system

Abstract

We provide the system. [Solution] Means of collecting information on traditional techniques, A means for analyzing the collected information and training a generated AI model, An interface means for providing technical details to the user, A sensor device means for collecting user movements in real time, A system including means for analyzing the aforementioned collected operational information and providing feedback to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

[0006] This provides a means to efficiently collect information on traditional techniques. This includes means to collect and store information such as history, materials, procedures, and craftsman's know-how in a database.

[0007] Next, we provide a means to analyze the collected information and train a generative AI model. This will enable the AI ​​to recognize the detailed steps and key points of the technology.

[0008] It also provides a user interface for providing users with technical details. This allows users who are responsible for the technology to intuitively access and verify the necessary information.

[0009] Furthermore, the system provides sensor devices for collecting user activity in real time. This allows for the collection and analysis of user activity and environmental data.

[0010] Finally, the collected motion information is analyzed, and a means is provided to give the user appropriate feedback. This allows the user to check the accuracy of their actions and techniques in real time and make necessary corrections.

[0011] By combining these methods, it becomes possible to accurately pass on traditional techniques and prevent their decline.

[0012] "Traditional techniques" refer to specific crafts, work methods, and related knowledge systems that are historically rooted in a region or culture.

[0013] "Information gathering means" refers to methods and devices for efficiently and systematically collecting specific data.

[0014] A "generative AI model" refers to an algorithm or program trained to solve a specific task or problem using artificial intelligence technology.

[0015] "User interface means" refers to means including a display device and an operating device for a user to interact with a system.

[0016] "Sensor device means" refers to a device for detecting a physical phenomenon and providing the data thereof to a system.

[0017] "Real-time analysis means" refers to a system or method for immediately processing acquired data and providing an analysis result in real time.

[0018] "Feedback means" refers to a method or device for providing appropriate information and advice for a user's operation or action.

[0019] "Database" refers to a system for efficiently managing and acquiring organized information.

[0020] "User" refers to an individual or group that uses this system to learn and practice traditional techniques.

[0021] "Operation information" refers to data indicating a user's body movement and the state of a usage environment.

[0022] "Analysis means" refers to a method or device for making a specific judgment or evaluation based on collected data.

Brief Description of Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0024] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0025] First, the terms used in the following description will be explained.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] As shown in Figure 1, the 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.

[0034] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0035] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0036] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0037] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0038] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0041] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0042] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] The system of this invention provides a mechanism for efficiently passing on traditional techniques. The program's processing flow is described in detail below.

[0045] Data collection methods

[0046] The server collects all kinds of information about traditional techniques. First, it gathers data from reliable sources such as historical documents, specialized books, videos, interviews with artisans, and instruction manuals. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used. The collected data is stored in a database to facilitate subsequent analysis.

[0047] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[0048] Forms of data analysis and model generation

[0049] The server performs data analysis based on the collected data. First, it performs preprocessing such as frame splitting of video data and tokenization of text data. Then, it trains a generative AI model, learning each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are optimized as needed to achieve higher accuracy.

[0050] As a concrete example, in the case of data analysis of the forging process of Japanese swords, the heating temperature of the material and the timing of the striking process are learned by the model.

[0051] User interface configuration

[0052] The device provides an interface that allows users to easily view details of traditional techniques. This includes viewing the history of collected technical data, watching videos and images of forging and work procedures, and real-time feedback. Users can quickly access the information they need through an intuitive interface.

[0053] For example, a new swordsmith can learn the procedures and points to note by watching videos of the forging process on a device.

[0054] Real-time guide format

[0055] The terminal collects user movement data in real time using sensor devices. For example, it may motion capture movements during work or acquire environmental data using a temperature sensor. The server analyzes this data in real time and generates appropriate feedback for the user. This feedback is provided to the user through the terminal, instructing them in real time on how to correct or improve their movements.

[0056] For example, if a blacksmith is actually performing a task and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[0057] Forms of feedback and corrections

[0058] The server stores the data collected in real time and performs detailed analysis. Based on the analysis results, it updates the generated AI model and uses it for the next training session. This continuously improves the model's performance and increases the accuracy of knowledge transfer.

[0059] As a concrete example, we upload video footage of a blacksmith at work to a server and use the analysis results to provide feedback on areas for improvement in the next session.

[0060] Summary

[0061] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture.

[0062] The following describes the processing flow.

[0063] Step 1:

[0064] The server collects information about traditional techniques. Specifically, it gathers data from historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and organizes and stores it in a database. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used.

[0065] Step 2:

[0066] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Necessary metadata is also extracted and stored in a database.

[0067] Step 3:

[0068] The server trains a generative AI model using pre-processed data. It first learns the basic technical steps and key points, and then incorporates more advanced techniques and condition-dependent parameters. The algorithm used in this process is selected to best suit the characteristics of the technology.

[0069] Step 4:

[0070] The server evaluates the accuracy of the trained AI model. A validation dataset is used for evaluation, measuring the model's performance. If the accuracy is insufficient, the parameters are adjusted and the model is trained again. This process is repeated to create the optimal model.

[0071] Step 5:

[0072] The terminal provides an interface that allows the user to access the system. This includes viewing the history of technology, viewing images and videos of procedures, and receiving real-time feedback. It must be designed to be user-friendly.

[0073] Step 6:

[0074] Users access necessary technical information using their devices. They review videos and images to learn technical procedures and key points. For example, they might watch a video of the Japanese sword forging process to understand the specific steps involved.

[0075] Step 7:

[0076] The device uses sensor devices to collect user movement data in real time. Typical examples include motion capture and temperature sensors. The collected data is temporarily stored within the device.

[0077] Step 8:

[0078] The server analyzes the action data sent from the terminal in real time. Based on the collected data, it determines whether the user's actions are appropriate and generates necessary corrections and advice.

[0079] Step 9:

[0080] The terminal provides the user with feedback sent from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then uses this information to correct their actions.

[0081] Step 10:

[0082] The server saves all session data and performs detailed analysis later. The analysis results are used to update the generated AI model and are used as training data for the next session. This improves the accuracy of the model and the efficiency of knowledge transfer.

[0083] Through this series of processing steps, it becomes possible to accurately pass on traditional techniques and prevent their decline.

[0084] (Example 1)

[0085] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] The transmission of traditional techniques faces challenges due to the aging of skilled craftsmen and the loss of knowledge. Furthermore, the detailed learning of traditional techniques requires advanced knowledge and experience, making it difficult to train new craftsmen. Overcoming this situation and ensuring the accurate transmission of these techniques is an urgent priority.

[0087] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0088] In this invention, the server includes means for collecting information relating to traditional techniques, means for preprocessing the collected information, means for training a generative AI model based on the preprocessed information, means for evaluating the accuracy of the trained generative AI model and optimizing its parameters, means for providing technical details to the user, means for a sensor device that collects the user's actions in real time, and means for analyzing the collected action information and providing feedback to the user. This enables the efficient and accurate transmission of traditional techniques.

[0089] "Traditional techniques" is a general term for skills and knowledge that have been formed against a backdrop of a long history and culture, and have been passed down through generations.

[0090] "Means of information gathering" refers to methods and devices for obtaining data in various formats from reliable sources such as historical documents, specialized books, videos, interviews, and procedural manuals.

[0091] "Preprocessing methods" refer to processes performed to convert collected data into an analyzable format, and specifically include frame splitting of video data and tokenization of text data.

[0092] A "generative AI model" is an artificial intelligence model that is trained on collected and pre-processed data, and is designed to learn the detailed steps of a specific task or technique.

[0093] "Training methods" refer to methods or devices for training a generative AI model using pre-processed data.

[0094] "Means for evaluating accuracy and optimizing parameters" refers to methods or devices for measuring the performance of a trained generative AI model and improving the model's settings based on the results.

[0095] "Interface means" refers to software or hardware that allows users to view and manipulate collected technical data.

[0096] "Sensor device means" refers to a device for collecting user movements and environmental data in real time, and includes motion capture sensors, temperature sensors, and the like.

[0097] "Means for analyzing motion information and providing feedback" refers to methods or devices that analyze data collected by sensor devices and suggest areas for correction or improvement to the user's actions.

[0098] "Means for maintaining a database" refers to methods and devices for efficiently and securely storing diverse collected data and making it immediately accessible when needed.

[0099] A "feedback analysis means" refers to a method or apparatus for reanalyzing collected data and generated feedback in order to improve the performance of the generated AI model.

[0100] The system of this invention operates in cooperation with a server, terminals, and users to efficiently and accurately pass on traditional techniques. This system combines means of collection, analysis, training, provision, collection, and feedback to pass on the detailed steps and knowledge of traditional techniques.

[0101] The server first collects information on traditional techniques from reliable sources. Specifically, it obtains historical documents, specialized books, videos, interviews with craftsmen, and procedural manuals from the internet and digital archives. This collected data is formatted in digital format and stored in a database. For example, in the case of Japanese sword making techniques, it collects historical documents on swordsmiths, production videos, interviews with craftsmen, and forging procedure manuals.

[0102] Next, the server preprocesses the collected data. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques. This extracts technical steps and theories. The preprocessed data is then used to train a generative AI model.

[0103] The server trains the generative AI model. For example, it uses a Transformer-based generative AI model to learn each step and key point of the technology based on the collected data. The trained model is evaluated for accuracy, and parameters are tuned as needed. This optimizes the model's performance.

[0104] The device provides an interface that allows users to easily view technical details. The user interface has an intuitive design, making it easy for users to view the history of technical data and watch videos and images of the forging process. For example, a new swordsmith can learn the procedures and precautions by watching videos of the forging process on the device.

[0105] Furthermore, the terminal uses sensor devices to collect user movements in real time. Motion capture sensors and temperature sensors are used to collect user movements and environmental data, which are then transmitted to the server. The server analyzes this movement data in real time and generates feedback such as "heating temperature is not appropriate" or "the timing of the tapping is off."

[0106] The collected data and generated feedback are stored on a server for detailed analysis. Based on the results of this analysis, the generated AI model is updated and used as training data for the next training session. This continuously improves the model's performance and enhances the accuracy of passing on traditional techniques.

[0107] As a concrete example, consider the techniques used to make Japanese swords. The server collects historical documents and forging procedures of swordsmiths and analyzes video data frame by frame. The generated AI model learns, for example, the timing and force of a "hammer swing." When a user actually performs the work, the terminal collects motion data using motion capture sensors, and the server provides real-time feedback of the analysis results, thereby streamlining the transmission of technical skills.

[0108] Examples of prompt statements for a generative AI model are shown below:

[0109] "Please explain the forging process of Japanese swords. Describe in detail the heating temperature, the timing of the striking process, and the materials and tools used."

[0110] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0111] Step 1: Data Collection

[0112] The server collects information on traditional techniques from multiple reliable sources. Specifically, it retrieves historical documents, specialized books, videos, interviews with artisans, and procedural manuals.

[0113] Input: The source of the data (e.g., the internet or digital archives).

[0114] Data processing: Integrating data from different formats, such as text and video, and formatting it into a digital format.

[0115] Output: Collected data (e.g., documents and videos related to Japanese sword making techniques).

[0116] Specific operation: The server downloads information via a specific URL or database query and stores it in the database.

[0117] Step 2: Data Preprocessing

[0118] The server converts the collected data into a format that is easy to analyze. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques.

[0119] Input: Collected data (video files, text files, etc.).

[0120] Data processing: Frame splitting of video data, tokenization of text data.

[0121] Output: Preprocessed data (split frames, token sequences).

[0122] Specific operation: The server uses video analysis software to divide the video data into frames and uses a natural language processing library to tokenize the text.

[0123] Step 3: Model Training

[0124] The server uses pre-processed data to train a generative AI model. For example, it might use a Transformer-based model to teach the model each step and key points of the technology.

[0125] Input: Preprocessed data (token sequence, frame image, etc.).

[0126] Data processing: Generate data, input it into the AI ​​model, and execute the learning process.

[0127] Output: Trained generative AI model.

[0128] Specific operation: The server uses a deep learning framework to train a model using preprocessed data. For example, it learns the timing and temperature control patterns of Japanese sword forging.

[0129] Step 4: Model Evaluation and Optimization

[0130] The server evaluates the accuracy of the trained generative AI model and optimizes the parameters as needed.

[0131] Input: Trained generative AI model, validation dataset.

[0132] Data processing: Measure the prediction accuracy of the model and adjust the parameters.

[0133] Output: Optimized generative AI model.

[0134] Specific operation: The server uses a validation dataset to compare the model's predictions with the actual results and adjusts the parameters to minimize the error.

[0135] Step 5: Provide technical details

[0136] The terminal provides an interface that offers users technical details. Users can view the history of technical data and watch videos and images of the forging process.

[0137] Input: Optimized generative AI model, technical data.

[0138] Data processing: Visualize technical data and convert it into a format suitable for the user interface.

[0139] Output: Display of technical details on the user interface.

[0140] Specific operation: The device interacts with the user through a web application or mobile application and provides collected technical data in the form of video playback or text.

[0141] Step 6: Real-time motion capture

[0142] The device uses sensor devices to collect user movement data in real time.

[0143] Input: User actions (e.g., hammer movement during forging), sensor device data.

[0144] Data processing: Collect data from sensor devices in real time and format it into a format that can be sent to the server.

[0145] Output: Real-time operation data.

[0146] Specific operation: The device uses motion capture sensors and temperature sensors to record and transmit user movements and environmental data in real time.

[0147] Step 7: Feedback analysis and provision

[0148] The server analyzes the collected operational data in real time and provides feedback to the user.

[0149] Input: Real-time operation data.

[0150] Data processing: Analyzes operational data and generates optimal feedback.

[0151] Output: Feedback information to the user (e.g., alerts, instructions).

[0152] Specific operation: The server analyzes operational data in real time, generates feedback such as "heating temperature is not appropriate" or "timing of striking is off," and sends it to the terminal.

[0153] Step 8: Feedback and Model Updates

[0154] The server stores all collected data and performs detailed analysis. Based on the results of this analysis, the generated AI model is updated and used for the next training session.

[0155] Input: Collected data and feedback information.

[0156] Data processing: Analyze feedback information and adjust the parameters of the generated AI model.

[0157] Output: Updated generative AI model.

[0158] Specific operation: The server analyzes the feedback information in detail and uses it as training data for the model, continuously improving the generated AI model.

[0159] In this way, the coordinated operation of each processing step enables the efficient and accurate transmission of traditional techniques.

[0160] (Application Example 1)

[0161] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0162] The transmission of traditional techniques requires advanced skills and experience, making it difficult to learn and accurately reproduce them efficiently. Furthermore, the presence of experienced instructors is essential for inexperienced technicians to acquire accurate procedures and techniques; a lack of such instructors can lead to a decline in the accuracy of technique transfer. Additionally, the lack of mechanisms for real-time work improvement and feedback makes it difficult for technicians to self-correct. These challenges make the efficient and accurate transmission of traditional techniques difficult.

[0163] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0164] In this invention, the server includes means for collecting information on traditional techniques, means for analyzing the collected information and training a generated AI model, user interface means for providing technical details to the user, sensor device means for collecting user motion data in real time, means for analyzing the collected motion information and providing appropriate feedback to the user, terminal means for allowing real-time feedback based on the motion data, and means for displaying work procedures and video data to the terminal. This makes it possible for even inexperienced technicians to efficiently learn traditional techniques and receive real-time feedback.

[0165] "Traditional techniques" refer to the artisan skills and manufacturing methods that have been passed down for generations in a region or specific cultural area.

[0166] An "information base" is a database that systematically organizes and stores information and data about traditional techniques that have been collected.

[0167] A "generative AI model" is an artificial intelligence model that analyzes data collected using machine learning techniques and is trained to perform a specific task.

[0168] "User interface means" refers to interface devices and software that allow users to intuitively operate a system and view information.

[0169] "Sensor device means" refers to a sensor device used to collect user actions and environmental data in real time.

[0170] "Terminal means" refers to hardware that is a device for user operation and has the function of displaying specific feedback or information.

[0171] "Feedback" refers to information that a system uses to analyze user actions and operations, providing suggestions for improvement and guidance for future actions.

[0172] Overall System Overview

[0173] This system is designed to efficiently and accurately pass on traditional techniques. The system consists of the following main components: a server, terminals, users, and various sensor devices.

[0174] Program processing

[0175] The server plays a primary role in collecting and analyzing information about traditional techniques. This includes ingesting data from books, videos, interviews, and instruction manuals. The collected information is stored in an information base. Next, a generative AI model is trained using this data. This model learns each step of the traditional technique and identifies key points in the process.

[0176] The terminal has an interface that provides technical details to the user. Specifically, smartphones, smart glasses, and head-mounted displays are used, through which the user can view work procedures and video data. Furthermore, sensor devices are used to collect user movement data in real time. This movement data is sent to a server for analysis. Based on the analysis results, the system provides appropriate feedback to the user.

[0177] Hardware and software to be used

[0178] Hardware: Smartphones, smart glasses, head-mounted displays, and various sensor devices (motion capture, temperature sensors, etc.).

[0179] Software: Python®-based video analysis libraries (e.g., OpenCV), HTTP requests for data transmission (e.g., requests library), and TENSORFLOW® / Keras for running AI models.

[0180] The server performs preprocessing such as frame segmentation and tokenization to train the generative AI model. The trained model analyzes video and text data and learns each step of the technology.

[0181] As users utilize the device, they receive real-time feedback, and if certain actions or operations are not performed correctly, they are immediately instructed to correct them. This allows even inexperienced technicians to efficiently learn accurate techniques.

[0182] Examples of specific cases and prompt statements

[0183] For example, imagine a welding operation in a factory where a user wears smart glasses. The smart glasses' camera and sensors collect motion data, monitoring the appropriate welding temperature and timing in real time. Based on the analysis results, feedback such as the following is displayed:

[0184] "Start welding. Read the current temperature and work at the appropriate temperature."

[0185] "The temperature is too high. Cooling is required."

[0186] "Your movements are correct. Please maintain this pace."

[0187] As described above, this system makes it possible to efficiently and accurately pass on traditional techniques.

[0188] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0189] Step 1:

[0190] The server collects information about traditional techniques. It uses data such as books, videos, interviews, and instruction manuals as input. This data is collected by the server and stored in an information base. The server then performs preprocessing on this information, such as frame splitting and tokenization of text data.

[0191] Step 2:

[0192] The server trains a generative AI model based on the collected data. Preprocessed video and text data are used as input. The server uses this data to perform machine learning on the AI ​​model, learning specific technical steps and work procedures. The output is the trained AI model.

[0193] Step 3:

[0194] The user views technical details using a device. Technical information generated from a trained AI model is used as input. The device displays work procedures and video data to the user via a smartphone, smart glasses, or head-mounted display. The output is a display of technical details in a format easily understood by the user.

[0195] Step 4:

[0196] When a user begins working, a sensor device collects motion data in real time. The inputs used are user motion data and ambient environmental data. The sensor device collects detailed motion information using motion capture, temperature sensors, and other methods. The output is real-time motion data.

[0197] Step 5:

[0198] The server analyzes motion data collected in real time. It uses motion data transmitted from sensor devices as input. The server analyzes the motion data using a generative AI model to determine whether the work is being performed according to the technical requirements. The output is the evaluation result of the motion.

[0199] Step 6:

[0200] The terminal receives evaluation results from the server and provides appropriate feedback to the user. It uses evaluation results from the server as input. The terminal displays feedback to the user via voice and display. This may include specific advice such as, "Operation is good. Please maintain this pace," or "The temperature is too high. Cooling is required." The output is real-time feedback to the user.

[0201] The above processing steps enable the efficient and accurate transmission of traditional techniques.

[0202] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0203] The system of the present invention provides a mechanism for efficiently passing on traditional techniques, and in particular, by combining it with an emotion engine, it enables feedback that takes into account the user's emotional state. The specific form of implementation is described below.

[0204] Data collection methods

[0205] The server collects a wide range of information on traditional techniques, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The collected information is organized and stored in a database, allowing for centralized management of detailed steps, background information, and details about the materials and tools used.

[0206] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[0207] Forms of data analysis and model generation

[0208] The server preprocesses the collected information and trains a generative AI model. Video data is segmented into frames, and text data is tokenized. The preprocessed data is used to train the generative AI model to learn each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are adjusted as needed.

[0209] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught things like the heating temperature of the material and the timing of the forging process.

[0210] User interface configuration

[0211] The device provides an interface that allows users to easily access technical details. This includes viewing the technical history, watching instructional videos and images, and receiving real-time feedback. Users can operate it intuitively and quickly access the information they need.

[0212] As a concrete example, a new swordsmith uses a device to watch videos of the forging process and confirm the specific steps.

[0213] Real-time guide format

[0214] The device collects user motion data in real time using sensor devices. Data acquired through motion capture and temperature sensors is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[0215] For example, if a blacksmith is actually working and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[0216] Forms of emotional engines

[0217] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is feeling fatigued or stressed.

[0218] For example, if fatigue is detected in a user's facial expressions during training, feedback will be displayed on the device suggesting a break or recommending an adjustment to the work pace based on that status.

[0219] Forms of feedback and corrections

[0220] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[0221] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[0222] Summary

[0223] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture. The introduction of an emotional engine provides optimal feedback that takes the user's state into account, improving the efficiency of skill acquisition.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server collects information on traditional techniques. This involves gathering data from a wide range of sources, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and storing it in a database. This allows for centralized management of information about the detailed steps of the techniques, the materials used, and the tools.

[0227] Step 2:

[0228] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Metadata is also extracted, and all information is stored in a database in a standardized format.

[0229] Step 3:

[0230] The server trains a generative AI model using pre-processed data. Model training involves learning each step and key point of traditional techniques. After training, the model's accuracy is evaluated, and parameters are adjusted and retrained as needed.

[0231] Step 4:

[0232] The device provides users with an interface to access technical information. This includes features such as viewing images and videos of procedures and receiving real-time feedback. Users can operate it intuitively and quickly obtain the information they need.

[0233] Step 5:

[0234] Users access detailed technical information using their devices. They review videos and images provided on their devices to learn technical procedures and key points. For example, they might watch a video about the Japanese sword forging process to understand the specific steps involved.

[0235] Step 6:

[0236] The device utilizes sensor devices to collect user motion data in real time. Data collected through motion capture and temperature sensors is temporarily stored on the device.

[0237] Step 7:

[0238] The server analyzes the action data sent from the terminal in real time. This allows it to determine whether the user's actions are correct and generate necessary corrections and advice.

[0239] Step 8:

[0240] The terminal provides the user with feedback received from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then corrects the operation based on this information.

[0241] Step 9:

[0242] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. If the user is feeling fatigued or stressed, it provides appropriate feedback.

[0243] Step 10:

[0244] The server saves all session data for later detailed analysis. Based on the collected behavioral and emotional data, the generative AI model is updated and the feedback algorithm is improved. In this way, the accuracy of traditional technology and the user experience are enhanced.

[0245] Through this series of processing steps, a system is realized that efficiently transmits traditional techniques and provides appropriate support to users. Furthermore, the introduction of an emotion engine enables personalized responses that take into account the user's state.

[0246] (Example 2)

[0247] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0248] Traditional techniques are difficult to pass on due to their uniqueness and complexity. Furthermore, if proper feedback is not provided during the skill transfer process, the reproducibility and precision of the techniques may decline. Additionally, the emotional state of those learning the skills is often overlooked, making effective learning difficult. Therefore, there is a need to develop a system that evaluates and provides feedback on both operational and emotional information in real time during the skill transfer process.

[0249] The identification processing 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 information relating to traditional techniques, means for preprocessing the collected information and training a generation AI model, interface means for providing technical details to the user, sensor device means for collecting user movements in real time, means for analyzing the collected movement information and the user's emotional state and providing feedback to the user, and emotion engine means for analyzing the collected user's facial expressions and voice data. This enables appropriate feedback that takes both movement information and emotional information into consideration, realizing effective skill transfer and improved learning efficiency.

[0250] "Traditional techniques" refer to skills and techniques that have been passed down through generations in a particular region or culture with a long history.

[0251] "Means of collecting information" refers to methods and technologies for obtaining specific data and knowledge from the internet, partner organizations, etc., and storing them in a database.

[0252] "Preprocessing" refers to the process of performing operations such as frame splitting, tokenization, and noise reduction to make the collected data easier to analyze.

[0253] A "generative AI model" refers to an artificial intelligence model that learns each step and key point of a specific technology or skill based on collected data, and then provides the results to the user.

[0254] "Interface means" refers to display and input devices, and their designs, that allow users to intuitively operate a system and access information.

[0255] "Sensor device means" refers to devices and methods for collecting user movement data in real time.

[0256] "Motion information" refers to data about the user's movements collected by sensor devices.

[0257] "Means of providing feedback" refers to methods and technologies for providing users with appropriate advice and warnings based on collected and analyzed data.

[0258] "Emotional engine means" refers to technologies and devices that analyze a user's facial expressions and voice data to identify their emotional state.

[0259] "Facial expression data" refers to data used to capture the user's facial expressions.

[0260] "Voice data" refers to data used to capture the characteristics of the user's voice.

[0261] This invention is a system aimed at the efficient transmission of traditional techniques, and provides technical feedback to the user through multiple steps.

[0262] In particular, this invention is centered on a server, a terminal, and a user. Each of these entities works together to provide the user with detailed information on traditional techniques and to offer real-time instruction and emotionally responsive feedback.

[0263] Data collection

[0264] The server collects a wide range of information on traditional techniques. Data sources include historical documents, specialized books, videos, interviews with artisans, and procedural manuals. The collected information is then organized and stored in a database.

[0265] As a specific example, regarding the techniques of making Japanese swords, the server collects historical documents on Japanese swordsmiths, videos of the manufacturing process, interviews with craftsmen, and detailed forging procedures, and stores them in a database.

[0266] Data preprocessing and model training

[0267] The server preprocesses the collected information and trains a generative AI model. Video data is split frame by frame, and text data is tokenized. The preprocessed data is used to train the generative AI model, teaching it each step of the technology and key points. The trained model is then evaluated for accuracy, and parameters are adjusted as needed.

[0268] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught the heating temperature of the iron and the timing of the hammering.

[0269] User Interface

[0270] The device provides an interface that allows users to easily check technical details. This interface allows users to view the technical history, watch procedural videos and images, and receive real-time feedback. Users can operate it intuitively and quickly access the information they need.

[0271] As a concrete example, a new swordsmith can use a device to watch videos of the forging process and check details of the specific steps and materials and tools used.

[0272] Real-time guide

[0273] The device uses sensor devices to collect user movement data in real time. This data is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[0274] For example, when a blacksmith is working, an alert may appear on the terminal if the temperature is not appropriate or if the timing of the strike is incorrect.

[0275] Emotion analysis

[0276] The terminal is equipped with an emotion engine for recognizing the user's emotions. The emotion engine acquires the user's facial expression data and voice data and analyzes it. Thereby, when the user feels fatigue or stress, appropriate feedback can be provided accordingly.

[0277] As a specific example, when a user's fatigue is detected from their facial expression during training, suggestions for rest and adjustments to the work pace according to the status are displayed on the terminal.

[0278] Feedback and Model Update

[0279] The server stores the data collected in real time and performs detailed analysis. Based on the collected operation information and emotion data, the generative AI model and feedback algorithm are updated. Thereby, the accuracy of traditional technologies and the improvement of the user experience are continuously achieved.

[0280] As a specific example, the working video and emotion data of a blacksmith are uploaded to the server, and the improvement points in the next session are fed back based on the analysis results.

[0281] Examples of Prompt Sentences [[ID=2​​​​​​​​​​​​​​​​​"Analyze the user's facial expression data and provide feedback to propose a break when fatigue is detected."

[0287] By using this system, traditional technologies can be efficiently inherited, and the learning effect of users can be improved. Also, through real-time guidance and feedback according to emotions, it is expected that optimal support will be provided even during the process of acquiring skills.

[0288] The flow of the specific process in Example 2 will be described using FIG. 13.

[0289] Step 1: Data collection

[0290] The server collects information related to traditional technologies. Specifically, it acquires data such as historical materials, professional books, videos, interviews with craftsmen, and manuals from the Internet or partner institutions and stores them in a database.

[0291] Input: Information related to traditional technologies collected from the Internet or partner institutions.

[0292] Output: Technical information stored in an organized database.

[0293] Specific operation: The server automatically collects data from various resources on the Internet using scraping tools or APIs. Then, it converts the collected data into an appropriate format and stores it in the database.

[0294] Step 2: Data preprocessing

[0295] The server performs preprocessing on the collected data. It removes noise and normalizes the data, divides video data frame by frame, and tokenizes text data.

[0296] Input: Unprocessed technical information in the database.

[0297] Output: Preprocessed dataset.

[0298] Specific operations: The server uses a text cleaning tool to remove noise and uses a video processing program to split the video frame by frame.

[0299] Step 3: Model training

[0300] The server trains a generative AI model using the preprocessed data. To learn important steps and technical points, the data is input into the AI model and its performance is evaluated.

[0301] Input: Preprocessed dataset.

[0302] Output: Trained generative AI model.

[0303] Specific operations: The server uses a machine learning library (e.g., TensorFlow or PyTorch) to input data into the AI model and perform learning repeatedly. The accuracy of the model is evaluated regularly, and hyperparameters are adjusted as needed.

[0304] Step 4: Provide user interface

[0305] The terminal provides an interface for the user to view technical details. It is possible to view history, watch procedure videos and images, and receive real-time feedback.

[0306] Input: User operation instructions.

[0307] Output: Technical detail information displayed on the interface.

[0308] Specific operations: The terminal provides a graphical user interface (GUI) that allows the user to operate intuitively, and retrieves and displays data from the server according to the user's request.

[0309] Step 5: Real-time guidance

[0310] The terminal uses sensor devices to collect user movement data in real time. This movement data is temporarily stored and then sent to the server.

[0311] Input: Motion data from a sensor device.

[0312] Output: Data sent to the server and real-time feedback.

[0313] Specific operation: The terminal acquires data in real time from devices such as motion capture devices and temperature sensors, and sends the necessary information to the server. The server analyzes this data and sends feedback to the terminal.

[0314] Step 6: Emotion Analysis

[0315] The device uses an emotion engine to analyze the user's facial expressions and voice data. If the user is experiencing fatigue or stress, it provides appropriate feedback.

[0316] Input: User facial expression data and voice data.

[0317] Output: Feedback based on emotional state.

[0318] Specific operation: The device uses an emotion recognition algorithm to analyze data in real time and generates feedback based on the results, such as suggesting breaks or adjusting the work pace.

[0319] Step 7: Feedback and Model Updates

[0320] The server stores and analyzes collected behavioral and emotional data. Based on the results, it updates the generative AI model and feedback algorithm.

[0321] Input: Behavioral and emotional data collected in real time.

[0322] Output: Updated generative AI model and feedback algorithm.

[0323] Specific operation: The server periodically analyzes the collected data and continues to learn by adding new information to the AI ​​model. It also fine-tunes the algorithm to improve the accuracy of the feedback.

[0324] These steps ensure that traditional techniques are efficiently passed down and that users can effectively acquire the skills.

[0325] (Application Example 2)

[0326] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0327] The difficulty in passing on traditional techniques stems from factors such as the lack of sufficient sharing of detailed technical steps and information, and the inability to accurately reflect the motivation and physical condition of learners. Furthermore, there is a need for efficient methods of learning while maintaining the precision of traditional techniques. In addition, modern manufacturing environments require real-time feedback on the emotional state and accuracy of movements of workers and robots to improve work efficiency.

[0328] The specific processing performed by the specific 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 information relating to traditional technology, means for analyzing the collected information and training a generated AI model, interface means for providing technical details to the user, sensor device means for collecting user actions in real time, means for analyzing the collected action information and providing feedback to the user, emotion engine means for recognizing the user's emotional state, and means for adjusting the feedback based on the recognized emotional state. As a result, not only is detailed step and background information of traditional technology centrally managed and provided, but real-time feedback is also possible according to the user's emotional state and the accuracy of their actions.

[0329] "Traditional techniques" refer to methods and skills that have been passed down through history and play an important role in specific cultures or professions.

[0330] "Means of collecting information" refers to systems and technologies for obtaining relevant data from various sources.

[0331] "Methods for training generative AI models" refer to the process of training an AI model based on collected information to improve its accuracy and performance.

[0332] An "interface means" refers to a method or device that allows a user to access a system and utilize its information and functions.

[0333] A "sensor device means" is a device for collecting user actions and environmental information in real time.

[0334] "Means of providing feedback" refer to systems or functions that provide appropriate responses or instructions to user actions or states.

[0335] An "emotional engine" is a technology that recognizes a user's emotional state and generates countermeasures based on that information.

[0336] "Means of adjusting feedback" refer to systems or technologies that appropriately modify the content of feedback provided according to the user's emotional state and behavior.

[0337] "Means of maintaining a database" refers to a system for centrally managing and storing collected information.

[0338] A "feedback analysis method" is an analytical technique that uses acquired data to improve the efficiency and accuracy of feedback.

[0339] This invention is a system for efficiently passing on traditional techniques. In particular, it uses an emotion engine to provide feedback that takes into account the user's emotional state, thereby supporting the acquisition of skills. The specific form of the system is described below.

[0340] System Configuration

[0341] server

[0342] The server collects and stores extensive information on traditional techniques in a database. This information is primarily gathered from historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server analyzes this information and trains a generative AI model. The AI ​​model learns the detailed steps and key points of the technique, and its accuracy is evaluated.

[0343] terminal

[0344] The device provides an interface that allows users to easily check the technical details. Users can use the device to view the technical history, watch instructional videos and images, and receive real-time feedback. The device also incorporates sensor devices to collect user activity in real time. Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state based on facial and voice data.

[0345] User

[0346] The users are individuals who wish to acquire traditional or specialized skills, such as swordsmiths or automobile manufacturing line workers. Using the device, users can receive real-time feedback on their actions and instructions based on their emotional state.

[0347] Processing flow

[0348] The server uses the collected information to train a generative AI model and improve its accuracy. The device collects the user's actions and emotional state in real time and sends it to the server. The server analyzes this data and generates appropriate feedback for the user. This feedback is adjusted according to the accuracy of the user's actions and emotional state.

[0349] Hardware and software

[0350] server:

[0351] High-performance servers (e.g., AWS® EC2 instances) are used to collect and analyze information.

[0352] Deep learning frameworks (e.g., TensorFlow, PyTorch) are used to train generative AI models.

[0353] Terminal:

[0354] To collect motion data, use motion capture devices (e.g., Microsoft® Kinect) or temperature sensors.

[0355] The emotion engine uses an emotion recognition library (e.g., OpenFace, Affectiva).

[0356] The user interface will utilize smart glasses or head-mounted displays (e.g., Google® Glass®, Microsoft HoloLens®).

[0357] Specific example

[0358] For example, in a robotic assistant application on a factory's automobile manufacturing line, workers wear smart glasses while assembling products. Cameras capture the worker's movements and facial expressions in real time, analyzing the accuracy of their work and their emotional state. If fatigue or stress is detected, feedback such as "You're doing great, we recommend you take a 5-minute break" is displayed on the smart glasses. Also, if there is a problem with the accuracy of the work, an alert will be issued saying, "The screw is not tightened enough, please check again."

[0359] Example of a prompt:

[0360] "Explain how to assess the emotions and behavior of production line workers in real time and provide appropriate feedback. This includes, in particular, advice on taking breaks when fatigue or stress is detected, and specific feedback based on work progress."

[0361] The above describes specific embodiments for carrying out this invention.

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] The server collects information on traditional techniques. Inputs include historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server stores this information in a database and manages it centrally, preparing it to provide detailed steps and background information on the techniques. The output is an organized database.

[0365] Step 2:

[0366] The server analyzes the collected information and trains a generative AI model. A stored database is used as input. For data processing, video data is segmented into frames, and text data is tokenized. This pre-processed data is then used to train the AI ​​model. The output is the trained generative AI model.

[0367] Step 3:

[0368] The terminal functions as an interface that provides technical details to the user. Input includes user actions such as operating the terminal and requesting necessary information. Data processing involves displaying technical information retrieved from the server to the user in an appropriate format. Output consists of technical information and step-by-step details that the user can review.

[0369] Step 4:

[0370] The user uses the terminal to check technical details and perform actual tasks. During this time, the sensor device built into the terminal collects the user's movements in real time. The input includes user movement data. The output is a stream of movement data.

[0371] Step 5:

[0372] The terminal transmits collected motion data to the server. Real-time motion data acquired from sensors is used as input. This data is transmitted to the server as data transmission. The output is motion data that the server can analyze.

[0373] Step 6:

[0374] The server analyzes the transmitted motion data in real time. A stream of motion data is used as input. The data is used to evaluate the accuracy and appropriateness of the motion. Then, feedback is generated based on the analysis results. The output is feedback information.

[0375] Step 7:

[0376] The device displays feedback sent from the server to the user. Feedback information is included as input. The data display shows the feedback in an appropriate format for smart glasses or a head-mounted display. The output is the feedback provided to the user visually and audibly.

[0377] Step 8:

[0378] The emotion engine built into the device recognizes the user's emotional state. It acquires user facial expression data and voice data as input. Data analysis evaluates the emotional state and sends this information to the server. The output is emotional state data.

[0379] Step 9:

[0380] The server analyzes emotional state data and adjusts the feedback accordingly. Emotional state data and the results of behavioral data analysis are used as input. Data adjustment involves customizing the feedback according to the user's state. The output is the adjusted feedback information.

[0381] Step 10:

[0382] The device then displays the adjusted feedback to the user again. The adjusted feedback information is included as input. The data display provides the feedback in a user-friendly format. The output is the final feedback provided to the user.

[0383] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0384] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0386] [Second Embodiment]

[0387] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0388] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0394] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0395] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0396] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0398] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0399] The system of this invention provides a mechanism for efficiently passing on traditional techniques. The program's processing flow is described in detail below.

[0400] Data collection methods

[0401] The server collects all kinds of information about traditional techniques. First, it gathers data from reliable sources such as historical documents, specialized books, videos, interviews with artisans, and instruction manuals. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used. The collected data is stored in a database to facilitate subsequent analysis.

[0402] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[0403] Forms of data analysis and model generation

[0404] The server performs data analysis based on the collected data. First, it performs preprocessing such as frame splitting of video data and tokenization of text data. Then, it trains a generative AI model, learning each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are optimized as needed to achieve higher accuracy.

[0405] As a concrete example, in the case of data analysis of the forging process of Japanese swords, the heating temperature of the material and the timing of the striking process are learned by the model.

[0406] User interface configuration

[0407] The device provides an interface that allows users to easily view details of traditional techniques. This includes viewing the history of collected technical data, watching videos and images of forging and work procedures, and real-time feedback. Users can quickly access the information they need through an intuitive interface.

[0408] For example, a new swordsmith can learn the procedures and points to note by watching videos of the forging process on a device.

[0409] Real-time guide format

[0410] The terminal collects user movement data in real time using sensor devices. For example, it may motion capture movements during work or acquire environmental data using a temperature sensor. The server analyzes this data in real time and generates appropriate feedback for the user. This feedback is provided to the user through the terminal, instructing them in real time on how to correct or improve their movements.

[0411] For example, if a blacksmith is actually performing a task and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[0412] Forms of feedback and corrections

[0413] The server stores the data collected in real time and performs detailed analysis. Based on the analysis results, it updates the generated AI model and uses it for the next training session. This continuously improves the model's performance and increases the accuracy of knowledge transfer.

[0414] As a concrete example, we upload video footage of a blacksmith at work to a server and use the analysis results to provide feedback on areas for improvement in the next session.

[0415] Summary

[0416] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The server collects information about traditional techniques. Specifically, it gathers data from historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and organizes and stores it in a database. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used.

[0420] Step 2:

[0421] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Necessary metadata is also extracted and stored in a database.

[0422] Step 3:

[0423] The server trains a generative AI model using pre-processed data. It first learns the basic technical steps and key points, and then incorporates more advanced techniques and condition-dependent parameters. The algorithm used in this process is selected to best suit the characteristics of the technology.

[0424] Step 4:

[0425] The server evaluates the accuracy of the trained AI model. A validation dataset is used for evaluation, measuring the model's performance. If the accuracy is insufficient, the parameters are adjusted and the model is trained again. This process is repeated to create the optimal model.

[0426] Step 5:

[0427] The terminal provides an interface that allows the user to access the system. This includes viewing the history of technology, viewing images and videos of procedures, and receiving real-time feedback. It must be designed to be user-friendly.

[0428] Step 6:

[0429] Users access necessary technical information using their devices. They review videos and images to learn technical procedures and key points. For example, they might watch a video of the Japanese sword forging process to understand the specific steps involved.

[0430] Step 7:

[0431] The device uses sensor devices to collect user movement data in real time. Typical examples include motion capture and temperature sensors. The collected data is temporarily stored within the device.

[0432] Step 8:

[0433] The server analyzes the action data sent from the terminal in real time. Based on the collected data, it determines whether the user's actions are appropriate and generates necessary corrections and advice.

[0434] Step 9:

[0435] The terminal provides the user with feedback sent from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then uses this information to correct their actions.

[0436] Step 10:

[0437] The server saves all session data and performs detailed analysis later. The analysis results are used to update the generated AI model and are used as training data for the next session. This improves the accuracy of the model and the efficiency of knowledge transfer.

[0438] Through this series of processing steps, it becomes possible to accurately pass on traditional techniques and prevent their decline.

[0439] (Example 1)

[0440] Next, we will describe Example 1. 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".

[0441] The transmission of traditional techniques faces challenges due to the aging of skilled craftsmen and the loss of knowledge. Furthermore, the detailed learning of traditional techniques requires advanced knowledge and experience, making it difficult to train new craftsmen. Overcoming this situation and ensuring the accurate transmission of these techniques is an urgent priority.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for collecting information relating to traditional techniques, means for preprocessing the collected information, means for training a generative AI model based on the preprocessed information, means for evaluating the accuracy of the trained generative AI model and optimizing its parameters, means for providing technical details to the user, means for a sensor device that collects the user's actions in real time, and means for analyzing the collected action information and providing feedback to the user. This enables the efficient and accurate transmission of traditional techniques.

[0444] "Traditional techniques" is a general term for skills and knowledge that have been formed against a backdrop of a long history and culture, and have been passed down through generations.

[0445] "Means of information gathering" refers to methods and devices for obtaining data in various formats from reliable sources such as historical documents, specialized books, videos, interviews, and procedural manuals.

[0446] "Preprocessing methods" refer to processes performed to convert collected data into an analyzable format, and specifically include frame splitting of video data and tokenization of text data.

[0447] A "generative AI model" is an artificial intelligence model that is trained on collected and pre-processed data, and is designed to learn the detailed steps of a specific task or technique.

[0448] "Training methods" refer to methods or devices for training a generative AI model using pre-processed data.

[0449] "Means for evaluating accuracy and optimizing parameters" refers to methods or devices for measuring the performance of a trained generative AI model and improving the model's settings based on the results.

[0450] "Interface means" refers to software or hardware that allows users to view and manipulate collected technical data.

[0451] "Sensor device means" refers to a device for collecting user movements and environmental data in real time, and includes motion capture sensors, temperature sensors, and the like.

[0452] "Means for analyzing motion information and providing feedback" refers to methods or devices that analyze data collected by sensor devices and suggest areas for correction or improvement to the user's actions.

[0453] "Means for maintaining a database" refers to methods and devices for efficiently and securely storing diverse collected data and making it immediately accessible when needed.

[0454] A "feedback analysis means" refers to a method or apparatus for reanalyzing collected data and generated feedback in order to improve the performance of the generated AI model.

[0455] The system of this invention operates in cooperation with a server, terminals, and users to efficiently and accurately pass on traditional techniques. This system combines means of collection, analysis, training, provision, collection, and feedback to pass on the detailed steps and knowledge of traditional techniques.

[0456] The server first collects information on traditional techniques from reliable sources. Specifically, it obtains historical documents, specialized books, videos, interviews with craftsmen, and procedural manuals from the internet and digital archives. This collected data is formatted in digital format and stored in a database. For example, in the case of Japanese sword making techniques, it collects historical documents on swordsmiths, production videos, interviews with craftsmen, and forging procedure manuals.

[0457] Next, the server preprocesses the collected data. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques. This extracts technical steps and theories. The preprocessed data is then used to train a generative AI model.

[0458] The server trains the generative AI model. For example, it uses a Transformer-based generative AI model to learn each step and key point of the technology based on the collected data. The trained model is evaluated for accuracy, and parameters are tuned as needed. This optimizes the model's performance.

[0459] The device provides an interface that allows users to easily view technical details. The user interface has an intuitive design, making it easy for users to view the history of technical data and watch videos and images of the forging process. For example, a new swordsmith can learn the procedures and precautions by watching videos of the forging process on the device.

[0460] Furthermore, the terminal uses sensor devices to collect user movements in real time. Motion capture sensors and temperature sensors are used to collect user movements and environmental data, which are then transmitted to the server. The server analyzes this movement data in real time and generates feedback such as "heating temperature is not appropriate" or "the timing of the tapping is off."

[0461] The collected data and generated feedback are stored on a server for detailed analysis. Based on the results of this analysis, the generated AI model is updated and used as training data for the next training session. This continuously improves the model's performance and enhances the accuracy of passing on traditional techniques.

[0462] As a concrete example, consider the techniques used to make Japanese swords. The server collects historical documents and forging procedures of swordsmiths and analyzes video data frame by frame. The generated AI model learns, for example, the timing and force of a "hammer swing." When a user actually performs the work, the terminal collects motion data using motion capture sensors, and the server provides real-time feedback of the analysis results, thereby streamlining the transmission of technical skills.

[0463] Examples of prompt statements for a generative AI model are shown below:

[0464] "Please explain the forging process of Japanese swords. Describe in detail the heating temperature, the timing of the striking process, and the materials and tools used."

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1: Data Collection

[0467] The server collects information on traditional techniques from multiple reliable sources. Specifically, it retrieves historical documents, specialized books, videos, interviews with artisans, and procedural manuals.

[0468] Input: The source of the data (e.g., the internet or digital archives).

[0469] Data processing: Integrating data from different formats, such as text and video, and formatting it into a digital format.

[0470] Output: Collected data (e.g., documents and videos related to Japanese sword making techniques).

[0471] Specific operation: The server downloads information via a specific URL or database query and stores it in the database.

[0472] Step 2: Data Preprocessing

[0473] The server converts the collected data into a format that is easy to analyze. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques.

[0474] Input: Collected data (video files, text files, etc.).

[0475] Data processing: Frame splitting of video data, tokenization of text data.

[0476] Output: Preprocessed data (split frames, token sequences).

[0477] Specific operation: The server uses video analysis software to divide the video data into frames and uses a natural language processing library to tokenize the text.

[0478] Step 3: Model Training

[0479] The server uses pre-processed data to train a generative AI model. For example, it might use a Transformer-based model to teach the model each step and key points of the technology.

[0480] Input: Preprocessed data (token sequence, frame image, etc.).

[0481] Data processing: Generate data, input it into the AI ​​model, and execute the learning process.

[0482] Output: Trained generative AI model.

[0483] Specific operation: The server uses a deep learning framework to train a model using preprocessed data. For example, it learns the timing and temperature control patterns of Japanese sword forging.

[0484] Step 4: Model Evaluation and Optimization

[0485] The server evaluates the accuracy of the trained generative AI model and optimizes the parameters as needed.

[0486] Input: Trained generative AI model, validation dataset.

[0487] Data processing: Measure the prediction accuracy of the model and adjust the parameters.

[0488] Output: Optimized generative AI model.

[0489] Specific operation: The server uses a validation dataset to compare the model's predictions with the actual results and adjusts the parameters to minimize the error.

[0490] Step 5: Provide technical details

[0491] The terminal provides an interface that offers users technical details. Users can view the history of technical data and watch videos and images of the forging process.

[0492] Input: Optimized generative AI model, technical data.

[0493] Data processing: Visualize technical data and convert it into a format suitable for the user interface.

[0494] Output: Display of technical details on the user interface.

[0495] Specific operation: The device interacts with the user through a web application or mobile application and provides collected technical data in the form of video playback or text.

[0496] Step 6: Real-time motion capture

[0497] The device uses sensor devices to collect user movement data in real time.

[0498] Input: User actions (e.g., hammer movement during forging), sensor device data.

[0499] Data processing: Collect data from sensor devices in real time and format it into a format that can be sent to the server.

[0500] Output: Real-time operation data.

[0501] Specific operation: The device uses motion capture sensors and temperature sensors to record and transmit user movements and environmental data in real time.

[0502] Step 7: Feedback analysis and provision

[0503] The server analyzes the collected operational data in real time and provides feedback to the user.

[0504] Input: Real-time operation data.

[0505] Data processing: Analyzes operational data and generates optimal feedback.

[0506] Output: Feedback information to the user (e.g., alerts, instructions).

[0507] Specific operation: The server analyzes operational data in real time, generates feedback such as "heating temperature is not appropriate" or "timing of striking is off," and sends it to the terminal.

[0508] Step 8: Feedback and Model Updates

[0509] The server stores all collected data and performs detailed analysis. Based on the results of this analysis, the generated AI model is updated and used for the next training session.

[0510] Input: Collected data and feedback information.

[0511] Data processing: Analyze feedback information and adjust the parameters of the generated AI model.

[0512] Output: Updated generative AI model.

[0513] Specific operation: The server analyzes the feedback information in detail and uses it as training data for the model, continuously improving the generated AI model.

[0514] In this way, the coordinated operation of each processing step enables the efficient and accurate transmission of traditional techniques.

[0515] (Application Example 1)

[0516] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0517] The transmission of traditional techniques requires advanced skills and experience, making it difficult to learn and accurately reproduce them efficiently. Furthermore, the presence of experienced instructors is essential for inexperienced technicians to acquire accurate procedures and techniques; a lack of such instructors can lead to a decline in the accuracy of technique transfer. Additionally, the lack of mechanisms for real-time work improvement and feedback makes it difficult for technicians to self-correct. These challenges make the efficient and accurate transmission of traditional techniques difficult.

[0518] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0519] In this invention, the server includes means for collecting information on traditional techniques, means for analyzing the collected information and training a generated AI model, user interface means for providing technical details to the user, sensor device means for collecting user motion data in real time, means for analyzing the collected motion information and providing appropriate feedback to the user, terminal means for allowing real-time feedback based on the motion data, and means for displaying work procedures and video data to the terminal. This makes it possible for even inexperienced technicians to efficiently learn traditional techniques and receive real-time feedback.

[0520] "Traditional techniques" refer to the artisan skills and manufacturing methods that have been passed down for generations in a region or specific cultural area.

[0521] An "information base" is a database that systematically organizes and stores information and data about traditional techniques that have been collected.

[0522] A "generative AI model" is an artificial intelligence model that analyzes data collected using machine learning techniques and is trained to perform a specific task.

[0523] "User interface means" refers to interface devices and software that allow users to intuitively operate a system and view information.

[0524] "Sensor device means" refers to a sensor device used to collect user actions and environmental data in real time.

[0525] "Terminal means" refers to hardware that is a device for user operation and has the function of displaying specific feedback or information.

[0526] "Feedback" refers to information that a system uses to analyze user actions and operations, providing suggestions for improvement and guidance for future actions.

[0527] Overall System Overview

[0528] This system is designed to efficiently and accurately pass on traditional techniques. The system consists of the following main components: a server, terminals, users, and various sensor devices.

[0529] Program processing

[0530] The server plays a primary role in collecting and analyzing information about traditional techniques. This includes ingesting data from books, videos, interviews, and instruction manuals. The collected information is stored in an information base. Next, a generative AI model is trained using this data. This model learns each step of the traditional technique and identifies key points in the process.

[0531] The terminal has an interface that provides technical details to the user. Specifically, smartphones, smart glasses, and head-mounted displays are used, through which the user can view work procedures and video data. Furthermore, sensor devices are used to collect user movement data in real time. This movement data is sent to a server for analysis. Based on the analysis results, the system provides appropriate feedback to the user.

[0532] Hardware and software to be used

[0533] Hardware: Smartphones, smart glasses, head-mounted displays, and various sensor devices (motion capture, temperature sensors, etc.).

[0534] Software: Python-based video analysis libraries (e.g., OpenCV), HTTP requests for data transmission (e.g., the requests library), and TensorFlow / Keras for running AI models.

[0535] The server performs preprocessing such as frame segmentation and tokenization to train the generative AI model. The trained model analyzes video and text data and learns each step of the technology.

[0536] As users utilize the device, they receive real-time feedback, and if certain actions or operations are not performed correctly, they are immediately instructed to correct them. This allows even inexperienced technicians to efficiently learn accurate techniques.

[0537] Examples of specific cases and prompt statements

[0538] For example, imagine a welding operation in a factory where a user wears smart glasses. The smart glasses' camera and sensors collect motion data, monitoring the appropriate welding temperature and timing in real time. Based on the analysis results, feedback such as the following is displayed:

[0539] "Start welding. Read the current temperature and work at the appropriate temperature."

[0540] "The temperature is too high. Cooling is required."

[0541] "Your movements are correct. Please maintain this pace."

[0542] As described above, this system makes it possible to efficiently and accurately pass on traditional techniques.

[0543] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0544] Step 1:

[0545] The server collects information about traditional techniques. It uses data such as books, videos, interviews, and instruction manuals as input. This data is collected by the server and stored in an information base. The server then performs preprocessing on this information, such as frame splitting and tokenization of text data.

[0546] Step 2:

[0547] The server trains a generative AI model based on the collected data. Preprocessed video and text data are used as input. The server uses this data to perform machine learning on the AI ​​model, learning specific technical steps and work procedures. The output is the trained AI model.

[0548] Step 3:

[0549] The user views technical details using a device. Technical information generated from a trained AI model is used as input. The device displays work procedures and video data to the user via a smartphone, smart glasses, or head-mounted display. The output is a display of technical details in a format easily understood by the user.

[0550] Step 4:

[0551] When a user begins working, a sensor device collects motion data in real time. The inputs used are user motion data and ambient environmental data. The sensor device collects detailed motion information using motion capture, temperature sensors, and other methods. The output is real-time motion data.

[0552] Step 5:

[0553] The server analyzes motion data collected in real time. It uses motion data transmitted from sensor devices as input. The server analyzes the motion data using a generative AI model to determine whether the work is being performed according to the technical requirements. The output is the evaluation result of the motion.

[0554] Step 6:

[0555] The terminal receives evaluation results from the server and provides appropriate feedback to the user. It uses evaluation results from the server as input. The terminal displays feedback to the user via voice and display. This may include specific advice such as, "Operation is good. Please maintain this pace," or "The temperature is too high. Cooling is required." The output is real-time feedback to the user.

[0556] The above processing steps enable the efficient and accurate transmission of traditional techniques.

[0557] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0558] The system of the present invention provides a mechanism for efficiently passing on traditional techniques, and in particular, by combining it with an emotion engine, it enables feedback that takes into account the user's emotional state. The specific form of implementation is described below.

[0559] Data collection methods

[0560] The server collects a wide range of information on traditional techniques, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The collected information is organized and stored in a database, allowing for centralized management of detailed steps, background information, and details about the materials and tools used.

[0561] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[0562] Forms of data analysis and model generation

[0563] The server preprocesses the collected information and trains a generative AI model. Video data is segmented into frames, and text data is tokenized. The preprocessed data is used to train the generative AI model to learn each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are adjusted as needed.

[0564] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught things like the heating temperature of the material and the timing of the forging process.

[0565] User interface configuration

[0566] The device provides an interface that allows users to easily access technical details. This includes viewing the technical history, watching instructional videos and images, and receiving real-time feedback. Users can operate it intuitively and quickly access the information they need.

[0567] As a concrete example, a new swordsmith uses a device to watch videos of the forging process and confirm the specific steps.

[0568] Real-time guide format

[0569] The device collects user motion data in real time using sensor devices. Data acquired through motion capture and temperature sensors is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[0570] For example, if a blacksmith is actually working and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[0571] Forms of emotional engines

[0572] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is feeling fatigued or stressed.

[0573] For example, if fatigue is detected in a user's facial expressions during training, feedback will be displayed on the device suggesting a break or recommending an adjustment to the work pace based on that status.

[0574] Forms of feedback and corrections

[0575] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[0576] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[0577] Summary

[0578] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture. The introduction of an emotional engine provides optimal feedback that takes the user's state into account, improving the efficiency of skill acquisition.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The server collects information on traditional techniques. This involves gathering data from a wide range of sources, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and storing it in a database. This allows for centralized management of information about the detailed steps of the techniques, the materials used, and the tools.

[0582] Step 2:

[0583] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Metadata is also extracted, and all information is stored in a database in a standardized format.

[0584] Step 3:

[0585] The server trains a generative AI model using pre-processed data. Model training involves learning each step and key point of traditional techniques. After training, the model's accuracy is evaluated, and parameters are adjusted and retrained as needed.

[0586] Step 4:

[0587] The device provides users with an interface to access technical information. This includes features such as viewing images and videos of procedures and receiving real-time feedback. Users can operate it intuitively and quickly obtain the information they need.

[0588] Step 5:

[0589] Users access detailed technical information using their devices. They review videos and images provided on their devices to learn technical procedures and key points. For example, they might watch a video about the Japanese sword forging process to understand the specific steps involved.

[0590] Step 6:

[0591] The device utilizes sensor devices to collect user motion data in real time. Data collected through motion capture and temperature sensors is temporarily stored on the device.

[0592] Step 7:

[0593] The server analyzes the action data sent from the terminal in real time. This allows it to determine whether the user's actions are correct and generate necessary corrections and advice.

[0594] Step 8:

[0595] The terminal provides the user with feedback received from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then corrects the operation based on this information.

[0596] Step 9:

[0597] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. If the user is feeling fatigued or stressed, it provides appropriate feedback.

[0598] Step 10:

[0599] The server saves all session data for later detailed analysis. Based on the collected behavioral and emotional data, the generative AI model is updated and the feedback algorithm is improved. In this way, the accuracy of traditional technology and the user experience are enhanced.

[0600] Through this series of processing steps, a system is realized that efficiently transmits traditional techniques and provides appropriate support to users. Furthermore, the introduction of an emotion engine enables personalized responses that take into account the user's state.

[0601] (Example 2)

[0602] Next, we will describe Example 2. 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".

[0603] Traditional techniques are difficult to pass on due to their uniqueness and complexity. Furthermore, if proper feedback is not provided during the skill transfer process, the reproducibility and precision of the techniques may decline. Additionally, the emotional state of those learning the skills is often overlooked, making effective learning difficult. Therefore, there is a need to develop a system that evaluates and provides feedback on both operational and emotional information in real time during the skill transfer process.

[0604] The identification processing 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 information relating to traditional techniques, means for preprocessing the collected information and training a generation AI model, interface means for providing technical details to the user, sensor device means for collecting user movements in real time, means for analyzing the collected movement information and the user's emotional state and providing feedback to the user, and emotion engine means for analyzing the collected user's facial expressions and voice data. This enables appropriate feedback that takes both movement information and emotional information into consideration, realizing effective skill transfer and improved learning efficiency.

[0605] "Traditional techniques" refer to skills and techniques that have been passed down through generations in a particular region or culture with a long history.

[0606] "Means of collecting information" refers to methods and technologies for obtaining specific data and knowledge from the internet, partner organizations, etc., and storing them in a database.

[0607] "Preprocessing" refers to the process of performing operations such as frame splitting, tokenization, and noise reduction to make the collected data easier to analyze.

[0608] A "generative AI model" refers to an artificial intelligence model that learns each step and key point of a specific technology or skill based on collected data, and then provides the results to the user.

[0609] "Interface means" refers to display and input devices, and their designs, that allow users to intuitively operate a system and access information.

[0610] "Sensor device means" refers to devices and methods for collecting user movement data in real time.

[0611] "Motion information" refers to data about the user's movements collected by sensor devices.

[0612] "Means of providing feedback" refers to methods and technologies for providing users with appropriate advice and warnings based on collected and analyzed data.

[0613] "Emotional engine means" refers to technologies and devices that analyze a user's facial expressions and voice data to identify their emotional state.

[0614] "Facial expression data" refers to data used to capture the user's facial expressions.

[0615] "Voice data" refers to data used to capture the characteristics of the user's voice.

[0616] This invention is a system aimed at the efficient transmission of traditional techniques, and provides technical feedback to the user through multiple steps.

[0617] In particular, this invention is centered on a server, a terminal, and a user. Each of these entities works together to provide the user with detailed information on traditional techniques and to offer real-time instruction and emotionally responsive feedback.

[0618] Data collection

[0619] The server collects a wide range of information on traditional techniques. Data sources include historical documents, specialized books, videos, interviews with artisans, and procedural manuals. The collected information is then organized and stored in a database.

[0620] As a specific example, regarding the techniques of making Japanese swords, the server collects historical documents on Japanese swordsmiths, videos of the manufacturing process, interviews with craftsmen, and detailed forging procedures, and stores them in a database.

[0621] Data preprocessing and model training

[0622] The server preprocesses the collected information and trains a generative AI model. Video data is split frame by frame, and text data is tokenized. The preprocessed data is used to train the generative AI model, teaching it each step of the technology and key points. The trained model is then evaluated for accuracy, and parameters are adjusted as needed.

[0623] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught the heating temperature of the iron and the timing of the hammering.

[0624] User Interface

[0625] The device provides an interface that allows users to easily check technical details. This interface allows users to view the technical history, watch procedural videos and images, and receive real-time feedback. Users can operate it intuitively and quickly access the information they need.

[0626] As a concrete example, a new swordsmith can use a device to watch videos of the forging process and check details of the specific steps and materials and tools used.

[0627] Real-time guide

[0628] The device uses sensor devices to collect user movement data in real time. This data is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[0629] For example, when a blacksmith is working, an alert may appear on the terminal if the temperature is not appropriate or if the timing of the strike is incorrect.

[0630] Emotion analysis

[0631] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is experiencing fatigue or stress.

[0632] For example, if fatigue is detected from a user's facial expressions during training, the device will display suggestions for taking a break or adjusting the work pace according to that status.

[0633] Feedback and Model Updates

[0634] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[0635] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[0636] Example of a prompt

[0637] "Collect historical documents on the process of making Japanese swords and save them in a database."

[0638] "Train an AI model based on the collected manufacturing process data and teach it Japanese sword forging techniques."

[0639] "Design an interface that allows new swordsmiths to watch videos of the forging process."

[0640] "Develop a system that provides real-time feedback on things like inappropriate temperature during operation and correct timing for hammering."

[0641] "Analyze the user's facial expression data and provide feedback suggesting a break if fatigue is detected."

[0642] This system will enable the efficient transmission of traditional techniques and improve user learning effectiveness. Furthermore, it is expected to provide optimal support throughout the skill acquisition process through real-time instruction and emotion-responsive feedback.

[0643] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0644] Step 1: Data Collection

[0645] The server collects information about traditional techniques. Specifically, it obtains data such as historical documents, specialized books, videos, interviews with artisans, and procedural manuals from the internet and partner organizations, and stores them in a database.

[0646] Input: Information on traditional techniques collected from the internet and partner organizations.

[0647] Output: Technical information stored in an organized database.

[0648] Specific operation: The server automatically collects data from various resources on the internet using scraping tools and APIs. It then converts the collected data into an appropriate format and stores it in a database.

[0649] Step 2: Data Preprocessing

[0650] The server preprocesses the collected data. It performs noise reduction and normalization, splits video data into frames, and tokenizes text data.

[0651] Input: Unprocessed technical information from the database.

[0652] Output: Preprocessed dataset.

[0653] Specific operation: The server uses a text cleaning tool to remove noise and a video processing program to split the video frame by frame.

[0654] Step 3: Model Training

[0655] The server trains a generative AI model using preprocessed data. It then feeds data into the AI ​​model to teach it key steps and technical points, and evaluates its performance.

[0656] Input: Preprocessed dataset.

[0657] Output: A trained generative AI model.

[0658] Specific operation: The server uses machine learning libraries (e.g., TensorFlow and PyTorch) to input data into an AI model and repeatedly trains it. It periodically evaluates the model's accuracy and adjusts hyperparameters as needed.

[0659] Step 4: Provide User Interface

[0660] The device provides an interface for users to view technical details. Users can view history, watch instructional videos and images, and receive real-time feedback.

[0661] Input: User instructions.

[0662] Output: Technical details displayed on the interface.

[0663] Specific operation: The terminal provides a graphical user interface (GUI) to allow users to operate it intuitively, and retrieves and displays data from the server in response to user requests.

[0664] Step 5: Real-time guide

[0665] The terminal uses sensor devices to collect user movement data in real time. This movement data is temporarily stored and then sent to the server.

[0666] Input: Motion data from a sensor device.

[0667] Output: Data sent to the server and real-time feedback.

[0668] Specific operation: The terminal acquires data in real time from devices such as motion capture devices and temperature sensors, and sends the necessary information to the server. The server analyzes this data and sends feedback to the terminal.

[0669] Step 6: Emotion Analysis

[0670] The device uses an emotion engine to analyze the user's facial expressions and voice data. If the user is experiencing fatigue or stress, it provides appropriate feedback.

[0671] Input: User facial expression data and voice data.

[0672] Output: Feedback based on emotional state.

[0673] Specific operation: The device uses an emotion recognition algorithm to analyze data in real time and generates feedback based on the results, such as suggesting breaks or adjusting the work pace.

[0674] Step 7: Feedback and Model Updates

[0675] The server stores and analyzes collected behavioral and emotional data. Based on the results, it updates the generative AI model and feedback algorithm.

[0676] Input: Behavioral and emotional data collected in real time.

[0677] Output: Updated generative AI model and feedback algorithm.

[0678] Specific operation: The server periodically analyzes the collected data and continues to learn by adding new information to the AI ​​model. It also fine-tunes the algorithm to improve the accuracy of the feedback.

[0679] These steps ensure that traditional techniques are efficiently passed down and that users can effectively acquire the skills.

[0680] (Application Example 2)

[0681] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0682] The difficulty in passing on traditional techniques stems from factors such as the lack of sufficient sharing of detailed technical steps and information, and the inability to accurately reflect the motivation and physical condition of learners. Furthermore, there is a need for efficient methods of learning while maintaining the precision of traditional techniques. In addition, modern manufacturing environments require real-time feedback on the emotional state and accuracy of movements of workers and robots to improve work efficiency.

[0683] The specific processing performed by the specific 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 information relating to traditional technology, means for analyzing the collected information and training a generated AI model, interface means for providing technical details to the user, sensor device means for collecting user actions in real time, means for analyzing the collected action information and providing feedback to the user, emotion engine means for recognizing the user's emotional state, and means for adjusting the feedback based on the recognized emotional state. As a result, not only is detailed step and background information of traditional technology centrally managed and provided, but real-time feedback is also possible according to the user's emotional state and the accuracy of their actions.

[0684] "Traditional techniques" refer to methods and skills that have been passed down through history and play an important role in specific cultures or professions.

[0685] "Means of collecting information" refers to systems and technologies for obtaining relevant data from various sources.

[0686] "Methods for training generative AI models" refer to the process of training an AI model based on collected information to improve its accuracy and performance.

[0687] An "interface means" refers to a method or device that allows a user to access a system and utilize its information and functions.

[0688] A "sensor device means" is a device for collecting user actions and environmental information in real time.

[0689] "Means of providing feedback" refer to systems or functions that provide appropriate responses or instructions to user actions or states.

[0690] An "emotional engine" is a technology that recognizes a user's emotional state and generates countermeasures based on that information.

[0691] "Means of adjusting feedback" refer to systems or technologies that appropriately modify the content of feedback provided according to the user's emotional state and behavior.

[0692] "Means of maintaining a database" refers to a system for centrally managing and storing collected information.

[0693] A "feedback analysis method" is an analytical technique that uses acquired data to improve the efficiency and accuracy of feedback.

[0694] This invention is a system for efficiently passing on traditional techniques. In particular, it uses an emotion engine to provide feedback that takes into account the user's emotional state, thereby supporting the acquisition of skills. The specific form of the system is described below.

[0695] System Configuration

[0696] server

[0697] The server collects and stores extensive information on traditional techniques in a database. This information is primarily gathered from historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server analyzes this information and trains a generative AI model. The AI ​​model learns the detailed steps and key points of the technique, and its accuracy is evaluated.

[0698] terminal

[0699] The device provides an interface that allows users to easily check the technical details. Users can use the device to view the technical history, watch instructional videos and images, and receive real-time feedback. The device also incorporates sensor devices to collect user activity in real time. Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state based on facial and voice data.

[0700] User

[0701] The users are individuals who wish to acquire traditional or specialized skills, such as swordsmiths or automobile manufacturing line workers. Using the device, users can receive real-time feedback on their actions and instructions based on their emotional state.

[0702] Processing flow

[0703] The server uses the collected information to train a generative AI model and improve its accuracy. The device collects the user's actions and emotional state in real time and sends it to the server. The server analyzes this data and generates appropriate feedback for the user. This feedback is adjusted according to the accuracy of the user's actions and emotional state.

[0704] Hardware and software

[0705] server:

[0706] High-performance servers (e.g., AWS EC2 instances) are used to collect and analyze information.

[0707] Deep learning frameworks (e.g., TensorFlow, PyTorch) are used to train generative AI models.

[0708] Terminal:

[0709] To collect motion data, use motion capture devices (e.g., Microsoft Kinect) or temperature sensors.

[0710] The emotion engine uses an emotion recognition library (e.g., OpenFace, Affectiva).

[0711] The user interface will utilize smart glasses or head-mounted displays (e.g., Google Glass, Microsoft HoloLens).

[0712] Specific example

[0713] For example, in a robotic assistant application on a factory's automobile manufacturing line, workers wear smart glasses while assembling products. Cameras capture the worker's movements and facial expressions in real time, analyzing the accuracy of their work and their emotional state. If fatigue or stress is detected, feedback such as "You're doing great, we recommend you take a 5-minute break" is displayed on the smart glasses. Also, if there is a problem with the accuracy of the work, an alert will be issued saying, "The screw is not tightened enough, please check again."

[0714] Example of a prompt:

[0715] "Explain how to assess the emotions and behavior of production line workers in real time and provide appropriate feedback. This includes, in particular, advice on taking breaks when fatigue or stress is detected, and specific feedback based on work progress."

[0716] The above describes specific embodiments for carrying out this invention.

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The server collects information on traditional techniques. Inputs include historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server stores this information in a database and manages it centrally, preparing it to provide detailed steps and background information on the techniques. The output is an organized database.

[0720] Step 2:

[0721] The server analyzes the collected information and trains a generative AI model. A stored database is used as input. For data processing, video data is segmented into frames, and text data is tokenized. This pre-processed data is then used to train the AI ​​model. The output is the trained generative AI model.

[0722] Step 3:

[0723] The terminal functions as an interface that provides technical details to the user. Input includes user actions such as operating the terminal and requesting necessary information. Data processing involves displaying technical information retrieved from the server to the user in an appropriate format. Output consists of technical information and step-by-step details that the user can review.

[0724] Step 4:

[0725] The user uses the terminal to check technical details and perform actual tasks. During this time, the sensor device built into the terminal collects the user's movements in real time. The input includes user movement data. The output is a stream of movement data.

[0726] Step 5:

[0727] The terminal transmits collected motion data to the server. Real-time motion data acquired from sensors is used as input. This data is transmitted to the server as data transmission. The output is motion data that the server can analyze.

[0728] Step 6:

[0729] The server analyzes the transmitted motion data in real time. A stream of motion data is used as input. The data is used to evaluate the accuracy and appropriateness of the motion. Then, feedback is generated based on the analysis results. The output is feedback information.

[0730] Step 7:

[0731] The device displays feedback sent from the server to the user. Feedback information is included as input. The data display shows the feedback in an appropriate format for smart glasses or a head-mounted display. The output is the feedback provided to the user visually and audibly.

[0732] Step 8:

[0733] The emotion engine built into the device recognizes the user's emotional state. It acquires user facial expression data and voice data as input. Data analysis evaluates the emotional state and sends this information to the server. The output is emotional state data.

[0734] Step 9:

[0735] The server analyzes emotional state data and adjusts the feedback accordingly. Emotional state data and the results of behavioral data analysis are used as input. Data adjustment involves customizing the feedback according to the user's state. The output is the adjusted feedback information.

[0736] Step 10:

[0737] The device then displays the adjusted feedback to the user again. The adjusted feedback information is included as input. The data display provides the feedback in a user-friendly format. The output is the final feedback provided to the user.

[0738] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0741] [Third Embodiment]

[0742] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0743] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0744] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0745] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0746] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0747] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0748] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0749] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0750] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0751] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0752] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0753] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0754] The system of this invention provides a mechanism for efficiently passing on traditional techniques. The program's processing flow is described in detail below.

[0755] Data collection methods

[0756] The server collects all kinds of information about traditional techniques. First, it gathers data from reliable sources such as historical documents, specialized books, videos, interviews with artisans, and instruction manuals. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used. The collected data is stored in a database to facilitate subsequent analysis.

[0757] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[0758] Forms of data analysis and model generation

[0759] The server performs data analysis based on the collected data. First, it performs preprocessing such as frame splitting of video data and tokenization of text data. Then, it trains a generative AI model, learning each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are optimized as needed to achieve higher accuracy.

[0760] As a concrete example, in the case of data analysis of the forging process of Japanese swords, the heating temperature of the material and the timing of the striking process are learned by the model.

[0761] User interface configuration

[0762] The device provides an interface that allows users to easily view details of traditional techniques. This includes viewing the history of collected technical data, watching videos and images of forging and work procedures, and real-time feedback. Users can quickly access the information they need through an intuitive interface.

[0763] For example, a new swordsmith can learn the procedures and points to note by watching videos of the forging process on a device.

[0764] Real-time guide format

[0765] The terminal collects user movement data in real time using sensor devices. For example, it may motion capture movements during work or acquire environmental data using a temperature sensor. The server analyzes this data in real time and generates appropriate feedback for the user. This feedback is provided to the user through the terminal, instructing them in real time on how to correct or improve their movements.

[0766] For example, if a blacksmith is actually performing a task and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[0767] Forms of feedback and corrections

[0768] The server stores the data collected in real time and performs detailed analysis. Based on the analysis results, it updates the generated AI model and uses it for the next training session. This continuously improves the model's performance and increases the accuracy of knowledge transfer.

[0769] As a concrete example, we upload video footage of a blacksmith at work to a server and use the analysis results to provide feedback on areas for improvement in the next session.

[0770] Summary

[0771] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture.

[0772] The following describes the processing flow.

[0773] Step 1:

[0774] The server collects information about traditional techniques. Specifically, it gathers data from historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and organizes and stores it in a database. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used.

[0775] Step 2:

[0776] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Necessary metadata is also extracted and stored in a database.

[0777] Step 3:

[0778] The server trains a generative AI model using pre-processed data. It first learns the basic technical steps and key points, and then incorporates more advanced techniques and condition-dependent parameters. The algorithm used in this process is selected to best suit the characteristics of the technology.

[0779] Step 4:

[0780] The server evaluates the accuracy of the trained AI model. A validation dataset is used for evaluation, measuring the model's performance. If the accuracy is insufficient, the parameters are adjusted and the model is trained again. This process is repeated to create the optimal model.

[0781] Step 5:

[0782] The terminal provides an interface that allows the user to access the system. This includes viewing the history of technology, viewing images and videos of procedures, and receiving real-time feedback. It must be designed to be user-friendly.

[0783] Step 6:

[0784] Users access necessary technical information using their devices. They review videos and images to learn technical procedures and key points. For example, they might watch a video of the Japanese sword forging process to understand the specific steps involved.

[0785] Step 7:

[0786] The device uses sensor devices to collect user movement data in real time. Typical examples include motion capture and temperature sensors. The collected data is temporarily stored within the device.

[0787] Step 8:

[0788] The server analyzes the action data sent from the terminal in real time. Based on the collected data, it determines whether the user's actions are appropriate and generates necessary corrections and advice.

[0789] Step 9:

[0790] The terminal provides the user with feedback sent from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then uses this information to correct their actions.

[0791] Step 10:

[0792] The server saves all session data and performs detailed analysis later. The analysis results are used to update the generated AI model and are used as training data for the next session. This improves the accuracy of the model and the efficiency of knowledge transfer.

[0793] Through this series of processing steps, it becomes possible to accurately pass on traditional techniques and prevent their decline.

[0794] (Example 1)

[0795] Next, we will describe Example 1. 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] The transmission of traditional techniques faces challenges due to the aging of skilled craftsmen and the loss of knowledge. Furthermore, the detailed learning of traditional techniques requires advanced knowledge and experience, making it difficult to train new craftsmen. Overcoming this situation and ensuring the accurate transmission of these techniques is an urgent priority.

[0797] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0798] In this invention, the server includes means for collecting information relating to traditional techniques, means for preprocessing the collected information, means for training a generative AI model based on the preprocessed information, means for evaluating the accuracy of the trained generative AI model and optimizing its parameters, means for providing technical details to the user, means for a sensor device that collects the user's actions in real time, and means for analyzing the collected action information and providing feedback to the user. This enables the efficient and accurate transmission of traditional techniques.

[0799] "Traditional techniques" is a general term for skills and knowledge that have been formed against a backdrop of a long history and culture, and have been passed down through generations.

[0800] "Means of information gathering" refers to methods and devices for obtaining data in various formats from reliable sources such as historical documents, specialized books, videos, interviews, and procedural manuals.

[0801] "Preprocessing methods" refer to processes performed to convert collected data into an analyzable format, and specifically include frame splitting of video data and tokenization of text data.

[0802] A "generative AI model" is an artificial intelligence model that is trained on collected and pre-processed data, and is designed to learn the detailed steps of a specific task or technique.

[0803] "Training methods" refer to methods or devices for training a generative AI model using pre-processed data.

[0804] "Means for evaluating accuracy and optimizing parameters" refers to methods or devices for measuring the performance of a trained generative AI model and improving the model's settings based on the results.

[0805] "Interface means" refers to software or hardware that allows users to view and manipulate collected technical data.

[0806] "Sensor device means" refers to a device for collecting user movements and environmental data in real time, and includes motion capture sensors, temperature sensors, and the like.

[0807] "Means for analyzing motion information and providing feedback" refers to methods or devices that analyze data collected by sensor devices and suggest areas for correction or improvement to the user's actions.

[0808] "Means for maintaining a database" refers to methods and devices for efficiently and securely storing diverse collected data and making it immediately accessible when needed.

[0809] A "feedback analysis means" refers to a method or apparatus for reanalyzing collected data and generated feedback in order to improve the performance of the generated AI model.

[0810] The system of this invention operates in cooperation with a server, terminals, and users to efficiently and accurately pass on traditional techniques. This system combines means of collection, analysis, training, provision, collection, and feedback to pass on the detailed steps and knowledge of traditional techniques.

[0811] The server first collects information on traditional techniques from reliable sources. Specifically, it obtains historical documents, specialized books, videos, interviews with craftsmen, and procedural manuals from the internet and digital archives. This collected data is formatted in digital format and stored in a database. For example, in the case of Japanese sword making techniques, it collects historical documents on swordsmiths, production videos, interviews with craftsmen, and forging procedure manuals.

[0812] Next, the server preprocesses the collected data. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques. This extracts technical steps and theories. The preprocessed data is then used to train a generative AI model.

[0813] The server trains the generative AI model. For example, it uses a Transformer-based generative AI model to learn each step and key point of the technology based on the collected data. The trained model is evaluated for accuracy, and parameters are tuned as needed. This optimizes the model's performance.

[0814] The device provides an interface that allows users to easily view technical details. The user interface has an intuitive design, making it easy for users to view the history of technical data and watch videos and images of the forging process. For example, a new swordsmith can learn the procedures and precautions by watching videos of the forging process on the device.

[0815] Furthermore, the terminal uses sensor devices to collect user movements in real time. Motion capture sensors and temperature sensors are used to collect user movements and environmental data, which are then transmitted to the server. The server analyzes this movement data in real time and generates feedback such as "heating temperature is not appropriate" or "the timing of the tapping is off."

[0816] The collected data and generated feedback are stored on a server for detailed analysis. Based on the results of this analysis, the generated AI model is updated and used as training data for the next training session. This continuously improves the model's performance and enhances the accuracy of passing on traditional techniques.

[0817] As a concrete example, consider the techniques used to make Japanese swords. The server collects historical documents and forging procedures of swordsmiths and analyzes video data frame by frame. The generated AI model learns, for example, the timing and force of a "hammer swing." When a user actually performs the work, the terminal collects motion data using motion capture sensors, and the server provides real-time feedback of the analysis results, thereby streamlining the transmission of technical skills.

[0818] Examples of prompt statements for a generative AI model are shown below:

[0819] "Please explain the forging process of Japanese swords. Describe in detail the heating temperature, the timing of the striking process, and the materials and tools used."

[0820] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0821] Step 1: Data Collection

[0822] The server collects information on traditional techniques from multiple reliable sources. Specifically, it retrieves historical documents, specialized books, videos, interviews with artisans, and procedural manuals.

[0823] Input: The source of the data (e.g., the internet or digital archives).

[0824] Data processing: Integrating data from different formats, such as text and video, and formatting it into a digital format.

[0825] Output: Collected data (e.g., documents and videos related to Japanese sword making techniques).

[0826] Specific operation: The server downloads information via a specific URL or database query and stores it in the database.

[0827] Step 2: Data Preprocessing

[0828] The server converts the collected data into a format that is easy to analyze. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques.

[0829] Input: Collected data (video files, text files, etc.).

[0830] Data processing: Frame splitting of video data, tokenization of text data.

[0831] Output: Preprocessed data (split frames, token sequences).

[0832] Specific operation: The server uses video analysis software to divide the video data into frames and uses a natural language processing library to tokenize the text.

[0833] Step 3: Model Training

[0834] The server uses pre-processed data to train a generative AI model. For example, it might use a Transformer-based model to teach the model each step and key points of the technology.

[0835] Input: Preprocessed data (token sequence, frame image, etc.).

[0836] Data processing: Generate data, input it into the AI ​​model, and execute the learning process.

[0837] Output: Trained generative AI model.

[0838] Specific operation: The server uses a deep learning framework to train a model using preprocessed data. For example, it learns the timing and temperature control patterns of Japanese sword forging.

[0839] Step 4: Model Evaluation and Optimization

[0840] The server evaluates the accuracy of the trained generative AI model and optimizes the parameters as needed.

[0841] Input: Trained generative AI model, validation dataset.

[0842] Data processing: Measure the prediction accuracy of the model and adjust the parameters.

[0843] Output: Optimized generative AI model.

[0844] Specific operation: The server uses a validation dataset to compare the model's predictions with the actual results and adjusts the parameters to minimize the error.

[0845] Step 5: Provide technical details

[0846] The terminal provides an interface that offers users technical details. Users can view the history of technical data and watch videos and images of the forging process.

[0847] Input: Optimized generative AI model, technical data.

[0848] Data processing: Visualize technical data and convert it into a format suitable for the user interface.

[0849] Output: Display of technical details on the user interface.

[0850] Specific operation: The device interacts with the user through a web application or mobile application and provides collected technical data in the form of video playback or text.

[0851] Step 6: Real-time motion capture

[0852] The device uses sensor devices to collect user movement data in real time.

[0853] Input: User actions (e.g., hammer movement during forging), sensor device data.

[0854] Data processing: Collect data from sensor devices in real time and format it into a format that can be sent to the server.

[0855] Output: Real-time operation data.

[0856] Specific operation: The device uses motion capture sensors and temperature sensors to record and transmit user movements and environmental data in real time.

[0857] Step 7: Feedback analysis and provision

[0858] The server analyzes the collected operational data in real time and provides feedback to the user.

[0859] Input: Real-time operation data.

[0860] Data processing: Analyzes operational data and generates optimal feedback.

[0861] Output: Feedback information to the user (e.g., alerts, instructions).

[0862] Specific operation: The server analyzes operational data in real time, generates feedback such as "heating temperature is not appropriate" or "timing of striking is off," and sends it to the terminal.

[0863] Step 8: Feedback and Model Updates

[0864] The server stores all collected data and performs detailed analysis. Based on the results of this analysis, the generated AI model is updated and used for the next training session.

[0865] Input: Collected data and feedback information.

[0866] Data processing: Analyze feedback information and adjust the parameters of the generated AI model.

[0867] Output: Updated generative AI model.

[0868] Specific operation: The server analyzes the feedback information in detail and uses it as training data for the model, continuously improving the generated AI model.

[0869] In this way, the coordinated operation of each processing step enables the efficient and accurate transmission of traditional techniques.

[0870] (Application Example 1)

[0871] Next, we will explain Application Example 1. In the following explanation, 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."

[0872] The transmission of traditional techniques requires advanced skills and experience, making it difficult to learn and accurately reproduce them efficiently. Furthermore, the presence of experienced instructors is essential for inexperienced technicians to acquire accurate procedures and techniques; a lack of such instructors can lead to a decline in the accuracy of technique transfer. Additionally, the lack of mechanisms for real-time work improvement and feedback makes it difficult for technicians to self-correct. These challenges make the efficient and accurate transmission of traditional techniques difficult.

[0873] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0874] In this invention, the server includes means for collecting information on traditional techniques, means for analyzing the collected information and training a generated AI model, user interface means for providing technical details to the user, sensor device means for collecting user motion data in real time, means for analyzing the collected motion information and providing appropriate feedback to the user, terminal means for allowing real-time feedback based on the motion data, and means for displaying work procedures and video data to the terminal. This makes it possible for even inexperienced technicians to efficiently learn traditional techniques and receive real-time feedback.

[0875] "Traditional techniques" refer to the artisan skills and manufacturing methods that have been passed down for generations in a region or specific cultural area.

[0876] An "information base" is a database that systematically organizes and stores information and data about traditional techniques that have been collected.

[0877] A "generative AI model" is an artificial intelligence model that analyzes data collected using machine learning techniques and is trained to perform a specific task.

[0878] "User interface means" refers to interface devices and software that allow users to intuitively operate a system and view information.

[0879] "Sensor device means" refers to a sensor device used to collect user actions and environmental data in real time.

[0880] "Terminal means" refers to hardware that is a device for user operation and has the function of displaying specific feedback or information.

[0881] "Feedback" refers to information that a system uses to analyze user actions and operations, providing suggestions for improvement and guidance for future actions.

[0882] Overall System Overview

[0883] This system is designed to efficiently and accurately pass on traditional techniques. The system consists of the following main components: a server, terminals, users, and various sensor devices.

[0884] Program processing

[0885] The server plays a primary role in collecting and analyzing information about traditional techniques. This includes ingesting data from books, videos, interviews, and instruction manuals. The collected information is stored in an information base. Next, a generative AI model is trained using this data. This model learns each step of the traditional technique and identifies key points in the process.

[0886] The terminal has an interface that provides technical details to the user. Specifically, smartphones, smart glasses, and head-mounted displays are used, through which the user can view work procedures and video data. Furthermore, sensor devices are used to collect user movement data in real time. This movement data is sent to a server for analysis. Based on the analysis results, the system provides appropriate feedback to the user.

[0887] Hardware and software to be used

[0888] Hardware: Smartphones, smart glasses, head-mounted displays, and various sensor devices (motion capture, temperature sensors, etc.).

[0889] Software: Python-based video analysis libraries (e.g., OpenCV), HTTP requests for data transmission (e.g., the requests library), and TensorFlow / Keras for running AI models.

[0890] The server performs preprocessing such as frame segmentation and tokenization to train the generative AI model. The trained model analyzes video and text data and learns each step of the technology.

[0891] As users utilize the device, they receive real-time feedback, and if certain actions or operations are not performed correctly, they are immediately instructed to correct them. This allows even inexperienced technicians to efficiently learn accurate techniques.

[0892] Examples of specific cases and prompt statements

[0893] For example, imagine a welding operation in a factory where a user wears smart glasses. The smart glasses' camera and sensors collect motion data, monitoring the appropriate welding temperature and timing in real time. Based on the analysis results, feedback such as the following is displayed:

[0894] "Start welding. Read the current temperature and work at the appropriate temperature."

[0895] "The temperature is too high. Cooling is required."

[0896] "Your movements are correct. Please maintain this pace."

[0897] As described above, this system makes it possible to efficiently and accurately pass on traditional techniques.

[0898] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0899] Step 1:

[0900] The server collects information about traditional techniques. It uses data such as books, videos, interviews, and instruction manuals as input. This data is collected by the server and stored in an information base. The server then performs preprocessing on this information, such as frame splitting and tokenization of text data.

[0901] Step 2:

[0902] The server trains a generative AI model based on the collected data. Preprocessed video and text data are used as input. The server uses this data to perform machine learning on the AI ​​model, learning specific technical steps and work procedures. The output is the trained AI model.

[0903] Step 3:

[0904] The user views technical details using a device. Technical information generated from a trained AI model is used as input. The device displays work procedures and video data to the user via a smartphone, smart glasses, or head-mounted display. The output is a display of technical details in a format easily understood by the user.

[0905] Step 4:

[0906] When a user begins working, a sensor device collects motion data in real time. The inputs used are user motion data and ambient environmental data. The sensor device collects detailed motion information using motion capture, temperature sensors, and other methods. The output is real-time motion data.

[0907] Step 5:

[0908] The server analyzes motion data collected in real time. It uses motion data transmitted from sensor devices as input. The server analyzes the motion data using a generative AI model to determine whether the work is being performed according to the technical requirements. The output is the evaluation result of the motion.

[0909] Step 6:

[0910] The terminal receives evaluation results from the server and provides appropriate feedback to the user. It uses evaluation results from the server as input. The terminal displays feedback to the user via voice and display. This may include specific advice such as, "Operation is good. Please maintain this pace," or "The temperature is too high. Cooling is required." The output is real-time feedback to the user.

[0911] The above processing steps enable the efficient and accurate transmission of traditional techniques.

[0912] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0913] The system of the present invention provides a mechanism for efficiently passing on traditional techniques, and in particular, by combining it with an emotion engine, it enables feedback that takes into account the user's emotional state. The specific form of implementation is described below.

[0914] Data collection methods

[0915] The server collects a wide range of information on traditional techniques, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The collected information is organized and stored in a database, allowing for centralized management of detailed steps, background information, and details about the materials and tools used.

[0916] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[0917] Forms of data analysis and model generation

[0918] The server preprocesses the collected information and trains a generative AI model. Video data is segmented into frames, and text data is tokenized. The preprocessed data is used to train the generative AI model to learn each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are adjusted as needed.

[0919] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught things like the heating temperature of the material and the timing of the forging process.

[0920] User interface configuration

[0921] The device provides an interface that allows users to easily access technical details. This includes viewing the technical history, watching instructional videos and images, and receiving real-time feedback. Users can operate it intuitively and quickly access the information they need.

[0922] As a concrete example, a new swordsmith uses a device to watch videos of the forging process and confirm the specific steps.

[0923] Real-time guide format

[0924] The device collects user motion data in real time using sensor devices. Data acquired through motion capture and temperature sensors is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[0925] For example, if a blacksmith is actually working and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[0926] Forms of emotional engines

[0927] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is feeling fatigued or stressed.

[0928] For example, if fatigue is detected in a user's facial expressions during training, feedback will be displayed on the device suggesting a break or recommending an adjustment to the work pace based on that status.

[0929] Forms of feedback and corrections

[0930] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[0931] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[0932] Summary

[0933] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture. The introduction of an emotional engine provides optimal feedback that takes the user's state into account, improving the efficiency of skill acquisition.

[0934] The following describes the processing flow.

[0935] Step 1:

[0936] The server collects information on traditional techniques. This involves gathering data from a wide range of sources, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and storing it in a database. This allows for centralized management of information about the detailed steps of the techniques, the materials used, and the tools.

[0937] Step 2:

[0938] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Metadata is also extracted, and all information is stored in a database in a standardized format.

[0939] Step 3:

[0940] The server trains a generative AI model using pre-processed data. Model training involves learning each step and key point of traditional techniques. After training, the model's accuracy is evaluated, and parameters are adjusted and retrained as needed.

[0941] Step 4:

[0942] The device provides users with an interface to access technical information. This includes features such as viewing images and videos of procedures and receiving real-time feedback. Users can operate it intuitively and quickly obtain the information they need.

[0943] Step 5:

[0944] Users access detailed technical information using their devices. They review videos and images provided on their devices to learn technical procedures and key points. For example, they might watch a video about the Japanese sword forging process to understand the specific steps involved.

[0945] Step 6:

[0946] The device utilizes sensor devices to collect user motion data in real time. Data collected through motion capture and temperature sensors is temporarily stored on the device.

[0947] Step 7:

[0948] The server analyzes the action data sent from the terminal in real time. This allows it to determine whether the user's actions are correct and generate necessary corrections and advice.

[0949] Step 8:

[0950] The terminal provides the user with feedback received from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then corrects the operation based on this information.

[0951] Step 9:

[0952] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. If the user is feeling fatigued or stressed, it provides appropriate feedback.

[0953] Step 10:

[0954] The server saves all session data for later detailed analysis. Based on the collected behavioral and emotional data, the generative AI model is updated and the feedback algorithm is improved. In this way, the accuracy of traditional technology and the user experience are enhanced.

[0955] Through this series of processing steps, a system is realized that efficiently transmits traditional techniques and provides appropriate support to users. Furthermore, the introduction of an emotion engine enables personalized responses that take into account the user's state.

[0956] (Example 2)

[0957] Next, we will describe Example 2. 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."

[0958] Traditional techniques are difficult to pass on due to their uniqueness and complexity. Furthermore, if proper feedback is not provided during the skill transfer process, the reproducibility and precision of the techniques may decline. Additionally, the emotional state of those learning the skills is often overlooked, making effective learning difficult. Therefore, there is a need to develop a system that evaluates and provides feedback on both operational and emotional information in real time during the skill transfer process.

[0959] The identification processing 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 information relating to traditional techniques, means for preprocessing the collected information and training a generation AI model, interface means for providing technical details to the user, sensor device means for collecting user movements in real time, means for analyzing the collected movement information and the user's emotional state and providing feedback to the user, and emotion engine means for analyzing the collected user's facial expressions and voice data. This enables appropriate feedback that takes both movement information and emotional information into consideration, realizing effective skill transfer and improved learning efficiency.

[0960] "Traditional techniques" refer to skills and techniques that have been passed down through generations in a particular region or culture with a long history.

[0961] "Means of collecting information" refers to methods and technologies for obtaining specific data and knowledge from the internet, partner organizations, etc., and storing them in a database.

[0962] "Preprocessing" refers to the process of performing operations such as frame splitting, tokenization, and noise reduction to make the collected data easier to analyze.

[0963] A "generative AI model" refers to an artificial intelligence model that learns each step and key point of a specific technology or skill based on collected data, and then provides the results to the user.

[0964] "Interface means" refers to display and input devices, and their designs, that allow users to intuitively operate a system and access information.

[0965] "Sensor device means" refers to devices and methods for collecting user movement data in real time.

[0966] "Motion information" refers to data about the user's movements collected by sensor devices.

[0967] "Means of providing feedback" refers to methods and technologies for providing users with appropriate advice and warnings based on collected and analyzed data.

[0968] "Emotional engine means" refers to technologies and devices that analyze a user's facial expressions and voice data to identify their emotional state.

[0969] "Facial expression data" refers to data used to capture the user's facial expressions.

[0970] "Voice data" refers to data used to capture the characteristics of the user's voice.

[0971] This invention is a system aimed at the efficient transmission of traditional techniques, and provides technical feedback to the user through multiple steps.

[0972] In particular, this invention is centered on a server, a terminal, and a user. Each of these entities works together to provide the user with detailed information on traditional techniques and to offer real-time instruction and emotionally responsive feedback.

[0973] Data collection

[0974] The server collects a wide range of information on traditional techniques. Data sources include historical documents, specialized books, videos, interviews with artisans, and procedural manuals. The collected information is then organized and stored in a database.

[0975] As a specific example, regarding the techniques of making Japanese swords, the server collects historical documents on Japanese swordsmiths, videos of the manufacturing process, interviews with craftsmen, and detailed forging procedures, and stores them in a database.

[0976] Data preprocessing and model training

[0977] The server preprocesses the collected information and trains a generative AI model. Video data is split frame by frame, and text data is tokenized. The preprocessed data is used to train the generative AI model, teaching it each step of the technology and key points. The trained model is then evaluated for accuracy, and parameters are adjusted as needed.

[0978] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught the heating temperature of the iron and the timing of the hammering.

[0979] User Interface

[0980] The device provides an interface that allows users to easily check technical details. This interface allows users to view the technical history, watch procedural videos and images, and receive real-time feedback. Users can operate it intuitively and quickly access the information they need.

[0981] As a concrete example, a new swordsmith can use a device to watch videos of the forging process and check details of the specific steps and materials and tools used.

[0982] Real-time guide

[0983] The device uses sensor devices to collect user movement data in real time. This data is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[0984] For example, when a blacksmith is working, an alert may appear on the terminal if the temperature is not appropriate or if the timing of the strike is incorrect.

[0985] Emotion analysis

[0986] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is experiencing fatigue or stress.

[0987] For example, if fatigue is detected from a user's facial expressions during training, the device will display suggestions for taking a break or adjusting the work pace according to that status.

[0988] Feedback and Model Updates

[0989] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[0990] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[0991] Example of a prompt

[0992] "Collect historical documents on the process of making Japanese swords and save them in a database."

[0993] "Train an AI model based on the collected manufacturing process data and teach it Japanese sword forging techniques."

[0994] "Design an interface that allows new swordsmiths to watch videos of the forging process."

[0995] "Develop a system that provides real-time feedback on things like inappropriate temperature during operation and correct timing for hammering."

[0996] "Analyze the user's facial expression data and provide feedback suggesting a break if fatigue is detected."

[0997] This system will enable the efficient transmission of traditional techniques and improve user learning effectiveness. Furthermore, it is expected to provide optimal support throughout the skill acquisition process through real-time instruction and emotion-responsive feedback.

[0998] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0999] Step 1: Data Collection

[1000] The server collects information about traditional techniques. Specifically, it obtains data such as historical documents, specialized books, videos, interviews with artisans, and procedural manuals from the internet and partner organizations, and stores them in a database.

[1001] Input: Information on traditional techniques collected from the internet and partner organizations.

[1002] Output: Technical information stored in an organized database.

[1003] Specific operation: The server automatically collects data from various resources on the internet using scraping tools and APIs. It then converts the collected data into an appropriate format and stores it in a database.

[1004] Step 2: Data Preprocessing

[1005] The server preprocesses the collected data. It performs noise reduction and normalization, splits video data into frames, and tokenizes text data.

[1006] Input: Unprocessed technical information from the database.

[1007] Output: Preprocessed dataset.

[1008] Specific operation: The server uses a text cleaning tool to remove noise and a video processing program to split the video frame by frame.

[1009] Step 3: Model Training

[1010] The server trains a generative AI model using preprocessed data. It then feeds data into the AI ​​model to teach it key steps and technical points, and evaluates its performance.

[1011] Input: Preprocessed dataset.

[1012] Output: A trained generative AI model.

[1013] Specific operation: The server uses machine learning libraries (e.g., TensorFlow and PyTorch) to input data into an AI model and repeatedly trains it. It periodically evaluates the model's accuracy and adjusts hyperparameters as needed.

[1014] Step 4: Provide User Interface

[1015] The device provides an interface for users to view technical details. Users can view history, watch instructional videos and images, and receive real-time feedback.

[1016] Input: User instructions.

[1017] Output: Technical details displayed on the interface.

[1018] Specific operation: The terminal provides a graphical user interface (GUI) to allow users to operate it intuitively, and retrieves and displays data from the server in response to user requests.

[1019] Step 5: Real-time guide

[1020] The terminal uses sensor devices to collect user movement data in real time. This movement data is temporarily stored and then sent to the server.

[1021] Input: Motion data from a sensor device.

[1022] Output: Data sent to the server and real-time feedback.

[1023] Specific operation: The terminal acquires data in real time from devices such as motion capture devices and temperature sensors, and sends the necessary information to the server. The server analyzes this data and sends feedback to the terminal.

[1024] Step 6: Emotion Analysis

[1025] The device uses an emotion engine to analyze the user's facial expressions and voice data. If the user is experiencing fatigue or stress, it provides appropriate feedback.

[1026] Input: User facial expression data and voice data.

[1027] Output: Feedback based on emotional state.

[1028] Specific operation: The device uses an emotion recognition algorithm to analyze data in real time and generates feedback based on the results, such as suggesting breaks or adjusting the work pace.

[1029] Step 7: Feedback and Model Updates

[1030] The server stores and analyzes collected behavioral and emotional data. Based on the results, it updates the generative AI model and feedback algorithm.

[1031] Input: Behavioral and emotional data collected in real time.

[1032] Output: Updated generative AI model and feedback algorithm.

[1033] Specific operation: The server periodically analyzes the collected data and continues to learn by adding new information to the AI ​​model. It also fine-tunes the algorithm to improve the accuracy of the feedback.

[1034] These steps ensure that traditional techniques are efficiently passed down and that users can effectively acquire the skills.

[1035] (Application Example 2)

[1036] Next, we will explain application example 2. In the following explanation, 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."

[1037] The difficulty in passing on traditional techniques stems from factors such as the lack of sufficient sharing of detailed technical steps and information, and the inability to accurately reflect the motivation and physical condition of learners. Furthermore, there is a need for efficient methods of learning while maintaining the precision of traditional techniques. In addition, modern manufacturing environments require real-time feedback on the emotional state and accuracy of movements of workers and robots to improve work efficiency.

[1038] The specific processing performed by the specific 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 information relating to traditional technology, means for analyzing the collected information and training a generated AI model, interface means for providing technical details to the user, sensor device means for collecting user actions in real time, means for analyzing the collected action information and providing feedback to the user, emotion engine means for recognizing the user's emotional state, and means for adjusting the feedback based on the recognized emotional state. As a result, not only is detailed step and background information of traditional technology centrally managed and provided, but real-time feedback is also possible according to the user's emotional state and the accuracy of their actions.

[1039] "Traditional techniques" refer to methods and skills that have been passed down through history and play an important role in specific cultures or professions.

[1040] "Means of collecting information" refers to systems and technologies for obtaining relevant data from various sources.

[1041] "Methods for training generative AI models" refer to the process of training an AI model based on collected information to improve its accuracy and performance.

[1042] An "interface means" refers to a method or device that allows a user to access a system and utilize its information and functions.

[1043] A "sensor device means" is a device for collecting user actions and environmental information in real time.

[1044] "Means of providing feedback" refer to systems or functions that provide appropriate responses or instructions to user actions or states.

[1045] An "emotional engine" is a technology that recognizes a user's emotional state and generates countermeasures based on that information.

[1046] "Means of adjusting feedback" refer to systems or technologies that appropriately modify the content of feedback provided according to the user's emotional state and behavior.

[1047] "Means of maintaining a database" refers to a system for centrally managing and storing collected information.

[1048] A "feedback analysis method" is an analytical technique that uses acquired data to improve the efficiency and accuracy of feedback.

[1049] This invention is a system for efficiently passing on traditional techniques. In particular, it uses an emotion engine to provide feedback that takes into account the user's emotional state, thereby supporting the acquisition of skills. The specific form of the system is described below.

[1050] System Configuration

[1051] server

[1052] The server collects and stores extensive information on traditional techniques in a database. This information is primarily gathered from historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server analyzes this information and trains a generative AI model. The AI ​​model learns the detailed steps and key points of the technique, and its accuracy is evaluated.

[1053] terminal

[1054] The device provides an interface that allows users to easily check the technical details. Users can use the device to view the technical history, watch instructional videos and images, and receive real-time feedback. The device also incorporates sensor devices to collect user activity in real time. Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state based on facial and voice data.

[1055] User

[1056] The users are individuals who wish to acquire traditional or specialized skills, such as swordsmiths or automobile manufacturing line workers. Using the device, users can receive real-time feedback on their actions and instructions based on their emotional state.

[1057] Processing flow

[1058] The server uses the collected information to train a generative AI model and improve its accuracy. The device collects the user's actions and emotional state in real time and sends it to the server. The server analyzes this data and generates appropriate feedback for the user. This feedback is adjusted according to the accuracy of the user's actions and emotional state.

[1059] Hardware and software

[1060] server:

[1061] High-performance servers (e.g., AWS EC2 instances) are used to collect and analyze information.

[1062] Deep learning frameworks (e.g., TensorFlow, PyTorch) are used to train generative AI models.

[1063] Terminal:

[1064] To collect motion data, use motion capture devices (e.g., Microsoft Kinect) or temperature sensors.

[1065] The emotion engine uses an emotion recognition library (e.g., OpenFace, Affectiva).

[1066] The user interface will utilize smart glasses or head-mounted displays (e.g., Google Glass, Microsoft HoloLens).

[1067] Specific example

[1068] For example, in a robotic assistant application on a factory's automobile manufacturing line, workers wear smart glasses while assembling products. Cameras capture the worker's movements and facial expressions in real time, analyzing the accuracy of their work and their emotional state. If fatigue or stress is detected, feedback such as "You're doing great, we recommend you take a 5-minute break" is displayed on the smart glasses. Also, if there is a problem with the accuracy of the work, an alert will be issued saying, "The screw is not tightened enough, please check again."

[1069] Example of a prompt:

[1070] "Explain how to assess the emotions and behavior of production line workers in real time and provide appropriate feedback. This includes, in particular, advice on taking breaks when fatigue or stress is detected, and specific feedback based on work progress."

[1071] The above describes specific embodiments for carrying out this invention.

[1072] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1073] Step 1:

[1074] The server collects information on traditional techniques. Inputs include historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server stores this information in a database and manages it centrally, preparing it to provide detailed steps and background information on the techniques. The output is an organized database.

[1075] Step 2:

[1076] The server analyzes the collected information and trains a generative AI model. A stored database is used as input. For data processing, video data is segmented into frames, and text data is tokenized. This pre-processed data is then used to train the AI ​​model. The output is the trained generative AI model.

[1077] Step 3:

[1078] The terminal functions as an interface that provides technical details to the user. Input includes user actions such as operating the terminal and requesting necessary information. Data processing involves displaying technical information retrieved from the server to the user in an appropriate format. Output consists of technical information and step-by-step details that the user can review.

[1079] Step 4:

[1080] The user uses the terminal to check technical details and perform actual tasks. During this time, the sensor device built into the terminal collects the user's movements in real time. The input includes user movement data. The output is a stream of movement data.

[1081] Step 5:

[1082] The terminal transmits collected motion data to the server. Real-time motion data acquired from sensors is used as input. This data is transmitted to the server as data transmission. The output is motion data that the server can analyze.

[1083] Step 6:

[1084] The server analyzes the transmitted motion data in real time. A stream of motion data is used as input. The data is used to evaluate the accuracy and appropriateness of the motion. Then, feedback is generated based on the analysis results. The output is feedback information.

[1085] Step 7:

[1086] The device displays feedback sent from the server to the user. Feedback information is included as input. The data display shows the feedback in an appropriate format for smart glasses or a head-mounted display. The output is the feedback provided to the user visually and audibly.

[1087] Step 8:

[1088] The emotion engine built into the device recognizes the user's emotional state. It acquires user facial expression data and voice data as input. Data analysis evaluates the emotional state and sends this information to the server. The output is emotional state data.

[1089] Step 9:

[1090] The server analyzes emotional state data and adjusts the feedback accordingly. Emotional state data and the results of behavioral data analysis are used as input. Data adjustment involves customizing the feedback according to the user's state. The output is the adjusted feedback information.

[1091] Step 10:

[1092] The device then displays the adjusted feedback to the user again. The adjusted feedback information is included as input. The data display provides the feedback in a user-friendly format. The output is the final feedback provided to the user.

[1093] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1094] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1095] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1096] [Fourth Embodiment]

[1097] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1098] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1100] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1101] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1104] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1105] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1106] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1107] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1108] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1109] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1110] The system of this invention provides a mechanism for efficiently passing on traditional techniques. The program's processing flow is described in detail below.

[1111] Data collection methods

[1112] The server collects all kinds of information about traditional techniques. First, it gathers data from reliable sources such as historical documents, specialized books, videos, interviews with artisans, and instruction manuals. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used. The collected data is stored in a database to facilitate subsequent analysis.

[1113] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[1114] Forms of data analysis and model generation

[1115] The server performs data analysis based on the collected data. First, it performs preprocessing such as frame splitting of video data and tokenization of text data. Then, it trains a generative AI model, learning each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are optimized as needed to achieve higher accuracy.

[1116] As a concrete example, in the case of data analysis of the forging process of Japanese swords, the heating temperature of the material and the timing of the striking process are learned by the model.

[1117] User interface configuration

[1118] The device provides an interface that allows users to easily view details of traditional techniques. This includes viewing the history of collected technical data, watching videos and images of forging and work procedures, and real-time feedback. Users can quickly access the information they need through an intuitive interface.

[1119] For example, a new swordsmith can learn the procedures and points to note by watching videos of the forging process on a device.

[1120] Real-time guide format

[1121] The terminal collects user movement data in real time using sensor devices. For example, it may motion capture movements during work or acquire environmental data using a temperature sensor. The server analyzes this data in real time and generates appropriate feedback for the user. This feedback is provided to the user through the terminal, instructing them in real time on how to correct or improve their movements.

[1122] For example, if a blacksmith is actually performing a task and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[1123] Forms of feedback and corrections

[1124] The server stores the data collected in real time and performs detailed analysis. Based on the analysis results, it updates the generated AI model and uses it for the next training session. This continuously improves the model's performance and increases the accuracy of knowledge transfer.

[1125] As a concrete example, we upload video footage of a blacksmith at work to a server and use the analysis results to provide feedback on areas for improvement in the next session.

[1126] Summary

[1127] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture.

[1128] The following describes the processing flow.

[1129] Step 1:

[1130] The server collects information about traditional techniques. Specifically, it gathers data from historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and organizes and stores it in a database. This information includes detailed steps and theories of the techniques, as well as descriptions of the materials and tools used.

[1131] Step 2:

[1132] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Necessary metadata is also extracted and stored in a database.

[1133] Step 3:

[1134] The server trains a generative AI model using pre-processed data. It first learns the basic technical steps and key points, and then incorporates more advanced techniques and condition-dependent parameters. The algorithm used in this process is selected to best suit the characteristics of the technology.

[1135] Step 4:

[1136] The server evaluates the accuracy of the trained AI model. A validation dataset is used for evaluation, measuring the model's performance. If the accuracy is insufficient, the parameters are adjusted and the model is trained again. This process is repeated to create the optimal model.

[1137] Step 5:

[1138] The terminal provides an interface that allows the user to access the system. This includes viewing the history of technology, viewing images and videos of procedures, and receiving real-time feedback. It must be designed to be user-friendly.

[1139] Step 6:

[1140] Users access necessary technical information using their devices. They review videos and images to learn technical procedures and key points. For example, they might watch a video of the Japanese sword forging process to understand the specific steps involved.

[1141] Step 7:

[1142] The device uses sensor devices to collect user movement data in real time. Typical examples include motion capture and temperature sensors. The collected data is temporarily stored within the device.

[1143] Step 8:

[1144] The server analyzes the action data sent from the terminal in real time. Based on the collected data, it determines whether the user's actions are appropriate and generates necessary corrections and advice.

[1145] Step 9:

[1146] The terminal provides the user with feedback sent from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then uses this information to correct their actions.

[1147] Step 10:

[1148] The server saves all session data and performs detailed analysis later. The analysis results are used to update the generated AI model and are used as training data for the next session. This improves the accuracy of the model and the efficiency of knowledge transfer.

[1149] Through this series of processing steps, it becomes possible to accurately pass on traditional techniques and prevent their decline.

[1150] (Example 1)

[1151] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1152] The transmission of traditional techniques faces challenges due to the aging of skilled craftsmen and the loss of knowledge. Furthermore, the detailed learning of traditional techniques requires advanced knowledge and experience, making it difficult to train new craftsmen. Overcoming this situation and ensuring the accurate transmission of these techniques is an urgent priority.

[1153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1154] In this invention, the server includes means for collecting information relating to traditional techniques, means for preprocessing the collected information, means for training a generative AI model based on the preprocessed information, means for evaluating the accuracy of the trained generative AI model and optimizing its parameters, means for providing technical details to the user, means for a sensor device that collects the user's actions in real time, and means for analyzing the collected action information and providing feedback to the user. This enables the efficient and accurate transmission of traditional techniques.

[1155] "Traditional techniques" is a general term for skills and knowledge that have been formed against a backdrop of a long history and culture, and have been passed down through generations.

[1156] "Means of information gathering" refers to methods and devices for obtaining data in various formats from reliable sources such as historical documents, specialized books, videos, interviews, and procedural manuals.

[1157] "Preprocessing methods" refer to processes performed to convert collected data into an analyzable format, and specifically include frame splitting of video data and tokenization of text data.

[1158] A "generative AI model" is an artificial intelligence model that is trained on collected and pre-processed data, and is designed to learn the detailed steps of a specific task or technique.

[1159] "Training methods" refer to methods or devices for training a generative AI model using pre-processed data.

[1160] "Means for evaluating accuracy and optimizing parameters" refers to methods or devices for measuring the performance of a trained generative AI model and improving the model's settings based on the results.

[1161] "Interface means" refers to software or hardware that allows users to view and manipulate collected technical data.

[1162] "Sensor device means" refers to a device for collecting user movements and environmental data in real time, and includes motion capture sensors, temperature sensors, and the like.

[1163] "Means for analyzing motion information and providing feedback" refers to methods or devices that analyze data collected by sensor devices and suggest areas for correction or improvement to the user's actions.

[1164] "Means for maintaining a database" refers to methods and devices for efficiently and securely storing diverse collected data and making it immediately accessible when needed.

[1165] A "feedback analysis means" refers to a method or apparatus for reanalyzing collected data and generated feedback in order to improve the performance of the generated AI model.

[1166] The system of this invention operates in cooperation with a server, terminals, and users to efficiently and accurately pass on traditional techniques. This system combines means of collection, analysis, training, provision, collection, and feedback to pass on the detailed steps and knowledge of traditional techniques.

[1167] The server first collects information on traditional techniques from reliable sources. Specifically, it obtains historical documents, specialized books, videos, interviews with craftsmen, and procedural manuals from the internet and digital archives. This collected data is formatted in digital format and stored in a database. For example, in the case of Japanese sword making techniques, it collects historical documents on swordsmiths, production videos, interviews with craftsmen, and forging procedure manuals.

[1168] Next, the server preprocesses the collected data. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques. This extracts technical steps and theories. The preprocessed data is then used to train a generative AI model.

[1169] The server trains the generative AI model. For example, it uses a Transformer-based generative AI model to learn each step and key point of the technology based on the collected data. The trained model is evaluated for accuracy, and parameters are tuned as needed. This optimizes the model's performance.

[1170] The device provides an interface that allows users to easily view technical details. The user interface has an intuitive design, making it easy for users to view the history of technical data and watch videos and images of the forging process. For example, a new swordsmith can learn the procedures and precautions by watching videos of the forging process on the device.

[1171] Furthermore, the terminal uses sensor devices to collect user movements in real time. Motion capture sensors and temperature sensors are used to collect user movements and environmental data, which are then transmitted to the server. The server analyzes this movement data in real time and generates feedback such as "heating temperature is not appropriate" or "the timing of the tapping is off."

[1172] The collected data and generated feedback are stored on a server for detailed analysis. Based on the results of this analysis, the generated AI model is updated and used as training data for the next training session. This continuously improves the model's performance and enhances the accuracy of passing on traditional techniques.

[1173] As a concrete example, consider the techniques used to make Japanese swords. The server collects historical documents and forging procedures of swordsmiths and analyzes video data frame by frame. The generated AI model learns, for example, the timing and force of a "hammer swing." When a user actually performs the work, the terminal collects motion data using motion capture sensors, and the server provides real-time feedback of the analysis results, thereby streamlining the transmission of technical skills.

[1174] Examples of prompt statements for a generative AI model are shown below:

[1175] "Please explain the forging process of Japanese swords. Describe in detail the heating temperature, the timing of the striking process, and the materials and tools used."

[1176] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1177] Step 1: Data Collection

[1178] The server collects information on traditional techniques from multiple reliable sources. Specifically, it retrieves historical documents, specialized books, videos, interviews with artisans, and procedural manuals.

[1179] Input: The source of the data (e.g., the internet or digital archives).

[1180] Data processing: Integrating data from different formats, such as text and video, and formatting it into a digital format.

[1181] Output: Collected data (e.g., documents and videos related to Japanese sword making techniques).

[1182] Specific operation: The server downloads information via a specific URL or database query and stores it in the database.

[1183] Step 2: Data Preprocessing

[1184] The server converts the collected data into a format that is easy to analyze. Video data is divided frame by frame, and text data is tokenized using natural language processing techniques.

[1185] Input: Collected data (video files, text files, etc.).

[1186] Data processing: Frame splitting of video data, tokenization of text data.

[1187] Output: Preprocessed data (split frames, token sequences).

[1188] Specific operation: The server uses video analysis software to divide the video data into frames and uses a natural language processing library to tokenize the text.

[1189] Step 3: Model Training

[1190] The server uses pre-processed data to train a generative AI model. For example, it might use a Transformer-based model to teach the model each step and key points of the technology.

[1191] Input: Preprocessed data (token sequence, frame image, etc.).

[1192] Data processing: Generate data, input it into the AI ​​model, and execute the learning process.

[1193] Output: Trained generative AI model.

[1194] Specific operation: The server uses a deep learning framework to train a model using preprocessed data. For example, it learns the timing and temperature control patterns of Japanese sword forging.

[1195] Step 4: Model Evaluation and Optimization

[1196] The server evaluates the accuracy of the trained generative AI model and optimizes the parameters as needed.

[1197] Input: Trained generative AI model, validation dataset.

[1198] Data processing: Measure the prediction accuracy of the model and adjust the parameters.

[1199] Output: Optimized generative AI model.

[1200] Specific operation: The server uses a validation dataset to compare the model's predictions with the actual results and adjusts the parameters to minimize the error.

[1201] Step 5: Provide technical details

[1202] The terminal provides an interface that offers users technical details. Users can view the history of technical data and watch videos and images of the forging process.

[1203] Input: Optimized generative AI model, technical data.

[1204] Data processing: Visualize technical data and convert it into a format suitable for the user interface.

[1205] Output: Display of technical details on the user interface.

[1206] Specific operation: The device interacts with the user through a web application or mobile application and provides collected technical data in the form of video playback or text.

[1207] Step 6: Real-time motion capture

[1208] The device uses sensor devices to collect user movement data in real time.

[1209] Input: User actions (e.g., hammer movement during forging), sensor device data.

[1210] Data processing: Collect data from sensor devices in real time and format it into a format that can be sent to the server.

[1211] Output: Real-time operation data.

[1212] Specific operation: The device uses motion capture sensors and temperature sensors to record and transmit user movements and environmental data in real time.

[1213] Step 7: Feedback analysis and provision

[1214] The server analyzes the collected operational data in real time and provides feedback to the user.

[1215] Input: Real-time operation data.

[1216] Data processing: Analyzes operational data and generates optimal feedback.

[1217] Output: Feedback information to the user (e.g., alerts, instructions).

[1218] Specific operation: The server analyzes operational data in real time, generates feedback such as "heating temperature is not appropriate" or "timing of striking is off," and sends it to the terminal.

[1219] Step 8: Feedback and Model Updates

[1220] The server stores all collected data and performs detailed analysis. Based on the results of this analysis, the generated AI model is updated and used for the next training session.

[1221] Input: Collected data and feedback information.

[1222] Data processing: Analyze feedback information and adjust the parameters of the generated AI model.

[1223] Output: Updated generative AI model.

[1224] Specific operation: The server analyzes the feedback information in detail and uses it as training data for the model, continuously improving the generated AI model.

[1225] In this way, the coordinated operation of each processing step enables the efficient and accurate transmission of traditional techniques.

[1226] (Application Example 1)

[1227] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1228] The transmission of traditional techniques requires advanced skills and experience, making it difficult to learn and accurately reproduce them efficiently. Furthermore, the presence of experienced instructors is essential for inexperienced technicians to acquire accurate procedures and techniques; a lack of such instructors can lead to a decline in the accuracy of technique transfer. Additionally, the lack of mechanisms for real-time work improvement and feedback makes it difficult for technicians to self-correct. These challenges make the efficient and accurate transmission of traditional techniques difficult.

[1229] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1230] In this invention, the server includes means for collecting information on traditional techniques, means for analyzing the collected information and training a generated AI model, user interface means for providing technical details to the user, sensor device means for collecting user motion data in real time, means for analyzing the collected motion information and providing appropriate feedback to the user, terminal means for allowing real-time feedback based on the motion data, and means for displaying work procedures and video data to the terminal. This makes it possible for even inexperienced technicians to efficiently learn traditional techniques and receive real-time feedback.

[1231] "Traditional techniques" refer to the artisan skills and manufacturing methods that have been passed down for generations in a region or specific cultural area.

[1232] An "information base" is a database that systematically organizes and stores information and data about traditional techniques that have been collected.

[1233] A "generative AI model" is an artificial intelligence model that analyzes data collected using machine learning techniques and is trained to perform a specific task.

[1234] "User interface means" refers to interface devices and software that allow users to intuitively operate a system and view information.

[1235] "Sensor device means" refers to a sensor device used to collect user actions and environmental data in real time.

[1236] "Terminal means" refers to hardware that is a device for user operation and has the function of displaying specific feedback or information.

[1237] "Feedback" refers to information that a system uses to analyze user actions and operations, providing suggestions for improvement and guidance for future actions.

[1238] Overall System Overview

[1239] This system is designed to efficiently and accurately pass on traditional techniques. The system consists of the following main components: a server, terminals, users, and various sensor devices.

[1240] Program processing

[1241] The server plays a primary role in collecting and analyzing information about traditional techniques. This includes ingesting data from books, videos, interviews, and instruction manuals. The collected information is stored in an information base. Next, a generative AI model is trained using this data. This model learns each step of the traditional technique and identifies key points in the process.

[1242] The terminal has an interface that provides technical details to the user. Specifically, smartphones, smart glasses, and head-mounted displays are used, through which the user can view work procedures and video data. Furthermore, sensor devices are used to collect user movement data in real time. This movement data is sent to a server for analysis. Based on the analysis results, the system provides appropriate feedback to the user.

[1243] Hardware and software to be used

[1244] Hardware: Smartphones, smart glasses, head-mounted displays, and various sensor devices (motion capture, temperature sensors, etc.).

[1245] Software: Python-based video analysis libraries (e.g., OpenCV), HTTP requests for data transmission (e.g., the requests library), and TensorFlow / Keras for running AI models.

[1246] The server performs preprocessing such as frame segmentation and tokenization to train the generative AI model. The trained model analyzes video and text data and learns each step of the technology.

[1247] As users utilize the device, they receive real-time feedback, and if certain actions or operations are not performed correctly, they are immediately instructed to correct them. This allows even inexperienced technicians to efficiently learn accurate techniques.

[1248] Examples of specific cases and prompt statements

[1249] For example, imagine a welding operation in a factory where a user wears smart glasses. The smart glasses' camera and sensors collect motion data, monitoring the appropriate welding temperature and timing in real time. Based on the analysis results, feedback such as the following is displayed:

[1250] "Start welding. Read the current temperature and work at the appropriate temperature."

[1251] "The temperature is too high. Cooling is required."

[1252] "Your movements are correct. Please maintain this pace."

[1253] As described above, this system makes it possible to efficiently and accurately pass on traditional techniques.

[1254] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1255] Step 1:

[1256] The server collects information about traditional techniques. It uses data such as books, videos, interviews, and instruction manuals as input. This data is collected by the server and stored in an information base. The server then performs preprocessing on this information, such as frame splitting and tokenization of text data.

[1257] Step 2:

[1258] The server trains a generative AI model based on the collected data. Preprocessed video and text data are used as input. The server uses this data to perform machine learning on the AI ​​model, learning specific technical steps and work procedures. The output is the trained AI model.

[1259] Step 3:

[1260] The user views technical details using a device. Technical information generated from a trained AI model is used as input. The device displays work procedures and video data to the user via a smartphone, smart glasses, or head-mounted display. The output is a display of technical details in a format easily understood by the user.

[1261] Step 4:

[1262] When a user begins working, a sensor device collects motion data in real time. The inputs used are user motion data and ambient environmental data. The sensor device collects detailed motion information using motion capture, temperature sensors, and other methods. The output is real-time motion data.

[1263] Step 5:

[1264] The server analyzes motion data collected in real time. It uses motion data transmitted from sensor devices as input. The server analyzes the motion data using a generative AI model to determine whether the work is being performed according to the technical requirements. The output is the evaluation result of the motion.

[1265] Step 6:

[1266] The terminal receives evaluation results from the server and provides appropriate feedback to the user. It uses evaluation results from the server as input. The terminal displays feedback to the user via voice and display. This may include specific advice such as, "Operation is good. Please maintain this pace," or "The temperature is too high. Cooling is required." The output is real-time feedback to the user.

[1267] The above processing steps enable the efficient and accurate transmission of traditional techniques.

[1268] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1269] The system of the present invention provides a mechanism for efficiently passing on traditional techniques, and in particular, by combining it with an emotion engine, it enables feedback that takes into account the user's emotional state. The specific form of implementation is described below.

[1270] Data collection methods

[1271] The server collects a wide range of information on traditional techniques, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The collected information is organized and stored in a database, allowing for centralized management of detailed steps, background information, and details about the materials and tools used.

[1272] As a concrete example, considering the techniques of making Japanese swords, the server would store historical documents about swordsmiths, production videos, interviews with craftsmen, and detailed forging procedures in its database.

[1273] Forms of data analysis and model generation

[1274] The server preprocesses the collected information and trains a generative AI model. Video data is segmented into frames, and text data is tokenized. The preprocessed data is used to train the generative AI model to learn each step of the technology and key points. The trained model's accuracy is evaluated, and parameters are adjusted as needed.

[1275] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught things like the heating temperature of the material and the timing of the forging process.

[1276] User interface configuration

[1277] The device provides an interface that allows users to easily access technical details. This includes viewing the technical history, watching instructional videos and images, and receiving real-time feedback. Users can operate it intuitively and quickly access the information they need.

[1278] As a concrete example, a new swordsmith uses a device to watch videos of the forging process and confirm the specific steps.

[1279] Real-time guide format

[1280] The device collects user motion data in real time using sensor devices. Data acquired through motion capture and temperature sensors is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[1281] For example, if a blacksmith is actually working and the temperature is not appropriate or the timing of the strike is wrong, an alert will be displayed on the terminal.

[1282] Forms of emotional engines

[1283] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is feeling fatigued or stressed.

[1284] For example, if fatigue is detected in a user's facial expressions during training, feedback will be displayed on the device suggesting a break or recommending an adjustment to the work pace based on that status.

[1285] Forms of feedback and corrections

[1286] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[1287] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[1288] Summary

[1289] This system allows traditional techniques, even if they temporarily decline, to be revived when new practitioners emerge. By ensuring accurate transmission of skills through collaboration between servers, terminals, and users, the system enables the preservation and development of culture. The introduction of an emotional engine provides optimal feedback that takes the user's state into account, improving the efficiency of skill acquisition.

[1290] The following describes the processing flow.

[1291] Step 1:

[1292] The server collects information on traditional techniques. This involves gathering data from a wide range of sources, including historical documents, specialized books, videos, interviews with artisans, and instruction manuals, and storing it in a database. This allows for centralized management of information about the detailed steps of the techniques, the materials used, and the tools.

[1293] Step 2:

[1294] The server preprocesses the collected information. Video data is divided into frames, and text data is tokenized. Metadata is also extracted, and all information is stored in a database in a standardized format.

[1295] Step 3:

[1296] The server trains a generative AI model using pre-processed data. Model training involves learning each step and key point of traditional techniques. After training, the model's accuracy is evaluated, and parameters are adjusted and retrained as needed.

[1297] Step 4:

[1298] The device provides users with an interface to access technical information. This includes features such as viewing images and videos of procedures and receiving real-time feedback. Users can operate it intuitively and quickly obtain the information they need.

[1299] Step 5:

[1300] Users access detailed technical information using their devices. They review videos and images provided on their devices to learn technical procedures and key points. For example, they might watch a video about the Japanese sword forging process to understand the specific steps involved.

[1301] Step 6:

[1302] The device utilizes sensor devices to collect user motion data in real time. Data collected through motion capture and temperature sensors is temporarily stored on the device.

[1303] Step 7:

[1304] The server analyzes the action data sent from the terminal in real time. This allows it to determine whether the user's actions are correct and generate necessary corrections and advice.

[1305] Step 8:

[1306] The terminal provides the user with feedback received from the server. For example, it displays an alert if the forging timing is off or the temperature is not appropriate. The user then corrects the operation based on this information.

[1307] Step 9:

[1308] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. If the user is feeling fatigued or stressed, it provides appropriate feedback.

[1309] Step 10:

[1310] The server saves all session data for later detailed analysis. Based on the collected behavioral and emotional data, the generative AI model is updated and the feedback algorithm is improved. In this way, the accuracy of traditional technology and the user experience are enhanced.

[1311] Through this series of processing steps, a system is realized that efficiently transmits traditional techniques and provides appropriate support to users. Furthermore, the introduction of an emotion engine enables personalized responses that take into account the user's state.

[1312] (Example 2)

[1313] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1314] Traditional techniques are difficult to pass on due to their uniqueness and complexity. Furthermore, if proper feedback is not provided during the skill transfer process, the reproducibility and precision of the techniques may decline. Additionally, the emotional state of those learning the skills is often overlooked, making effective learning difficult. Therefore, there is a need to develop a system that evaluates and provides feedback on both operational and emotional information in real time during the skill transfer process.

[1315] The identification processing 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 information relating to traditional techniques, means for preprocessing the collected information and training a generation AI model, interface means for providing technical details to the user, sensor device means for collecting user movements in real time, means for analyzing the collected movement information and the user's emotional state and providing feedback to the user, and emotion engine means for analyzing the collected user's facial expressions and voice data. This enables appropriate feedback that takes both movement information and emotional information into consideration, realizing effective skill transfer and improved learning efficiency.

[1316] "Traditional techniques" refer to skills and techniques that have been passed down through generations in a particular region or culture with a long history.

[1317] "Means of collecting information" refers to methods and technologies for obtaining specific data and knowledge from the internet, partner organizations, etc., and storing them in a database.

[1318] "Preprocessing" refers to the process of performing operations such as frame splitting, tokenization, and noise reduction to make the collected data easier to analyze.

[1319] A "generative AI model" refers to an artificial intelligence model that learns each step and key point of a specific technology or skill based on collected data, and then provides the results to the user.

[1320] "Interface means" refers to display and input devices, and their designs, that allow users to intuitively operate a system and access information.

[1321] "Sensor device means" refers to devices and methods for collecting user movement data in real time.

[1322] "Motion information" refers to data about the user's movements collected by sensor devices.

[1323] "Means of providing feedback" refers to methods and technologies for providing users with appropriate advice and warnings based on collected and analyzed data.

[1324] "Emotional engine means" refers to technologies and devices that analyze a user's facial expressions and voice data to identify their emotional state.

[1325] "Facial expression data" refers to data used to capture the user's facial expressions.

[1326] "Voice data" refers to data used to capture the characteristics of the user's voice.

[1327] This invention is a system aimed at the efficient transmission of traditional techniques, and provides technical feedback to the user through multiple steps.

[1328] In particular, this invention is centered on a server, a terminal, and a user. Each of these entities works together to provide the user with detailed information on traditional techniques and to offer real-time instruction and emotionally responsive feedback.

[1329] Data collection

[1330] The server collects a wide range of information on traditional techniques. Data sources include historical documents, specialized books, videos, interviews with artisans, and procedural manuals. The collected information is then organized and stored in a database.

[1331] As a specific example, regarding the techniques of making Japanese swords, the server collects historical documents on Japanese swordsmiths, videos of the manufacturing process, interviews with craftsmen, and detailed forging procedures, and stores them in a database.

[1332] Data preprocessing and model training

[1333] The server preprocesses the collected information and trains a generative AI model. Video data is split frame by frame, and text data is tokenized. The preprocessed data is used to train the generative AI model, teaching it each step of the technology and key points. The trained model is then evaluated for accuracy, and parameters are adjusted as needed.

[1334] As a concrete example, data from the forging process of Japanese swords is analyzed, and the model is taught the heating temperature of the iron and the timing of the hammering.

[1335] User Interface

[1336] The device provides an interface that allows users to easily check technical details. This interface allows users to view the technical history, watch procedural videos and images, and receive real-time feedback. Users can operate it intuitively and quickly access the information they need.

[1337] As a concrete example, a new swordsmith can use a device to watch videos of the forging process and check details of the specific steps and materials and tools used.

[1338] Real-time guide

[1339] The device uses sensor devices to collect user movement data in real time. This data is temporarily stored on the device and then sent to the server. The server analyzes this data in real time and generates appropriate feedback.

[1340] For example, when a blacksmith is working, an alert may appear on the terminal if the temperature is not appropriate or if the timing of the strike is incorrect.

[1341] Emotion analysis

[1342] The device is equipped with an emotion engine to recognize the user's emotions. The emotion engine acquires and analyzes the user's facial expression and voice data. This allows it to provide appropriate feedback if the user is experiencing fatigue or stress.

[1343] For example, if fatigue is detected from a user's facial expressions during training, the device will display suggestions for taking a break or adjusting the work pace according to that status.

[1344] Feedback and Model Updates

[1345] The server stores data collected in real time and performs detailed analysis. Based on the collected behavioral and emotional data, it updates the generative AI model and feedback algorithm. This continuously improves the accuracy of traditional techniques and the user experience.

[1346] As a concrete example, video footage of a blacksmith at work and their emotional data are uploaded to a server, and based on the analysis results, feedback is provided on areas for improvement in the next session.

[1347] Example of a prompt

[1348] "Collect historical documents on the process of making Japanese swords and save them in a database."

[1349] "Train an AI model based on the collected manufacturing process data and teach it Japanese sword forging techniques."

[1350] "Design an interface that allows new swordsmiths to watch videos of the forging process."

[1351] "Develop a system that provides real-time feedback on things like inappropriate temperature during operation and correct timing for hammering."

[1352] "Analyze the user's facial expression data and provide feedback suggesting a break if fatigue is detected."

[1353] This system will enable the efficient transmission of traditional techniques and improve user learning effectiveness. Furthermore, it is expected to provide optimal support throughout the skill acquisition process through real-time instruction and emotion-responsive feedback.

[1354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1355] Step 1: Data Collection

[1356] The server collects information about traditional techniques. Specifically, it obtains data such as historical documents, specialized books, videos, interviews with artisans, and procedural manuals from the internet and partner organizations, and stores them in a database.

[1357] Input: Information on traditional techniques collected from the internet and partner organizations.

[1358] Output: Technical information stored in an organized database.

[1359] Specific operation: The server automatically collects data from various resources on the internet using scraping tools and APIs. It then converts the collected data into an appropriate format and stores it in a database.

[1360] Step 2: Data Preprocessing

[1361] The server preprocesses the collected data. It performs noise reduction and normalization, splits video data into frames, and tokenizes text data.

[1362] Input: Unprocessed technical information from the database.

[1363] Output: Preprocessed dataset.

[1364] Specific operation: The server uses a text cleaning tool to remove noise and a video processing program to split the video frame by frame.

[1365] Step 3: Model Training

[1366] The server trains a generative AI model using preprocessed data. It then feeds data into the AI ​​model to teach it key steps and technical points, and evaluates its performance.

[1367] Input: Preprocessed dataset.

[1368] Output: A trained generative AI model.

[1369] Specific operation: The server uses machine learning libraries (e.g., TensorFlow and PyTorch) to input data into an AI model and repeatedly trains it. It periodically evaluates the model's accuracy and adjusts hyperparameters as needed.

[1370] Step 4: Provide User Interface

[1371] The device provides an interface for users to view technical details. Users can view history, watch instructional videos and images, and receive real-time feedback.

[1372] Input: User instructions.

[1373] Output: Technical details displayed on the interface.

[1374] Specific operation: The terminal provides a graphical user interface (GUI) to allow users to operate it intuitively, and retrieves and displays data from the server in response to user requests.

[1375] Step 5: Real-time guide

[1376] The terminal uses sensor devices to collect user movement data in real time. This movement data is temporarily stored and then sent to the server.

[1377] Input: Motion data from a sensor device.

[1378] Output: Data sent to the server and real-time feedback.

[1379] Specific operation: The terminal acquires data in real time from devices such as motion capture devices and temperature sensors, and sends the necessary information to the server. The server analyzes this data and sends feedback to the terminal.

[1380] Step 6: Emotion Analysis

[1381] The device uses an emotion engine to analyze the user's facial expressions and voice data. If the user is experiencing fatigue or stress, it provides appropriate feedback.

[1382] Input: User facial expression data and voice data.

[1383] Output: Feedback based on emotional state.

[1384] Specific operation: The device uses an emotion recognition algorithm to analyze data in real time and generates feedback based on the results, such as suggesting breaks or adjusting the work pace.

[1385] Step 7: Feedback and Model Updates

[1386] The server stores and analyzes collected behavioral and emotional data. Based on the results, it updates the generative AI model and feedback algorithm.

[1387] Input: Behavioral and emotional data collected in real time.

[1388] Output: Updated generative AI model and feedback algorithm.

[1389] Specific operation: The server periodically analyzes the collected data and continues to learn by adding new information to the AI ​​model. It also fine-tunes the algorithm to improve the accuracy of the feedback.

[1390] These steps ensure that traditional techniques are efficiently passed down and that users can effectively acquire the skills.

[1391] (Application Example 2)

[1392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1393] The difficulty in passing on traditional techniques stems from factors such as the lack of sufficient sharing of detailed technical steps and information, and the inability to accurately reflect the motivation and physical condition of learners. Furthermore, there is a need for efficient methods of learning while maintaining the precision of traditional techniques. In addition, modern manufacturing environments require real-time feedback on the emotional state and accuracy of movements of workers and robots to improve work efficiency.

[1394] The specific processing performed by the specific 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 information relating to traditional technology, means for analyzing the collected information and training a generated AI model, interface means for providing technical details to the user, sensor device means for collecting user actions in real time, means for analyzing the collected action information and providing feedback to the user, emotion engine means for recognizing the user's emotional state, and means for adjusting the feedback based on the recognized emotional state. As a result, not only is detailed step and background information of traditional technology centrally managed and provided, but real-time feedback is also possible according to the user's emotional state and the accuracy of their actions.

[1395] "Traditional techniques" refer to methods and skills that have been passed down through history and play an important role in specific cultures or professions.

[1396] "Means of collecting information" refers to systems and technologies for obtaining relevant data from various sources.

[1397] "Methods for training generative AI models" refer to the process of training an AI model based on collected information to improve its accuracy and performance.

[1398] An "interface means" refers to a method or device that allows a user to access a system and utilize its information and functions.

[1399] A "sensor device means" is a device for collecting user actions and environmental information in real time.

[1400] "Means of providing feedback" refer to systems or functions that provide appropriate responses or instructions to user actions or states.

[1401] An "emotional engine" is a technology that recognizes a user's emotional state and generates countermeasures based on that information.

[1402] "Means of adjusting feedback" refer to systems or technologies that appropriately modify the content of feedback provided according to the user's emotional state and behavior.

[1403] "Means of maintaining a database" refers to a system for centrally managing and storing collected information.

[1404] A "feedback analysis method" is an analytical technique that uses acquired data to improve the efficiency and accuracy of feedback.

[1405] This invention is a system for efficiently passing on traditional techniques. In particular, it uses an emotion engine to provide feedback that takes into account the user's emotional state, thereby supporting the acquisition of skills. The specific form of the system is described below.

[1406] System Configuration

[1407] server

[1408] The server collects and stores extensive information on traditional techniques in a database. This information is primarily gathered from historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server analyzes this information and trains a generative AI model. The AI ​​model learns the detailed steps and key points of the technique, and its accuracy is evaluated.

[1409] terminal

[1410] The device provides an interface that allows users to easily check the technical details. Users can use the device to view the technical history, watch instructional videos and images, and receive real-time feedback. The device also incorporates sensor devices to collect user activity in real time. Furthermore, the device is equipped with an emotion engine that analyzes the user's emotional state based on facial and voice data.

[1411] User

[1412] The users are individuals who wish to acquire traditional or specialized skills, such as swordsmiths or automobile manufacturing line workers. Using the device, users can receive real-time feedback on their actions and instructions based on their emotional state.

[1413] Processing flow

[1414] The server uses the collected information to train a generative AI model and improve its accuracy. The device collects the user's actions and emotional state in real time and sends it to the server. The server analyzes this data and generates appropriate feedback for the user. This feedback is adjusted according to the accuracy of the user's actions and emotional state.

[1415] Hardware and software

[1416] server:

[1417] High-performance servers (e.g., AWS EC2 instances) are used to collect and analyze information.

[1418] Deep learning frameworks (e.g., TensorFlow, PyTorch) are used to train generative AI models.

[1419] Terminal:

[1420] To collect motion data, use motion capture devices (e.g., Microsoft Kinect) or temperature sensors.

[1421] The emotion engine uses an emotion recognition library (e.g., OpenFace, Affectiva).

[1422] The user interface will utilize smart glasses or head-mounted displays (e.g., Google Glass, Microsoft HoloLens).

[1423] Specific example

[1424] For example, in a robotic assistant application on a factory's automobile manufacturing line, workers wear smart glasses while assembling products. Cameras capture the worker's movements and facial expressions in real time, analyzing the accuracy of their work and their emotional state. If fatigue or stress is detected, feedback such as "You're doing great, we recommend you take a 5-minute break" is displayed on the smart glasses. Also, if there is a problem with the accuracy of the work, an alert will be issued saying, "The screw is not tightened enough, please check again."

[1425] Example of a prompt:

[1426] "Explain how to assess the emotions and behavior of production line workers in real time and provide appropriate feedback. This includes, in particular, advice on taking breaks when fatigue or stress is detected, and specific feedback based on work progress."

[1427] The above describes specific embodiments for carrying out this invention.

[1428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1429] Step 1:

[1430] The server collects information on traditional techniques. Inputs include historical documents, specialized books, videos, interviews with artisans, and instruction manuals. The server stores this information in a database and manages it centrally, preparing it to provide detailed steps and background information on the techniques. The output is an organized database.

[1431] Step 2:

[1432] The server analyzes the collected information and trains a generative AI model. A stored database is used as input. For data processing, video data is segmented into frames, and text data is tokenized. This pre-processed data is then used to train the AI ​​model. The output is the trained generative AI model.

[1433] Step 3:

[1434] The terminal functions as an interface that provides technical details to the user. Input includes user actions such as operating the terminal and requesting necessary information. Data processing involves displaying technical information retrieved from the server to the user in an appropriate format. Output consists of technical information and step-by-step details that the user can review.

[1435] Step 4:

[1436] The user uses the terminal to check technical details and perform actual tasks. During this time, the sensor device built into the terminal collects the user's movements in real time. The input includes user movement data. The output is a stream of movement data.

[1437] Step 5:

[1438] The terminal transmits collected motion data to the server. Real-time motion data acquired from sensors is used as input. This data is transmitted to the server as data transmission. The output is motion data that the server can analyze.

[1439] Step 6:

[1440] The server analyzes the transmitted motion data in real time. A stream of motion data is used as input. The data is used to evaluate the accuracy and appropriateness of the motion. Then, feedback is generated based on the analysis results. The output is feedback information.

[1441] Step 7:

[1442] The device displays feedback sent from the server to the user. Feedback information is included as input. The data display shows the feedback in an appropriate format for smart glasses or a head-mounted display. The output is the feedback provided to the user visually and audibly.

[1443] Step 8:

[1444] The emotion engine built into the device recognizes the user's emotional state. It acquires user facial expression data and voice data as input. Data analysis evaluates the emotional state and sends this information to the server. The output is emotional state data.

[1445] Step 9:

[1446] The server analyzes emotional state data and adjusts the feedback accordingly. Emotional state data and the results of behavioral data analysis are used as input. Data adjustment involves customizing the feedback according to the user's state. The output is the adjusted feedback information.

[1447] Step 10:

[1448] The device then displays the adjusted feedback to the user again. The adjusted feedback information is included as input. The data display provides the feedback in a user-friendly format. The output is the final feedback provided to the user.

[1449] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1450] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1451] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1452] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1453] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1454] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1455] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1456] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1457] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1458] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1459] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1460] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1461] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1462] 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.

[1463] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1464] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1465] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1466] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1467] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1468] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1469] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1470] The following is further disclosed regarding the embodiments described above.

[1471] (Claim 1)

[1472] Means of collecting information on traditional techniques,

[1473] A means for analyzing the collected information and training a generated AI model,

[1474] An interface means for providing technical details to the user,

[1475] A sensor device means for collecting user movements in real time,

[1476] A system including means for analyzing the aforementioned collected operational information and providing feedback to the user.

[1477] (Claim 2)

[1478] The system according to claim 1, further comprising means for maintaining a database containing collected technical information.

[1479] (Claim 3)

[1480] The system according to claim 1, further comprising a feedback analysis means for updating the generated AI model and improving the accuracy of technology transfer.

[1481] "Example 1"

[1482] (Claim 1)

[1483] Means of collecting information on traditional techniques,

[1484] means for preprocessing the collected information,

[1485] A means for training a generated AI model based on the aforementioned preprocessed information,

[1486] A means for evaluating the accuracy of a trained generative AI model and optimizing its parameters,

[1487] An interface means for providing technical details to the user,

[1488] A sensor device means for collecting user movements in real time,

[1489] A system including means for analyzing the aforementioned collected operational information and providing feedback to the user.

[1490] (Claim 2)

[1491] The system according to claim 1, further comprising means for maintaining a database containing collected technical information.

[1492] (Claim 3)

[1493] The system according to claim 1, further comprising a feedback analysis means for updating the generated AI model and improving the accuracy of technology transfer.

[1494] "Application Example 1"

[1495] (Claim 1)

[1496] Means of collecting information on traditional techniques,

[1497] A means for analyzing the collected information and training a generated AI model,

[1498] A user interface means for providing technical details to the user,

[1499] A sensor device means for collecting user motion data in real time,

[1500] A means for analyzing the collected operational information and providing appropriate feedback to the user,

[1501] A terminal means that allows real-time feedback based on operational data,

[1502] A means for displaying work procedures and video data on the aforementioned terminal,

[1503] A system that includes this.

[1504] (Claim 2)

[1505] The system according to claim 1, further comprising means for maintaining an information base containing collected technical information.

[1506] (Claim 3)

[1507] The system according to claim 1, further comprising evaluation and analysis means for updating the generated AI model and improving the accuracy of technology transfer.

[1508] "Example 2 of combining an emotion engine"

[1509] (Claim 1)

[1510] Means of collecting information on traditional techniques,

[1511] A means for preprocessing the collected information and training a generative AI model,

[1512] An interface means for providing technical details to the user,

[1513] A sensor device means for collecting user movements in real time,

[1514] A means for analyzing the collected behavioral information and the user's emotional state and providing feedback to the user,

[1515] The emotion engine means for analyzing the collected user facial expressions and voice data,

[1516] A system that includes this.

[1517] (Claim 2)

[1518] The system according to claim 1, further comprising means for maintaining a database containing collected technical information.

[1519] (Claim 3)

[1520] The system according to claim 1, further comprising a feedback analysis means for updating the generated AI model and improving the accuracy of technology transfer.

[1521] "Application example 2 when combining with an emotional engine"

[1522] (Claim 1)

[1523] Means of collecting information on traditional techniques,

[1524] A means for analyzing the collected information and training a generated AI model,

[1525] An interface means for providing technical details to the user,

[1526] A sensor device means for collecting user movements in real time,

[1527] A means for analyzing the collected operational information and providing feedback to the user,

[1528] An emotion engine means for recognizing the user's emotional state,

[1529] A means for adjusting feedback based on the recognized emotional state,

[1530] A system that includes this.

[1531] (Claim 2)

[1532] The system according to claim 1, further comprising means for maintaining a database containing collected technical information.

[1533] (Claim 3)

[1534] The system according to claim 1, further comprising a feedback analysis means for updating the generated AI model and improving the accuracy of technology transfer. [Explanation of Symbols]

[1535] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting information on traditional techniques, A means for analyzing the collected information and training a generated AI model, An interface means for providing technical details to the user, A sensor device means for collecting user movements in real time, A system including means for analyzing the aforementioned collected operational information and providing feedback to the user.

2. The system according to claim 1, further comprising means for maintaining a database containing collected technical information.

3. The system according to claim 1, further comprising a feedback analysis means for updating the generated AI model and improving the accuracy of technology transfer.

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