system

Abstract

Provide a system. An input means for inputting technical details; a generating means for automatically generating advanced training materials based on the generated technical content; VR / Metaverse construction methods for constructing virtual spaces, A means of communication for learners and instructors to communicate in real time; an assessment instrument to evaluate learner performance and generate feedback after the session; A system including:

Description

[Technical Field]

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

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

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

[0004] The purpose of this invention is to address the social challenges of a declining labor force and a lack of business successors. It also aims to provide a platform for technology transfer to meet the needs of developing countries for technical assistance and overseas learners of Japanese technology and culture. Conventional technology transfer methods require instructors and learners to be physically in the same location, which is time-consuming and costly, and also poses the problem of inconsistent quality of instruction. New and innovative technologies are needed to solve these issues. [Means for solving the problem]

[0005] The present invention provides a system that includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, and an evaluation means for evaluating learners' performance and generating feedback after the session ends. This eliminates physical constraints and enables efficient and high-quality technical transfer. Furthermore, the inclusion of a storage means and model placement means provides an environment in which learners can review the content of past sessions, enabling consistent learning support.

[0006] "Technical content" refers to information about the specific techniques, methods, and knowledge that the instructor wants to convey.

[0007] "Input means" refers to an interface or device that allows a user to input technical content into the system.

[0008] "Generation means" refers to a program or algorithm that automatically generates advanced training materials based on input technical content.

[0009] "VR / Metaverse construction means" refers to systems and software for creating virtual reality or metaverse spaces based on technical content.

[0010] "Communication means" refers to networks and platforms that allow learners and instructors to communicate in real time.

[0011] "Assessment tool" refers to the system used to analyze learner performance throughout the session and generate assessment results and feedback.

[0012] "Storage means" refers to a data storage or database for storing input technical content and generated training materials.

[0013] "Model placement means" refers to software or algorithms for properly placing 3D models within a virtual space.

[0014] "System" refers to a set of hardware and software for realizing technology transfer, consisting of input means, generation means, VR / metaverse construction means, communication means, evaluation means, etc. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI and VR / Metaverse technology, it realizes efficient and realistic technology transfer.

[0037] User (instructor) operation

[0038] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[0039] Server Operation

[0040] The server receives and stores the technical details sent by the user. Based on the stored data, generative AI is used to automatically generate detailed training materials. For example, it generates instruction manuals, video explanations, and 3D models for the washi paper production process. Based on these materials, a VR / Metaverse space is designed to create a virtual space. In the designed virtual space, the tools and materials used in washi paper production are realistically placed as 3D models.

[0041] Device operation

[0042] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0043] Real-time technology transfer

[0044] Once in the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[0045] Session Closure and Evaluation

[0046] After the session ends, the server collects the learner's behavior log and evaluates their performance using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[0047] Specific examples

[0048] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[0049] In this way, the present invention is a system that incorporates advanced technology in order to improve the efficiency and quality of technical transfer.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[0053] Step 2:

[0054] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[0055] Step 3:

[0056] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[0057] Step 4:

[0058] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[0059] Step 5:

[0060] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[0061] Step 6:

[0062] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[0063] Step 7:

[0064] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[0065] Step 8:

[0066] Learners put on the VR device and join the virtual space using a link provided by the server.

[0067] Step 9:

[0068] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[0069] Step 10:

[0070] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[0071] Step 11:

[0072] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[0073] Step 12:

[0074] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[0075] Step 13:

[0076] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[0077] This step realizes a system that allows instructors and students to transfer skills efficiently and in a realistic manner.

[0078] Example 1

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

[0080] Accurate and efficient transmission of knowledge and skills is a challenge in modern technology transfer. Manual and artisanal techniques require real-time instruction and detailed feedback, but traditional methods make it difficult to achieve this appropriately. Communication between learners and instructors is also important, and an environment in which this can occur effectively is necessary. Furthermore, quantitative and objective feedback is required for skill evaluation, but traditional methods often rely on subjective evaluation. To solve these challenges, a new technology transfer system is needed.

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

[0082] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, and a virtual environment creation means for constructing a virtual space, thereby improving the accuracy and efficiency of technical transfer and enabling real-time technical instruction and objective performance evaluation.

[0083] "Input means" refers to the device or software that allows the user to input technical content into the system.

[0084] "Generation means" refers to devices or software that automatically generate advanced training materials based on input technical content.

[0085] "Virtual environment construction means" refers to devices and software for designing and constructing virtual spaces.

[0086] "Communication means" refers to devices and software that allow learners and instructors to communicate in real time.

[0087] "Evaluation tools" refer to devices or software that collect learners' behavioral logs after the session ends, evaluate their performance using generative AI models, and generate feedback.

[0088] "Storage means" refers to devices or software for storing technical content in a database or the like.

[0089] "Model placement means" refers to devices or software for placing 3D models in virtual space.

[0090] A "generative AI model" refers to an artificial intelligence model that automatically generates training materials and evaluations based on input data.

[0091] "Technical content" refers to the knowledge and skills that should be passed on, specifically the procedure manuals, tools to be used, details of materials, etc.

[0092] "Training materials" refers to teaching materials, manuals, videos, etc. that contain the information a learner needs to master a skill.

[0093] "Virtual space" refers to a simulated three-dimensional environment that a user can access through a virtual reality (VR) device.

[0094] "Behavior log" refers to data that records the operations and actions performed by a learner during a session.

[0095] "Feedback" refers to information, including advice and suggestions for improvement, given to a learner on their performance using assessment tools.

[0096] The "technology transfer system" refers to the entire system that integrates the above means and enables efficient and effective technology transfer.

[0097] The technology transfer platform of the present invention is a system that consists of users, a server, and terminals, and realizes efficient and realistic technology transfer by utilizing generative AI models and VR / Metaverse technology. Specific embodiments of this system are described in detail below.

[0098] User operations

[0099] First, the user logs in to the system using a device (e.g., a PC or smartphone). After logging in, the user accesses the dashboard and clicks the "Create a new session" button. In the form that appears, the user enters details of the technical content they want to teach (e.g., how to make washi paper). For example, they enter information such as the tools and materials used and the steps, and upload images and video files as needed. This operation allows the system to collect realistic technical content.

[0100] Server Operation

[0101] The server receives the technical details sent by the user and stores them in a database. Based on the stored data, a generative AI model (such as OpenAI's GPT-3 (registered trademark) or DALL-E) is used to automatically generate detailed training materials. Specifically, it generates instruction manuals, video explanations, 3D models, and other materials related to the washi paper production process. Based on these materials, a VR / Metaverse space is designed using game engines such as Unity or Unreal Engine as a means of building the virtual environment. In the virtual space, tools and materials used in washi paper production are realistically placed as 3D models.

[0102] Device operation

[0103] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners then put on a VR device (e.g., Oculus Rift or HTC Vive) and use the link to enter the virtual space.

[0104] Real-time technology transfer

[0105] Once inside the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[0106] Session Closure and Evaluation

[0107] After the session ends, the server collects the learner's behavior log and evaluates their performance using a generative AI model. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[0108] Specific examples

[0109] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[0110] Prompt Sentence Examples

[0111] "Please enter the detailed steps for making washi paper. Please include the tools and materials you will use, the specific steps, and images and videos if necessary."

[0112] The above is a detailed description of the embodiment of the present invention, and this system can improve the efficiency and quality of technology transfer.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] A user logs into the system using a terminal.

[0116] Input: Username, Password

[0117] Specific actions: Open a browser, access the system URL to display the login page, enter your username and password, and click the "Login" button.

[0118] Output: Dashboard screen after successful login

[0119] Step 2:

[0120] The user accesses the dashboard and clicks the "Create a new session" button.

[0121] Specific operation: After logging in, access the dashboard and click the "Create a new session" button.

[0122] Input: None

[0123] Output: Technical content input form

[0124] Step 3:

[0125] The user enters the technical details (e.g., how to make washi paper) into the displayed form and uploads the necessary information.

[0126] Specific operations: In the "Technical Details" field, users enter detailed instructions for making washi paper, add information about the tools and materials used, and upload images and video files to complement the explanation.

[0127] Input: Text information on technical content, image files, video files

[0128] Output: Technical data entered by the user

[0129] Step 4:

[0130] The server receives the technical content sent by the user and stores it in a database.

[0131] What happens: The server receives the HTTP POST request, parses the form data, and saves it in a database.

[0132] Input: Technical data entered by the user

[0133] Output: Technical details stored in the database

[0134] Step 5:

[0135] The server automatically generates detailed training materials using generative AI based on the stored data.

[0136] How it works: The server inputs the stored data on the washi paper production process into a generative AI model, which then generates instructions, video explanations, and 3D models. For example, algorithms such as OpenAI's GPT-3 and DALL-E are used.

[0137] Input: Technical details stored in the database

[0138] Output: Generated instructions, video instructions, 3D models

[0139] Step 6:

[0140] The server designs and constructs the VR / Metaverse space based on the generated materials.

[0141] Specific operation: The server uses a game engine such as Unity or Unreal Engine to create a virtual space and places a washi paper workshop as a 3D model.

[0142] Input: Generated instructions, video instructions, 3D models

[0143] Output: Designed and constructed VR / Metaverse space

[0144] Step 7:

[0145] The server generates and provides the learner with an access URL or QR code.

[0146] What happens: The server generates an access link for the learner and sends it via email. A QR code is also generated, which can be printed if desired.

[0147] Input: Access information for the designed and constructed VR / Metaverse space

[0148] Output: Generated URL, QR code

[0149] Step 8:

[0150] Learners access the system using a device and a provided URL or QR code.

[0151] Specific actions: Learners scan the QR code with their smartphone and tap the displayed link to access the system.

[0152] Input: QR code or URL

[0153] Output: System login screen

[0154] Step 9:

[0155] Learners log in by entering their credentials and are provided with a link to join the VR / Metaverse space.

[0156] Specific operation: The learner enters their username and password on the login page and clicks the "Login" button. If successful, a link to join the VR space will appear on the screen.

[0157] Input: Username, Password

[0158] Output: VR / Metaverse space participation link

[0159] Step 10:

[0160] Learners put on the VR device and use the link to enter the virtual space.

[0161] Specific operation: Learners put on a VR device such as Oculus Rift or HTC Vive, click on the link to enter the virtual space, and after logging in, move to the washi paper making workshop.

[0162] Input: VR / Metaverse space participation link

[0163] Output: A virtual washi paper workshop

[0164] Step 11:

[0165] The instructor will demonstrate the technique using a VR device.

[0166] Specific actions: The instructor operates tools in the VR space and demonstrates the steps for making washi paper. The learners observe the process in real time.

[0167] Input: None

[0168] Output: An environment where technology can be demonstrated in real time

[0169] Step 12:

[0170] Learners simulate skills in a virtual space and receive feedback from instructors.

[0171] Specific actions: Learners process raw materials for washi paper in a VR space and make paper using simulated tools. The instructor observes the process and provides appropriate feedback.

[0172] Input: None

[0173] Output: Simulation and feedback of technology

[0174] Step 13:

[0175] After the session ends, the server collects the learner's behavioral log and evaluates their performance using generative AI.

[0176] What it does: The server analyzes the data recorded during the session and performs an evaluation using a generative AI model (e.g., Azure® Cognitive Services or Google® Cloud AI).

[0177] Input: Learner behavior log

[0178] Output: Evaluation results and feedback

[0179] Step 14:

[0180] The assessment results and feedback are generated as a report and provided to the instructor.

[0181] What it does: The server generates a detailed report of the assessment results and feedback and uploads it to the instructor's dashboard, where the instructor can review and add comments.

[0182] Input: Evaluation results, feedback

[0183] Output: Report

[0184] Step 15:

[0185] Once the instructor's feedback is complete, the final report will be sent to the learner.

[0186] Specific operation: The instructor sends feedback from the dashboard, the server sends a notification email to the learner, and the learner logs in and checks the feedback.

[0187] Input: Report with feedback

[0188] Output: Feedback communicated to the learner

[0189] (Application example 1)

[0190] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] Traditional technology transfer methods are inefficient because they require a lot of time and effort to convey technical information and for learners to acquire practical skills, and there is a risk of mistakes. In particular, in fields requiring advanced skills, such as training to operate factory robots, training in an actual work environment poses safety risks and requires a lot of time and money. For this reason, an effective method for simultaneously improving the efficiency of technology transfer and quality has been sought.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0193] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, a means for constructing a 3D simulation model based on the generated training materials, a log collection means for collecting operation data in real time, and an evaluation generation means for automatically providing evaluations and feedback based on the collected operation data. This allows learners to acquire practical skills safely and efficiently, and instructors to provide accurate evaluations and feedback.

[0194] The "input means for inputting technical content" is an interface that allows instructors to input technical information and data into the system.

[0195] The "means for automatically generating advanced training materials based on generated technical content" is a mechanism for automatically generating detailed training materials and guides based on input technical content.

[0196] "VR / Metaverse construction method for constructing virtual spaces" is a technology that creates a training environment in virtual reality or the metaverse based on generated training materials.

[0197] "A means of communication for real-time communication between learners and instructors" refers to a communication technology that enables learners and instructors to communicate in real-time within a virtual space.

[0198] "Evaluation means for evaluating learner performance and generating feedback after the session" is a function that analyzes learner behavior and results after the training session and provides evaluation and feedback using generative AI.

[0199] The "means for constructing a 3D simulation model based on the generated training materials" refers to a technology for creating a three-dimensional simulation model based on the training materials.

[0200] The "log collection means for collecting operation data in real time" is a system that collects data on the operations and actions performed by learners in the virtual space in real time.

[0201] The "evaluation generation means that automatically provides evaluation and feedback based on collected operation data" refers to an algorithm and system that analyzes data obtained by the log collection means and automatically generates an evaluation approach and specific feedback.

[0202] This invention is a system for efficiently and effectively transferring technical content, which combines generative AI models with VR / Metaverse technology. Specific implementation methods are described below.

[0203] Program Generation and Processing Description

[0204] Server Roles

[0205] The server has an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, and an evaluation means for evaluating the learner's performance and generating feedback after the session ends.

[0206] Furthermore, the server includes a log collection means for constructing a 3D simulation model based on the generated training materials, collecting learner operation data in real time, and an evaluation generation means for automatically providing evaluation and feedback based on the collected operation data.

[0207] Hardware used

[0208] VR device: Used by learners to learn practical skills in a virtual space (e.g., Oculus Quest 2).

[0209] Server: Uses cloud-based services as computing resources (e.g., AWS or Google Cloud).

[0210] User terminal: A device used by instructors and students to access the system (e.g., PC or smartphone).

[0211] Software used

[0212] Generative AI models: Use natural language processing models to automatically generate training materials from technical content (e.g., GPT-4 (registered trademark)).

[0213] 3D modeling software: Create 3D models to be used in virtual space (e.g. Blender, Unity)

[0214] Database: Stores technical content and student data (e.g., MySQL (registered trademark), NoSQL).

[0215] Communication protocol: Realizes real-time communication (e.g., WebSocket, REST API).

[0216] Detailed processing

[0217] The server receives and stores the technical content entered by the instructor, and then uses a generative AI model to automatically generate detailed training materials, including text instructions, video explanations, and 3D models. Based on these materials, a virtual space is designed to recreate a real-world work environment, such as a washi paper workshop.

[0218] In addition, learners' operation data is collected in real time and evaluation and feedback is automatically performed using a generative AI model, which makes the transfer of skills more efficient and enables high-quality training.

[0219] Specific examples

[0220] For example, in training on the operation of a welding robot, an instructor inputs detailed instructions for operating the robot, and the AI ​​generator creates training materials based on these instructions. Learners put on VR devices and enter a virtual welding environment, practicing operations while receiving real-time instruction. During the training, the learner's operation data is monitored in real time by a log collection means, and an evaluation generation means provides evaluations and feedback after the session ends.

[0221] Prompt Sentence Examples

[0222] "The operating procedure for the welding robot is to first secure the handle and release the safety device. Then set the speed and temperature on the operation panel and press the welding start button. Maintain a constant speed during welding, and when finished, reset the safety device and release the handle. Please create a detailed training manual and 3D model based on this."

[0223] In this way, the transfer of technical content can be carried out more efficiently and effectively.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] The user (instructor) logs in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. They enter details of the technical content (e.g., welding robot operating procedures) in the displayed form. Specifically, they enter information such as the tools and materials to be used and the procedures, and upload images and video files as needed. The entered data is sent to the server. The input data includes the technical content, attachments, and prompts. The output is a technical content dataset.

[0227] Step 2:

[0228] The server receives and stores the technical content dataset sent by the user. Based on the stored data, it uses a generative AI model to automatically generate detailed training materials. The generated training materials include instructions, video explanations, 3D models, etc. The input data is the technical content dataset, and the output is the generated training materials.

[0229] Step 3:

[0230] The server uses a VR / Metaverse construction tool to construct a virtual space based on the generated training materials. Specifically, it uses 3D modeling software to arrange the work environment, tools, materials, etc. in the virtual space. The virtual space is realistically reproduced as a learning environment. The input data are the training materials, and the output is the constructed virtual space.

[0231] Step 4:

[0232] The user (learner) accesses the system from a device using the provided URL or QR code and logs in by entering their credentials. Once the necessary authentication is completed, a link to join the designated virtual space is provided. The learner puts on the VR device and uses the link to enter the virtual space. The input data is the credentials, and the output is an access link to the virtual space.

[0233] Step 5:

[0234] Users (learners) actually learn skills in a virtual space. Instructors provide instruction while communicating in real time through VR devices. Learners practice procedures in a simulated environment and receive feedback and real-time advice from the instructor. Input data is operation data in the virtual space, and output is the learner's operation log.

[0235] Step 6:

[0236] After the session ends, the server collects the learner's operation data log. The log collection means uses the data collected in real time to pass it to the evaluation generation means. The generation AI analyzes the learner's performance and generates specific evaluations and feedback. The input data is the operation data log, and the output is an evaluation report and feedback.

[0237] Step 7:

[0238] The server generates a detailed report of the assessment results and feedback and provides it to the instructor. The instructor reviews this report and adds additional advice and comments to complete the final report. The completed report is notified to the learner and can be used for the next session or self-study. The input data are the assessment report, feedback, and instructor comments, and the output is the final learning report.

[0239] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0240] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and realistic technology transfer.

[0241] User (instructor) operation

[0242] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[0243] Server Operation

[0244] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[0245] Emotion Engine Operation

[0246] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback to be more encouraging. If an instructor is frustrated, it can suggest a break to ease their feelings.

[0247] Device operation

[0248] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0249] Real-time technology transfer

[0250] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[0251] Session Closure and Evaluation

[0252] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[0253] Specific examples

[0254] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production steps. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. The emotion engine would assess in real time whether the learner was confident or anxious about the operation, and automatically provide support accordingly. After the session, the learner's work would be evaluated and feedback would be provided, highlighting specific areas for improvement.

[0255] In this way, the present invention is a system that incorporates cutting-edge technology to improve the efficiency and quality of skill transfer. The addition of an emotion engine makes it possible to understand the psychological states of learners and instructors in real time and provide optimal instruction and learning support.

[0256] The processing flow will be explained below.

[0257] Step 1:

[0258] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[0259] Step 2:

[0260] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[0261] Step 3:

[0262] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[0263] Step 4:

[0264] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[0265] Step 5:

[0266] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[0267] Step 6:

[0268] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[0269] Step 7:

[0270] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[0271] Step 8:

[0272] Learners put on the VR device and join the virtual space using a link provided by the server.

[0273] Step 9:

[0274] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[0275] Step 10:

[0276] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[0277] Step 11:

[0278] The server activates an emotion engine to analyze the emotional states of the learner and instructor in real time. For example, if the learner is feeling anxious, the server will suggest to the instructor to encourage the learner.

[0279] Step 12:

[0280] The emotion engine also monitors the emotional state of the instructor and, for example, provides suggestions for short breaks if the instructor feels frustrated, thereby maintaining the quality of instruction.

[0281] Step 13:

[0282] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[0283] Step 14:

[0284] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[0285] Step 15:

[0286] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[0287] This step will realize a system that allows instructors and students to transfer skills efficiently and in a realistic manner. In particular, the introduction of an emotion engine will enable the real-time analysis of the emotional states of both parties and provide appropriate feedback and support.

[0288] Example 2

[0289] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Current technology transfer methods lack the mechanisms for providing efficient and immersive education. They also often lack the means to grasp the psychological state of learners and instructors in real time and provide appropriate feedback and support. As a result, technology transfer is inefficient, and there are challenges in improving learners' understanding and the quality of their learning experience.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0292] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a virtual reality construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an emotion analysis means for analyzing the emotional states of the learner and the instructor in real time and adjusting feedback, an evaluation means for evaluating the learner's performance after the session ends and generating feedback, and an access means for accessing the virtual space using a provided URL or QR code. This enables efficient and realistic skill transfer and makes it possible to provide an optimal learning environment for both the learner and the instructor through real-time emotion analysis and appropriate feedback.

[0293] The "input means for inputting technical details" is an interface that allows the user to input detailed information about the technology they wish to transfer to the system.

[0294] The "means for automatically generating advanced training materials based on generated technical content" refers to an algorithm and system for automatically generating training materials based on input technical content.

[0295] The "virtual reality construction means for constructing a virtual space" is a system for designing and constructing a virtual reality space based on input technical content and generated training materials.

[0296] "A means of communication for real-time communication between learners and instructors" is a system that allows learners and instructors to communicate two-way in real time using the Internet or other networks.

[0297] The "emotion analysis means for analyzing the emotional states of learners and instructors in real time and adjusting feedback" is a system that analyzes the voices and facial expressions of learners and instructors in real time and adaptively changes feedback based on their emotional states.

[0298] The "assessment means for evaluating the learner's performance and generating feedback after the session" is a system for analyzing the learner's behavioral log and training results, evaluating the learner's performance, and generating feedback.

[0299] "Means of access to the virtual space using the provided URL or QR code" refers to the terminal and interface that allows learners to access the virtual space using the URL or QR code provided by the server.

[0300] This invention is a technology transfer platform consisting of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and immersive technology transfer.

[0301] User operations

[0302] Users first log in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technical content they want to teach. For example, they enter information about how to make washi paper, such as the tools, materials, and steps used, and can upload images and video files as needed.

[0303] Server Operation

[0304] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instruction videos, 3D models, etc.) based on the input content. To achieve this, the server inputs prompts like the following into the generative AI:

[0305] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[0306] 1. Tools: aquarium, spoon, net, etc.

[0307] 2. Materials: Raw materials for Japanese paper, glue, etc.

[0308] 3. Steps:

[0309] a. Soaking the raw materials for washi paper in water

[0310] b. Add glue

[0311] c. Shape it with a net

[0312] d. Drying

[0313] 4. Attachments:

[0314] Images of tools and materials

[0315] Instructional Video

[0316] Based on the training materials generated by the generative AI, the server begins constructing the VR / Metaverse space. The required 3D models are selected from the library and placed in the virtual space. The designed virtual space is saved on a dedicated VR server. The server then generates a session URL or QR code and provides it to the user.

[0317] Emotion Engine Operation

[0318] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, the system will automatically adjust the feedback to send encouraging words. If an instructor is frustrated, the system will suggest taking a break to ease their feelings.

[0319] Device operation

[0320] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0321] Real-time technology transfer

[0322] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[0323] Session Closure and Evaluation

[0324] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the analysis results of the emotion engine, indicating the learner's level of understanding and areas for improvement.

[0325] As described above, the technology transfer platform of the present invention can revolutionize conventional technology transfer methods by utilizing generative AI, VR / metaverse, and emotion engines, and provide an efficient and immersive learning experience.

[0326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0327] Step 1:

[0328] A user logs in to the system.

[0329] Input: Username, Password

[0330] How it works: A user opens a web browser, accesses the system's login page, enters their username and password, and clicks the "Login" button.

[0331] Output: Login success or failure message, dashboard screen

[0332] Step 2:

[0333] A user creates a new session.

[0334] Input: Technical content, images, video files

[0335] How it works: Users click the "Create a new session" button on their dashboard, then fill out a form to describe their technical needs, uploading images and video files as needed.

[0336] Output: Form data with technical content entered

[0337] Step 3:

[0338] The server receives the technical content and stores it in a database.

[0339] Input: Technical form data entered by the user

[0340] How it works: The server receives user input data and stores it in a database using SQL queries.

[0341] Output: Technical details stored in the database

[0342] Step 4:

[0343] The server launches a generation AI to automatically generate training materials.

[0344] Input: Technical details stored in the database

[0345] Operation: The server uses prompts to instruct the generative AI model to generate technical training materials (procedures, instruction videos, 3D models, etc.). The generative AI model generates the training materials based on the input data.

[0346] Example prompt:

[0347] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[0348] 1. Tools: aquarium, spoon, net, etc.

[0349] 2. Materials: Raw materials for Japanese paper, glue, etc.

[0350] 3. Steps:

[0351] a. Soaking the raw materials for washi paper in water

[0352] b. Add glue

[0353] c. Shape it with a net

[0354] d. Drying

[0355] 4. Attachments:

[0356] Images of tools and materials

[0357] Instructional Video

[0358] Output: Generated training materials (instructions, instructional videos, 3D models, etc.)

[0359] Step 5:

[0360] The server creates the VR / Metaverse space.

[0361] Input: Generated training materials

[0362] How it works: The server selects the necessary 3D models from the library based on the training materials and designs the layout of the virtual space. The designed virtual space is saved on a dedicated VR server.

[0363] Output: Virtual space data, virtual space stored on a dedicated VR server

[0364] Step 6:

[0365] The server generates a session URL or QR code and provides it to the user.

[0366] Input: Virtual space data

[0367] How it works: The server generates a URL or QR code for accessing the virtual space and displays it on the user's dashboard.

[0368] Output: Access URL, QR code

[0369] Step 7:

[0370] Learners access the system and participate in the virtual space.

[0371] Input: URL or QR code, credentials (student name, password)

[0372] How it works: Learners access the system by entering the provided URL into their browser or by scanning the QR code with their camera. They enter their credentials and click the "Login" button. Once the necessary authentication is completed, they are provided with a link to join the virtual space. They put on their VR device and use the link to enter the virtual space.

[0373] Output: Authentication result, link to join the virtual space

[0374] Step 8:

[0375] Real-time technology transfer takes place in a virtual space.

[0376] Input: Link to join the virtual space, VR device

[0377] Operation: The instructor and the student communicate in real time through audio and video in the virtual space. The instructor demonstrates the technology, and the student operates it according to the instruction.

[0378] Output: Learner's operation results, progress of skill transfer session

[0379] Step 9:

[0380] The emotion engine analyzes the emotional state of both parties in real time.

[0381] Input: Audio data, video data

[0382] How it works: The emotion engine analyzes the voice and facial expression data of learners and instructors to assess their emotional state in real time, for example measuring stress levels and excitement levels using voice recognition technology and machine learning algorithms.

[0383] Output: Real-time emotion evaluation results

[0384] Step 10:

[0385] The server evaluates the learner's performance after the session and generates feedback.

[0386] Input: Learner's operation log, emotion evaluation results

[0387] How it works: After the session ends, the server collects the learner's behavior log and analyzes it using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review the report and enter additional advice or comments.

[0388] Output: Evaluation report, feedback

[0389] (Application example 2)

[0390] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0391] Conventional technology transfer systems have difficulty effectively transferring complex techniques and procedures, posing particular challenges for transferring skilled techniques on factory production lines. Furthermore, they lacked the ability to grasp the learner's emotional state and provide appropriate feedback, which could lead to reduced learning efficiency. Furthermore, relying solely on technology transfer meant there was no way to effectively transfer technology to factory robots with automated equipment.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0393] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, an emotion analysis means for analyzing the learner's emotional state and adjusting the feedback, and a robot control means for controlling the robot's operation based on the evaluation results. This makes it possible to effectively transfer complex skills to factory robots while providing optimal feedback according to the learner's emotional state.

[0394] "Technical content" refers to information such as the specific knowledge, skills, procedures, tools, and materials to be passed on.

[0395] "Input means" refers to an interface or device that allows a user to input technical content into the system.

[0396] "Generation means" refers to a function or system that automatically generates advanced training materials based on input technical content.

[0397] "VR / Metaverse construction means" refers to the technology and software used to construct virtual reality or metaverse spaces.

[0398] "Communication means" refers to the network infrastructure and devices that enable learners and instructors to communicate in real time.

[0399] "Evaluation tools" are functions or systems that evaluate learner performance after a session and generate feedback.

[0400] "Emotion analysis means" refers to a function or system that senses the emotional state of learners and instructors and provides feedback accordingly.

[0401] "Robot control means" refers to a function or system for controlling the operation of the robot based on the evaluation results.

[0402] This invention is a system for transferring technical content in an efficient and immersive way, and is realized by combining generative AI, VR / Metaverse technology, and an emotion engine. The specific operation of the system and the hardware and software used are described below.

[0403] User (instructor) operations:

[0404] First, the instructor logs into the technology transfer system. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technology they want to transfer (e.g., complex part assembly procedures). They enter information such as the tools and materials used and the procedures, and upload images and video files as needed.

[0405] Server behavior:

[0406] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generation AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[0407] Emotion Engine in action:

[0408] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback and send encouraging words. If an instructor is frustrated, it can suggest a break to ease their feelings.

[0409] Terminal operations:

[0410] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0411] Real-time technical transfer:

[0412] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the procedure to the learner. The learner can then simulate assembling parts in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[0413] Session Closure and Evaluation:

[0414] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[0415] Examples:

[0416] For example, when transferring complex parts assembly techniques in a factory, the instructor inputs the assembly steps. Based on this, generative AI creates training materials and constructs a factory line in a virtual space. The learner then joins the virtual space and experiences the assembly work under the guidance of the instructor. The emotion engine evaluates in real time whether the learner is confident or anxious about the operation, and automatically provides support accordingly. After the session, the learner's work is evaluated and feedback is provided with specific areas for improvement.

[0417] Example prompt sentence:

[0418] "Generate appropriate work instructions for the robot based on real-time work data obtained from the VR device. These instructions should include the work flow, detailed steps, and points to note, and also provide feedback based on the worker's emotional state."

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1:

[0421] The user logs in to the technology transfer system and clicks the "Create a new session" button. They enter the details of the technology (e.g., the assembly procedure for a part) and upload any necessary images or video files.

[0422] Input: Technical content, images, video files

[0423] Output: Uploaded technical content data

[0424] Step 2:

[0425] The server receives the technical content data sent by the user and stores it in a database, thereby permanently storing the entered technical content.

[0426] Input: Technical content data

[0427] Output: Saved technical content data (storage in database)

[0428] Step 3:

[0429] The server activates a generative AI that automatically generates advanced training materials (such as instruction manuals, explanatory videos, and 3D models) based on the stored technical content data.

[0430] Input: Saved technical content data

[0431] Output: Generated training materials (instructions, instructional videos, 3D models)

[0432] Step 4:

[0433] The server begins constructing the VR / Metaverse space based on the generated training materials, placing the 3D model in the virtual space, and saving the designed virtual space.

[0434] Input: Generated training materials (instructions, instructional videos, 3D models)

[0435] Output: Constructed VR / Metaverse space

[0436] Step 5:

[0437] The server generates a session URL or QR code and provides it to the user, allowing the learner to access the virtual space.

[0438] Input: Constructed VR / Metaverse space

[0439] Output: Session URL or QR code

[0440] Step 6:

[0441] Learners access the system using the provided URL or QR code, put on a VR device, enter the virtual space, and participate in the virtual space after entering specific credentials and completing the necessary authentication.

[0442] Input: Session URL or QR code, credentials

[0443] Output: Virtual space in which learners participated

[0444] Step 7:

[0445] The server uses an emotion engine to analyze the emotional state of learners and instructors in real time, for example, automatically adjusting the feedback if a learner is nervous.

[0446] Input: Behavioral and emotional data of students and instructors

[0447] Output: Regulated Feedback

[0448] Step 8:

[0449] Once inside the virtual space, the instructor and learners begin communicating in real time. The instructor demonstrates the technology using a VR device, and the learners simulate assembling the parts in the virtual space.

[0450] Input: Real-time communication data between instructors and students

[0451] Output: Technical transfer activities in virtual space

[0452] Step 9:

[0453] The emotion engine monitors the emotional state of both parties in real time and provides appropriate feedback and assistance.

[0454] Input: Real-time emotion data

[0455] Output: Appropriate feedback and assistance

[0456] Step 10:

[0457] After the session ends, the server collects the learner's behavior log and uses generative AI to analyze the learner's performance. A detailed report containing the evaluation results and feedback is generated and provided to the instructor. The instructor can review the report and provide additional advice or comments.

[0458] Input: Learner behavior log

[0459] Output: Detailed assessment report

[0460] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0462] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0463] [Second embodiment]

[0464] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0465] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0466] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0467] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0468] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0469] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0470] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0471] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0472] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0473] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0474] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0475] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0476] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI and VR / Metaverse technology, it realizes efficient and realistic technology transfer.

[0477] User (instructor) operation

[0478] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[0479] Server Operation

[0480] The server receives and stores the technical details sent by the user. Based on the stored data, generative AI is used to automatically generate detailed training materials. For example, it generates instruction manuals, video explanations, and 3D models for the washi paper production process. Based on these materials, a VR / Metaverse space is designed to create a virtual space. In the designed virtual space, the tools and materials used in washi paper production are realistically placed as 3D models.

[0481] Device operation

[0482] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0483] Real-time technology transfer

[0484] Once in the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[0485] Session Closure and Evaluation

[0486] After the session ends, the server collects the learner's behavior log and evaluates their performance using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[0487] Specific examples

[0488] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[0489] In this way, the present invention is a system that incorporates advanced technology in order to improve the efficiency and quality of technical transfer.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[0493] Step 2:

[0494] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[0495] Step 3:

[0496] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[0497] Step 4:

[0498] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[0499] Step 5:

[0500] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[0501] Step 6:

[0502] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[0503] Step 7:

[0504] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[0505] Step 8:

[0506] Learners put on the VR device and join the virtual space using a link provided by the server.

[0507] Step 9:

[0508] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[0509] Step 10:

[0510] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[0511] Step 11:

[0512] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[0513] Step 12:

[0514] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[0515] Step 13:

[0516] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[0517] This step realizes a system that allows instructors and students to transfer skills efficiently and in a realistic manner.

[0518] Example 1

[0519] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0520] Accurate and efficient transmission of knowledge and skills is a challenge in modern technology transfer. Manual and artisanal techniques require real-time instruction and detailed feedback, but traditional methods make it difficult to achieve this appropriately. Communication between learners and instructors is also important, and an environment in which this can occur effectively is necessary. Furthermore, quantitative and objective feedback is required for skill evaluation, but traditional methods often rely on subjective evaluation. To solve these challenges, a new technology transfer system is needed.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0522] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, and a virtual environment creation means for constructing a virtual space, thereby improving the accuracy and efficiency of technical transfer and enabling real-time technical instruction and objective performance evaluation.

[0523] "Input means" refers to the device or software that allows the user to input technical content into the system.

[0524] "Generation means" refers to devices or software that automatically generate advanced training materials based on input technical content.

[0525] "Virtual environment construction means" refers to devices and software for designing and constructing virtual spaces.

[0526] "Communication means" refers to devices and software that allow learners and instructors to communicate in real time.

[0527] "Evaluation tools" refer to devices or software that collect learners' behavioral logs after the session ends, evaluate their performance using generative AI models, and generate feedback.

[0528] "Storage means" refers to devices or software for storing technical content in a database or the like.

[0529] "Model placement means" refers to devices or software for placing 3D models in virtual space.

[0530] A "generative AI model" refers to an artificial intelligence model that automatically generates training materials and evaluations based on input data.

[0531] "Technical content" refers to the knowledge and skills that should be passed on, specifically the procedure manuals, tools to be used, details of materials, etc.

[0532] "Training materials" refers to teaching materials, manuals, videos, etc. that contain the information a learner needs to master a skill.

[0533] "Virtual space" refers to a simulated three-dimensional environment that a user can access through a virtual reality (VR) device.

[0534] "Behavior log" refers to data that records the operations and actions performed by a learner during a session.

[0535] "Feedback" refers to information, including advice and suggestions for improvement, given to a learner on their performance using assessment tools.

[0536] The "technology transfer system" refers to the entire system that integrates the above means and enables efficient and effective technology transfer.

[0537] The technology transfer platform of the present invention is a system that consists of users, a server, and terminals, and realizes efficient and realistic technology transfer by utilizing generative AI models and VR / Metaverse technology. Specific embodiments of this system are described in detail below.

[0538] User operations

[0539] First, the user logs in to the system using a device (e.g., a PC or smartphone). After logging in, the user accesses the dashboard and clicks the "Create a new session" button. In the form that appears, the user enters details of the technical content they want to teach (e.g., how to make washi paper). For example, they enter information such as the tools and materials used and the steps, and upload images and video files as needed. This operation allows the system to collect realistic technical content.

[0540] Server Operation

[0541] The server receives the technical details sent by the user and stores them in a database. Based on the stored data, a generative AI model (such as OpenAI's GPT-3 or DALL-E) is used to automatically generate detailed training materials. Specifically, it generates instruction manuals, video explanations, 3D models, and other materials related to the washi paper production process. To create a virtual space based on these materials, a VR / Metaverse space is designed using game engines such as Unity or Unreal Engine as a means of building the virtual environment. In the virtual space, tools and materials used in washi paper production are realistically placed as 3D models.

[0542] Device operation

[0543] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners then put on a VR device (e.g., Oculus Rift or HTC Vive) and use the link to enter the virtual space.

[0544] Real-time technology transfer

[0545] Once inside the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[0546] Session Closure and Evaluation

[0547] After the session ends, the server collects the learner's behavior log and evaluates their performance using a generative AI model. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[0548] Specific examples

[0549] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[0550] Prompt Sentence Examples

[0551] "Please enter the detailed steps for making washi paper. Please include the tools and materials you will use, the specific steps, and images and videos if necessary."

[0552] The above is a detailed description of the embodiment of the present invention, and this system can improve the efficiency and quality of technology transfer.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] Step 1:

[0555] A user logs into the system using a terminal.

[0556] Input: Username, Password

[0557] Specific actions: Open a browser, access the system URL to display the login page, enter your username and password, and click the "Login" button.

[0558] Output: Dashboard screen after successful login

[0559] Step 2:

[0560] The user accesses the dashboard and clicks the "Create a new session" button.

[0561] Specific operation: After logging in, access the dashboard and click the "Create a new session" button.

[0562] Input: None

[0563] Output: Technical content input form

[0564] Step 3:

[0565] The user enters the technical details (e.g., how to make washi paper) into the displayed form and uploads the necessary information.

[0566] Specific operations: In the "Technical Details" field, users enter detailed instructions for making washi paper, add information about the tools and materials used, and upload images and video files to complement the explanation.

[0567] Input: Text information on technical content, image files, video files

[0568] Output: Technical data entered by the user

[0569] Step 4:

[0570] The server receives the technical content sent by the user and stores it in a database.

[0571] What happens: The server receives the HTTP POST request, parses the form data, and saves it in a database.

[0572] Input: Technical data entered by the user

[0573] Output: Technical details stored in the database

[0574] Step 5:

[0575] The server automatically generates detailed training materials using generative AI based on the stored data.

[0576] How it works: The server inputs the stored data on the washi paper production process into a generative AI model, which then generates instructions, video explanations, and 3D models. For example, algorithms such as OpenAI's GPT-3 and DALL-E are used.

[0577] Input: Technical details stored in the database

[0578] Output: Generated instructions, video instructions, 3D models

[0579] Step 6:

[0580] The server designs and constructs the VR / Metaverse space based on the generated materials.

[0581] Specific operation: The server uses a game engine such as Unity or Unreal Engine to create a virtual space and places a washi paper workshop as a 3D model.

[0582] Input: Generated instructions, video instructions, 3D models

[0583] Output: Designed and constructed VR / Metaverse space

[0584] Step 7:

[0585] The server generates and provides the learner with an access URL or QR code.

[0586] What happens: The server generates an access link for the learner and sends it via email. A QR code is also generated, which can be printed if desired.

[0587] Input: Access information for the designed and constructed VR / Metaverse space

[0588] Output: Generated URL, QR code

[0589] Step 8:

[0590] Learners access the system using a device and a provided URL or QR code.

[0591] Specific actions: Learners scan the QR code with their smartphone and tap the displayed link to access the system.

[0592] Input: QR code or URL

[0593] Output: System login screen

[0594] Step 9:

[0595] Learners log in by entering their credentials and are provided with a link to join the VR / Metaverse space.

[0596] Specific operation: The learner enters their username and password on the login page and clicks the "Login" button. If successful, a link to join the VR space will appear on the screen.

[0597] Input: Username, Password

[0598] Output: VR / Metaverse space participation link

[0599] Step 10:

[0600] Learners put on the VR device and use the link to enter the virtual space.

[0601] Specific operation: Learners put on a VR device such as Oculus Rift or HTC Vive, click on the link to enter the virtual space, and after logging in, move to the washi paper making workshop.

[0602] Input: VR / Metaverse space participation link

[0603] Output: A virtual washi paper workshop

[0604] Step 11:

[0605] The instructor will demonstrate the technique using a VR device.

[0606] Specific actions: The instructor operates tools in the VR space and demonstrates the steps for making washi paper. The learners observe the process in real time.

[0607] Input: None

[0608] Output: An environment where technology can be demonstrated in real time

[0609] Step 12:

[0610] Learners simulate skills in a virtual space and receive feedback from instructors.

[0611] Specific actions: Learners process raw materials for washi paper in a VR space and make paper using simulated tools. The instructor observes the process and provides appropriate feedback.

[0612] Input: None

[0613] Output: Simulation and feedback of technology

[0614] Step 13:

[0615] After the session ends, the server collects the learner's behavioral log and evaluates their performance using generative AI.

[0616] What it does: The server analyzes the data recorded during the session and performs an evaluation using a generative AI model (e.g., Azure Cognitive Services or Google Cloud AI).

[0617] Input: Learner behavior log

[0618] Output: Evaluation results and feedback

[0619] Step 14:

[0620] The assessment results and feedback are generated as a report and provided to the instructor.

[0621] What it does: The server generates a detailed report of the assessment results and feedback and uploads it to the instructor's dashboard, where the instructor can review and add comments.

[0622] Input: Evaluation results, feedback

[0623] Output: Report

[0624] Step 15:

[0625] Once the instructor's feedback is complete, the final report will be sent to the learner.

[0626] Specific operation: The instructor sends feedback from the dashboard, the server sends a notification email to the learner, and the learner logs in and checks the feedback.

[0627] Input: Report with feedback

[0628] Output: Feedback communicated to the learner

[0629] (Application example 1)

[0630] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] Traditional technology transfer methods are inefficient because they require a lot of time and effort to convey technical information and for learners to acquire practical skills, and there is a risk of mistakes. In particular, in fields requiring advanced skills, such as training to operate factory robots, training in an actual work environment poses safety risks and requires a lot of time and money. For this reason, an effective method for simultaneously improving the efficiency of technology transfer and quality has been sought.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0633] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, a means for constructing a 3D simulation model based on the generated training materials, a log collection means for collecting operation data in real time, and an evaluation generation means for automatically providing evaluations and feedback based on the collected operation data. This allows learners to acquire practical skills safely and efficiently, and instructors to provide accurate evaluations and feedback.

[0634] The "input means for inputting technical content" is an interface that allows instructors to input technical information and data into the system.

[0635] The "means for automatically generating advanced training materials based on generated technical content" is a mechanism for automatically generating detailed training materials and guides based on input technical content.

[0636] "VR / Metaverse construction method for constructing virtual spaces" is a technology that creates a training environment in virtual reality or the metaverse based on generated training materials.

[0637] "A means of communication for real-time communication between learners and instructors" refers to a communication technology that enables learners and instructors to communicate in real-time within a virtual space.

[0638] "Evaluation means for evaluating learner performance and generating feedback after the session" is a function that analyzes learner behavior and results after the training session and provides evaluation and feedback using generative AI.

[0639] The "means for constructing a 3D simulation model based on the generated training materials" refers to a technology for creating a three-dimensional simulation model based on the training materials.

[0640] The "log collection means for collecting operation data in real time" is a system that collects data on the operations and actions performed by learners in the virtual space in real time.

[0641] The "evaluation generation means that automatically provides evaluation and feedback based on collected operation data" refers to an algorithm and system that analyzes data obtained by the log collection means and automatically generates an evaluation approach and specific feedback.

[0642] This invention is a system for efficiently and effectively transferring technical content, which combines generative AI models with VR / Metaverse technology. Specific implementation methods are described below.

[0643] Program Generation and Processing Description

[0644] Server Roles

[0645] The server has an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, and an evaluation means for evaluating the learner's performance and generating feedback after the session ends.

[0646] Furthermore, the server includes a log collection means for constructing a 3D simulation model based on the generated training materials, collecting learner operation data in real time, and an evaluation generation means for automatically providing evaluation and feedback based on the collected operation data.

[0647] Hardware used

[0648] VR device: Used by learners to learn practical skills in a virtual space (e.g., Oculus Quest 2).

[0649] Server: Use cloud-based services for computing resources (e.g., AWS or Google Cloud).

[0650] User terminal: A device used by instructors and students to access the system (e.g., PC or smartphone).

[0651] Software used

[0652] Generative AI models: use natural language processing models to automatically generate training materials from technical content (e.g., GPT-4).

[0653] 3D modeling software: Create 3D models to be used in virtual space (e.g. Blender, Unity)

[0654] Database: Stores technical content and learner data (e.g., MySQL, NoSQL).

[0655] Communication protocol: Realizes real-time communication (e.g., WebSocket, REST API).

[0656] Detailed processing

[0657] The server receives and stores the technical content entered by the instructor, and then uses a generative AI model to automatically generate detailed training materials, including text instructions, video explanations, and 3D models. Based on these materials, a virtual space is designed to recreate a real-world work environment, such as a washi paper workshop.

[0658] In addition, learners' operation data is collected in real time and evaluation and feedback is automatically performed using a generative AI model, which makes the transfer of skills more efficient and enables high-quality training.

[0659] Specific examples

[0660] For example, in training on the operation of a welding robot, an instructor inputs detailed instructions for operating the robot, and the AI ​​generator creates training materials based on these instructions. Learners put on VR devices and enter a virtual welding environment, practicing operations while receiving real-time instruction. During the training, the learner's operation data is monitored in real time by a log collection means, and an evaluation generation means provides evaluations and feedback after the session ends.

[0661] Prompt Sentence Examples

[0662] "The operating procedure for the welding robot is to first secure the handle and release the safety device. Then set the speed and temperature on the operation panel and press the welding start button. Maintain a constant speed during welding, and when finished, reset the safety device and release the handle. Please create a detailed training manual and 3D model based on this."

[0663] In this way, the transfer of technical content can be carried out more efficiently and effectively.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1:

[0666] The user (instructor) logs in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. They enter details of the technical content (e.g., welding robot operating procedures) in the displayed form. Specifically, they enter information such as the tools and materials to be used and the procedures, and upload images and video files as needed. The entered data is sent to the server. The input data includes the technical content, attachments, and prompts. The output is a technical content dataset.

[0667] Step 2:

[0668] The server receives and stores the technical content dataset sent by the user. Based on the stored data, it uses a generative AI model to automatically generate detailed training materials. The generated training materials include instructions, video explanations, 3D models, etc. The input data is the technical content dataset, and the output is the generated training materials.

[0669] Step 3:

[0670] The server uses a VR / Metaverse construction tool to construct a virtual space based on the generated training materials. Specifically, it uses 3D modeling software to arrange the work environment, tools, materials, etc. in the virtual space. The virtual space is realistically reproduced as a learning environment. The input data are the training materials, and the output is the constructed virtual space.

[0671] Step 4:

[0672] The user (learner) accesses the system from a device using the provided URL or QR code and logs in by entering their credentials. Once the necessary authentication is completed, a link to join the designated virtual space is provided. The learner puts on the VR device and uses the link to enter the virtual space. The input data is the credentials, and the output is an access link to the virtual space.

[0673] Step 5:

[0674] Users (learners) actually learn skills in a virtual space. Instructors provide instruction while communicating in real time through VR devices. Learners practice procedures in a simulated environment and receive feedback and real-time advice from the instructor. Input data is operation data in the virtual space, and output is the learner's operation log.

[0675] Step 6:

[0676] After the session ends, the server collects the learner's operation data log. The log collection means uses the data collected in real time to pass it to the evaluation generation means. The generation AI analyzes the learner's performance and generates specific evaluations and feedback. The input data is the operation data log, and the output is an evaluation report and feedback.

[0677] Step 7:

[0678] The server generates a detailed report of the assessment results and feedback and provides it to the instructor. The instructor reviews this report and adds additional advice and comments to complete the final report. The completed report is notified to the learner and can be used for the next session or self-study. The input data are the assessment report, feedback, and instructor comments, and the output is the final learning report.

[0679] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0680] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and realistic technology transfer.

[0681] User (instructor) operation

[0682] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[0683] Server Operation

[0684] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[0685] Emotion Engine Operation

[0686] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback to be more encouraging. If an instructor is frustrated, it can suggest a break to ease their feelings.

[0687] Device operation

[0688] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0689] Real-time technology transfer

[0690] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[0691] Session Closure and Evaluation

[0692] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[0693] Specific examples

[0694] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production steps. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. The emotion engine would assess in real time whether the learner was confident or anxious about the operation, and automatically provide support accordingly. After the session, the learner's work would be evaluated and feedback would be provided, highlighting specific areas for improvement.

[0695] In this way, the present invention is a system that incorporates cutting-edge technology to improve the efficiency and quality of skill transfer. The addition of an emotion engine makes it possible to understand the psychological states of learners and instructors in real time and provide optimal instruction and learning support.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[0699] Step 2:

[0700] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[0701] Step 3:

[0702] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[0703] Step 4:

[0704] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[0705] Step 5:

[0706] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[0707] Step 6:

[0708] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[0709] Step 7:

[0710] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[0711] Step 8:

[0712] Learners put on the VR device and join the virtual space using a link provided by the server.

[0713] Step 9:

[0714] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[0715] Step 10:

[0716] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[0717] Step 11:

[0718] The server activates an emotion engine to analyze the emotional states of the learner and instructor in real time. For example, if the learner is feeling anxious, the server will suggest to the instructor to encourage the learner.

[0719] Step 12:

[0720] The emotion engine also monitors the emotional state of the instructor and, for example, provides suggestions for short breaks if the instructor feels frustrated, thereby maintaining the quality of instruction.

[0721] Step 13:

[0722] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[0723] Step 14:

[0724] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[0725] Step 15:

[0726] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[0727] This step will realize a system that allows instructors and students to transfer skills efficiently and in a realistic manner. In particular, the introduction of an emotion engine will enable the real-time analysis of the emotional states of both parties and provide appropriate feedback and support.

[0728] Example 2

[0729] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] Current technology transfer methods lack the mechanisms for providing efficient and immersive education. They also often lack the means to grasp the psychological state of learners and instructors in real time and provide appropriate feedback and support. As a result, technology transfer is inefficient, and there are challenges in improving learners' understanding and the quality of their learning experience.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0732] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a virtual reality construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an emotion analysis means for analyzing the emotional states of the learner and the instructor in real time and adjusting feedback, an evaluation means for evaluating the learner's performance after the session ends and generating feedback, and an access means for accessing the virtual space using a provided URL or QR code. This enables efficient and realistic skill transfer and makes it possible to provide an optimal learning environment for both the learner and the instructor through real-time emotion analysis and appropriate feedback.

[0733] The "input means for inputting technical details" is an interface that allows the user to input detailed information about the technology they wish to transfer to the system.

[0734] The "means for automatically generating advanced training materials based on generated technical content" refers to an algorithm and system for automatically generating training materials based on input technical content.

[0735] The "virtual reality construction means for constructing a virtual space" is a system for designing and constructing a virtual reality space based on input technical content and generated training materials.

[0736] "A means of communication for real-time communication between learners and instructors" is a system that allows learners and instructors to communicate two-way in real time using the Internet or other networks.

[0737] The "emotion analysis means for analyzing the emotional states of learners and instructors in real time and adjusting feedback" is a system that analyzes the voices and facial expressions of learners and instructors in real time and adaptively changes feedback based on their emotional states.

[0738] The "assessment means for evaluating the learner's performance and generating feedback after the session" is a system for analyzing the learner's behavioral log and training results, evaluating the learner's performance, and generating feedback.

[0739] "Means of access to the virtual space using the provided URL or QR code" refers to the terminal and interface that allows learners to access the virtual space using the URL or QR code provided by the server.

[0740] This invention is a technology transfer platform consisting of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and immersive technology transfer.

[0741] User operations

[0742] Users first log in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technical content they want to teach. For example, they enter information about how to make washi paper, such as the tools, materials, and steps used, and can upload images and video files as needed.

[0743] Server Operation

[0744] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instruction videos, 3D models, etc.) based on the input content. To achieve this, the server inputs prompts like the following into the generative AI:

[0745] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[0746] 1. Tools: aquarium, spoon, net, etc.

[0747] 2. Materials: Raw materials for Japanese paper, glue, etc.

[0748] 3. Steps:

[0749] a. Soaking the raw materials for washi paper in water

[0750] b. Add glue

[0751] c. Shape it with a net

[0752] d. Drying

[0753] 4. Attachments:

[0754] Images of tools and materials

[0755] Instructional Video

[0756] Based on the training materials generated by the generative AI, the server begins constructing the VR / Metaverse space. The required 3D models are selected from the library and placed in the virtual space. The designed virtual space is saved on a dedicated VR server. The server then generates a session URL or QR code and provides it to the user.

[0757] Emotion Engine Operation

[0758] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, the system will automatically adjust the feedback to send encouraging words. If an instructor is frustrated, the system will suggest taking a break to ease their feelings.

[0759] Device operation

[0760] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0761] Real-time technology transfer

[0762] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[0763] Session Closure and Evaluation

[0764] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the analysis results of the emotion engine, indicating the learner's level of understanding and areas for improvement.

[0765] As described above, the technology transfer platform of the present invention can revolutionize conventional technology transfer methods by utilizing generative AI, VR / metaverse, and emotion engines, and provide an efficient and immersive learning experience.

[0766] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0767] Step 1:

[0768] A user logs in to the system.

[0769] Input: Username, Password

[0770] How it works: A user opens a web browser, accesses the system's login page, enters their username and password, and clicks the "Login" button.

[0771] Output: Login success or failure message, dashboard screen

[0772] Step 2:

[0773] A user creates a new session.

[0774] Input: Technical content, images, video files

[0775] How it works: Users click the "Create a new session" button on their dashboard, then fill out a form to describe their technical needs, uploading images and video files as needed.

[0776] Output: Form data with technical content entered

[0777] Step 3:

[0778] The server receives the technical content and stores it in a database.

[0779] Input: Technical form data entered by the user

[0780] How it works: The server receives user input data and stores it in a database using SQL queries.

[0781] Output: Technical details stored in the database

[0782] Step 4:

[0783] The server launches a generation AI to automatically generate training materials.

[0784] Input: Technical details stored in the database

[0785] Operation: The server uses prompts to instruct the generative AI model to generate technical training materials (procedures, instruction videos, 3D models, etc.). The generative AI model generates the training materials based on the input data.

[0786] Example prompt:

[0787] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[0788] 1. Tools: aquarium, spoon, net, etc.

[0789] 2. Materials: Raw materials for Japanese paper, glue, etc.

[0790] 3. Steps:

[0791] a. Soaking the raw materials for washi paper in water

[0792] b. Add glue

[0793] c. Shape it with a net

[0794] d. Drying

[0795] 4. Attachments:

[0796] Images of tools and materials

[0797] Instructional Video

[0798] Output: Generated training materials (instructions, instructional videos, 3D models, etc.)

[0799] Step 5:

[0800] The server creates the VR / Metaverse space.

[0801] Input: Generated training materials

[0802] How it works: The server selects the necessary 3D models from the library based on the training materials and designs the layout of the virtual space. The designed virtual space is saved on a dedicated VR server.

[0803] Output: Virtual space data, virtual space stored on a dedicated VR server

[0804] Step 6:

[0805] The server generates a session URL or QR code and provides it to the user.

[0806] Input: Virtual space data

[0807] How it works: The server generates a URL or QR code for accessing the virtual space and displays it on the user's dashboard.

[0808] Output: Access URL, QR code

[0809] Step 7:

[0810] Learners access the system and participate in the virtual space.

[0811] Input: URL or QR code, credentials (student name, password)

[0812] How it works: Learners access the system by entering the provided URL into their browser or by scanning the QR code with their camera. They enter their credentials and click the "Login" button. Once the necessary authentication is completed, they are provided with a link to join the virtual space. They put on their VR device and use the link to enter the virtual space.

[0813] Output: Authentication result, link to join the virtual space

[0814] Step 8:

[0815] Real-time technology transfer takes place in a virtual space.

[0816] Input: Link to join the virtual space, VR device

[0817] Operation: The instructor and the student communicate in real time through audio and video in the virtual space. The instructor demonstrates the technology, and the student operates it according to the instruction.

[0818] Output: Learner's operation results, progress of skill transfer session

[0819] Step 9:

[0820] The emotion engine analyzes the emotional state of both parties in real time.

[0821] Input: Audio data, video data

[0822] How it works: The emotion engine analyzes the voice and facial expression data of learners and instructors to assess their emotional state in real time, for example measuring stress levels and excitement levels using voice recognition technology and machine learning algorithms.

[0823] Output: Real-time emotion evaluation results

[0824] Step 10:

[0825] The server evaluates the learner's performance after the session and generates feedback.

[0826] Input: Learner's operation log, emotion evaluation results

[0827] How it works: After the session ends, the server collects the learner's behavior log and analyzes it using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review the report and enter additional advice or comments.

[0828] Output: Evaluation report, feedback

[0829] (Application example 2)

[0830] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0831] Conventional technology transfer systems have difficulty effectively transferring complex techniques and procedures, posing particular challenges for transferring skilled techniques on factory production lines. Furthermore, they lacked the ability to grasp the learner's emotional state and provide appropriate feedback, which could lead to reduced learning efficiency. Furthermore, relying solely on technology transfer meant there was no way to effectively transfer technology to factory robots with automated equipment.

[0832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0833] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, an emotion analysis means for analyzing the learner's emotional state and adjusting the feedback, and a robot control means for controlling the robot's operation based on the evaluation results. This makes it possible to effectively transfer complex skills to factory robots while providing optimal feedback according to the learner's emotional state.

[0834] "Technical content" refers to information such as the specific knowledge, skills, procedures, tools, and materials to be passed on.

[0835] "Input means" refers to an interface or device that allows a user to input technical content into the system.

[0836] "Generation means" refers to a function or system that automatically generates advanced training materials based on input technical content.

[0837] "VR / Metaverse construction means" refers to the technology and software used to construct virtual reality or metaverse spaces.

[0838] "Communication means" refers to the network infrastructure and devices that enable learners and instructors to communicate in real time.

[0839] "Evaluation tools" are functions or systems that evaluate learner performance after a session and generate feedback.

[0840] "Emotion analysis means" refers to a function or system that senses the emotional state of learners and instructors and provides feedback accordingly.

[0841] "Robot control means" refers to a function or system for controlling the operation of the robot based on the evaluation results.

[0842] This invention is a system for transferring technical content in an efficient and immersive way, and is realized by combining generative AI, VR / Metaverse technology, and an emotion engine. The specific operation of the system and the hardware and software used are described below.

[0843] User (instructor) operations:

[0844] First, the instructor logs into the technology transfer system. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technology they want to transfer (e.g., complex part assembly procedures). They enter information such as the tools and materials used and the procedures, and upload images and video files as needed.

[0845] Server behavior:

[0846] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generation AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[0847] Emotion Engine in action:

[0848] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback and send encouraging words. If an instructor is frustrated, it can suggest a break to ease their feelings.

[0849] Terminal operations:

[0850] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0851] Real-time technical transfer:

[0852] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the procedure to the learner. The learner can then simulate assembling parts in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[0853] Session Closure and Evaluation:

[0854] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[0855] Examples:

[0856] For example, when transferring complex parts assembly techniques in a factory, the instructor inputs the assembly steps. Based on this, generative AI creates training materials and constructs a factory line in a virtual space. The learner then joins the virtual space and experiences the assembly work under the guidance of the instructor. The emotion engine evaluates in real time whether the learner is confident or anxious about the operation, and automatically provides support accordingly. After the session, the learner's work is evaluated and feedback is provided with specific areas for improvement.

[0857] Example prompt sentence:

[0858] "Generate appropriate work instructions for the robot based on real-time work data obtained from the VR device. These instructions should include the work flow, detailed steps, and points to note, and also provide feedback based on the worker's emotional state."

[0859] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0860] Step 1:

[0861] The user logs in to the technology transfer system and clicks the "Create a new session" button. They enter the details of the technology (e.g., the assembly procedure for a part) and upload any necessary images or video files.

[0862] Input: Technical content, images, video files

[0863] Output: Uploaded technical content data

[0864] Step 2:

[0865] The server receives the technical content data sent by the user and stores it in a database, thereby permanently storing the entered technical content.

[0866] Input: Technical content data

[0867] Output: Saved technical content data (storage in database)

[0868] Step 3:

[0869] The server activates a generative AI that automatically generates advanced training materials (such as instruction manuals, explanatory videos, and 3D models) based on the stored technical content data.

[0870] Input: Saved technical content data

[0871] Output: Generated training materials (instructions, instructional videos, 3D models)

[0872] Step 4:

[0873] The server begins constructing the VR / Metaverse space based on the generated training materials, placing the 3D model in the virtual space, and saving the designed virtual space.

[0874] Input: Generated training materials (instructions, instructional videos, 3D models)

[0875] Output: Constructed VR / Metaverse space

[0876] Step 5:

[0877] The server generates a session URL or QR code and provides it to the user, allowing the learner to access the virtual space.

[0878] Input: Constructed VR / Metaverse space

[0879] Output: Session URL or QR code

[0880] Step 6:

[0881] Learners access the system using the provided URL or QR code, put on a VR device, enter the virtual space, and participate in the virtual space after entering specific credentials and completing the necessary authentication.

[0882] Input: Session URL or QR code, credentials

[0883] Output: Virtual space in which learners participated

[0884] Step 7:

[0885] The server uses an emotion engine to analyze the emotional state of learners and instructors in real time, for example, automatically adjusting the feedback if a learner is nervous.

[0886] Input: Behavioral and emotional data of students and instructors

[0887] Output: Regulated Feedback

[0888] Step 8:

[0889] Once inside the virtual space, the instructor and learners begin communicating in real time. The instructor demonstrates the technology using a VR device, and the learners simulate assembling the parts in the virtual space.

[0890] Input: Real-time communication data between instructors and students

[0891] Output: Technical transfer activities in virtual space

[0892] Step 9:

[0893] The emotion engine monitors the emotional state of both parties in real time and provides appropriate feedback and assistance.

[0894] Input: Real-time emotion data

[0895] Output: Appropriate feedback and assistance

[0896] Step 10:

[0897] After the session ends, the server collects the learner's behavior log and uses generative AI to analyze the learner's performance. A detailed report containing the evaluation results and feedback is generated and provided to the instructor. The instructor can review the report and provide additional advice or comments.

[0898] Input: Learner behavior log

[0899] Output: Detailed assessment report

[0900] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0901] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0902] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0903] [Third embodiment]

[0904] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0905] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0906] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0907] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0908] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0909] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0910] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0911] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0912] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0913] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0914] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0915] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0916] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI and VR / Metaverse technology, it realizes efficient and realistic technology transfer.

[0917] User (instructor) operation

[0918] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[0919] Server Operation

[0920] The server receives and stores the technical details sent by the user. Based on the stored data, generative AI is used to automatically generate detailed training materials. For example, it generates instruction manuals, video explanations, and 3D models for the washi paper production process. Based on these materials, a VR / Metaverse space is designed to create a virtual space. In the designed virtual space, the tools and materials used in washi paper production are realistically placed as 3D models.

[0921] Device operation

[0922] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[0923] Real-time technology transfer

[0924] Once in the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[0925] Session Closure and Evaluation

[0926] After the session ends, the server collects the learner's behavior log and evaluates their performance using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[0927] Specific examples

[0928] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[0929] In this way, the present invention is a system that incorporates advanced technology in order to improve the efficiency and quality of technical transfer.

[0930] The processing flow will be explained below.

[0931] Step 1:

[0932] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[0933] Step 2:

[0934] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[0935] Step 3:

[0936] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[0937] Step 4:

[0938] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[0939] Step 5:

[0940] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[0941] Step 6:

[0942] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[0943] Step 7:

[0944] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[0945] Step 8:

[0946] Learners put on the VR device and join the virtual space using a link provided by the server.

[0947] Step 9:

[0948] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[0949] Step 10:

[0950] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[0951] Step 11:

[0952] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[0953] Step 12:

[0954] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[0955] Step 13:

[0956] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[0957] This step realizes a system that allows instructors and students to transfer skills efficiently and in a realistic manner.

[0958] Example 1

[0959] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0960] Accurate and efficient transmission of knowledge and skills is a challenge in modern technology transfer. Manual and artisanal techniques require real-time instruction and detailed feedback, but traditional methods make it difficult to achieve this appropriately. Communication between learners and instructors is also important, and an environment in which this can occur effectively is necessary. Furthermore, quantitative and objective feedback is required for skill evaluation, but traditional methods often rely on subjective evaluation. To solve these challenges, a new technology transfer system is needed.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0962] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, and a virtual environment creation means for constructing a virtual space, thereby improving the accuracy and efficiency of technical transfer and enabling real-time technical instruction and objective performance evaluation.

[0963] "Input means" refers to the device or software that allows the user to input technical content into the system.

[0964] "Generation means" refers to devices or software that automatically generate advanced training materials based on input technical content.

[0965] "Virtual environment construction means" refers to devices and software for designing and constructing virtual spaces.

[0966] "Communication means" refers to devices and software that allow learners and instructors to communicate in real time.

[0967] "Evaluation tools" refer to devices or software that collect learners' behavioral logs after the session ends, evaluate their performance using generative AI models, and generate feedback.

[0968] "Storage means" refers to devices or software for storing technical content in a database or the like.

[0969] "Model placement means" refers to devices or software for placing 3D models in virtual space.

[0970] A "generative AI model" refers to an artificial intelligence model that automatically generates training materials and evaluations based on input data.

[0971] "Technical content" refers to the knowledge and skills that should be passed on, specifically the procedure manuals, tools to be used, details of materials, etc.

[0972] "Training materials" refers to teaching materials, manuals, videos, etc. that contain the information a learner needs to master a skill.

[0973] "Virtual space" refers to a simulated three-dimensional environment that a user can access through a virtual reality (VR) device.

[0974] "Behavior log" refers to data that records the operations and actions performed by a learner during a session.

[0975] "Feedback" refers to information, including advice and suggestions for improvement, given to a learner on their performance using assessment tools.

[0976] The "technology transfer system" refers to the entire system that integrates the above means and enables efficient and effective technology transfer.

[0977] The technology transfer platform of the present invention is a system that consists of users, a server, and terminals, and realizes efficient and realistic technology transfer by utilizing generative AI models and VR / Metaverse technology. Specific embodiments of this system are described in detail below.

[0978] User operations

[0979] First, the user logs in to the system using a device (e.g., a PC or smartphone). After logging in, the user accesses the dashboard and clicks the "Create a new session" button. In the form that appears, the user enters details of the technical content they want to teach (e.g., how to make washi paper). For example, they enter information such as the tools and materials used and the steps, and upload images and video files as needed. This operation allows the system to collect realistic technical content.

[0980] Server Operation

[0981] The server receives the technical details sent by the user and stores them in a database. Based on the stored data, a generative AI model (such as OpenAI's GPT-3 or DALL-E) is used to automatically generate detailed training materials. Specifically, it generates instruction manuals, video explanations, 3D models, and other materials related to the washi paper production process. To create a virtual space based on these materials, a VR / Metaverse space is designed using game engines such as Unity or Unreal Engine as a means of building the virtual environment. In the virtual space, tools and materials used in washi paper production are realistically placed as 3D models.

[0982] Device operation

[0983] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners then put on a VR device (e.g., Oculus Rift or HTC Vive) and use the link to enter the virtual space.

[0984] Real-time technology transfer

[0985] Once inside the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[0986] Session Closure and Evaluation

[0987] After the session ends, the server collects the learner's behavior log and evaluates their performance using a generative AI model. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[0988] Specific examples

[0989] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[0990] Prompt Sentence Examples

[0991] "Please enter the detailed steps for making washi paper. Please include the tools and materials you will use, the specific steps, and images and videos if necessary."

[0992] The above is a detailed description of the embodiment of the present invention, and this system can improve the efficiency and quality of technology transfer.

[0993] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0994] Step 1:

[0995] A user logs into the system using a terminal.

[0996] Input: Username, Password

[0997] Specific actions: Open a browser, access the system URL to display the login page, enter your username and password, and click the "Login" button.

[0998] Output: Dashboard screen after successful login

[0999] Step 2:

[1000] The user accesses the dashboard and clicks the "Create a new session" button.

[1001] Specific operation: After logging in, access the dashboard and click the "Create a new session" button.

[1002] Input: None

[1003] Output: Technical content input form

[1004] Step 3:

[1005] The user enters the technical details (e.g., how to make washi paper) into the displayed form and uploads the necessary information.

[1006] Specific operations: In the "Technical Details" field, users enter detailed instructions for making washi paper, add information about the tools and materials used, and upload images and video files to complement the explanation.

[1007] Input: Text information on technical content, image files, video files

[1008] Output: Technical data entered by the user

[1009] Step 4:

[1010] The server receives the technical content sent by the user and stores it in a database.

[1011] What happens: The server receives the HTTP POST request, parses the form data, and saves it in a database.

[1012] Input: Technical data entered by the user

[1013] Output: Technical details stored in the database

[1014] Step 5:

[1015] The server automatically generates detailed training materials using generative AI based on the stored data.

[1016] How it works: The server inputs the stored data on the washi paper production process into a generative AI model, which then generates instructions, video explanations, and 3D models. For example, algorithms such as OpenAI's GPT-3 and DALL-E are used.

[1017] Input: Technical details stored in the database

[1018] Output: Generated instructions, video instructions, 3D models

[1019] Step 6:

[1020] The server designs and constructs the VR / Metaverse space based on the generated materials.

[1021] Specific operation: The server uses a game engine such as Unity or Unreal Engine to create a virtual space and places a washi paper workshop as a 3D model.

[1022] Input: Generated instructions, video instructions, 3D models

[1023] Output: Designed and constructed VR / Metaverse space

[1024] Step 7:

[1025] The server generates and provides the learner with an access URL or QR code.

[1026] What happens: The server generates an access link for the learner and sends it via email. A QR code is also generated, which can be printed if desired.

[1027] Input: Access information for the designed and constructed VR / Metaverse space

[1028] Output: Generated URL, QR code

[1029] Step 8:

[1030] Learners access the system using a device and a provided URL or QR code.

[1031] Specific actions: Learners scan the QR code with their smartphone and tap the displayed link to access the system.

[1032] Input: QR code or URL

[1033] Output: System login screen

[1034] Step 9:

[1035] Learners log in by entering their credentials and are provided with a link to join the VR / Metaverse space.

[1036] Specific operation: The learner enters their username and password on the login page and clicks the "Login" button. If successful, a link to join the VR space will appear on the screen.

[1037] Input: Username, Password

[1038] Output: VR / Metaverse space participation link

[1039] Step 10:

[1040] Learners put on the VR device and use the link to enter the virtual space.

[1041] Specific operation: Learners put on a VR device such as Oculus Rift or HTC Vive, click on the link to enter the virtual space, and after logging in, move to the washi paper making workshop.

[1042] Input: VR / Metaverse space participation link

[1043] Output: A virtual washi paper workshop

[1044] Step 11:

[1045] The instructor will demonstrate the technique using a VR device.

[1046] Specific actions: The instructor operates tools in the VR space and demonstrates the steps for making washi paper. The learners observe the process in real time.

[1047] Input: None

[1048] Output: An environment where technology can be demonstrated in real time

[1049] Step 12:

[1050] Learners simulate skills in a virtual space and receive feedback from instructors.

[1051] Specific actions: Learners process raw materials for washi paper in a VR space and make paper using simulated tools. The instructor observes the process and provides appropriate feedback.

[1052] Input: None

[1053] Output: Simulation and feedback of technology

[1054] Step 13:

[1055] After the session ends, the server collects the learner's behavioral log and evaluates their performance using generative AI.

[1056] What it does: The server analyzes the data recorded during the session and performs an evaluation using a generative AI model (e.g., Azure Cognitive Services or Google Cloud AI).

[1057] Input: Learner behavior log

[1058] Output: Evaluation results and feedback

[1059] Step 14:

[1060] The assessment results and feedback are generated as a report and provided to the instructor.

[1061] What it does: The server generates a detailed report of the assessment results and feedback and uploads it to the instructor's dashboard, where the instructor can review and add comments.

[1062] Input: Evaluation results, feedback

[1063] Output: Report

[1064] Step 15:

[1065] Once the instructor's feedback is complete, the final report will be sent to the learner.

[1066] Specific operation: The instructor sends feedback from the dashboard, the server sends a notification email to the learner, and the learner logs in and checks the feedback.

[1067] Input: Report with feedback

[1068] Output: Feedback communicated to the learner

[1069] (Application example 1)

[1070] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1071] Traditional technology transfer methods are inefficient because they require a lot of time and effort to convey technical information and for learners to acquire practical skills, and there is a risk of mistakes. In particular, in fields requiring advanced skills, such as training to operate factory robots, training in an actual work environment poses safety risks and requires a lot of time and money. For this reason, an effective method for simultaneously improving the efficiency of technology transfer and quality has been sought.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1073] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, a means for constructing a 3D simulation model based on the generated training materials, a log collection means for collecting operation data in real time, and an evaluation generation means for automatically providing evaluations and feedback based on the collected operation data. This allows learners to acquire practical skills safely and efficiently, and instructors to provide accurate evaluations and feedback.

[1074] The "input means for inputting technical content" is an interface that allows instructors to input technical information and data into the system.

[1075] The "means for automatically generating advanced training materials based on generated technical content" is a mechanism for automatically generating detailed training materials and guides based on input technical content.

[1076] "VR / Metaverse construction method for constructing virtual spaces" is a technology that creates a training environment in virtual reality or the metaverse based on generated training materials.

[1077] "A means of communication for real-time communication between learners and instructors" refers to a communication technology that enables learners and instructors to communicate in real-time within a virtual space.

[1078] "Evaluation means for evaluating learner performance and generating feedback after the session" is a function that analyzes learner behavior and results after the training session and provides evaluation and feedback using generative AI.

[1079] The "means for constructing a 3D simulation model based on the generated training materials" refers to a technology for creating a three-dimensional simulation model based on the training materials.

[1080] The "log collection means for collecting operation data in real time" is a system that collects data on the operations and actions performed by learners in the virtual space in real time.

[1081] The "evaluation generation means that automatically provides evaluation and feedback based on collected operation data" refers to an algorithm and system that analyzes data obtained by the log collection means and automatically generates an evaluation approach and specific feedback.

[1082] This invention is a system for efficiently and effectively transferring technical content, which combines generative AI models with VR / Metaverse technology. Specific implementation methods are described below.

[1083] Program Generation and Processing Description

[1084] Server Roles

[1085] The server has an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, and an evaluation means for evaluating the learner's performance and generating feedback after the session ends.

[1086] Furthermore, the server includes a log collection means for constructing a 3D simulation model based on the generated training materials, collecting learner operation data in real time, and an evaluation generation means for automatically providing evaluation and feedback based on the collected operation data.

[1087] Hardware used

[1088] VR device: Used by learners to learn practical skills in a virtual space (e.g., Oculus Quest 2).

[1089] Server: Use cloud-based services for computing resources (e.g., AWS or Google Cloud).

[1090] User terminal: A device used by instructors and students to access the system (e.g., PC or smartphone).

[1091] Software used

[1092] Generative AI models: use natural language processing models to automatically generate training materials from technical content (e.g., GPT-4).

[1093] 3D modeling software: Create 3D models to be used in virtual space (e.g. Blender, Unity)

[1094] Database: Stores technical content and learner data (e.g., MySQL, NoSQL).

[1095] Communication protocol: Realizes real-time communication (e.g., WebSocket, REST API).

[1096] Detailed processing

[1097] The server receives and stores the technical content entered by the instructor, and then uses a generative AI model to automatically generate detailed training materials, including text instructions, video explanations, and 3D models. Based on these materials, a virtual space is designed to recreate a real-world work environment, such as a washi paper workshop.

[1098] In addition, learners' operation data is collected in real time and evaluation and feedback is automatically performed using a generative AI model, which makes the transfer of skills more efficient and enables high-quality training.

[1099] Specific examples

[1100] For example, in training on the operation of a welding robot, an instructor inputs detailed instructions for operating the robot, and the AI ​​generator creates training materials based on these instructions. Learners put on VR devices and enter a virtual welding environment, practicing operations while receiving real-time instruction. During the training, the learner's operation data is monitored in real time by a log collection means, and an evaluation generation means provides evaluations and feedback after the session ends.

[1101] Prompt Sentence Examples

[1102] "The operating procedure for the welding robot is to first secure the handle and release the safety device. Then set the speed and temperature on the operation panel and press the welding start button. Maintain a constant speed during welding, and when finished, reset the safety device and release the handle. Please create a detailed training manual and 3D model based on this."

[1103] In this way, the transfer of technical content can be carried out more efficiently and effectively.

[1104] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1105] Step 1:

[1106] The user (instructor) logs in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. They enter details of the technical content (e.g., welding robot operating procedures) in the displayed form. Specifically, they enter information such as the tools and materials to be used and the procedures, and upload images and video files as needed. The entered data is sent to the server. The input data includes the technical content, attachments, and prompts. The output is a technical content dataset.

[1107] Step 2:

[1108] The server receives and stores the technical content dataset sent by the user. Based on the stored data, it uses a generative AI model to automatically generate detailed training materials. The generated training materials include instructions, video explanations, 3D models, etc. The input data is the technical content dataset, and the output is the generated training materials.

[1109] Step 3:

[1110] The server uses a VR / Metaverse construction tool to construct a virtual space based on the generated training materials. Specifically, it uses 3D modeling software to arrange the work environment, tools, materials, etc. in the virtual space. The virtual space is realistically reproduced as a learning environment. The input data are the training materials, and the output is the constructed virtual space.

[1111] Step 4:

[1112] The user (learner) accesses the system from a device using the provided URL or QR code and logs in by entering their credentials. Once the necessary authentication is completed, a link to join the designated virtual space is provided. The learner puts on the VR device and uses the link to enter the virtual space. The input data is the credentials, and the output is an access link to the virtual space.

[1113] Step 5:

[1114] Users (learners) actually learn skills in a virtual space. Instructors provide instruction while communicating in real time through VR devices. Learners practice procedures in a simulated environment and receive feedback and real-time advice from the instructor. Input data is operation data in the virtual space, and output is the learner's operation log.

[1115] Step 6:

[1116] After the session ends, the server collects the learner's operation data log. The log collection means uses the data collected in real time to pass it to the evaluation generation means. The generation AI analyzes the learner's performance and generates specific evaluations and feedback. The input data is the operation data log, and the output is an evaluation report and feedback.

[1117] Step 7:

[1118] The server generates a detailed report of the assessment results and feedback and provides it to the instructor. The instructor reviews this report and adds additional advice and comments to complete the final report. The completed report is notified to the learner and can be used for the next session or self-study. The input data are the assessment report, feedback, and instructor comments, and the output is the final learning report.

[1119] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1120] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and realistic technology transfer.

[1121] User (instructor) operation

[1122] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[1123] Server Operation

[1124] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[1125] Emotion Engine Operation

[1126] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback to be more encouraging. If an instructor is frustrated, it can suggest a break to ease their feelings.

[1127] Device operation

[1128] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1129] Real-time technology transfer

[1130] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[1131] Session Closure and Evaluation

[1132] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[1133] Specific examples

[1134] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production steps. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. The emotion engine would assess in real time whether the learner was confident or anxious about the operation, and automatically provide support accordingly. After the session, the learner's work would be evaluated and feedback would be provided, highlighting specific areas for improvement.

[1135] In this way, the present invention is a system that incorporates cutting-edge technology to improve the efficiency and quality of skill transfer. The addition of an emotion engine makes it possible to understand the psychological states of learners and instructors in real time and provide optimal instruction and learning support.

[1136] The processing flow will be explained below.

[1137] Step 1:

[1138] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[1139] Step 2:

[1140] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[1141] Step 3:

[1142] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[1143] Step 4:

[1144] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[1145] Step 5:

[1146] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[1147] Step 6:

[1148] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[1149] Step 7:

[1150] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[1151] Step 8:

[1152] Learners put on the VR device and join the virtual space using a link provided by the server.

[1153] Step 9:

[1154] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[1155] Step 10:

[1156] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[1157] Step 11:

[1158] The server activates an emotion engine to analyze the emotional states of the learner and instructor in real time. For example, if the learner is feeling anxious, the server will suggest to the instructor to encourage the learner.

[1159] Step 12:

[1160] The emotion engine also monitors the emotional state of the instructor and, for example, provides suggestions for short breaks if the instructor feels frustrated, thereby maintaining the quality of instruction.

[1161] Step 13:

[1162] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[1163] Step 14:

[1164] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[1165] Step 15:

[1166] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[1167] This step will realize a system that allows instructors and students to transfer skills efficiently and in a realistic manner. In particular, the introduction of an emotion engine will enable the real-time analysis of the emotional states of both parties and provide appropriate feedback and support.

[1168] Example 2

[1169] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1170] Current technology transfer methods lack the mechanisms for providing efficient and immersive education. They also often lack the means to grasp the psychological state of learners and instructors in real time and provide appropriate feedback and support. As a result, technology transfer is inefficient, and there are challenges in improving learners' understanding and the quality of their learning experience.

[1171] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1172] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a virtual reality construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an emotion analysis means for analyzing the emotional states of the learner and the instructor in real time and adjusting feedback, an evaluation means for evaluating the learner's performance after the session ends and generating feedback, and an access means for accessing the virtual space using a provided URL or QR code. This enables efficient and realistic skill transfer and makes it possible to provide an optimal learning environment for both the learner and the instructor through real-time emotion analysis and appropriate feedback.

[1173] The "input means for inputting technical details" is an interface that allows the user to input detailed information about the technology they wish to transfer to the system.

[1174] The "means for automatically generating advanced training materials based on generated technical content" refers to an algorithm and system for automatically generating training materials based on input technical content.

[1175] The "virtual reality construction means for constructing a virtual space" is a system for designing and constructing a virtual reality space based on input technical content and generated training materials.

[1176] "A means of communication for real-time communication between learners and instructors" is a system that allows learners and instructors to communicate two-way in real time using the Internet or other networks.

[1177] The "emotion analysis means for analyzing the emotional states of learners and instructors in real time and adjusting feedback" is a system that analyzes the voices and facial expressions of learners and instructors in real time and adaptively changes feedback based on their emotional states.

[1178] The "assessment means for evaluating the learner's performance and generating feedback after the session" is a system for analyzing the learner's behavioral log and training results, evaluating the learner's performance, and generating feedback.

[1179] "Means of access to the virtual space using the provided URL or QR code" refers to the terminal and interface that allows learners to access the virtual space using the URL or QR code provided by the server.

[1180] This invention is a technology transfer platform consisting of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and immersive technology transfer.

[1181] User operations

[1182] Users first log in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technical content they want to teach. For example, they enter information about how to make washi paper, such as the tools, materials, and steps used, and can upload images and video files as needed.

[1183] Server Operation

[1184] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instruction videos, 3D models, etc.) based on the input content. To achieve this, the server inputs prompts like the following into the generative AI:

[1185] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[1186] 1. Tools: aquarium, spoon, net, etc.

[1187] 2. Materials: Raw materials for Japanese paper, glue, etc.

[1188] 3. Steps:

[1189] a. Soaking the raw materials for washi paper in water

[1190] b. Add glue

[1191] c. Shape it with a net

[1192] d. Drying

[1193] 4. Attachments:

[1194] Images of tools and materials

[1195] Instructional Video

[1196] Based on the training materials generated by the generative AI, the server begins constructing the VR / Metaverse space. The required 3D models are selected from the library and placed in the virtual space. The designed virtual space is saved on a dedicated VR server. The server then generates a session URL or QR code and provides it to the user.

[1197] Emotion Engine Operation

[1198] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, the system will automatically adjust the feedback to send encouraging words. If an instructor is frustrated, the system will suggest taking a break to ease their feelings.

[1199] Device operation

[1200] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1201] Real-time technology transfer

[1202] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[1203] Session Closure and Evaluation

[1204] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the analysis results of the emotion engine, indicating the learner's level of understanding and areas for improvement.

[1205] As described above, the technology transfer platform of the present invention can revolutionize conventional technology transfer methods by utilizing generative AI, VR / metaverse, and emotion engines, and provide an efficient and immersive learning experience.

[1206] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1207] Step 1:

[1208] A user logs in to the system.

[1209] Input: Username, Password

[1210] How it works: A user opens a web browser, accesses the system's login page, enters their username and password, and clicks the "Login" button.

[1211] Output: Login success or failure message, dashboard screen

[1212] Step 2:

[1213] A user creates a new session.

[1214] Input: Technical content, images, video files

[1215] How it works: Users click the "Create a new session" button on their dashboard, then fill out a form to describe their technical needs, uploading images and video files as needed.

[1216] Output: Form data with technical content entered

[1217] Step 3:

[1218] The server receives the technical content and stores it in a database.

[1219] Input: Technical form data entered by the user

[1220] How it works: The server receives user input data and stores it in a database using SQL queries.

[1221] Output: Technical details stored in the database

[1222] Step 4:

[1223] The server launches a generation AI to automatically generate training materials.

[1224] Input: Technical details stored in the database

[1225] Operation: The server uses prompts to instruct the generative AI model to generate technical training materials (procedures, instruction videos, 3D models, etc.). The generative AI model generates the training materials based on the input data.

[1226] Example prompt:

[1227] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[1228] 1. Tools: aquarium, spoon, net, etc.

[1229] 2. Materials: Raw materials for Japanese paper, glue, etc.

[1230] 3. Steps:

[1231] a. Soaking the raw materials for washi paper in water

[1232] b. Add glue

[1233] c. Shape it with a net

[1234] d. Drying

[1235] 4. Attachments:

[1236] Images of tools and materials

[1237] Instructional Video

[1238] Output: Generated training materials (instructions, instructional videos, 3D models, etc.)

[1239] Step 5:

[1240] The server creates the VR / Metaverse space.

[1241] Input: Generated training materials

[1242] How it works: The server selects the necessary 3D models from the library based on the training materials and designs the layout of the virtual space. The designed virtual space is saved on a dedicated VR server.

[1243] Output: Virtual space data, virtual space stored on a dedicated VR server

[1244] Step 6:

[1245] The server generates a session URL or QR code and provides it to the user.

[1246] Input: Virtual space data

[1247] How it works: The server generates a URL or QR code for accessing the virtual space and displays it on the user's dashboard.

[1248] Output: Access URL, QR code

[1249] Step 7:

[1250] Learners access the system and participate in the virtual space.

[1251] Input: URL or QR code, credentials (student name, password)

[1252] How it works: Learners access the system by entering the provided URL into their browser or by scanning the QR code with their camera. They enter their credentials and click the "Login" button. Once the necessary authentication is completed, they are provided with a link to join the virtual space. They put on their VR device and use the link to enter the virtual space.

[1253] Output: Authentication result, link to join the virtual space

[1254] Step 8:

[1255] Real-time technology transfer takes place in a virtual space.

[1256] Input: Link to join the virtual space, VR device

[1257] Operation: The instructor and the student communicate in real time through audio and video in the virtual space. The instructor demonstrates the technology, and the student operates it according to the instruction.

[1258] Output: Learner's operation results, progress of skill transfer session

[1259] Step 9:

[1260] The emotion engine analyzes the emotional state of both parties in real time.

[1261] Input: Audio data, video data

[1262] How it works: The emotion engine analyzes the voice and facial expression data of learners and instructors to assess their emotional state in real time, for example measuring stress levels and excitement levels using voice recognition technology and machine learning algorithms.

[1263] Output: Real-time emotion evaluation results

[1264] Step 10:

[1265] The server evaluates the learner's performance after the session and generates feedback.

[1266] Input: Learner's operation log, emotion evaluation results

[1267] How it works: After the session ends, the server collects the learner's behavior log and analyzes it using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review the report and enter additional advice or comments.

[1268] Output: Evaluation report, feedback

[1269] (Application example 2)

[1270] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1271] Conventional technology transfer systems have difficulty effectively transferring complex techniques and procedures, posing particular challenges for transferring skilled techniques on factory production lines. Furthermore, they lacked the ability to grasp the learner's emotional state and provide appropriate feedback, which could lead to reduced learning efficiency. Furthermore, relying solely on technology transfer meant there was no way to effectively transfer technology to factory robots with automated equipment.

[1272] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1273] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, an emotion analysis means for analyzing the learner's emotional state and adjusting the feedback, and a robot control means for controlling the robot's operation based on the evaluation results. This makes it possible to effectively transfer complex skills to factory robots while providing optimal feedback according to the learner's emotional state.

[1274] "Technical content" refers to information such as the specific knowledge, skills, procedures, tools, and materials to be passed on.

[1275] "Input means" refers to an interface or device that allows a user to input technical content into the system.

[1276] "Generation means" refers to a function or system that automatically generates advanced training materials based on input technical content.

[1277] "VR / Metaverse construction means" refers to the technology and software used to construct virtual reality or metaverse spaces.

[1278] "Communication means" refers to the network infrastructure and devices that enable learners and instructors to communicate in real time.

[1279] "Evaluation tools" are functions or systems that evaluate learner performance after a session and generate feedback.

[1280] "Emotion analysis means" refers to a function or system that senses the emotional state of learners and instructors and provides feedback accordingly.

[1281] "Robot control means" refers to a function or system for controlling the operation of the robot based on the evaluation results.

[1282] This invention is a system for transferring technical content in an efficient and immersive way, and is realized by combining generative AI, VR / Metaverse technology, and an emotion engine. The specific operation of the system and the hardware and software used are described below.

[1283] User (instructor) operations:

[1284] First, the instructor logs into the technology transfer system. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technology they want to transfer (e.g., complex part assembly procedures). They enter information such as the tools and materials used and the procedures, and upload images and video files as needed.

[1285] Server behavior:

[1286] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generation AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[1287] Emotion Engine in action:

[1288] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback and send encouraging words. If an instructor is frustrated, it can suggest a break to ease their feelings.

[1289] Terminal operations:

[1290] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1291] Real-time technical transfer:

[1292] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the procedure to the learner. The learner can then simulate assembling parts in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[1293] Session Closure and Evaluation:

[1294] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[1295] Examples:

[1296] For example, when transferring complex parts assembly techniques in a factory, the instructor inputs the assembly steps. Based on this, generative AI creates training materials and constructs a factory line in a virtual space. The learner then joins the virtual space and experiences the assembly work under the guidance of the instructor. The emotion engine evaluates in real time whether the learner is confident or anxious about the operation, and automatically provides support accordingly. After the session, the learner's work is evaluated and feedback is provided with specific areas for improvement.

[1297] Example prompt sentence:

[1298] "Generate appropriate work instructions for the robot based on real-time work data obtained from the VR device. These instructions should include the work flow, detailed steps, and points to note, and also provide feedback based on the worker's emotional state."

[1299] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1300] Step 1:

[1301] The user logs in to the technology transfer system and clicks the "Create a new session" button. They enter the details of the technology (e.g., the assembly procedure for a part) and upload any necessary images or video files.

[1302] Input: Technical content, images, video files

[1303] Output: Uploaded technical content data

[1304] Step 2:

[1305] The server receives the technical content data sent by the user and stores it in a database, thereby permanently storing the entered technical content.

[1306] Input: Technical content data

[1307] Output: Saved technical content data (storage in database)

[1308] Step 3:

[1309] The server activates a generative AI that automatically generates advanced training materials (such as instruction manuals, explanatory videos, and 3D models) based on the stored technical content data.

[1310] Input: Saved technical content data

[1311] Output: Generated training materials (instructions, instructional videos, 3D models)

[1312] Step 4:

[1313] The server begins constructing the VR / Metaverse space based on the generated training materials, placing the 3D model in the virtual space, and saving the designed virtual space.

[1314] Input: Generated training materials (instructions, instructional videos, 3D models)

[1315] Output: Constructed VR / Metaverse space

[1316] Step 5:

[1317] The server generates a session URL or QR code and provides it to the user, allowing the learner to access the virtual space.

[1318] Input: Constructed VR / Metaverse space

[1319] Output: Session URL or QR code

[1320] Step 6:

[1321] Learners access the system using the provided URL or QR code, put on a VR device, enter the virtual space, and participate in the virtual space after entering specific credentials and completing the necessary authentication.

[1322] Input: Session URL or QR code, credentials

[1323] Output: Virtual space in which learners participated

[1324] Step 7:

[1325] The server uses an emotion engine to analyze the emotional state of learners and instructors in real time, for example, automatically adjusting the feedback if a learner is nervous.

[1326] Input: Behavioral and emotional data of students and instructors

[1327] Output: Regulated Feedback

[1328] Step 8:

[1329] Once inside the virtual space, the instructor and learners begin communicating in real time. The instructor demonstrates the technology using a VR device, and the learners simulate assembling the parts in the virtual space.

[1330] Input: Real-time communication data between instructors and students

[1331] Output: Technical transfer activities in virtual space

[1332] Step 9:

[1333] The emotion engine monitors the emotional state of both parties in real time and provides appropriate feedback and assistance.

[1334] Input: Real-time emotion data

[1335] Output: Appropriate feedback and assistance

[1336] Step 10:

[1337] After the session ends, the server collects the learner's behavior log and uses generative AI to analyze the learner's performance. A detailed report containing the evaluation results and feedback is generated and provided to the instructor. The instructor can review the report and provide additional advice or comments.

[1338] Input: Learner behavior log

[1339] Output: Detailed assessment report

[1340] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1341] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1342] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1343] [Fourth embodiment]

[1344] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1345] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1346] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1347] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1348] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1349] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1350] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1351] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1352] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1353] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1354] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1355] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1356] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1357] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI and VR / Metaverse technology, it realizes efficient and realistic technology transfer.

[1358] User (instructor) operation

[1359] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[1360] Server Operation

[1361] The server receives and stores the technical details sent by the user. Based on the stored data, generative AI is used to automatically generate detailed training materials. For example, it generates instruction manuals, video explanations, and 3D models for the washi paper production process. Based on these materials, a VR / Metaverse space is designed to create a virtual space. In the designed virtual space, the tools and materials used in washi paper production are realistically placed as 3D models.

[1362] Device operation

[1363] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1364] Real-time technology transfer

[1365] Once in the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[1366] Session Closure and Evaluation

[1367] After the session ends, the server collects the learner's behavior log and evaluates their performance using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[1368] Specific examples

[1369] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[1370] In this way, the present invention is a system that incorporates advanced technology in order to improve the efficiency and quality of technical transfer.

[1371] The processing flow will be explained below.

[1372] Step 1:

[1373] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[1374] Step 2:

[1375] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[1376] Step 3:

[1377] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[1378] Step 4:

[1379] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[1380] Step 5:

[1381] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[1382] Step 6:

[1383] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[1384] Step 7:

[1385] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[1386] Step 8:

[1387] Learners put on the VR device and join the virtual space using a link provided by the server.

[1388] Step 9:

[1389] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[1390] Step 10:

[1391] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[1392] Step 11:

[1393] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[1394] Step 12:

[1395] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[1396] Step 13:

[1397] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[1398] This step realizes a system that allows instructors and students to transfer skills efficiently and in a realistic manner.

[1399] Example 1

[1400] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1401] Accurate and efficient transmission of knowledge and skills is a challenge in modern technology transfer. Manual and artisanal techniques require real-time instruction and detailed feedback, but traditional methods make it difficult to achieve this appropriately. Communication between learners and instructors is also important, and an environment in which this can occur effectively is necessary. Furthermore, quantitative and objective feedback is required for skill evaluation, but traditional methods often rely on subjective evaluation. To solve these challenges, a new technology transfer system is needed.

[1402] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1403] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, and a virtual environment creation means for constructing a virtual space, thereby improving the accuracy and efficiency of technical transfer and enabling real-time technical instruction and objective performance evaluation.

[1404] "Input means" refers to the device or software that allows the user to input technical content into the system.

[1405] "Generation means" refers to devices or software that automatically generate advanced training materials based on input technical content.

[1406] "Virtual environment construction means" refers to devices and software for designing and constructing virtual spaces.

[1407] "Communication means" refers to devices and software that allow learners and instructors to communicate in real time.

[1408] "Evaluation tools" refer to devices or software that collect learners' behavioral logs after the session ends, evaluate their performance using generative AI models, and generate feedback.

[1409] "Storage means" refers to devices or software for storing technical content in a database or the like.

[1410] "Model placement means" refers to devices or software for placing 3D models in virtual space.

[1411] A "generative AI model" refers to an artificial intelligence model that automatically generates training materials and evaluations based on input data.

[1412] "Technical content" refers to the knowledge and skills that should be passed on, specifically the procedure manuals, tools to be used, details of materials, etc.

[1413] "Training materials" refers to teaching materials, manuals, videos, etc. that contain the information a learner needs to master a skill.

[1414] "Virtual space" refers to a simulated three-dimensional environment that a user can access through a virtual reality (VR) device.

[1415] "Behavior log" refers to data that records the operations and actions performed by a learner during a session.

[1416] "Feedback" refers to information, including advice and suggestions for improvement, given to a learner on their performance using assessment tools.

[1417] The "technology transfer system" refers to the entire system that integrates the above means and enables efficient and effective technology transfer.

[1418] The technology transfer platform of the present invention is a system that consists of users, a server, and terminals, and realizes efficient and realistic technology transfer by utilizing generative AI models and VR / Metaverse technology. Specific embodiments of this system are described in detail below.

[1419] User operations

[1420] First, the user logs in to the system using a device (e.g., a PC or smartphone). After logging in, the user accesses the dashboard and clicks the "Create a new session" button. In the form that appears, the user enters details of the technical content they want to teach (e.g., how to make washi paper). For example, they enter information such as the tools and materials used and the steps, and upload images and video files as needed. This operation allows the system to collect realistic technical content.

[1421] Server Operation

[1422] The server receives the technical details sent by the user and stores them in a database. Based on the stored data, a generative AI model (such as OpenAI's GPT-3 or DALL-E) is used to automatically generate detailed training materials. Specifically, it generates instruction manuals, video explanations, 3D models, and other materials related to the washi paper production process. To create a virtual space based on these materials, a VR / Metaverse space is designed using game engines such as Unity or Unreal Engine as a means of building the virtual environment. In the virtual space, tools and materials used in washi paper production are realistically placed as 3D models.

[1423] Device operation

[1424] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners then put on a VR device (e.g., Oculus Rift or HTC Vive) and use the link to enter the virtual space.

[1425] Real-time technology transfer

[1426] Once inside the virtual space, the instructor and students begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the students. The students can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor.

[1427] Session Closure and Evaluation

[1428] After the session ends, the server collects the learner's behavior log and evaluates their performance using a generative AI model. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The final report is notified to the learner and used to prepare for the next session.

[1429] Specific examples

[1430] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production process. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. After the session, the AI ​​would evaluate the learners' work and provide specific feedback on areas for improvement.

[1431] Prompt Sentence Examples

[1432] "Please enter the detailed steps for making washi paper. Please include the tools and materials you will use, the specific steps, and images and videos if necessary."

[1433] The above is a detailed description of the embodiment of the present invention, and this system can improve the efficiency and quality of technology transfer.

[1434] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1435] Step 1:

[1436] A user logs into the system using a terminal.

[1437] Input: Username, Password

[1438] Specific actions: Open a browser, access the system URL to display the login page, enter your username and password, and click the "Login" button.

[1439] Output: Dashboard screen after successful login

[1440] Step 2:

[1441] The user accesses the dashboard and clicks the "Create a new session" button.

[1442] Specific operation: After logging in, access the dashboard and click the "Create a new session" button.

[1443] Input: None

[1444] Output: Technical content input form

[1445] Step 3:

[1446] The user enters the technical details (e.g., how to make washi paper) into the displayed form and uploads the necessary information.

[1447] Specific operations: In the "Technical Details" field, users enter detailed instructions for making washi paper, add information about the tools and materials used, and upload images and video files to complement the explanation.

[1448] Input: Text information on technical content, image files, video files

[1449] Output: Technical data entered by the user

[1450] Step 4:

[1451] The server receives the technical content sent by the user and stores it in a database.

[1452] What happens: The server receives the HTTP POST request, parses the form data, and saves it in a database.

[1453] Input: Technical data entered by the user

[1454] Output: Technical details stored in the database

[1455] Step 5:

[1456] The server automatically generates detailed training materials using generative AI based on the stored data.

[1457] How it works: The server inputs the stored data on the washi paper production process into a generative AI model, which then generates instructions, video explanations, and 3D models. For example, algorithms such as OpenAI's GPT-3 and DALL-E are used.

[1458] Input: Technical details stored in the database

[1459] Output: Generated instructions, video instructions, 3D models

[1460] Step 6:

[1461] The server designs and constructs the VR / Metaverse space based on the generated materials.

[1462] Specific operation: The server uses a game engine such as Unity or Unreal Engine to create a virtual space and places a washi paper workshop as a 3D model.

[1463] Input: Generated instructions, video instructions, 3D models

[1464] Output: Designed and constructed VR / Metaverse space

[1465] Step 7:

[1466] The server generates and provides the learner with an access URL or QR code.

[1467] What happens: The server generates an access link for the learner and sends it via email. A QR code is also generated, which can be printed if desired.

[1468] Input: Access information for the designed and constructed VR / Metaverse space

[1469] Output: Generated URL, QR code

[1470] Step 8:

[1471] Learners access the system using a device and a provided URL or QR code.

[1472] Specific actions: Learners scan the QR code with their smartphone and tap the displayed link to access the system.

[1473] Input: QR code or URL

[1474] Output: System login screen

[1475] Step 9:

[1476] Learners log in by entering their credentials and are provided with a link to join the VR / Metaverse space.

[1477] Specific operation: The learner enters their username and password on the login page and clicks the "Login" button. If successful, a link to join the VR space will appear on the screen.

[1478] Input: Username, Password

[1479] Output: VR / Metaverse space participation link

[1480] Step 10:

[1481] Learners put on the VR device and use the link to enter the virtual space.

[1482] Specific operation: Learners put on a VR device such as Oculus Rift or HTC Vive, click on the link to enter the virtual space, and after logging in, move to the washi paper making workshop.

[1483] Input: VR / Metaverse space participation link

[1484] Output: A virtual washi paper workshop

[1485] Step 11:

[1486] The instructor will demonstrate the technique using a VR device.

[1487] Specific actions: The instructor operates tools in the VR space and demonstrates the steps for making washi paper. The learners observe the process in real time.

[1488] Input: None

[1489] Output: An environment where technology can be demonstrated in real time

[1490] Step 12:

[1491] Learners simulate skills in a virtual space and receive feedback from instructors.

[1492] Specific actions: Learners process raw materials for washi paper in a VR space and make paper using simulated tools. The instructor observes the process and provides appropriate feedback.

[1493] Input: None

[1494] Output: Simulation and feedback of technology

[1495] Step 13:

[1496] After the session ends, the server collects the learner's behavioral log and evaluates their performance using generative AI.

[1497] What it does: The server analyzes the data recorded during the session and performs an evaluation using a generative AI model (e.g., Azure Cognitive Services or Google Cloud AI).

[1498] Input: Learner behavior log

[1499] Output: Evaluation results and feedback

[1500] Step 14:

[1501] The assessment results and feedback are generated as a report and provided to the instructor.

[1502] What it does: The server generates a detailed report of the assessment results and feedback and uploads it to the instructor's dashboard, where the instructor can review and add comments.

[1503] Input: Evaluation results, feedback

[1504] Output: Report

[1505] Step 15:

[1506] Once the instructor's feedback is complete, the final report will be sent to the learner.

[1507] Specific operation: The instructor sends feedback from the dashboard, the server sends a notification email to the learner, and the learner logs in and checks the feedback.

[1508] Input: Report with feedback

[1509] Output: Feedback communicated to the learner

[1510] (Application example 1)

[1511] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1512] Traditional technology transfer methods are inefficient because they require a lot of time and effort to convey technical information and for learners to acquire practical skills, and there is a risk of mistakes. In particular, in fields requiring advanced skills, such as training to operate factory robots, training in an actual work environment poses safety risks and requires a lot of time and money. For this reason, an effective method for simultaneously improving the efficiency of technology transfer and quality has been sought.

[1513] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1514] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, a means for constructing a 3D simulation model based on the generated training materials, a log collection means for collecting operation data in real time, and an evaluation generation means for automatically providing evaluations and feedback based on the collected operation data. This allows learners to acquire practical skills safely and efficiently, and instructors to provide accurate evaluations and feedback.

[1515] The "input means for inputting technical content" is an interface that allows instructors to input technical information and data into the system.

[1516] The "means for automatically generating advanced training materials based on generated technical content" is a mechanism for automatically generating detailed training materials and guides based on input technical content.

[1517] "VR / Metaverse construction method for constructing virtual spaces" is a technology that creates a training environment in virtual reality or the metaverse based on generated training materials.

[1518] "A means of communication for real-time communication between learners and instructors" refers to a communication technology that enables learners and instructors to communicate in real-time within a virtual space.

[1519] "Evaluation means for evaluating learner performance and generating feedback after the session" is a function that analyzes learner behavior and results after the training session and provides evaluation and feedback using generative AI.

[1520] The "means for constructing a 3D simulation model based on the generated training materials" refers to a technology for creating a three-dimensional simulation model based on the training materials.

[1521] The "log collection means for collecting operation data in real time" is a system that collects data on the operations and actions performed by learners in the virtual space in real time.

[1522] The "evaluation generation means that automatically provides evaluation and feedback based on collected operation data" refers to an algorithm and system that analyzes data obtained by the log collection means and automatically generates an evaluation approach and specific feedback.

[1523] This invention is a system for efficiently and effectively transferring technical content, which combines generative AI models with VR / Metaverse technology. Specific implementation methods are described below.

[1524] Program Generation and Processing Description

[1525] Server Roles

[1526] The server has an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between learners and instructors, and an evaluation means for evaluating the learner's performance and generating feedback after the session ends.

[1527] Furthermore, the server includes a log collection means for constructing a 3D simulation model based on the generated training materials, collecting learner operation data in real time, and an evaluation generation means for automatically providing evaluation and feedback based on the collected operation data.

[1528] Hardware used

[1529] VR device: Used by learners to learn practical skills in a virtual space (e.g., Oculus Quest 2).

[1530] Server: Use cloud-based services for computing resources (e.g., AWS or Google Cloud).

[1531] User terminal: A device used by instructors and students to access the system (e.g., PC or smartphone).

[1532] Software used

[1533] Generative AI models: use natural language processing models to automatically generate training materials from technical content (e.g., GPT-4).

[1534] 3D modeling software: Create 3D models to be used in virtual space (e.g. Blender, Unity)

[1535] Database: Stores technical content and learner data (e.g., MySQL, NoSQL).

[1536] Communication protocol: Realizes real-time communication (e.g., WebSocket, REST API).

[1537] Detailed processing

[1538] The server receives and stores the technical content entered by the instructor, and then uses a generative AI model to automatically generate detailed training materials, including text instructions, video explanations, and 3D models. Based on these materials, a virtual space is designed to recreate a real-world work environment, such as a washi paper workshop.

[1539] In addition, learners' operation data is collected in real time and evaluation and feedback is automatically performed using a generative AI model, which makes the transfer of skills more efficient and enables high-quality training.

[1540] Specific examples

[1541] For example, in training on the operation of a welding robot, an instructor inputs detailed instructions for operating the robot, and the AI ​​generator creates training materials based on these instructions. Learners put on VR devices and enter a virtual welding environment, practicing operations while receiving real-time instruction. During the training, the learner's operation data is monitored in real time by a log collection means, and an evaluation generation means provides evaluations and feedback after the session ends.

[1542] Prompt Sentence Examples

[1543] "The operating procedure for the welding robot is to first secure the handle and release the safety device. Then set the speed and temperature on the operation panel and press the welding start button. Maintain a constant speed during welding, and when finished, reset the safety device and release the handle. Please create a detailed training manual and 3D model based on this."

[1544] In this way, the transfer of technical content can be carried out more efficiently and effectively.

[1545] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1546] Step 1:

[1547] The user (instructor) logs in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. They enter details of the technical content (e.g., welding robot operating procedures) in the displayed form. Specifically, they enter information such as the tools and materials to be used and the procedures, and upload images and video files as needed. The entered data is sent to the server. The input data includes the technical content, attachments, and prompts. The output is a technical content dataset.

[1548] Step 2:

[1549] The server receives and stores the technical content dataset sent by the user. Based on the stored data, it uses a generative AI model to automatically generate detailed training materials. The generated training materials include instructions, video explanations, 3D models, etc. The input data is the technical content dataset, and the output is the generated training materials.

[1550] Step 3:

[1551] The server uses a VR / Metaverse construction tool to construct a virtual space based on the generated training materials. Specifically, it uses 3D modeling software to arrange the work environment, tools, materials, etc. in the virtual space. The virtual space is realistically reproduced as a learning environment. The input data are the training materials, and the output is the constructed virtual space.

[1552] Step 4:

[1553] The user (learner) accesses the system from a device using the provided URL or QR code and logs in by entering their credentials. Once the necessary authentication is completed, a link to join the designated virtual space is provided. The learner puts on the VR device and uses the link to enter the virtual space. The input data is the credentials, and the output is an access link to the virtual space.

[1554] Step 5:

[1555] Users (learners) actually learn skills in a virtual space. Instructors provide instruction while communicating in real time through VR devices. Learners practice procedures in a simulated environment and receive feedback and real-time advice from the instructor. Input data is operation data in the virtual space, and output is the learner's operation log.

[1556] Step 6:

[1557] After the session ends, the server collects the learner's operation data log. The log collection means uses the data collected in real time to pass it to the evaluation generation means. The generation AI analyzes the learner's performance and generates specific evaluations and feedback. The input data is the operation data log, and the output is an evaluation report and feedback.

[1558] Step 7:

[1559] The server generates a detailed report of the assessment results and feedback and provides it to the instructor. The instructor reviews this report and adds additional advice and comments to complete the final report. The completed report is notified to the learner and can be used for the next session or self-study. The input data are the assessment report, feedback, and instructor comments, and the output is the final learning report.

[1560] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1561] The technology transfer platform of this invention is composed of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and realistic technology transfer.

[1562] User (instructor) operation

[1563] The instructor (user) first logs into the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technique they want to teach (e.g., how to make washi paper). Specifically, they enter information such as the tools and materials to be used and the procedure, and upload images and video files as needed.

[1564] Server Operation

[1565] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[1566] Emotion Engine Operation

[1567] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback to be more encouraging. If an instructor is frustrated, it can suggest a break to ease their feelings.

[1568] Device operation

[1569] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1570] Real-time technology transfer

[1571] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[1572] Session Closure and Evaluation

[1573] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[1574] Specific examples

[1575] For example, when passing on the traditional Japanese craft of washi papermaking, an instructor would input the history of washi paper and the production steps. Based on this, the generative AI would create training materials and build a washi paper-making workshop in a virtual space. Learners would then join the virtual space and experience making washi paper under the guidance of the instructor. The emotion engine would assess in real time whether the learner was confident or anxious about the operation, and automatically provide support accordingly. After the session, the learner's work would be evaluated and feedback would be provided, highlighting specific areas for improvement.

[1576] In this way, the present invention is a system that incorporates cutting-edge technology to improve the efficiency and quality of skill transfer. The addition of an emotion engine makes it possible to understand the psychological states of learners and instructors in real time and provide optimal instruction and learning support.

[1577] The processing flow will be explained below.

[1578] Step 1:

[1579] The user (instructor) logs in to the system using a terminal. The user enters the username and password on the login screen on the terminal.

[1580] Step 2:

[1581] The server receives the user's credentials, authenticates them against the registration information in the database, and if authentication is successful, presents the user with a dashboard screen.

[1582] Step 3:

[1583] The user clicks the "Create a new session" button on the dashboard. They fill out the form with the technical details (e.g., how to make washi paper), the tools, materials, and procedures they will use. They can also upload images and video files as needed.

[1584] Step 4:

[1585] The server receives the input technical content and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedure manuals, instructional videos, 3D models, etc.) based on the input content.

[1586] Step 5:

[1587] The server starts constructing the VR / Metaverse space based on the generated training materials, selects the required 3D models from the library and places them in the virtual space, saves the designed virtual space, and generates a session URL or QR code to provide to the user.

[1588] Step 6:

[1589] The learner accesses the system from the provided URL or QR code using their device, enters their credentials, and logs in to the system.

[1590] Step 7:

[1591] The server authenticates the learner's credentials and grants access to the specified skill transfer session. If authentication is successful, it sends the learner's device a link to enter the VR / Metaverse space.

[1592] Step 8:

[1593] Learners put on the VR device and join the virtual space using a link provided by the server.

[1594] Step 9:

[1595] The user (instructor) puts on the VR device and starts a skill transfer session in the virtual space. The user demonstrates the procedure and explains it to the learners.

[1596] Step 10:

[1597] Learners practice skills and ask questions in the virtual space, and the user (instructor) answers questions and provides instructions and guidance in real time.

[1598] Step 11:

[1599] The server activates an emotion engine to analyze the emotional states of the learner and instructor in real time. For example, if the learner is feeling anxious, the server will suggest to the instructor to encourage the learner.

[1600] Step 12:

[1601] The emotion engine also monitors the emotional state of the instructor and, for example, provides suggestions for short breaks if the instructor feels frustrated, thereby maintaining the quality of instruction.

[1602] Step 13:

[1603] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. A report is then automatically generated, including the evaluation results and feedback.

[1604] Step 14:

[1605] The server provides the generated report to the user's (instructor's) dashboard, where the user can review the report and enter additional comments or advice.

[1606] Step 15:

[1607] A final feedback report is sent to the learner, who can review it and prepare for the next session.

[1608] This step will realize a system that allows instructors and students to transfer skills efficiently and in a realistic manner. In particular, the introduction of an emotion engine will enable the real-time analysis of the emotional states of both parties and provide appropriate feedback and support.

[1609] Example 2

[1610] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1611] Current technology transfer methods lack the mechanisms for providing efficient and immersive education. They also often lack the means to grasp the psychological state of learners and instructors in real time and provide appropriate feedback and support. As a result, technology transfer is inefficient, and there are challenges in improving learners' understanding and the quality of their learning experience.

[1612] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1613] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a virtual reality construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an emotion analysis means for analyzing the emotional states of the learner and the instructor in real time and adjusting feedback, an evaluation means for evaluating the learner's performance after the session ends and generating feedback, and an access means for accessing the virtual space using a provided URL or QR code. This enables efficient and realistic skill transfer and makes it possible to provide an optimal learning environment for both the learner and the instructor through real-time emotion analysis and appropriate feedback.

[1614] The "input means for inputting technical details" is an interface that allows the user to input detailed information about the technology they wish to transfer to the system.

[1615] The "means for automatically generating advanced training materials based on generated technical content" refers to an algorithm and system for automatically generating training materials based on input technical content.

[1616] The "virtual reality construction means for constructing a virtual space" is a system for designing and constructing a virtual reality space based on input technical content and generated training materials.

[1617] "A means of communication for real-time communication between learners and instructors" is a system that allows learners and instructors to communicate two-way in real time using the Internet or other networks.

[1618] The "emotion analysis means for analyzing the emotional states of learners and instructors in real time and adjusting feedback" is a system that analyzes the voices and facial expressions of learners and instructors in real time and adaptively changes feedback based on their emotional states.

[1619] The "assessment means for evaluating the learner's performance and generating feedback after the session" is a system for analyzing the learner's behavioral log and training results, evaluating the learner's performance, and generating feedback.

[1620] "Means of access to the virtual space using the provided URL or QR code" refers to the terminal and interface that allows learners to access the virtual space using the URL or QR code provided by the server.

[1621] This invention is a technology transfer platform consisting of users, servers, and terminals, and by utilizing generative AI, VR / metaverse technology, and an emotion engine, it realizes efficient and immersive technology transfer.

[1622] User operations

[1623] Users first log in to the system using a terminal. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technical content they want to teach. For example, they enter information about how to make washi paper, such as the tools, materials, and steps used, and can upload images and video files as needed.

[1624] Server Operation

[1625] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generative AI that automatically generates advanced training materials (procedures, instruction videos, 3D models, etc.) based on the input content. To achieve this, the server inputs prompts like the following into the generative AI:

[1626] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[1627] 1. Tools: aquarium, spoon, net, etc.

[1628] 2. Materials: Raw materials for Japanese paper, glue, etc.

[1629] 3. Steps:

[1630] a. Soaking the raw materials for washi paper in water

[1631] b. Add glue

[1632] c. Shape it with a net

[1633] d. Drying

[1634] 4. Attachments:

[1635] Images of tools and materials

[1636] Instructional Video

[1637] Based on the training materials generated by the generative AI, the server begins constructing the VR / Metaverse space. The required 3D models are selected from the library and placed in the virtual space. The designed virtual space is saved on a dedicated VR server. The server then generates a session URL or QR code and provides it to the user.

[1638] Emotion Engine Operation

[1639] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, the system will automatically adjust the feedback to send encouraging words. If an instructor is frustrated, the system will suggest taking a break to ease their feelings.

[1640] Device operation

[1641] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1642] Real-time technology transfer

[1643] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the steps to the learner. The learner can then simulate the actual production of washi paper in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[1644] Session Closure and Evaluation

[1645] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the analysis results of the emotion engine, indicating the learner's level of understanding and areas for improvement.

[1646] As described above, the technology transfer platform of the present invention can revolutionize conventional technology transfer methods by utilizing generative AI, VR / metaverse, and emotion engines, and provide an efficient and immersive learning experience.

[1647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1648] Step 1:

[1649] A user logs in to the system.

[1650] Input: Username, Password

[1651] How it works: A user opens a web browser, accesses the system's login page, enters their username and password, and clicks the "Login" button.

[1652] Output: Login success or failure message, dashboard screen

[1653] Step 2:

[1654] A user creates a new session.

[1655] Input: Technical content, images, video files

[1656] How it works: Users click the "Create a new session" button on their dashboard, then fill out a form to describe their technical needs, uploading images and video files as needed.

[1657] Output: Form data with technical content entered

[1658] Step 3:

[1659] The server receives the technical content and stores it in a database.

[1660] Input: Technical form data entered by the user

[1661] How it works: The server receives user input data and stores it in a database using SQL queries.

[1662] Output: Technical details stored in the database

[1663] Step 4:

[1664] The server launches a generation AI to automatically generate training materials.

[1665] Input: Technical details stored in the database

[1666] Operation: The server uses prompts to instruct the generative AI model to generate technical training materials (procedures, instruction videos, 3D models, etc.). The generative AI model generates the training materials based on the input data.

[1667] Example prompt:

[1668] Generate training materials for washi paper making using the following data. We've included detailed descriptions of the tools, materials, and procedures you'll need. Select the necessary 3D models from the library.

[1669] 1. Tools: aquarium, spoon, net, etc.

[1670] 2. Materials: Raw materials for Japanese paper, glue, etc.

[1671] 3. Steps:

[1672] a. Soaking the raw materials for washi paper in water

[1673] b. Add glue

[1674] c. Shape it with a net

[1675] d. Drying

[1676] 4. Attachments:

[1677] Images of tools and materials

[1678] Instructional Video

[1679] Output: Generated training materials (instructions, instructional videos, 3D models, etc.)

[1680] Step 5:

[1681] The server creates the VR / Metaverse space.

[1682] Input: Generated training materials

[1683] How it works: The server selects the necessary 3D models from the library based on the training materials and designs the layout of the virtual space. The designed virtual space is saved on a dedicated VR server.

[1684] Output: Virtual space data, virtual space stored on a dedicated VR server

[1685] Step 6:

[1686] The server generates a session URL or QR code and provides it to the user.

[1687] Input: Virtual space data

[1688] How it works: The server generates a URL or QR code for accessing the virtual space and displays it on the user's dashboard.

[1689] Output: Access URL, QR code

[1690] Step 7:

[1691] Learners access the system and participate in the virtual space.

[1692] Input: URL or QR code, credentials (student name, password)

[1693] How it works: Learners access the system by entering the provided URL into their browser or by scanning the QR code with their camera. They enter their credentials and click the "Login" button. Once the necessary authentication is completed, they are provided with a link to join the virtual space. They put on their VR device and use the link to enter the virtual space.

[1694] Output: Authentication result, link to join the virtual space

[1695] Step 8:

[1696] Real-time technology transfer takes place in a virtual space.

[1697] Input: Link to join the virtual space, VR device

[1698] Operation: The instructor and the student communicate in real time through audio and video in the virtual space. The instructor demonstrates the technology, and the student operates it according to the instruction.

[1699] Output: Learner's operation results, progress of skill transfer session

[1700] Step 9:

[1701] The emotion engine analyzes the emotional state of both parties in real time.

[1702] Input: Audio data, video data

[1703] How it works: The emotion engine analyzes the voice and facial expression data of learners and instructors to assess their emotional state in real time, for example measuring stress levels and excitement levels using voice recognition technology and machine learning algorithms.

[1704] Output: Real-time emotion evaluation results

[1705] Step 10:

[1706] The server evaluates the learner's performance after the session and generates feedback.

[1707] Input: Learner's operation log, emotion evaluation results

[1708] How it works: After the session ends, the server collects the learner's behavior log and analyzes it using generative AI. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review the report and enter additional advice or comments.

[1709] Output: Evaluation report, feedback

[1710] (Application example 2)

[1711] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1712] Conventional technology transfer systems have difficulty effectively transferring complex techniques and procedures, posing particular challenges for transferring skilled techniques on factory production lines. Furthermore, they lacked the ability to grasp the learner's emotional state and provide appropriate feedback, which could lead to reduced learning efficiency. Furthermore, relying solely on technology transfer meant there was no way to effectively transfer technology to factory robots with automated equipment.

[1713] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1714] In this invention, the server includes an input means for inputting technical content, a generation means for automatically generating advanced training materials based on the generated technical content, a VR / metaverse construction means for constructing a virtual space, a communication means for real-time communication between the learner and the instructor, an evaluation means for evaluating the learner's performance and generating feedback after the session ends, an emotion analysis means for analyzing the learner's emotional state and adjusting the feedback, and a robot control means for controlling the robot's operation based on the evaluation results. This makes it possible to effectively transfer complex skills to factory robots while providing optimal feedback according to the learner's emotional state.

[1715] "Technical content" refers to information such as the specific knowledge, skills, procedures, tools, and materials to be passed on.

[1716] "Input means" refers to an interface or device that allows a user to input technical content into the system.

[1717] "Generation means" refers to a function or system that automatically generates advanced training materials based on input technical content.

[1718] "VR / Metaverse construction means" refers to the technology and software used to construct virtual reality or metaverse spaces.

[1719] "Communication means" refers to the network infrastructure and devices that enable learners and instructors to communicate in real time.

[1720] "Evaluation tools" are functions or systems that evaluate learner performance after a session and generate feedback.

[1721] "Emotion analysis means" refers to a function or system that senses the emotional state of learners and instructors and provides feedback accordingly.

[1722] "Robot control means" refers to a function or system for controlling the operation of the robot based on the evaluation results.

[1723] This invention is a system for transferring technical content in an efficient and immersive way, and is realized by combining generative AI, VR / Metaverse technology, and an emotion engine. The specific operation of the system and the hardware and software used are described below.

[1724] User (instructor) operations:

[1725] First, the instructor logs into the technology transfer system. After logging in, they access the dashboard and click the "Create a new session" button. In the form that appears, they enter details of the technology they want to transfer (e.g., complex part assembly procedures). They enter information such as the tools and materials used and the procedures, and upload images and video files as needed.

[1726] Server behavior:

[1727] The server receives the technical content sent by the user and stores it in a database. At the same time, it activates a generation AI that automatically generates advanced training materials (procedures, instructional videos, 3D models, etc.) based on the input content. It then begins constructing the VR / Metaverse space based on the generated training materials. It selects the necessary 3D models from the library and places them in the virtual space. It saves the designed virtual space and generates a session URL or QR code to provide to the user.

[1728] Emotion Engine in action:

[1729] The server uses an emotion engine to analyze the emotional states of learners and instructors in real time. For example, if a learner is nervous, it can automatically adjust the feedback and send encouraging words. If an instructor is frustrated, it can suggest a break to ease their feelings.

[1730] Terminal operations:

[1731] Learners access the system from their devices using the provided URL or QR code. After logging in by entering their credentials and completing the necessary authentication, they are provided with a link to join the designated VR / Metaverse space. Learners put on their VR device and use the link to enter the virtual space.

[1732] Real-time technical transfer:

[1733] Once inside the virtual space, the instructor and learner begin communicating in real time. The instructor uses a VR device to demonstrate the technique and explain the procedure to the learner. The learner can then simulate assembling parts in the virtual space and receive advice and guidance from the instructor. The emotion engine monitors the emotional states of both parties in real time and provides appropriate feedback and assistance.

[1734] Session Closure and Evaluation:

[1735] After the session ends, the server collects the learner's behavioral log and uses generative AI to analyze the learner's performance. The evaluation results and feedback are generated as a detailed report and provided to the instructor. The instructor can review this report and enter additional advice or comments. The feedback is provided in a format that also reflects the results of emotion analysis by an emotion engine, and indicates the learner's level of understanding and areas for improvement.

[1736] Examples:

[1737] For example, when transferring complex parts assembly techniques in a factory, the instructor inputs the assembly steps. Based on this, generative AI creates training materials and constructs a factory line in a virtual space. The learner then joins the virtual space and experiences the assembly work under the guidance of the instructor. The emotion engine evaluates in real time whether the learner is confident or anxious about the operation, and automatically provides support accordingly. After the session, the learner's work is evaluated and feedback is provided with specific areas for improvement.

[1738] Example prompt sentence:

[1739] "Generate appropriate work instructions for the robot based on real-time work data obtained from the VR device. These instructions should include the work flow, detailed steps, and points to note, and also provide feedback based on the worker's emotional state."

[1740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1741] Step 1:

[1742] The user logs in to the technology transfer system and clicks the "Create a new session" button. They enter the details of the technology (e.g., the assembly procedure for a part) and upload any necessary images or video files.

[1743] Input: Technical content, images, video files

[1744] Output: Uploaded technical content data

[1745] Step 2:

[1746] The server receives the technical content data sent by the user and stores it in a database, thereby permanently storing the entered technical content.

[1747] Input: Technical content data

[1748] Output: Saved technical content data (storage in database)

[1749] Step 3:

[1750] The server activates a generative AI that automatically generates advanced training materials (such as instruction manuals, explanatory videos, and 3D models) based on the stored technical content data.

[1751] Input: Saved technical content data

[1752] Output: Generated training materials (instructions, instructional videos, 3D models)

[1753] Step 4:

[1754] The server begins constructing the VR / Metaverse space based on the generated training materials, placing the 3D model in the virtual space, and saving the designed virtual space.

[1755] Input: Generated training materials (instructions, instructional videos, 3D models)

[1756] Output: Constructed VR / Metaverse space

[1757] Step 5:

[1758] The server generates a session URL or QR code and provides it to the user, allowing the learner to access the virtual space.

[1759] Input: Constructed VR / Metaverse space

[1760] Output: Session URL or QR code

[1761] Step 6:

[1762] Learners access the system using the provided URL or QR code, put on a VR device, enter the virtual space, and participate in the virtual space after entering specific credentials and completing the necessary authentication.

[1763] Input: Session URL or QR code, credentials

[1764] Output: Virtual space in which learners participated

[1765] Step 7:

[1766] The server uses an emotion engine to analyze the emotional state of learners and instructors in real time, for example, automatically adjusting the feedback if a learner is nervous.

[1767] Input: Behavioral and emotional data of students and instructors

[1768] Output: Regulated Feedback

[1769] Step 8:

[1770] Once inside the virtual space, the instructor and learners begin communicating in real time. The instructor demonstrates the technology using a VR device, and the learners simulate assembling the parts in the virtual space.

[1771] Input: Real-time communication data between instructors and students

[1772] Output: Technical transfer activities in virtual space

[1773] Step 9:

[1774] The emotion engine monitors the emotional state of both parties in real time and provides appropriate feedback and assistance.

[1775] Input: Real-time emotion data

[1776] Output: Appropriate feedback and assistance

[1777] Step 10:

[1778] After the session ends, the server collects the learner's behavior log and uses generative AI to analyze the learner's performance. A detailed report containing the evaluation results and feedback is generated and provided to the instructor. The instructor can review the report and provide additional advice or comments.

[1779] Input: Learner behavior log

[1780] Output: Detailed assessment report

[1781] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1782] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1784] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1785] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1786] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1787] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1788] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1789] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1790] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1791] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1792] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1793] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1794] 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.

[1795] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1796] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1797] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1798] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1799] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1800] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1801] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1802] The following is further disclosed regarding the above embodiment.

[1803] (Claim 1)

[1804] an input means for inputting technical details;

[1805] a generating means for automatically generating advanced training materials based on the generated technical content;

[1806] VR / Metaverse construction methods for constructing virtual spaces,

[1807] A means of communication for learners and instructors to communicate in real time;

[1808] an assessment instrument to evaluate learner performance and generate feedback after the session;

[1809] A system including:

[1810] (Claim 2)

[1811] 10. The system of claim 1, further comprising a storage means for storing the technical content.

[1812] (Claim 3)

[1813] 10. The system of claim 1, further comprising a model placement means for placing the 3D model in the virtual space.

[1814] "Example 1"

[1815] (Claim 1)

[1816] an input means for inputting technical details;

[1817] a generating means for automatically generating advanced training materials based on the generated technical content;

[1818] a virtual environment construction means for constructing a virtual space;

[1819] A means of communication for learners and instructors to communicate in real time;

[1820] An evaluation method that collects learners' behavioral logs after the session, evaluates their performance using a generative AI model, and generates feedback;

[1821] A system including:

[1822] (Claim 2)

[1823] 10. The system of claim 1, further comprising a storage means for storing the technical content.

[1824] (Claim 3)

[1825] 2. The system according to claim 1, further comprising a model placement means for placing the 3D model in the virtual space.

[1826] "Application Example 1"

[1827] (Claim 1)

[1828] an input means for inputting technical details;

[1829] a generating means for automatically generating advanced training materials based on the generated technical content;

[1830] VR / Metaverse construction methods for constructing virtual spaces,

[1831] A means of communication for learners and instructors to communicate in real time;

[1832] an assessment instrument to evaluate learner performance and generate feedback after the session;

[1833] means for constructing a 3D simulation model based on the generated training materials;

[1834] a log collection means for collecting operation data in real time;

[1835] A rating generation means that automatically provides ratings and feedback based on collected operation data;

[1836] A system including:

[1837] (Claim 2)

[1838] 10. The system of claim 1, further comprising a storage means for storing the technical content.

[1839] (Claim 3)

[1840] 10. The system of claim 1, further comprising a model placement means for placing the 3D model in the virtual space.

[1841] "Example 2: Combining Emotion Engines"

[1842] (Claim 1)

[1843] an input means for inputting technical details;

[1844] a generating means for automatically generating advanced training materials based on the generated technical content;

[1845] a virtual reality construction means for constructing a virtual space;

[1846] A means of communication for learners and instructors to communicate in real time;

[1847] An emotion analysis means for analyzing the emotional states of learners and instructors in real time and adjusting feedback;

[1848] an assessment instrument to evaluate learner performance and generate feedback after the session;

[1849] Access to the virtual space using the provided URL or QR code;

[1850] A system including:

[1851] (Claim 2)

[1852] 10. The system of claim 1, further comprising a storage means for storing the technical content.

[1853] (Claim 3)

[1854] 10. The system of claim 1, further comprising a model placement means for placing the three-dimensional model in the virtual space.

[1855] "Application example 2 when combining emotion engines"

[1856] (Claim 1)

[1857] an input means for inputting technical details;

[1858] a generating means for automatically generating advanced training materials based on the generated technical content;

[1859] VR / Metaverse construction methods for constructing virtual spaces,

[1860] A means of communication for learners and instructors to communicate in real time;

[1861] an assessment instrument to evaluate learner performance and generate feedback after the session;

[1862] an emotion analysis means for analyzing the emotional state and adjusting the feedback;

[1863] a robot control means for controlling the operation of the robot based on the evaluation result;

[1864] A system including:

[1865] (Claim 2)

[1866] 10. The system of claim 1, further comprising a storage means for storing the technical content.

[1867] (Claim 3)

[1868] 10. The system of claim 1, further comprising a model placement means for placing the 3D model in the virtual space. [Explanation of symbols]

[1869] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting technical details; a generating means for automatically generating advanced training materials based on the generated technical content; VR / Metaverse construction methods for constructing virtual spaces, A means of communication for learners and instructors to communicate in real time; an assessment instrument to evaluate learner performance and generate feedback after the session; A system including:

2. 10. The system of claim 1, further comprising a storage means for storing technical content.

3. The system according to claim 1 , further comprising a model placement means for placing the 3D model in the virtual space.

Images