to the server.

[1353] Step 4:

[1354] The server generates optimal learning content based on user information. It determines the appropriate format of learning content based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the actions in detail, annotates the video, and inserts text and arrows to indicate important actions. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[1355] Step 5:

[1356] While the user is studying, the device uses an emotion engine to monitor the user's facial expressions and tone of voice in real time. The emotion engine analyzes whether the user is concentrating or feeling stressed. The emotion data is sent to the server, which dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the user is having difficulty understanding the content, it may lower the difficulty level or provide a refresher guide.

[1357] Step 6:

[1358] The device provides the generated learning content to the user. The user uses the device to progress through the learning process using videos, audio guides, and text explanations. The user's level of understanding is confirmed by answering the provided interactive quizzes and checkpoints. The device then sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[1359] Example 2

[1360] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1361] Current technical education systems lack support functions for efficiently and effectively acquiring craftsmanship skills. Furthermore, it is difficult to dynamically adjust learning content according to the user's learning style and emotional state. This results in insufficient improvement in user understanding and a poor quality learning experience.

[1362] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, means for collecting information on user preferences and strengths and weaknesses, means for generating optimal learning content based on the analyzed data and user information, means for providing the generated learning content to the user and checking their level of understanding, and means for analyzing the user's emotional data in real time and dynamically adjusting the learning content and presentation method. This enables efficient and effective skill acquisition and enables dynamic adjustments based on the user's emotional state.

[1363] A "craftsman" is a specialist who possesses specific skills and techniques and is responsible for teaching those skills to others.

[1364] "Technical footage" is video data recorded to visualize the techniques and work procedures of craftsmen.

[1365] "Audio data" refers to recorded data of craftsmen giving oral explanations about techniques and work procedures.

[1366] "Text data" refers to data documenting technical procedures and background information provided by craftsmen.

[1367] "Means of collection" refers to the devices and technologies used to capture video, audio, and text data.

[1368] "Means of analysis" refers to the techniques and devices used to analyze collected data and extract meaningful information.

[1369] "Video segmentation" is the process of dividing video data into meaningful segments.

[1370] "Image recognition" is a technology that identifies specific objects or actions within a video.

[1371] "Information about user preferences and strengths and weaknesses" is data about the user's learning style and level of understanding of technology.

[1372] "Means for generating optimal learning content" refers to technologies and devices that provide learning information in the most optimal form for users based on collected and analyzed data.

[1373] "Means for checking comprehension" is a process for assessing how well a user has understood the learning content.

[1374] "Emotion data" refers to data relating to the user's emotional state based on facial expressions, tone of voice, and the like.

[1375] "Dynamic adjustment means" refers to technology that changes the learning content and presentation method in response to the user's changing situation in real time.

[1376] An embodiment of the present invention is described below: The system is composed of three main components: a server, a terminal, and a user, and further includes an emotion engine that recognizes the user's emotions.

[1377] Data collection and analysis

[1378] First, the server collects video, audio, and text data of the craftsman's technique from the craftsman.

[1379] 1. The server uses a video camera to record the craftsman's technical demonstration and records it as video data.

[1380] 2. The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[1381] 3. The server stores the technical procedures and background information provided by the craftsman in a database as text data.

[1382] The server then analyzes the collected data.

[1383] 1. The server uses a video data analysis module to identify the motion segments of the technology.

[1384] 2. The server uses image recognition technology to highlight important action points in the video (e.g., hand position or tool usage).

[1385] 3. The server transcribes the audio data and extracts keywords and important phrases.

[1386] 4. The server analyzes the text data, extracts important procedures and points to note, and structures them.

[1387] Collection of User Information

[1388] The terminal collects information about the user's preferences and strengths and weaknesses.

[1389] 1. The device displays questions in the form of a survey to collect information about preferred learning styles, experience with technology, and level of understanding.

[1390] 2. The user answers a questionnaire and enters their preferences, strengths and weaknesses into the device.

[1391] 3. The device sends the collected information to the server.

[1392] Generating learning content

[1393] The server generates learning content based on the user information.

[1394] 1. The server determines the optimal learning format based on the user's preferences and characteristics (e.g., for a user who prefers video, slow-motion video that shows the movements in detail is selected).

[1395] 2. The server adds annotations to the video data, inserting text and arrows at important action points (e.g., adding annotations such as "Turn your hand 45 degrees here" at action points).

[1396] 3. The server uses the audio data to generate audio guidance that emphasizes important points (e.g., adding guidance such as "Keep the soil moist until this step is complete").

[1397] 4. The server uses the text data to create instructions including interactive quizzes and checkpoints (e.g., a checkpoint such as "Please check the dampness checkpoint before proceeding to the next step").

[1398] Emotion engine integration

[1399] In this system, the device is equipped with an emotion engine that recognizes the user's emotions.

[1400] 1. During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[1401] 2. The device sends emotional data to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user expresses surprise, it adds a detailed explanation).

[1402] 3. If the server determines that the user is having difficulty understanding, it will lower the difficulty of the learning content or display a refresher guide (e.g., "It's time to take a short break").

[1403] Learning support

[1404] The terminal provides the generated learning content to the user.

[1405] 1. The device displays customized images, text, and audio guides to help users progress through their learning.

[1406] 2. The user uses the device to answer the provided interactive quizzes and checkpoints (e.g., answering a quiz such as "What will the condition of the soil be after the next step?").

[1407] 3. The device sends the user's answers and progress information to the server, which then evaluates the user's level of understanding based on this information.

[1408] Examples of specific examples and prompts

[1409] Specific examples

[1410] For users who wish to learn pottery techniques:

[1411] 1. The server collects and analyzes technical ceramic data from skilled artisans and generates slow-motion footage and annotated commentary.

[1412] 2. Users begin learning on their devices and learn pottery techniques through video and audio guides.

[1413] 3. The device's emotion engine monitors the user's concentration and stress levels and adjusts the learning content as needed.

[1414] 4. The device provides interactive quizzes as the user progresses to assess their understanding.

[1415] Prompt Sentence Examples

[1416] "Generate specific slow-motion footage and annotated explanations for users who want to learn the pottery techniques of master artisans. Also include the ability to analyze the user's emotional state in real time while learning and dynamically adjust the learning content."

[1417] As described above, this system allows for efficient and effective skill acquisition, and improves the quality of learning by dynamically adjusting according to the user's emotional state.

[1418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1419] Step 1: Data collection

[1420] The server collects video, audio, and text data of the craftsman's technique from the craftsman.

[1421] Input: Craftsman demonstrations, explanations, and technical procedures

[1422] Specific behavior:

[1423] The server uses a video camera to capture the craftsman's demonstration of his skills and records it as video data.

[1424] The server uses a microphone to record the craftsman's explanation of the technique and saves it as audio data.

[1425] After completing the work, the user scans the handwritten technical instructions and background information and uploads them to the server, which stores them as text data.

[1426] Output: Video data, audio data, text data

[1427] Step 2: Data analysis

[1428] The server analyzes the collected data.

[1429] Input: Output data from Step 1 (video data, audio data, text data)

[1430] Specific behavior:

[1431] The server invokes a video data analysis module to identify the motion segments of the technique.

[1432] The server uses image recognition technology to identify and highlight important points in the video (e.g., hand position or tool usage).

[1433] The server transcribes the audio data and uses an analysis engine to extract keywords and important phrases (e.g., important phrases such as "soil moisture is important").

[1434] The server analyzes the text data, extracts important procedures and points to note, and structures them in a tree-like structure.

[1435] Output: Segmented video data, enhanced image points, text phrases, structured procedural data

[1436] Step 3: Collect user information

[1437] The terminal collects information about the user's preferences and strengths and weaknesses.

[1438] Input: User learning preferences, experience and comprehension information

[1439] Specific behavior:

[1440] The device displays a questionnaire form on the screen, asking about the user's learning style preferences and experience with technology.

[1441] Users answer surveys and enter information into forms.

[1442] The device transmits the collected information to the server in real time.

[1443] Output: Information about the user's learning preferences, experience, and comprehension

[1444] Step 4: Generate learning content

[1445] The server generates learning content based on the user information.

[1446] Input: Output data from Step 2, user information from Step 3

[1447] Specific behavior:

[1448] The server plans the optimal learning format based on the user's preferences and characteristics. For example, for a user who prioritizes visuals, it uses a lot of slow-motion footage to show important movements in detail.

[1449] The server adds annotations to the video data, overlaying text and arrows on important action scenes (e.g., annotations such as "Turn your hand 45 degrees here").

[1450] The server uses the audio data to generate audio instructions that emphasize important points (e.g., "Keep the soil moist until this step is complete").

[1451] The server uses the text data to generate instructions that include interactive quizzes and checkpoints (e.g., "Please check the dampness checkpoint before proceeding to the next step").

[1452] Output: customized video, text, audio guide, interactive quizzes, checkpoints

[1453] Step 5: Integrating the Emotion Engine

[1454] The device is equipped with an emotion engine that recognizes the user's emotions.

[1455] Input: Facial expressions and tone of voice of the user during training

[1456] Specific behavior:

[1457] During learning, the device monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine (e.g., to detect a user's surprised expression).

[1458] The device sends emotional data to the server, and the server dynamically adjusts the learning content and presentation method based on the analysis results (e.g., if the user reacts with surprise, it adds a detailed explanation).

[1459] If the server determines that the user is having difficulty understanding the content, it will lower the difficulty level of the learning content or display a refresher guide (for example, displaying "It's time to take a short break").

[1460] Output: Dynamically adjusted learning

[1461] Step 6: Learning support

[1462] The terminal provides the generated learning content to the user.

[1463] Input: customized video, text, audio guide, interactive quizzes, checkpoints

[1464] Specific behavior:

[1465] The device displays customized images and text guides on the screen to help users progress through their studies.

[1466] Users use the terminal to answer the provided interactive quizzes and checkpoints (e.g., answering questions such as "What will the condition of the soil be after the next step?").

[1467] The device sends the user's answers and progress information to the server, and the server evaluates the user's level of understanding based on this information.

[1468] Output: User's learning progress, understanding assessment

[1469] (Application example 2)

[1470] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1471] In recent years, there has been a demand for systems that can efficiently transfer skills and knowledge. However, conventional systems have had difficulty generating dynamic learning content that reflects the user's emotions and level of understanding in real time. Furthermore, they have not been able to provide personalized content based on the learner's preferences and characteristics, which has led to issues such as reduced learning effectiveness.

[1472] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting video, audio, and text data of skills from craftsmen, means for analyzing the collected data and performing video segmentation and image recognition, and means for collecting information on user preferences and strengths and weaknesses. This makes it possible to generate optimal learning content based on the analysis data and user information. Furthermore, by including means for analyzing the user's facial expressions and voice in real time and collecting emotional data, it is possible to dynamically adjust the learning content based on the emotional data, providing an optimized learning experience for each individual user.

[1473] A "craftsman" is a specialist with a particular skill or craftsmanship.

[1474] "Technical footage" is video data that records the specific actions and procedures performed by craftsmen.

[1475] "Audio" is a recording of the sounds made when a craftsman provides commentary on a technique or procedure.

[1476] "Text data" refers to textual information such as technical procedures and background information.

[1477] The "means for collecting data" is a system for capturing video, audio, and text data of the technology into a server.

[1478] The "means for analyzing data" refers to a system that uses collected video, audio, and text data to perform technical action segmentation and image recognition.

[1479] "Video segmentation" is the process of dividing video data into meaningful segments.

[1480] "Image recognition" is a technology that highlights and identifies important points within a video.

[1481] "Information on user preferences and strengths and weaknesses" is data on what types of learning formats the user prefers and in what areas the user has strengths and weaknesses.

[1482] The "means for generating optimal learning content" is a system that generates learning content in a form that is most suitable for the user based on analytical data and collected user information.

[1483] The "means for providing learning content and checking comprehension" is a system that presents the generated learning content to the user and evaluates the user's level of comprehension.

[1484] "Means for analyzing facial expressions and voice in real time and collecting emotional data" refers to a system that monitors the user's facial expressions and tone of voice, analyzes their emotions, and digitizes them.

[1485] The "means for dynamically adjusting learning content based on emotional data" is a system that adaptively changes the difficulty level and presentation method of learning content for users based on emotional data obtained in real time.

[1486] The embodiment of the present invention will be specifically described below. This system mainly consists of three main components: a server, a terminal, and a user. It is also characterized by including an emotion engine that recognizes the user's emotions.

[1487] Data collection and analysis

[1488] First, the server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, the craftsman's explanation of the technique is recorded with a microphone and saved as audio data on the server. Furthermore, the technical procedure manual and background information provided by the craftsman are collected as text data.

[1489] The server then analyzes the collected data. It uses a video data analysis module to identify technology operation segments and image recognition technology to highlight important points in the video. It also transcribes the audio data and extracts keywords and important phrases. It also analyzes the text data and structures important steps and points to note.

[1490] Collection of User Information

[1491] The device collects information about the user's preferences, strengths, and weaknesses, presents questions in the form of a questionnaire, and collects information entered by the user. This information is then sent to the server.

[1492] Generating learning content

[1493] The server determines the optimal learning format based on the user's preferences and characteristics. For example, for a user who prefers video, it selects slow-motion video that shows the action in detail and adds annotations to the video to highlight important movements. It also uses audio data to generate audio guides that emphasize important points. It also creates interactive quizzes and instruction manuals with checkpoints based on text data.

[1494] Emotion engine integration

[1495] While the device is learning, it monitors the user's facial expressions and tone of voice in real time and analyzes them using an emotion engine. The emotion data is sent to the server, which then dynamically adjusts the learning content and presentation method based on the analysis results. For example, if the device determines that the user is struggling to understand or feeling stressed, it will lower the difficulty of the content or provide a refresher guide. This makes the user's learning experience more effective and comfortable.

[1496] Learning support

[1497] The device provides the generated learning content to the user and checks their level of understanding. The user can check their level of understanding by answering interactive quizzes and checkpoints. The device also sends the user's answers and progress information to the server, which then evaluates the user's level of understanding.

[1498] Specific examples

[1499] For example, when providing training support for new staff at a flagship store, the system works as follows: The server collects and analyzes data on customer service skills and product knowledge from experienced staff, and generates slow-motion video and annotated explanations. The user begins learning on the device, learning customer service skills through video and audio guidance. The device's emotion engine monitors the user's concentration and stress levels, and adjusts the learning content as necessary. Interactive quizzes are also provided as the user progresses to check their level of understanding.

[1500] Example of prompt input to a generative AI model

[1501] "Analyze the customer service scenario in this video and highlight the important points (e.g., how to converse with customers, explaining product features)."

[1502] In this way, the system provides a learning experience optimized for each user and supports effective skill acquisition.

[1503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1504] Step 1:

[1505] Collecting user information. The device presents questions in a questionnaire format and collects information entered by the user about their preferences, strengths, and weaknesses. This information is sent to the server and used to generate learning content later.

[1506] Input: Survey results (preferences, weaknesses, experience level)

[1507] Output: User information data

[1508] Specific operation: The user answers questions displayed on the device screen, and the device collects this as data and sends it to the server.

[1509] Step 2:

[1510] Technical data is collected. The server collects video, audio, and text data of the craftsman's techniques. The craftsman's demonstration of the technique is recorded with a video camera and the video data is sent to the server. In addition, explanations of the technique are recorded with a microphone and saved as text data.

[1511] Input: Video camera footage, microphone audio, text data

[1512] Output: Technical video data, technical audio data, technical text data

[1513] Specific operation: A technical demonstration is recorded with a video camera and the video is sent to a server. Commentary audio is also recorded with a microphone and saved on the server.

[1514] Step 3:

[1515] Analyzes technical data. The server uses a video data analysis module to segment technical actions and uses image recognition technology to highlight important points in the video. It also transcribes audio data and extracts keywords and important phrases. It analyzes text data and structures important procedures and points to note.

[1516] Input: Technical video data, technical audio data, technical text data

[1517] Output: Analysis result data (segmented video data, highlighted points, transcribed text, structured procedural data)

[1518] Specific operations: The server runs a video analysis module to identify the operation segments of the technology. Image recognition technology highlights important points. Audio analysis software transcribes the audio and extracts keywords and important phrases. Text analysis software structures important steps and important points.

[1519] Step 4:

[1520] The server generates learning content. Based on the user's preferences and characteristics, the server determines the optimal learning format. If video is preferred, it generates slow-motion video and adds annotations with text and arrows. It generates audio guides based on audio data to emphasize important points, and creates interactive quizzes and instruction manuals with checkpoints.

[1521] Input: User information data, analysis result data

[1522] Output: Learning content (annotated videos, audio guides, interactive instructions)

[1523] Specific operations: The server selects the optimal learning format based on user information, adds annotations and generates audio guides using video editing software, and adds interactive quizzes and checkpoints using text editing software.

[1524] Step 5:

[1525] Provides learning content. The device presents the generated learning content to the user and checks their level of understanding. The user solidifies the learning content by answering interactive quizzes and checkpoints. The device sends the user's answers to the server and evaluates their level of understanding.

[1526] Input: Learning content, user answers

[1527] Output: User progress and understanding assessment data

[1528] How it works: The device displays video, audio, and text, and the user answers interactive quizzes. The device then sends the answers to a server, which then runs an algorithm to assess comprehension.

[1529] Step 6:

[1530] The device monitors the user's emotions. The device analyzes the user's facial expressions and tone of voice in real time while they are learning, and collects emotional data using an emotion engine. The collected emotional data is sent to a server, and the difficulty level and presentation method of the content are dynamically adjusted based on the analysis results.

[1531] Input: User's facial expression data, voice data

[1532] Output: Sentiment analysis data, dynamically adjusted learning content

[1533] Specific operation: The device collects the user's facial expressions and voice using the camera and microphone, analyzes them with the emotion analysis engine, sends the emotion data to the server, and executes the content adjustment algorithm.

[1534] Through each step, it has been demonstrated that the system can provide a personalized learning experience for each user.

[1535] 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.

[1536] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1537] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1538] 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.

[1539] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1540] 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.

[1541] 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).

[1542] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1543] 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."

[1544] 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.

[1545] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1546] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1551] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] The following is further disclosed regarding the above embodiment.

[1557] (Claim 1)

[1558] A means for collecting video, audio, and text data of techniques from craftsmen;

[1559] A means of analyzing the collected data and performing video segmentation and image recognition;

[1560] A means for collecting information about user preferences, strengths and weaknesses;

[1561] A means for generating optimal learning content based on analytical data and user information;

[1562] A means for providing the generated learning content to the user and checking the user's level of understanding;

[1563] A system including:

[1564] (Claim 2)

[1565] The system according to claim 1, further comprising means for generating slow-motion video showing the action in detail or audio guidance emphasizing important points based on the analysis data.

[1566] (Claim 3)

[1567] 10. The system of claim 1, further comprising means for including interactive quizzes and checkpoints for the generated learning content.

[1568] "Example 1"

[1569] (Claim 1)

[1570] A means for collecting video, audio, and text data of techniques from craftsmen;

[1571] A means of analyzing the collected data and performing video segmentation and image recognition;

[1572] A means for collecting information about user preferences, strengths and weaknesses;

[1573] A means for generating optimal learning content based on analytical data and user information;

[1574] a means of annotating learning content;

[1575] A means for providing the generated learning content to the user and checking the user's level of understanding;

[1576] A system including:

[1577] (Claim 2)

[1578] The system according to claim 1, further comprising means for generating slow-motion video showing the action in detail or audio guidance emphasizing important points based on the analysis data.

[1579] (Claim 3)

[1580] 10. The system of claim 1, further comprising means for including interactive quizzes and checkpoints for the generated learning content.

[1581] "Application Example 1"

[1582] (Claim 1)

[1583] A means for collecting video, audio, and text data of techniques from craftsmen;

[1584] A means of analyzing the collected data and performing video segmentation and image recognition;

[1585] A means for collecting information about user preferences, strengths and weaknesses;

[1586] A means for generating optimal learning content based on analytical data and user information;

[1587] A means for providing the generated learning content to the user and checking the user's level of understanding;

[1588] A means for collecting and analyzing technical demonstration data related to the operation and maintenance of factory robots;

[1589] A means for generating educational content for learning operating procedures and maintenance methods for factory robots based on the collected data; and

[1590] A system including:

[1591] (Claim 2)

[1592] The system according to claim 1, further comprising means for generating slow-motion video showing the action in detail or audio guidance emphasizing important points based on the analysis data.

[1593] (Claim 3)

[1594] 10. The system of claim 1, further comprising means for including interactive quizzes and checkpoints for the generated learning content.

[1595] "Example 2: Combining Emotion Engines"

[1596] (Claim 1)

[1597] A means for collecting video, audio, and text data of techniques from craftsmen;

[1598] A means of analyzing the collected data and performing video segmentation and image recognition;

[1599] A means for collecting information about user preferences, strengths and weaknesses;

[1600] A means for generating optimal learning content based on analytical data and user information;

[1601] A means for providing the generated learning content to the user and checking the user's level of understanding;

[1602] A means to analyze user emotional data in real time and dynamically adjust the learning content and presentation method;

[1603] A system including:

[1604] (Claim 2)

[1605] The system according to claim 1, further comprising means for generating slow-motion video showing the action in detail or audio guidance emphasizing important points based on the analysis data.

[1606] (Claim 3)

[1607] 10. The system of claim 1, further comprising means for including interactive quizzes and checkpoints for the generated learning content.

[1608] (Claim 4)

[1609] 10. The system of claim 1, further comprising means for monitoring the user's facial expressions and tone of voice and transmitting emotion data to the server.

[1610] "Application example 2 when combining emotion engines"

[1611] (Claim 1)

[1612] A means for collecting video, audio, and text data of techniques from craftsmen;

[1613] A means of analyzing the collected data and performing video segmentation and image recognition;

[1614] A means for collecting information about user preferences, strengths and weaknesses;

[1615] A means for generating optimal learning content based on analytical data and user information;

[1616] A means for providing the generated learning content to the user and checking the user's level of understanding;

[1617] A means for analyzing the user's facial expressions and voice in real time to collect emotional data;

[1618] a means for dynamically adjusting learning content based on the emotional data;

[1619] A system including:

[1620] (Claim 2)

[1621] The system according to claim 1, further comprising means for generating slow-motion video showing the action in detail or audio guidance emphasizing important points based on the analysis data.

[1622] (Claim 3)

[1623] 10. The system of claim 1, further comprising means for including interactive quizzes and checkpoints for the generated learning content. [Explanation of symbols]

[1624] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting video, audio, and text data of techniques from craftsmen; A means of analyzing the collected data and performing video segmentation and image recognition; A means for collecting information about user preferences, strengths and weaknesses; A means for generating optimal learning content based on analytical data and user information; A means for providing the generated learning content to the user and checking the user's level of understanding; A system including:

2. The system according to claim 1, further comprising means for generating slow-motion images showing details of the action or audio guides emphasizing important points based on the analysis data.

3. 10. The system of claim 1, further comprising means for including interactive quizzes and checkpoints in the generated learning content.

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