System

Abstract

A system is provided.SOLUTION: A system, comprising: means for generating a specific scenario for a selected business simulation using generation artificial intelligence; means for providing a visual and auditory experience to a user based on the generated scenario using a mixed reality technology; means for detecting a user's operation in real time and performing a simulation based on the detected user's operation; and means for analyzing user's operation data and generating feedback after the simulation is completed.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's workplace, it is often difficult for new hires and job seekers to accurately understand the job content, resulting in a discrepancy between workplace expectations and reality. This can lead to early turnover and mismatches. Furthermore, in educational settings, students and trainees have difficulty understanding the specifics of their jobs, limiting their future career choices. This invention aims to solve these issues by utilizing generative AI and mixed reality technology to provide a realistic work experience without being present at the workplace. [Means for solving the problem]

[0005] The present invention includes the following means. First, it provides a means for generating a specific scenario for a selected business simulation using generative artificial intelligence. Second, it includes a means for providing a user with a visual and auditory experience based on the scenario generated using mixed reality technology. It also provides a means for detecting user operations in real time and progressing the simulation based on the detected operations. It also includes a means for analyzing user operation data and generating feedback after the simulation is completed. When a user selects a business simulation, the system also includes a means for transmitting the selection information to a server, and the server for transmitting initial setting data for the selected business simulation to the user's terminal. It also includes a means for generating 3D models, audio guides, and text data for task guides using mixed reality technology and displaying them on the user's terminal.

[0006] "Generative AI" is an AI technology that automatically generates the scenarios and situations required for a specified business simulation.

[0007] "Mixed reality technology" is a technology that provides users with images and sounds that combine the real world and the virtual world.

[0008] A "scenario" is a sequence of specific situations and events that unfold during a business simulation that a user experiences.

[0009] A "visual and auditory experience" is an experience that includes the images and video that a user can see and the audio and guidance that a user can hear during the simulation.

[0010] "Real-time detection" refers to immediately identifying user actions and inputs without delay and processing that information.

[0011] "Feedback" is information that evaluates the user's operations and behavior after the simulation is completed, and specifically indicates areas for improvement and good points.

[0012] A "work simulation" is a program that virtually recreates a specific work environment and work content, allowing users to experience the work in that environment.

[0013] "Initial setting data" refers to data that includes basic information and initial parameters required to start a business simulation.

[0014] A "3D model" is digital data that reproduces real-world objects or environments in three-dimensional space.

[0015] "Audio guide" refers to voice messages that guide or instruct the user during a simulation.

[0016] "Work guide text data" is text data that includes work procedures and explanations for the user to refer to during the simulation. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention relates to a system that combines generative artificial intelligence and mixed reality technology to enable users to have a realistic work experience even when they are not at the actual work site. This system is composed of three main entities: a server, a user's terminal, and the user.

[0039] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0040] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0041] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0042] The server also uses mixed reality technology to generate 3D models, audio guides, and text data for task guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0043] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. The device displays this to the user, allowing the simulation to proceed.

[0044] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0045] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their performance and understand areas for improvement.

[0046] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved educational effectiveness, and reduced early employee turnover. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0050] Step 2:

[0051] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0052] Step 3:

[0053] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0054] Step 4:

[0055] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0056] Step 5:

[0057] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0058] Step 6:

[0059] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0060] Step 7:

[0061] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0062] Step 8:

[0063] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[0064] Step 9:

[0065] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. Based on the analysis results, feedback evaluating the user's work accuracy, speed, and response accuracy is sent to the device.

[0066] Step 10:

[0067] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[0068] Example 1

[0069] 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."

[0070] With conventional work simulation systems, it was difficult for users to gain realistic work experience when they were not present at the site, and the lack of real-time feedback and evaluation based on user operations made it difficult to achieve effective work training.

[0071] 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.

[0072] In this invention, the server includes: a means for a user to transmit authentication information from a terminal to the server and for the server to verify the information; a means for a user to select a business simulation and transmit the selection information to the server; and a means for the server to generate initial setting data for the selected business simulation and transmit the initial setting data to the terminal. This allows the user to experience a realistic business operation even when not at the site. The server also includes a means for generating a specific scenario using generative artificial intelligence, a means for providing a visual and auditory experience using mixed reality technology, a means for the terminal to detect user operations in real time and transmit the data to the server, and a means for the server to analyze the operation data, generate next steps and feedback, and transmit the data to the terminal. This enables real-time feedback and evaluation based on the user's operations, resulting in more effective business training.

[0073] "User authentication" is the process in which a user provides authentication information, such as an ID and password, required to prove his or her identity to a server, which then verifies the information.

[0074] "Business simulation" is a system that virtually recreates a specific business environment and procedures, allowing users to experience simulated business operations within that environment.

[0075] "Initial setting data" refers to data that includes basic information and conditions necessary to start a business simulation, such as a line layout diagram and the types of tools to be used.

[0076] "Generative AI" is an AI technology that has the ability to automatically generate scenarios and data according to specific purposes.

[0077] A "specific scenario" is a plan that describes in detail the series of tasks and procedures that a user will experience in a business simulation.

[0078] "Mixed reality technology" is a technology that provides an environment that combines the real world and the virtual world, allowing users to experience 3D models and audio guides visually and aurally in real space.

[0079] "Operation data" refers to data related to specific operations (for example, hand movements or actions following instructions) performed by the user during the task simulation.

[0080] "Feedback" is information that includes evaluations and instructions on a user's actions and behaviors, allowing the user to check their performance and understand areas for improvement.

[0081] This invention relates to a system that allows users to gain a realistic work experience even when they are not at the site. This system is composed of three main components: a server, a user's terminal, and the user.

[0082] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends this information to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0083] The user selects the task they wish to simulate from the menu (e.g., factory line work, office work, safety training, etc.) and sends the selection information from the terminal to the server. The server generates initial setting data for the selected task simulation (e.g., line layout diagram, types of tools to be used, etc.) and sends it to the terminal.

[0084] Next, the server uses generative artificial intelligence to generate specific scenarios (e.g., "product check," "packaging," "response when the line stops," etc.). This scenario data is sent to the device and displayed on the user's display screen. The server then uses mixed reality technology to generate 3D models, audio guides, and text data for work guides corresponding to the scenario. These data are then delivered to the user's device, allowing them to experience them visually and audibly through an MR headset or display.

[0085] When a user starts a business simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The terminal displays this to the user, allowing the simulation to proceed.

[0086] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0087] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user. The user can check their own performance and understand areas for improvement.

[0088] This allows users to experience work that is close to the actual work even when they are not on-site, which is expected to have the effects of improving work understanding, improving educational effectiveness, and even preventing early turnover. Specific examples include simulations of factory line work, training in work procedures in the office, and safety management scenarios. The present invention can be applied to these wide-ranging work simulations.

[0089] Examples of prompts include:

[0090] "Please start the factory line work simulation training."

[0091] "Please explain the packaging steps in this business scenario."

[0092] "Show emergency shutdown procedures in safety training scenarios."

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1: User authentication

[0095] Input: Authentication information (ID and password) entered by the user on the device

[0096] Processing: The terminal sends the entered authentication information to the server, which checks the information against a database to authenticate the user.

[0097] Output: If authentication is successful, the server sends a business simulation menu to the terminal, which displays it to the user.

[0098] Specific operation: The user enters "user123" and "password1234" on the login screen, and the terminal sends that information to the server. The server checks the database, and if authentication is successful, it sends the message "Authentication successful" along with a business simulation menu to the terminal. The terminal displays it to the user.

[0099] Step 2: Simulation Selection

[0100] Input: User-selected business simulation information

[0101] Processing: The device sends information about the selected simulation to the server, which then generates initial setting data based on that information.

[0102] Output: The server sends the generated initialization data to the terminal, which displays it to the user.

[0103] Specific operation: The user selects "Line Work Simulation" from the menu, and the terminal sends the selection result to the server. The server generates initial setting data such as "Line Layout Diagram" and "Tool List" and sends it back to the terminal. The terminal displays the initial setting data on the screen.

[0104] Step 3: Scenario generation

[0105] Input: Initial setting data and business simulation selection information sent to the server

[0106] Processing: The server uses generative artificial intelligence (generative AI model) to generate specific scenarios.

[0107] Output: The server sends the generated scenario data to the terminal, which displays it to the user.

[0108] Specific operation: The server generates scenarios such as "product check" and "response when line is stopped" based on the initial setting data and sends them to the terminal. The terminal displays "Next step: Please check the product."

[0109] Step 4: Guide Data Generation

[0110] Input: Scenario data sent to the server

[0111] Processing: The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the task guide corresponding to the scenario.

[0112] Output: The generated data is sent to the terminal, which presents it to the user visually and audibly.

[0113] Specific operation: The server generates a 3D model of how to check product A and an audio guide, and sends them to the device. The device displays the 3D model on the headset and plays the audio guide.

[0114] Step 5: Run the simulation and send and receive data

[0115] Input: User operation information (hand movements, gaze, etc.) and scenario data

[0116] Processing: The device detects user actions in real time and sends the information to the server, which analyzes the action data and generates next steps and feedback.

[0117] Output: Instructions and feedback data from the server are sent to the device, which displays them to the user.

[0118] Specific operation: The user puts on the headset and performs the action of "checking product A." The device detects this movement and sends it to the server. The server analyzes the operation data and sends an instruction to the device saying, "Next, check product B." The device displays this on the screen.

[0119] Step 6: Generate feedback upon completion

[0120] Input: All operational data collected during the simulation

[0121] Processing: At the end of the simulation, the device sends all operation data to the server, which analyzes the data and evaluates the accuracy, efficiency, and speed of the operation.

[0122] Output: The server generates the evaluation result and sends it to the terminal, which displays it to the user.

[0123] Specific operation: At the end of the simulation, the device sends all operation data to the server. The server generates feedback such as "Product A check: successful" or "Product B check: failed (details of the error)" and sends it back to the device. The device displays the evaluation results to the user.

[0124] (Application example 1)

[0125] 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."

[0126] There is a need for a method that allows users to learn factory robot operation and maintenance work in a realistic way without visiting the site. There is also a need to improve the effectiveness of learning by capturing user operations in real time and providing appropriate feedback.

[0127] 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.

[0128] In this invention, the server includes means for generating a specific scenario for a selected business simulation using generative artificial intelligence, means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting the user's operations in real time and progressing the simulation based on the detected operations, means for analyzing the user's operation data after the simulation is completed and generating feedback, means for learning factory robot operation and maintenance work, and means for capturing the user's operations and transmitting them to the server. This allows the user to realistically experience actual factory work even when they are not on-site, thereby improving the learning effect.

[0129] "Generative AI" is a technology that generates specific scenarios and instructions for a selected business simulation.

[0130] "Mixed reality technology" is a technology that provides users with a visual and auditory experience, displaying a combination of the real world and the virtual world.

[0131] A "scenario" is a storyline that includes a series of tasks and instructions that a user experiences in a business simulation.

[0132] A "3D model" is a virtual object displayed in three-dimensional space using computer graphics, and is intended for users to visually recognize.

[0133] "Audio guide" is a system that provides audio guidance to users on tasks and operation methods in business simulations.

[0134] "User operations" refer to inputs and actions that a user performs to progress the simulation.

[0135] "Real-time detection" means that the system recognizes user actions immediately and on the spot.

[0136] "Feedback" refers to evaluations and instructions generated based on a user's operations, including the accuracy and efficiency of the operations.

[0137] A "factory robot" is an automated machine used to perform manufacturing and maintenance work within a factory.

[0138] "Capture" means recording a user's actions and behavior.

[0139] A "server" is a computer system that controls the entire business simulation system and generates and analyzes data.

[0140] This invention is a system for learning factory robot operation and maintenance work that combines generative artificial intelligence and mixed reality technology. This system is composed of three main entities: a server, a user's terminal, and the user.

[0141] The server uses generative artificial intelligence to generate a specific scenario for the selected task simulation. Specifically, a scenario is generated that includes tasks such as "robot oil change," "oil filter removal," and "emergency shutdown procedure." During this generation process, the server sets a prompt sentence based on the user's selection. For example, a prompt sentence such as "The user is changing the oil on a factory robot. Please instruct the next step, how to remove the oil filter." is used.

[0142] The user's device uses mixed reality technology to display a 3D model based on the generated scenario, along with audio and text guides. By wearing an optical head-mounted display (HMD), the user can experience the realistic operating environment of an actual factory robot.

[0143] When a user starts a work simulation, the device detects the user's actions in real time and sends them to the server. For example, when a user removes an oil filter from a factory robot, a sensor on the device detects hand movements and sends the data to the server. The server analyzes this data, generates instructions and feedback for proceeding to the next step, and sends it to the device. The user can refer to this to continue the simulation.

[0144] After the simulation is complete, the server analyzes the collected operation data and provides the results as feedback. This feedback includes an evaluation of the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand areas for improvement.

[0145] As a concrete example, a user wearing a mixed reality headset simulates an oil change operation by a factory robot, and the system captures and analyzes the operation and provides appropriate feedback. This process contributes to improving the user's skills, enabling efficient training without going to the site.

[0146] As described above, the present invention provides an effective means for virtually experiencing the operation and maintenance of factory robots and acquiring skills.

[0147] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0148] Step 1:

[0149] The user accesses the system using a device (smartphone, tablet, PC, etc.) and logs in. The user enters authentication information (ID and password) and sends it to the server. The server verifies this authentication information, and if authentication is successful, displays a business simulation menu on the device.

[0150] Input: User authentication information (ID, password)

[0151] Output: Authentication results, business simulation menu display

[0152] Step 2:

[0153] The user selects a simulation from the business simulation menu. The selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The terminal prepares the received initial setting data and prepares for the simulation.

[0154] Input: User's simulation selection information

[0155] Output: Initial setting data, simulation ready

[0156] Step 3:

[0157] The server uses generative AI to generate specific scenarios required for the selected task simulation. For example, it generates a scenario for the task of "changing oil for a robot." In this case, it uses prompt statements to input the generative AI model and generates an appropriate scenario.

[0158] Input: Simulation selection information, prompt text

[0159] Output: Scenario data (specific task flow)

[0160] Step 4:

[0161] The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, and sends them to the user's device, which receives the data and provides the user with a visual and auditory experience through an MR headset or display.

[0162] Input: Scenario data

[0163] Output: 3D model, audio guide, text data of operation guide

[0164] Step 5:

[0165] The user starts the task simulation, and the device detects the user's operations in real time. For example, when a user wears an MR headset and performs an action to remove an oil filter from a robot, the device's sensors capture the user's hand movements. The device then transmits the captured operation data to the server.

[0166] Input: User operation (hand movement)

[0167] Output: Captured operation data

[0168] Step 6:

[0169] The server receives and analyzes the user's operation data. Based on the analyzed data, it generates instructions for the next step and real-time feedback and sends them to the device. The device then displays the instructions and feedback to the user, progressing through the simulation.

[0170] Input: Captured operation data

[0171] Output: Next step instructions, feedback

[0172] Step 7:

[0173] After the simulation is over, the server analyzes the collected operation data and generates feedback, including the accuracy, efficiency, and speed of response. The evaluation results are sent to the terminal and displayed to the user, allowing them to check their performance and understand areas for improvement.

[0174] Input: All operation data

[0175] Output: Evaluation results, feedback

[0176] These steps allow users to experience and learn factory robot operation and maintenance tasks in a realistic way, even when they are not on-site, thereby efficiently improving their skills.

[0177] 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.

[0178] This invention relates to a system that combines generative artificial intelligence, mixed reality technology, and an emotion engine to enable users to have a realistic work experience even when they are not at the actual work site. This system consists of three entities: a server, a user terminal, and the user.

[0179] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0180] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0181] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0182] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0183] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and adjusts the progress of the simulation and the content of the feedback according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or a simple task.

[0184] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's actions and sends that information to the device. The user continues the simulation while referring to this. The device also detects emotions from the user's facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that the user is tired.

[0185] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[0186] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0187] The processing flow will be explained below.

[0188] Step 1:

[0189] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0190] Step 2:

[0191] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0192] Step 3:

[0193] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0194] Step 4:

[0195] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0196] Step 5:

[0197] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0198] Step 6:

[0199] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0200] Step 7:

[0201] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0202] Step 8:

[0203] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[0204] Step 9:

[0205] The emotion engine detects emotions from the user's facial expressions and voice and sends the emotion data to the server, which analyzes the emotion data and adjusts the scenario and feedback content based on the user's emotional state.

[0206] Step 10:

[0207] If the user feels stressed during the simulation, the server generates instructions suggesting a temporary break or a simple task and sends them to the device, which then displays the suggested instructions to the user.

[0208] Step 11:

[0209] Once the simulation is complete, the server analyzes the collected operation and emotion data and generates feedback. Based on the analysis results, feedback evaluating the user's task accuracy, speed, and response accuracy, as well as their emotional state, is sent to the device.

[0210] Step 12:

[0211] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[0212] Example 2

[0213] 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."

[0214] Conventional business simulation systems have the problem that it is difficult for users to experience realistic business operations when they are not present at the site, and it is also difficult to provide appropriate feedback based on the user's operations and emotional state. Furthermore, conventional systems cannot provide feedback that takes into account the user's emotional state, which limits the effectiveness of user learning and the improvement of business understanding.

[0215] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for transmitting authentication information via an information processing device operated by a user and displaying a menu of a business simulation if the authentication is successful; means for generating a specific scenario for the selected business simulation using a generative artificial intelligence; means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology; means for detecting the user's operations in real time and progressing the simulation based on the detected operations; means for analyzing the user's emotional state using an emotion engine and generating feedback accordingly; and means for analyzing the user's operation data and emotion data after the simulation is completed and generating feedback. This allows the user to have a realistic business experience even when they are not present at the site, and makes it possible to provide appropriate feedback based on the operation data and emotion data.

[0216] "Information processing device" is a general term for devices and systems that are operated by a user to input, display, and transmit data.

[0217] "Authentication Information" refers to personal identification information such as ID and password used by a User to access the System.

[0218] "Generative AI" refers to AI technology that has the ability to analyze data and generate scenarios and feedback for specific tasks or purposes.

[0219] "Business simulation" refers to a simulated business process that allows users to experience real business processes in a virtual environment for practice and training.

[0220] "Mixed reality technology" refers to technology that blends the real world with the virtual world, providing users with a real-time visual and auditory experience.

[0221] An "emotion engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice, and generates appropriate feedback based on the analysis results.

[0222] A "three-dimensional model" refers to a visual object constructed in three-dimensional space using computer graphics (CG).

[0223] "Voice guide" refers to a function that provides instructions and guidance to the user by voice.

[0224] "Work guide text data" refers to data that explains work procedures to the user in text and illustrations.

[0225] "Operation data" refers to data related to a series of operations performed by a user during a business simulation.

[0226] "Emotion data" refers to data relating to the emotional state detected by the user's facial expressions and voice.

[0227] This invention relates to a system that allows users to have a realistic work experience even when they are not at the site, and achieves this by combining generative artificial intelligence, mixed reality technology, and an emotion engine. The system consists of three entities: a server, a user's terminal, and the user.

[0228] The server, as the central control device of the present invention, utilizes the following hardware and software: the hardware includes a CPU, memory, storage device, and network interface, while the software includes a live artificial intelligence algorithm, mixed reality (MR) technology, an emotion engine, and a database system for managing user data.

[0229] The user's device may be a personal computer (PC), tablet, smartphone, etc., and provides an interface for the user to operate, including a keyboard, mouse, touchscreen, and MR headset.

[0230] Users access the system using their own terminal, enter their authentication information (ID and password) on the login screen, and send it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a work simulation. The user selects the work simulation they want to experience from this menu (e.g., factory line work, office work, safety training, etc.).

[0231] The user's selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The server then uses the generative AI model to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[0232] The server also uses mixed reality technology to generate a three-dimensional model, audio guide, and text data for operation guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0233] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[0234] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's operation and sends that information to the device. The user continues the simulation while referring to this. The device also detects the user's emotions from their facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that they are tired.

[0235] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[0236] As a specific example, the following prompt sentence can be used as input to a generative AI model: "Generate a simulation scenario of factory line work. Specific tasks should include 'product inspection,' 'packaging,' and 'response when the line stops.' Also, add a function to suggest breaks based on the user's emotional state."

[0237] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0239] Step 1:

[0240] Authentication Process

[0241] User: Enter your ID and password on the device login screen.

[0242] Terminal: Sends the entered authentication information to the server. Specifically, it obtains the "ID" and "password" from the text fields and sends an HTTP request to the server.

[0243] Server: Receives authentication information and checks it against a database. If authentication is successful, it generates an authentication success message and sends a business simulation menu to the terminal. For example, it checks user information using a database query and returns the results to the terminal in JSON format.

[0244] Input: User ID and password.

[0245] Output: Authentication success message and menu screen.

[0246] Step 2:

[0247] Selection of business simulations

[0248] User: Select the simulation they want to experience from the business simulation menu displayed on their device. For example, click on "Product Check Simulation."

[0249] Terminal: Sends the selection information to the server. Gets the ID of the selected simulation and sends an HTTP request to the server.

[0250] Server: Receives the selection information and generates initial setup data. It sends the generated initial setup data to the device. Specifically, it invokes the built-in algorithm that generates the initial setup data and returns the result to the device in JSON format.

[0251] Input: The ID of the selected business simulation.

[0252] Output: Initial setting data.

[0253] Step 3:

[0254] Simulation scenario generation

[0255] Server: Using the generative AI model, it generates the scenarios required for the selected business simulation. For example, it creates detailed scenarios including "product checks," "packaging," and "responses when the line stops."

[0256] Server: Sends the generated scenario data to the user terminal. The scenario data is sent to the terminal as an HTTP response.

[0257] Input: Initial setting data

[0258] Output: Scenario data

[0259] Step 4:

[0260] Mixed reality data generation

[0261] Server: Using mixed reality technology, it generates a 3D model, audio guide, and text data for the operation guide corresponding to the scenario.

[0262] Server: Sends the generated 3D model, audio guide, and text data of the operation guide to the terminal.

[0263] Terminal: displays this data and provides the user with a visual and auditory experience.

[0264] Input: Scenario data

[0265] Output: 3D model, audio guide, text data of operation guide

[0266] Step 5:

[0267] Operation detection during simulation

[0268] User: Puts on the MR headset and starts the simulation. For example, performs a simulated action of "checking the product."

[0269] Terminal: Detects user operations in real time and sends that information to the server. For example, a camera sensor can be used to track hand movements, acquire coordinate data, and send it to the server.

[0270] Server: Analyzes the received operation data and generates instructions and feedback for the next step.

[0271] Input: User operation data

[0272] Output: Next step instructions and feedback

[0273] Step 6:

[0274] Emotional state analysis and response

[0275] Device: Detects the user's facial expressions and voice in real time and sends the data to a server. For example, it uses a camera and microphone to perform facial expression recognition and voice analysis.

[0276] Server: Analyzes the received emotion data using the emotion engine and evaluates the user's emotional state. If stress or fatigue is detected, it generates a suggestion for a break or an instruction for a simple task and sends it to the device.

[0277] Input: User emotion data

[0278] Output: Emotion-based feedback

[0279] Step 7:

[0280] Feedback after the simulation

[0281] Server: Analyzes the operation and emotion data collected during the simulation and generates final feedback. Evaluations include evaluations based on operation accuracy, efficiency, response speed, and emotional state.

[0282] Server: Sends the generated feedback to the device.

[0283] Terminal: Displays received feedback to the user.

[0284] User: See the results of their evaluation based on their performance and emotional state. For example, "Your product inspection was accurate, but you need to improve your efficiency. Your emotional state showed positive fluctuations."

[0285] Input: Operation data and emotion data

[0286] Output: Final feedback

[0287] (Application example 2)

[0288] 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."

[0289] Conventional work simulation systems lack the ability to provide feedback that takes into account both realism and the user's emotional state, limiting the effectiveness of user learning and the improvement of their work comprehension. Furthermore, providing training without on-site experience carries risks in terms of safety and work efficiency. In response to these issues, there is a need to introduce emotion recognition technology to provide appropriate feedback based on the user's emotional state, enabling more practical and effective training.

[0290] 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.

[0291] In this invention, the server includes means for generating a specific scenario for a selected business simulation using a generative artificial intelligence, means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting user operations in real time and progressing the simulation based on the detected operations, means for analyzing user operation data after the simulation is completed and generating feedback, and means for adjusting the feedback content using emotion recognition means for recognizing the user's emotional state. This provides appropriate feedback according to the user's emotional state, enabling a more practical and effective business simulation experience.

[0292] "Generative AI" refers to artificial intelligence techniques used to generate specific scenarios for selected business simulations.

[0293] "Mixed reality technology" is a technology that provides users with visual and auditory experiences based on generated scenarios.

[0294] "Emotion recognition means" is a technology that recognizes the user's emotional state in real time and adjusts the feedback content based on that.

[0295] "Operation data" refers to information about operations recorded when a user performs a business simulation.

[0296] "Feedback" refers to evaluation and instruction information provided after the simulation is completed, which is generated by analyzing the user's operation data and emotional data, in order to improve the user's learning and understanding of the task.

[0297] A "scenario" refers to a virtual business procedure that includes specific tasks and procedures that users must perform in a business simulation.

[0298] "Visual experience" refers to the information that users see and feel through mixed reality technology.

[0299] "Auditory experience" refers to the information that users hear and feel through mixed reality technology.

[0300] This invention relates to a system that allows users to experience real-life work from remote locations. This system is realized by combining generative artificial intelligence, mixed reality technology, and emotion recognition means.

[0301] The main components of this system are as follows:

[0302] 1. User devices (personal computers, tablets, smartphones, etc.)

[0303] 2. Server

[0304] 3. Users

[0305] (Communication between user terminal and server)

[0306] Users access the system using their own terminal. The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu from which users can select a work simulation. When the user selects the work simulation they want to experience (e.g., factory line work, office work, safety training, etc.), the information is sent from the terminal to the server.

[0307] (Generating scenarios for business simulation)

[0308] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "robot maintenance," "parts replacement," "error code confirmation," and "system reboot procedure" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[0309] (Providing visual and auditory experiences using MR technology)

[0310] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0311] (Real-time action detection and emotion recognition)

[0312] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates emotion recognition means that recognize the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[0313] (Feedback after the simulation)

[0314] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with their emotional state.

[0315] Examples:

[0316] The user logs into the application and selects a robot maintenance task. The server generates a scenario and guides the user through the headset to actually perform the robot maintenance. The system monitors the user's actions and emotional state, providing feedback on correct operations and suggesting a break if the user feels stressed.

[0317] Example prompts to input to a generative AI model:

[0318] "Generate a simulation scenario in which users can practice robot maintenance in a factory. Include specific steps (e.g., robot arm part replacement, error code checking, system reboot procedure, etc.) along with emotion-aware feedback."

[0319] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0320] Step 1:

[0321] The user accesses the login screen using their own device, enters authentication information (ID and password), and sends it to the server. The server receives this authentication information and checks it against a database. If authentication is successful, the server displays a menu on the user's device for selecting a business simulation. The input is the user's authentication information, and the output is the business simulation selection menu.

[0322] Step 2:

[0323] The user selects the business simulation they wish to experience from a menu. This selection information is sent from the terminal to the server. The server generates the initial setting data required for the selected business simulation and sends it to the terminal. The input is the selected business simulation information, and the output is the initial setting data.

[0324] Step 3:

[0325] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. The generated scenarios include specific tasks and procedures, and use appropriate prompt statements as input. The output is scenario data.

[0326] Step 4:

[0327] The server uses mixed reality technology to generate 3D models, audio guides, and text data for task guides based on the generated scenario data. These data are then sent to the user's device, where they can experience them visually and audibly through an MR headset or display. The input is the scenario data, and the output is the 3D models, audio guides, and text data for task guides.

[0328] Step 5:

[0329] When a user starts a work simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data in real time, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The input is the user's operation data, and the output is instructions and feedback for proceeding to the next step.

[0330] Step 6:

[0331] The server uses emotion recognition to recognize the user's emotional state in real time, and adjusts the feedback content based on the recognized emotional data. For example, if the user is feeling stressed, it may suggest a temporary break or a simple task. The input is the emotional data, and the output is the adjusted feedback content.

[0332] Step 7:

[0333] When the simulation is finished, the server analyzes the collected operation data and emotional data to generate comprehensive feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user. The input is the operation data and emotional data, and the output is the feedback evaluation results.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] [Second embodiment]

[0338] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0339] 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.

[0340] 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).

[0341] 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.

[0342] 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.

[0343] 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).

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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."

[0350] This invention relates to a system that combines generative artificial intelligence and mixed reality technology to enable users to have a realistic work experience even when they are not at the actual work site. This system is composed of three main entities: a server, a user's terminal, and the user.

[0351] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0352] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0353] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0354] The server also uses mixed reality technology to generate 3D models, audio guides, and text data for task guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0355] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. The device displays this to the user, allowing the simulation to proceed.

[0356] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0357] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their performance and understand areas for improvement.

[0358] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved educational effectiveness, and reduced early employee turnover. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0359] The processing flow will be explained below.

[0360] Step 1:

[0361] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0362] Step 2:

[0363] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0364] Step 3:

[0365] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0366] Step 4:

[0367] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0368] Step 5:

[0369] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0370] Step 6:

[0371] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0372] Step 7:

[0373] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0374] Step 8:

[0375] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[0376] Step 9:

[0377] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. Based on the analysis results, feedback evaluating the user's work accuracy, speed, and response accuracy is sent to the device.

[0378] Step 10:

[0379] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[0380] Example 1

[0381] 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."

[0382] With conventional work simulation systems, it was difficult for users to gain realistic work experience when they were not present at the site, and the lack of real-time feedback and evaluation based on user operations made it difficult to achieve effective work training.

[0383] 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.

[0384] In this invention, the server includes: a means for a user to transmit authentication information from a terminal to the server and for the server to verify the information; a means for a user to select a business simulation and transmit the selection information to the server; and a means for the server to generate initial setting data for the selected business simulation and transmit the initial setting data to the terminal. This allows the user to experience a realistic business operation even when not at the site. The server also includes a means for generating a specific scenario using generative artificial intelligence, a means for providing a visual and auditory experience using mixed reality technology, a means for the terminal to detect user operations in real time and transmit the data to the server, and a means for the server to analyze the operation data, generate next steps and feedback, and transmit the data to the terminal. This enables real-time feedback and evaluation based on the user's operations, resulting in more effective business training.

[0385] "User authentication" is the process in which a user provides authentication information, such as an ID and password, required to prove his or her identity to a server, which then verifies the information.

[0386] "Business simulation" is a system that virtually recreates a specific business environment and procedures, allowing users to experience simulated business operations within that environment.

[0387] "Initial setting data" refers to data that includes basic information and conditions necessary to start a business simulation, such as a line layout diagram and the types of tools to be used.

[0388] "Generative AI" is an AI technology that has the ability to automatically generate scenarios and data according to specific purposes.

[0389] A "specific scenario" is a plan that describes in detail the series of tasks and procedures that a user will experience in a business simulation.

[0390] "Mixed reality technology" is a technology that provides an environment that combines the real world and the virtual world, allowing users to experience 3D models and audio guides visually and aurally in real space.

[0391] "Operation data" refers to data related to specific operations (for example, hand movements or actions following instructions) performed by the user during the task simulation.

[0392] "Feedback" is information that includes evaluations and instructions on a user's actions and behaviors, allowing the user to check their performance and understand areas for improvement.

[0393] This invention relates to a system that allows users to gain a realistic work experience even when they are not at the site. This system is composed of three main components: a server, a user's terminal, and the user.

[0394] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends this information to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0395] The user selects the task they wish to simulate from the menu (e.g., factory line work, office work, safety training, etc.) and sends the selection information from the terminal to the server. The server generates initial setting data for the selected task simulation (e.g., line layout diagram, types of tools to be used, etc.) and sends it to the terminal.

[0396] Next, the server uses generative artificial intelligence to generate specific scenarios (e.g., "product check," "packaging," "response when the line stops," etc.). This scenario data is sent to the device and displayed on the user's display screen. The server then uses mixed reality technology to generate 3D models, audio guides, and text data for work guides corresponding to the scenario. These data are then delivered to the user's device, allowing them to experience them visually and audibly through an MR headset or display.

[0397] When a user starts a business simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The terminal displays this to the user, allowing the simulation to proceed.

[0398] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0399] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user. The user can check their own performance and understand areas for improvement.

[0400] This allows users to experience work that is close to the actual work even when they are not on-site, which is expected to have the effects of improving work understanding, improving educational effectiveness, and even preventing early turnover. Specific examples include simulations of factory line work, training in work procedures in the office, and safety management scenarios. The present invention can be applied to these wide-ranging work simulations.

[0401] Examples of prompts include:

[0402] "Please start the factory line work simulation training."

[0403] "Please explain the packaging steps in this business scenario."

[0404] "Show emergency shutdown procedures in safety training scenarios."

[0405] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0406] Step 1: User authentication

[0407] Input: Authentication information (ID and password) entered by the user on the device

[0408] Processing: The terminal sends the entered authentication information to the server, which checks the information against a database to authenticate the user.

[0409] Output: If authentication is successful, the server sends a business simulation menu to the terminal, which displays it to the user.

[0410] Specific operation: The user enters "user123" and "password1234" on the login screen, and the terminal sends that information to the server. The server checks the database, and if authentication is successful, it sends the message "Authentication successful" along with a business simulation menu to the terminal. The terminal displays it to the user.

[0411] Step 2: Simulation Selection

[0412] Input: User-selected business simulation information

[0413] Processing: The device sends information about the selected simulation to the server, which then generates initial setting data based on that information.

[0414] Output: The server sends the generated initialization data to the terminal, which displays it to the user.

[0415] Specific operation: The user selects "Line Work Simulation" from the menu, and the terminal sends the selection result to the server. The server generates initial setting data such as "Line Layout Diagram" and "Tool List" and sends it back to the terminal. The terminal displays the initial setting data on the screen.

[0416] Step 3: Scenario generation

[0417] Input: Initial setting data and business simulation selection information sent to the server

[0418] Processing: The server uses generative artificial intelligence (generative AI model) to generate specific scenarios.

[0419] Output: The server sends the generated scenario data to the terminal, which displays it to the user.

[0420] Specific operation: The server generates scenarios such as "product check" and "response when line is stopped" based on the initial setting data and sends them to the terminal. The terminal displays "Next step: Please check the product."

[0421] Step 4: Guide Data Generation

[0422] Input: Scenario data sent to the server

[0423] Processing: The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the task guide corresponding to the scenario.

[0424] Output: The generated data is sent to the terminal, which presents it to the user visually and audibly.

[0425] Specific operation: The server generates a 3D model of how to check product A and an audio guide, and sends them to the device. The device displays the 3D model on the headset and plays the audio guide.

[0426] Step 5: Run the simulation and send and receive data

[0427] Input: User operation information (hand movements, gaze, etc.) and scenario data

[0428] Processing: The device detects user actions in real time and sends the information to the server, which analyzes the action data and generates next steps and feedback.

[0429] Output: Instructions and feedback data from the server are sent to the device, which displays them to the user.

[0430] Specific operation: The user puts on the headset and performs the action of "checking product A." The device detects this movement and sends it to the server. The server analyzes the operation data and sends an instruction to the device saying, "Next, check product B." The device displays this on the screen.

[0431] Step 6: Generate feedback upon completion

[0432] Input: All operational data collected during the simulation

[0433] Processing: At the end of the simulation, the device sends all operation data to the server, which analyzes the data and evaluates the accuracy, efficiency, and speed of the operation.

[0434] Output: The server generates the evaluation result and sends it to the terminal, which displays it to the user.

[0435] Specific operation: At the end of the simulation, the device sends all operation data to the server. The server generates feedback such as "Product A check: successful" or "Product B check: failed (details of the error)" and sends it back to the device. The device displays the evaluation results to the user.

[0436] (Application example 1)

[0437] 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."

[0438] There is a need for a method that allows users to learn factory robot operation and maintenance work in a realistic way without visiting the site. There is also a need to improve the effectiveness of learning by capturing user operations in real time and providing appropriate feedback.

[0439] 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.

[0440] In this invention, the server includes means for generating a specific scenario for a selected business simulation using generative artificial intelligence, means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting the user's operations in real time and progressing the simulation based on the detected operations, means for analyzing the user's operation data after the simulation is completed and generating feedback, means for learning factory robot operation and maintenance work, and means for capturing the user's operations and transmitting them to the server. This allows the user to realistically experience actual factory work even when they are not on-site, thereby improving the learning effect.

[0441] "Generative AI" is a technology that generates specific scenarios and instructions for a selected business simulation.

[0442] "Mixed reality technology" is a technology that provides users with a visual and auditory experience, displaying a combination of the real world and the virtual world.

[0443] A "scenario" is a storyline that includes a series of tasks and instructions that a user experiences in a business simulation.

[0444] A "3D model" is a virtual object displayed in three-dimensional space using computer graphics, and is intended for users to visually recognize.

[0445] "Audio guide" is a system that provides audio guidance to users on tasks and operation methods in business simulations.

[0446] "User operations" refer to inputs and actions that a user performs to progress the simulation.

[0447] "Real-time detection" means that the system recognizes user actions immediately and on the spot.

[0448] "Feedback" refers to evaluations and instructions generated based on a user's operations, including the accuracy and efficiency of the operations.

[0449] A "factory robot" is an automated machine used to perform manufacturing and maintenance work within a factory.

[0450] "Capture" means recording a user's actions and behavior.

[0451] A "server" is a computer system that controls the entire business simulation system and generates and analyzes data.

[0452] This invention is a system for learning factory robot operation and maintenance work that combines generative artificial intelligence and mixed reality technology. This system is composed of three main entities: a server, a user's terminal, and the user.

[0453] The server uses generative artificial intelligence to generate a specific scenario for the selected task simulation. Specifically, a scenario is generated that includes tasks such as "robot oil change," "oil filter removal," and "emergency shutdown procedure." During this generation process, the server sets a prompt sentence based on the user's selection. For example, a prompt sentence such as "The user is changing the oil on a factory robot. Please instruct the next step, how to remove the oil filter." is used.

[0454] The user's device uses mixed reality technology to display a 3D model based on the generated scenario, along with audio and text guides. By wearing an optical head-mounted display (HMD), the user can experience the realistic operating environment of an actual factory robot.

[0455] When a user starts a work simulation, the device detects the user's actions in real time and sends them to the server. For example, when a user removes an oil filter from a factory robot, a sensor on the device detects hand movements and sends the data to the server. The server analyzes this data, generates instructions and feedback for proceeding to the next step, and sends it to the device. The user can refer to this to continue the simulation.

[0456] After the simulation is complete, the server analyzes the collected operation data and provides the results as feedback. This feedback includes an evaluation of the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand areas for improvement.

[0457] As a concrete example, a user wearing a mixed reality headset simulates an oil change operation by a factory robot, and the system captures and analyzes the operation and provides appropriate feedback. This process contributes to improving the user's skills, enabling efficient training without going to the site.

[0458] As described above, the present invention provides an effective means for virtually experiencing the operation and maintenance of factory robots and acquiring skills.

[0459] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0460] Step 1:

[0461] The user accesses the system using a device (smartphone, tablet, PC, etc.) and logs in. The user enters authentication information (ID and password) and sends it to the server. The server verifies this authentication information, and if authentication is successful, displays a business simulation menu on the device.

[0462] Input: User authentication information (ID, password)

[0463] Output: Authentication results, business simulation menu display

[0464] Step 2:

[0465] The user selects a simulation from the business simulation menu. The selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The terminal prepares the received initial setting data and prepares for the simulation.

[0466] Input: User's simulation selection information

[0467] Output: Initial setting data, simulation ready

[0468] Step 3:

[0469] The server uses generative AI to generate specific scenarios required for the selected task simulation. For example, it generates a scenario for the task of "changing oil for a robot." In this case, it uses prompt statements to input the generative AI model and generates an appropriate scenario.

[0470] Input: Simulation selection information, prompt text

[0471] Output: Scenario data (specific task flow)

[0472] Step 4:

[0473] The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, and sends them to the user's device, which receives the data and provides the user with a visual and auditory experience through an MR headset or display.

[0474] Input: Scenario data

[0475] Output: 3D model, audio guide, text data of operation guide

[0476] Step 5:

[0477] The user starts the task simulation, and the device detects the user's operations in real time. For example, when a user wears an MR headset and performs an action to remove an oil filter from a robot, the device's sensors capture the user's hand movements. The device then transmits the captured operation data to the server.

[0478] Input: User operation (hand movement)

[0479] Output: Captured operation data

[0480] Step 6:

[0481] The server receives and analyzes the user's operation data. Based on the analyzed data, it generates instructions for the next step and real-time feedback and sends them to the device. The device then displays the instructions and feedback to the user, progressing through the simulation.

[0482] Input: Captured operation data

[0483] Output: Next step instructions, feedback

[0484] Step 7:

[0485] After the simulation is over, the server analyzes the collected operation data and generates feedback, including the accuracy, efficiency, and speed of response. The evaluation results are sent to the terminal and displayed to the user, allowing them to check their performance and understand areas for improvement.

[0486] Input: All operation data

[0487] Output: Evaluation results, feedback

[0488] These steps allow users to experience and learn factory robot operation and maintenance tasks in a realistic way, even when they are not on-site, thereby efficiently improving their skills.

[0489] 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.

[0490] This invention relates to a system that combines generative artificial intelligence, mixed reality technology, and an emotion engine to enable users to have a realistic work experience even when they are not at the actual work site. This system consists of three entities: a server, a user terminal, and the user.

[0491] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0492] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0493] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0494] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0495] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and adjusts the progress of the simulation and the content of the feedback according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or a simple task.

[0496] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's actions and sends that information to the device. The user continues the simulation while referring to this. The device also detects emotions from the user's facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that the user is tired.

[0497] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[0498] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0499] The processing flow will be explained below.

[0500] Step 1:

[0501] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0502] Step 2:

[0503] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0504] Step 3:

[0505] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0506] Step 4:

[0507] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0508] Step 5:

[0509] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0510] Step 6:

[0511] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0512] Step 7:

[0513] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0514] Step 8:

[0515] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[0516] Step 9:

[0517] The emotion engine detects emotions from the user's facial expressions and voice and sends the emotion data to the server, which analyzes the emotion data and adjusts the scenario and feedback content based on the user's emotional state.

[0518] Step 10:

[0519] If the user feels stressed during the simulation, the server generates instructions suggesting a temporary break or a simple task and sends them to the device, which then displays the suggested instructions to the user.

[0520] Step 11:

[0521] Once the simulation is complete, the server analyzes the collected operation and emotion data and generates feedback. Based on the analysis results, feedback evaluating the user's task accuracy, speed, and response accuracy, as well as their emotional state, is sent to the device.

[0522] Step 12:

[0523] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[0524] Example 2

[0525] 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."

[0526] Conventional business simulation systems have the problem that it is difficult for users to experience realistic business operations when they are not present at the site, and it is also difficult to provide appropriate feedback based on the user's operations and emotional state. Furthermore, conventional systems cannot provide feedback that takes into account the user's emotional state, which limits the effectiveness of user learning and the improvement of business understanding.

[0527] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for transmitting authentication information via an information processing device operated by a user and displaying a menu of a business simulation if the authentication is successful; means for generating a specific scenario for the selected business simulation using a generative artificial intelligence; means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology; means for detecting the user's operations in real time and progressing the simulation based on the detected operations; means for analyzing the user's emotional state using an emotion engine and generating feedback accordingly; and means for analyzing the user's operation data and emotion data after the simulation is completed and generating feedback. This allows the user to have a realistic business experience even when they are not present at the site, and makes it possible to provide appropriate feedback based on the operation data and emotion data.

[0528] "Information processing device" is a general term for devices and systems that are operated by a user to input, display, and transmit data.

[0529] "Authentication Information" refers to personal identification information such as ID and password used by a User to access the System.

[0530] "Generative AI" refers to AI technology that has the ability to analyze data and generate scenarios and feedback for specific tasks or purposes.

[0531] "Business simulation" refers to a simulated business process that allows users to experience real business processes in a virtual environment for practice and training.

[0532] "Mixed reality technology" refers to technology that blends the real world with the virtual world, providing users with a real-time visual and auditory experience.

[0533] An "emotion engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice, and generates appropriate feedback based on the analysis results.

[0534] A "three-dimensional model" refers to a visual object constructed in three-dimensional space using computer graphics (CG).

[0535] "Voice guide" refers to a function that provides instructions and guidance to the user by voice.

[0536] "Work guide text data" refers to data that explains work procedures to the user in text and illustrations.

[0537] "Operation data" refers to data related to a series of operations performed by a user during a business simulation.

[0538] "Emotion data" refers to data relating to the emotional state detected by the user's facial expressions and voice.

[0539] This invention relates to a system that allows users to have a realistic work experience even when they are not at the site, and achieves this by combining generative artificial intelligence, mixed reality technology, and an emotion engine. The system consists of three entities: a server, a user's terminal, and the user.

[0540] The server, as the central control device of the present invention, utilizes the following hardware and software: the hardware includes a CPU, memory, storage device, and network interface, while the software includes a live artificial intelligence algorithm, mixed reality (MR) technology, an emotion engine, and a database system for managing user data.

[0541] The user's device may be a personal computer (PC), tablet, smartphone, etc., and provides an interface for the user to operate, including a keyboard, mouse, touchscreen, and MR headset.

[0542] Users access the system using their own terminal, enter their authentication information (ID and password) on the login screen, and send it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a work simulation. The user selects the work simulation they want to experience from this menu (e.g., factory line work, office work, safety training, etc.).

[0543] The user's selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The server then uses the generative AI model to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[0544] The server also uses mixed reality technology to generate a three-dimensional model, audio guide, and text data for operation guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0545] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[0546] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's operation and sends that information to the device. The user continues the simulation while referring to this. The device also detects the user's emotions from their facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that they are tired.

[0547] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[0548] As a specific example, the following prompt sentence can be used as input to a generative AI model: "Generate a simulation scenario of factory line work. Specific tasks should include 'product inspection,' 'packaging,' and 'response when the line stops.' Also, add a function to suggest breaks based on the user's emotional state."

[0549] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0550] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0551] Step 1:

[0552] Authentication Process

[0553] User: Enter your ID and password on the device login screen.

[0554] Terminal: Sends the entered authentication information to the server. Specifically, it obtains the "ID" and "password" from the text field and sends an HTTP request to the server.

[0555] Server: Receives authentication information and checks it against a database. If authentication is successful, it generates an authentication success message and sends a business simulation menu to the terminal. For example, it checks user information using a database query and returns the results to the terminal in JSON format.

[0556] Input: User ID and password.

[0557] Output: Authentication success message and menu screen.

[0558] Step 2:

[0559] Selection of business simulations

[0560] User: Select the simulation they want to experience from the business simulation menu displayed on their device. For example, click on "Product Check Simulation."

[0561] Terminal: Sends the selection information to the server. Gets the ID of the selected simulation and sends an HTTP request to the server.

[0562] Server: Receives the selection information and generates initial setup data. It sends the generated initial setup data to the device. Specifically, it invokes the built-in algorithm that generates the initial setup data and returns the result to the device in JSON format.

[0563] Input: The ID of the selected business simulation.

[0564] Output: Initial setting data.

[0565] Step 3:

[0566] Simulation scenario generation

[0567] Server: Using the generative AI model, it generates the scenarios required for the selected business simulation. For example, it creates detailed scenarios including "product checks," "packaging," and "responses when the line stops."

[0568] Server: Sends the generated scenario data to the user terminal. The scenario data is sent to the terminal as an HTTP response.

[0569] Input: Initial setting data

[0570] Output: Scenario data

[0571] Step 4:

[0572] Mixed reality data generation

[0573] Server: Using mixed reality technology, it generates a 3D model, audio guide, and text data for the operation guide corresponding to the scenario.

[0574] Server: Sends the generated 3D model, audio guide, and text data of the operation guide to the terminal.

[0575] Terminal: displays this data and provides the user with a visual and auditory experience.

[0576] Input: Scenario data

[0577] Output: 3D model, audio guide, text data of operation guide

[0578] Step 5:

[0579] Operation detection during simulation

[0580] User: Puts on the MR headset and starts the simulation. For example, performs a simulated action of "checking the product."

[0581] Terminal: Detects user operations in real time and sends that information to the server. For example, a camera sensor can be used to track hand movements, acquire coordinate data, and send it to the server.

[0582] Server: Analyzes the received operation data and generates instructions and feedback for the next step.

[0583] Input: User operation data

[0584] Output: Next step instructions and feedback

[0585] Step 6:

[0586] Emotional state analysis and response

[0587] Device: Detects the user's facial expressions and voice in real time and sends the data to a server. For example, it uses a camera and microphone to perform facial expression recognition and voice analysis.

[0588] Server: Analyzes the received emotion data using the emotion engine and evaluates the user's emotional state. If stress or fatigue is detected, it generates a suggestion for a break or an instruction for a simple task and sends it to the device.

[0589] Input: User emotion data

[0590] Output: Emotion-based feedback

[0591] Step 7:

[0592] Feedback after the simulation

[0593] Server: Analyzes the operation and emotion data collected during the simulation and generates final feedback. Evaluations include evaluations based on operation accuracy, efficiency, response speed, and emotional state.

[0594] Server: Sends the generated feedback to the device.

[0595] Terminal: Displays received feedback to the user.

[0596] User: See the results of their evaluation based on their performance and emotional state. For example, "Your product inspection was accurate, but you need to improve your efficiency. Your emotional state showed positive fluctuations."

[0597] Input: Operation data and emotion data

[0598] Output: Final feedback

[0599] (Application example 2)

[0600] 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."

[0601] Conventional work simulation systems lack the ability to provide feedback that takes into account both realism and the user's emotional state, limiting the effectiveness of user learning and the improvement of their work comprehension. Furthermore, providing training without on-site experience carries risks in terms of safety and work efficiency. In response to these issues, there is a need to introduce emotion recognition technology to provide appropriate feedback based on the user's emotional state, enabling more practical and effective training.

[0602] 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.

[0603] In this invention, the server includes means for generating a specific scenario for a selected business simulation using a generative artificial intelligence, means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting user operations in real time and progressing the simulation based on the detected operations, means for analyzing user operation data after the simulation is completed and generating feedback, and means for adjusting the feedback content using emotion recognition means for recognizing the user's emotional state. This provides appropriate feedback according to the user's emotional state, enabling a more practical and effective business simulation experience.

[0604] "Generative AI" refers to artificial intelligence techniques used to generate specific scenarios for selected business simulations.

[0605] "Mixed reality technology" is a technology that provides users with visual and auditory experiences based on generated scenarios.

[0606] "Emotion recognition means" is a technology that recognizes the user's emotional state in real time and adjusts the feedback content based on that.

[0607] "Operation data" refers to information about operations recorded when a user performs a business simulation.

[0608] "Feedback" refers to evaluation and instruction information provided after the simulation is completed, which is generated by analyzing the user's operation data and emotional data, in order to improve the user's learning and understanding of the task.

[0609] A "scenario" refers to a virtual business procedure that includes specific tasks and procedures that users must perform in a business simulation.

[0610] "Visual experience" refers to the information that users see and feel through mixed reality technology.

[0611] "Auditory experience" refers to the information that users hear and feel through mixed reality technology.

[0612] This invention relates to a system that allows users to experience real-life work from remote locations. This system is realized by combining generative artificial intelligence, mixed reality technology, and emotion recognition means.

[0613] The main components of this system are as follows:

[0614] 1. User devices (personal computers, tablets, smartphones, etc.)

[0615] 2. Server

[0616] 3. Users

[0617] (Communication between user terminal and server)

[0618] Users access the system using their own terminal. The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu from which users can select a work simulation. When the user selects the work simulation they want to experience (e.g., factory line work, office work, safety training, etc.), the information is sent from the terminal to the server.

[0619] (Generating scenarios for business simulation)

[0620] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "robot maintenance," "parts replacement," "error code confirmation," and "system reboot procedure" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[0621] (Providing visual and auditory experiences using MR technology)

[0622] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0623] (Real-time action detection and emotion recognition)

[0624] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates emotion recognition means that recognize the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[0625] (Feedback after the simulation)

[0626] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with their emotional state.

[0627] Examples:

[0628] The user logs into the application and selects a robot maintenance task. The server generates a scenario and guides the user through the headset to actually perform the robot maintenance. The system monitors the user's actions and emotional state, providing feedback on correct operations and suggesting a break if the user feels stressed.

[0629] Example prompts to input to a generative AI model:

[0630] "Generate a simulation scenario in which users can practice robot maintenance in a factory. Include specific steps (e.g., robot arm part replacement, error code checking, system reboot procedure, etc.) along with emotion-aware feedback."

[0631] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0632] Step 1:

[0633] The user accesses the login screen using their own device, enters authentication information (ID and password), and sends it to the server. The server receives this authentication information and checks it against a database. If authentication is successful, the server displays a menu on the user's device for selecting a business simulation. The input is the user's authentication information, and the output is the business simulation selection menu.

[0634] Step 2:

[0635] The user selects the business simulation they wish to experience from a menu. This selection information is sent from the terminal to the server. The server generates the initial setting data required for the selected business simulation and sends it to the terminal. The input is the selected business simulation information, and the output is the initial setting data.

[0636] Step 3:

[0637] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. The generated scenarios include specific tasks and procedures, and use appropriate prompt statements as input. The output is scenario data.

[0638] Step 4:

[0639] The server uses mixed reality technology to generate 3D models, audio guides, and text data for task guides based on the generated scenario data. These data are then sent to the user's device, where they can experience them visually and audibly through an MR headset or display. The input is the scenario data, and the output is the 3D models, audio guides, and text data for task guides.

[0640] Step 5:

[0641] When a user starts a work simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data in real time, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The input is the user's operation data, and the output is instructions and feedback for proceeding to the next step.

[0642] Step 6:

[0643] The server uses emotion recognition to recognize the user's emotional state in real time, and adjusts the feedback content based on the recognized emotional data. For example, if the user is feeling stressed, it may suggest a temporary break or a simple task. The input is the emotional data, and the output is the adjusted feedback content.

[0644] Step 7:

[0645] When the simulation is finished, the server analyzes the collected operation data and emotional data to generate comprehensive feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user. The input is the operation data and emotional data, and the output is the feedback evaluation results.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] [Third embodiment]

[0650] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0651] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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."

[0662] This invention relates to a system that combines generative artificial intelligence and mixed reality technology to enable users to have a realistic work experience even when they are not at the actual work site. This system is composed of three main entities: a server, a user's terminal, and the user.

[0663] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0664] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0665] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0666] The server also uses mixed reality technology to generate 3D models, audio guides, and text data for task guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0667] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. The device displays this to the user, allowing the simulation to proceed.

[0668] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0669] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their performance and understand areas for improvement.

[0670] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved educational effectiveness, and reduced early employee turnover. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0671] The processing flow will be explained below.

[0672] Step 1:

[0673] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0674] Step 2:

[0675] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0676] Step 3:

[0677] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0678] Step 4:

[0679] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0680] Step 5:

[0681] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0682] Step 6:

[0683] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0684] Step 7:

[0685] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0686] Step 8:

[0687] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[0688] Step 9:

[0689] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. Based on the analysis results, feedback evaluating the user's work accuracy, speed, and response accuracy is sent to the device.

[0690] Step 10:

[0691] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[0692] Example 1

[0693] 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."

[0694] With conventional work simulation systems, it was difficult for users to gain realistic work experience when they were not present at the site, and the lack of real-time feedback and evaluation based on user operations made it difficult to achieve effective work training.

[0695] 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.

[0696] In this invention, the server includes: a means for a user to transmit authentication information from a terminal to the server and for the server to verify the information; a means for a user to select a business simulation and transmit the selection information to the server; and a means for the server to generate initial setting data for the selected business simulation and transmit the initial setting data to the terminal. This allows the user to experience a realistic business operation even when not at the site. The server also includes a means for generating a specific scenario using generative artificial intelligence, a means for providing a visual and auditory experience using mixed reality technology, a means for the terminal to detect user operations in real time and transmit the data to the server, and a means for the server to analyze the operation data, generate next steps and feedback, and transmit the data to the terminal. This enables real-time feedback and evaluation based on the user's operations, resulting in more effective business training.

[0697] "User authentication" is the process in which a user provides authentication information, such as an ID and password, required to prove his or her identity to a server, which then verifies the information.

[0698] "Business simulation" is a system that virtually recreates a specific business environment and procedures, allowing users to experience simulated business operations within that environment.

[0699] "Initial setting data" refers to data that includes basic information and conditions necessary to start a business simulation, such as a line layout diagram and the types of tools to be used.

[0700] "Generative AI" is an AI technology that has the ability to automatically generate scenarios and data according to specific purposes.

[0701] A "specific scenario" is a plan that describes in detail the series of tasks and procedures that a user will experience in a business simulation.

[0702] "Mixed reality technology" is a technology that provides an environment that combines the real world and the virtual world, allowing users to experience 3D models and audio guides visually and aurally in real space.

[0703] "Operation data" refers to data related to specific operations (for example, hand movements or actions following instructions) performed by the user during the task simulation.

[0704] "Feedback" is information that includes evaluations and instructions on a user's actions and behaviors, allowing the user to check their performance and understand areas for improvement.

[0705] This invention relates to a system that allows users to gain a realistic work experience even when they are not at the site. This system is composed of three main components: a server, a user's terminal, and the user.

[0706] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends this information to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0707] The user selects the task they wish to simulate from the menu (e.g., factory line work, office work, safety training, etc.) and sends the selection information from the terminal to the server. The server generates initial setting data for the selected task simulation (e.g., line layout diagram, types of tools to be used, etc.) and sends it to the terminal.

[0708] Next, the server uses generative artificial intelligence to generate specific scenarios (e.g., "product check," "packaging," "response when the line stops," etc.). This scenario data is sent to the device and displayed on the user's display screen. The server then uses mixed reality technology to generate 3D models, audio guides, and text data for work guides corresponding to the scenario. These data are then delivered to the user's device, allowing them to experience them visually and audibly through an MR headset or display.

[0709] When a user starts a business simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The terminal displays this to the user, allowing the simulation to proceed.

[0710] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0711] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user. The user can check their own performance and understand areas for improvement.

[0712] This allows users to experience work that is close to the actual work even when they are not on-site, which is expected to have the effects of improving work understanding, improving educational effectiveness, and even preventing early turnover. Specific examples include simulations of factory line work, training in work procedures in the office, and safety management scenarios. The present invention can be applied to these wide-ranging work simulations.

[0713] Examples of prompts include:

[0714] "Please start the factory line work simulation training."

[0715] "Please explain the packaging steps in this business scenario."

[0716] "Show emergency shutdown procedures in safety training scenarios."

[0717] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0718] Step 1: User authentication

[0719] Input: Authentication information (ID and password) entered by the user on the device

[0720] Processing: The terminal sends the entered authentication information to the server, which checks the information against a database to authenticate the user.

[0721] Output: If authentication is successful, the server sends a business simulation menu to the terminal, which displays it to the user.

[0722] Specific operation: The user enters "user123" and "password1234" on the login screen, and the terminal sends that information to the server. The server checks the database, and if authentication is successful, it sends the message "Authentication successful" along with a business simulation menu to the terminal. The terminal displays it to the user.

[0723] Step 2: Simulation Selection

[0724] Input: User-selected business simulation information

[0725] Processing: The device sends information about the selected simulation to the server, which then generates initial setting data based on that information.

[0726] Output: The server sends the generated initialization data to the terminal, which displays it to the user.

[0727] Specific operation: The user selects "Line Work Simulation" from the menu, and the terminal sends the selection result to the server. The server generates initial setting data such as "Line Layout Diagram" and "Tool List" and sends it back to the terminal. The terminal displays the initial setting data on the screen.

[0728] Step 3: Scenario generation

[0729] Input: Initial setting data and business simulation selection information sent to the server

[0730] Processing: The server uses generative artificial intelligence (generative AI model) to generate specific scenarios.

[0731] Output: The server sends the generated scenario data to the terminal, which displays it to the user.

[0732] Specific operation: The server generates scenarios such as "product check" and "response when line is stopped" based on the initial setting data and sends them to the terminal. The terminal displays "Next step: Please check the product."

[0733] Step 4: Guide Data Generation

[0734] Input: Scenario data sent to the server

[0735] Processing: The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the task guide corresponding to the scenario.

[0736] Output: The generated data is sent to the terminal, which presents it to the user visually and audibly.

[0737] Specific operation: The server generates a 3D model of how to check product A and an audio guide, and sends them to the device. The device displays the 3D model on the headset and plays the audio guide.

[0738] Step 5: Run the simulation and send and receive data

[0739] Input: User operation information (hand movements, gaze, etc.) and scenario data

[0740] Processing: The device detects user actions in real time and sends the information to the server, which analyzes the action data and generates next steps and feedback.

[0741] Output: Instructions and feedback data from the server are sent to the device, which displays them to the user.

[0742] Specific operation: The user puts on the headset and performs the action of "checking product A." The device detects this movement and sends it to the server. The server analyzes the operation data and sends an instruction to the device saying, "Next, check product B." The device displays this on the screen.

[0743] Step 6: Generate feedback upon completion

[0744] Input: All operational data collected during the simulation

[0745] Processing: At the end of the simulation, the device sends all operation data to the server, which analyzes the data and evaluates the accuracy, efficiency, and speed of the operation.

[0746] Output: The server generates the evaluation result and sends it to the terminal, which displays it to the user.

[0747] Specific operation: At the end of the simulation, the device sends all operation data to the server. The server generates feedback such as "Product A check: successful" or "Product B check: failed (details of the error)" and sends it back to the device. The device displays the evaluation results to the user.

[0748] (Application example 1)

[0749] 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."

[0750] There is a need for a method that allows users to learn factory robot operation and maintenance work in a realistic way without visiting the site. There is also a need to improve the effectiveness of learning by capturing user operations in real time and providing appropriate feedback.

[0751] 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.

[0752] In this invention, the server includes means for generating a specific scenario for a selected business simulation using generative artificial intelligence, means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting the user's operations in real time and progressing the simulation based on the detected operations, means for analyzing the user's operation data after the simulation is completed and generating feedback, means for learning factory robot operation and maintenance work, and means for capturing the user's operations and transmitting them to the server. This allows the user to realistically experience actual factory work even when they are not on-site, thereby improving the learning effect.

[0753] "Generative AI" is a technology that generates specific scenarios and instructions for a selected business simulation.

[0754] "Mixed reality technology" is a technology that provides users with a visual and auditory experience, displaying a combination of the real world and the virtual world.

[0755] A "scenario" is a storyline that includes a series of tasks and instructions that a user experiences in a business simulation.

[0756] A "3D model" is a virtual object displayed in three-dimensional space using computer graphics, and is intended for users to visually recognize.

[0757] "Audio guide" is a system that provides audio guidance to users on tasks and operation methods in business simulations.

[0758] "User operations" refer to inputs and actions that a user performs to progress the simulation.

[0759] "Real-time detection" means that the system recognizes user actions immediately and on the spot.

[0760] "Feedback" refers to evaluations and instructions generated based on a user's operations, including the accuracy and efficiency of the operations.

[0761] A "factory robot" is an automated machine used to perform manufacturing and maintenance work within a factory.

[0762] "Capture" means recording a user's actions and behavior.

[0763] A "server" is a computer system that controls the entire business simulation system and generates and analyzes data.

[0764] This invention is a system for learning factory robot operation and maintenance work that combines generative artificial intelligence and mixed reality technology. This system is composed of three main entities: a server, a user's terminal, and the user.

[0765] The server uses generative artificial intelligence to generate a specific scenario for the selected task simulation. Specifically, a scenario is generated that includes tasks such as "robot oil change," "oil filter removal," and "emergency shutdown procedure." During this generation process, the server sets a prompt sentence based on the user's selection. For example, a prompt sentence such as "The user is changing the oil on a factory robot. Please instruct the next step, how to remove the oil filter." is used.

[0766] The user's device uses mixed reality technology to display a 3D model based on the generated scenario, along with audio and text guides. By wearing an optical head-mounted display (HMD), the user can experience the realistic operating environment of an actual factory robot.

[0767] When a user starts a work simulation, the device detects the user's actions in real time and sends them to the server. For example, when a user removes an oil filter from a factory robot, a sensor on the device detects hand movements and sends the data to the server. The server analyzes this data, generates instructions and feedback for proceeding to the next step, and sends it to the device. The user can refer to this to continue the simulation.

[0768] After the simulation is complete, the server analyzes the collected operation data and provides the results as feedback. This feedback includes an evaluation of the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand areas for improvement.

[0769] As a concrete example, a user wearing a mixed reality headset simulates an oil change operation by a factory robot, and the system captures and analyzes the operation and provides appropriate feedback. This process contributes to improving the user's skills, enabling efficient training without going to the site.

[0770] As described above, the present invention provides an effective means for virtually experiencing the operation and maintenance of factory robots and acquiring skills.

[0771] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0772] Step 1:

[0773] The user accesses the system using a device (smartphone, tablet, PC, etc.) and logs in. The user enters authentication information (ID and password) and sends it to the server. The server verifies this authentication information, and if authentication is successful, displays a business simulation menu on the device.

[0774] Input: User authentication information (ID, password)

[0775] Output: Authentication results, business simulation menu display

[0776] Step 2:

[0777] The user selects a simulation from the business simulation menu. The selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The terminal prepares the received initial setting data and prepares for the simulation.

[0778] Input: User's simulation selection information

[0779] Output: Initial setting data, simulation ready

[0780] Step 3:

[0781] The server uses generative AI to generate specific scenarios required for the selected task simulation. For example, it generates a scenario for the task of "changing oil for a robot." In this case, it uses prompt statements to input the generative AI model and generates an appropriate scenario.

[0782] Input: Simulation selection information, prompt text

[0783] Output: Scenario data (specific task flow)

[0784] Step 4:

[0785] The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, and sends them to the user's device, which receives the data and provides the user with a visual and auditory experience through an MR headset or display.

[0786] Input: Scenario data

[0787] Output: 3D model, audio guide, text data of operation guide

[0788] Step 5:

[0789] The user starts the task simulation, and the device detects the user's operations in real time. For example, when a user wears an MR headset and performs an action to remove an oil filter from a robot, the device's sensors capture the user's hand movements. The device then transmits the captured operation data to the server.

[0790] Input: User operation (hand movement)

[0791] Output: Captured operation data

[0792] Step 6:

[0793] The server receives and analyzes the user's operation data. Based on the analyzed data, it generates instructions for the next step and real-time feedback and sends them to the device. The device then displays the instructions and feedback to the user, progressing through the simulation.

[0794] Input: Captured operation data

[0795] Output: Next step instructions, feedback

[0796] Step 7:

[0797] After the simulation is over, the server analyzes the collected operation data and generates feedback, including the accuracy, efficiency, and speed of response. The evaluation results are sent to the terminal and displayed to the user, allowing them to check their performance and understand areas for improvement.

[0798] Input: All operation data

[0799] Output: Evaluation results, feedback

[0800] These steps allow users to experience and learn factory robot operation and maintenance tasks in a realistic way, even when they are not on-site, thereby efficiently improving their skills.

[0801] 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.

[0802] This invention relates to a system that combines generative artificial intelligence, mixed reality technology, and an emotion engine to enable users to have a realistic work experience even when they are not at the actual work site. This system consists of three entities: a server, a user terminal, and the user.

[0803] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0804] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0805] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0806] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0807] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and adjusts the progress of the simulation and the content of the feedback according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or a simple task.

[0808] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's actions and sends that information to the device. The user continues the simulation while referring to this. The device also detects emotions from the user's facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that the user is tired.

[0809] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[0810] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0811] The processing flow will be explained below.

[0812] Step 1:

[0813] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0814] Step 2:

[0815] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0816] Step 3:

[0817] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0818] Step 4:

[0819] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0820] Step 5:

[0821] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0822] Step 6:

[0823] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0824] Step 7:

[0825] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0826] Step 8:

[0827] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[0828] Step 9:

[0829] The emotion engine detects emotions from the user's facial expressions and voice and sends the emotion data to the server, which analyzes the emotion data and adjusts the scenario and feedback content based on the user's emotional state.

[0830] Step 10:

[0831] If the user feels stressed during the simulation, the server generates instructions suggesting a temporary break or a simple task and sends them to the device, which then displays the suggested instructions to the user.

[0832] Step 11:

[0833] Once the simulation is complete, the server analyzes the collected operation and emotion data and generates feedback. Based on the analysis results, feedback evaluating the user's task accuracy, speed, and response accuracy, as well as their emotional state, is sent to the device.

[0834] Step 12:

[0835] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[0836] Example 2

[0837] 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."

[0838] Conventional business simulation systems have the problem that it is difficult for users to experience realistic business operations when they are not present at the site, and it is also difficult to provide appropriate feedback based on the user's operations and emotional state. Furthermore, conventional systems cannot provide feedback that takes into account the user's emotional state, which limits the effectiveness of user learning and the improvement of business understanding.

[0839] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for transmitting authentication information via an information processing device operated by a user and displaying a menu of a business simulation if the authentication is successful; means for generating a specific scenario for the selected business simulation using a generative artificial intelligence; means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology; means for detecting the user's operations in real time and progressing the simulation based on the detected operations; means for analyzing the user's emotional state using an emotion engine and generating feedback accordingly; and means for analyzing the user's operation data and emotion data after the simulation is completed and generating feedback. This allows the user to have a realistic business experience even when they are not present at the site, and makes it possible to provide appropriate feedback based on the operation data and emotion data.

[0840] "Information processing device" is a general term for devices and systems that are operated by a user to input, display, and transmit data.

[0841] "Authentication Information" refers to personal identification information such as ID and password used by a User to access the System.

[0842] "Generative AI" refers to AI technology that has the ability to analyze data and generate scenarios and feedback for specific tasks or purposes.

[0843] "Business simulation" refers to a simulated business process that allows users to experience real business processes in a virtual environment for practice and training.

[0844] "Mixed reality technology" refers to technology that blends the real world with the virtual world, providing users with a real-time visual and auditory experience.

[0845] An "emotion engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice, and generates appropriate feedback based on the analysis results.

[0846] A "three-dimensional model" refers to a visual object constructed in three-dimensional space using computer graphics (CG).

[0847] "Voice guide" refers to a function that provides instructions and guidance to the user by voice.

[0848] "Work guide text data" refers to data that explains work procedures to the user in text and illustrations.

[0849] "Operation data" refers to data related to a series of operations performed by a user during a business simulation.

[0850] "Emotion data" refers to data relating to the emotional state detected by the user's facial expressions and voice.

[0851] This invention relates to a system that allows users to have a realistic work experience even when they are not at the site, and achieves this by combining generative artificial intelligence, mixed reality technology, and an emotion engine. The system consists of three entities: a server, a user's terminal, and the user.

[0852] The server, as the central control device of the present invention, utilizes the following hardware and software: the hardware includes a CPU, memory, storage device, and network interface, while the software includes a live artificial intelligence algorithm, mixed reality (MR) technology, an emotion engine, and a database system for managing user data.

[0853] The user's device may be a personal computer (PC), tablet, smartphone, etc., and provides an interface for the user to operate, including a keyboard, mouse, touchscreen, and MR headset.

[0854] Users access the system using their own terminal, enter their authentication information (ID and password) on the login screen, and send it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a work simulation. The user selects the work simulation they want to experience from this menu (e.g., factory line work, office work, safety training, etc.).

[0855] The user's selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The server then uses the generative AI model to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[0856] The server also uses mixed reality technology to generate a three-dimensional model, audio guide, and text data for operation guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0857] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[0858] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's operation and sends that information to the device. The user continues the simulation while referring to this. The device also detects the user's emotions from their facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that they are tired.

[0859] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[0860] As a specific example, the following prompt sentence can be used as input to a generative AI model: "Generate a simulation scenario of factory line work. Specific tasks should include 'product inspection,' 'packaging,' and 'response when the line stops.' Also, add a function to suggest breaks based on the user's emotional state."

[0861] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Step 1:

[0864] Authentication Process

[0865] User: Enter your ID and password on the device login screen.

[0866] Terminal: Sends the entered authentication information to the server. Specifically, it obtains the "ID" and "password" from the text field and sends an HTTP request to the server.

[0867] Server: Receives authentication information and checks it against a database. If authentication is successful, it generates an authentication success message and sends a business simulation menu to the terminal. For example, it checks user information using a database query and returns the results to the terminal in JSON format.

[0868] Input: User ID and password.

[0869] Output: Authentication success message and menu screen.

[0870] Step 2:

[0871] Selection of business simulations

[0872] User: Select the simulation they want to experience from the business simulation menu displayed on their device. For example, click on "Product Check Simulation."

[0873] Terminal: Sends the selection information to the server. Gets the ID of the selected simulation and sends an HTTP request to the server.

[0874] Server: Receives the selection information and generates initial setup data. It sends the generated initial setup data to the device. Specifically, it invokes the built-in algorithm that generates the initial setup data and returns the result to the device in JSON format.

[0875] Input: The ID of the selected business simulation.

[0876] Output: Initial setting data.

[0877] Step 3:

[0878] Simulation scenario generation

[0879] Server: Using the generative AI model, it generates the scenarios required for the selected business simulation. For example, it creates detailed scenarios including "product checks," "packaging," and "responses when the line stops."

[0880] Server: Sends the generated scenario data to the user terminal. The scenario data is sent to the terminal as an HTTP response.

[0881] Input: Initial setting data

[0882] Output: Scenario data

[0883] Step 4:

[0884] Mixed reality data generation

[0885] Server: Using mixed reality technology, it generates a 3D model, audio guide, and text data for the operation guide corresponding to the scenario.

[0886] Server: Sends the generated 3D model, audio guide, and text data of the operation guide to the terminal.

[0887] Terminal: displays this data and provides the user with a visual and auditory experience.

[0888] Input: Scenario data

[0889] Output: 3D model, audio guide, text data of operation guide

[0890] Step 5:

[0891] Operation detection during simulation

[0892] User: Puts on the MR headset and starts the simulation. For example, performs a simulated action of "checking the product."

[0893] Terminal: Detects user operations in real time and sends that information to the server. For example, a camera sensor can be used to track hand movements, acquire coordinate data, and send it to the server.

[0894] Server: Analyzes the received operation data and generates instructions and feedback for the next step.

[0895] Input: User operation data

[0896] Output: Next step instructions and feedback

[0897] Step 6:

[0898] Emotional state analysis and response

[0899] Device: Detects the user's facial expressions and voice in real time and sends the data to a server. For example, it uses a camera and microphone to perform facial expression recognition and voice analysis.

[0900] Server: Analyzes the received emotion data using the emotion engine and evaluates the user's emotional state. If stress or fatigue is detected, it generates a suggestion for a break or an instruction for a simple task and sends it to the device.

[0901] Input: User emotion data

[0902] Output: Emotion-based feedback

[0903] Step 7:

[0904] Feedback after the simulation

[0905] Server: Analyzes the operation and emotion data collected during the simulation and generates final feedback. Evaluations include evaluations based on operation accuracy, efficiency, response speed, and emotional state.

[0906] Server: Sends the generated feedback to the device.

[0907] Terminal: Displays received feedback to the user.

[0908] User: See the results of their evaluation based on their performance and emotional state. For example, "Your product inspection was accurate, but you need to improve your efficiency. Your emotional state showed positive fluctuations."

[0909] Input: Operation data and emotion data

[0910] Output: Final feedback

[0911] (Application example 2)

[0912] 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."

[0913] Conventional work simulation systems lack the ability to provide feedback that takes into account both realism and the user's emotional state, limiting the effectiveness of user learning and the improvement of their work comprehension. Furthermore, providing training without on-site experience carries risks in terms of safety and work efficiency. In response to these issues, there is a need to introduce emotion recognition technology to provide appropriate feedback based on the user's emotional state, enabling more practical and effective training.

[0914] 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.

[0915] In this invention, the server includes means for generating a specific scenario for a selected business simulation using a generative artificial intelligence, means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting user operations in real time and progressing the simulation based on the detected operations, means for analyzing user operation data after the simulation is completed and generating feedback, and means for adjusting the feedback content using emotion recognition means for recognizing the user's emotional state. This provides appropriate feedback according to the user's emotional state, enabling a more practical and effective business simulation experience.

[0916] "Generative AI" refers to artificial intelligence techniques used to generate specific scenarios for selected business simulations.

[0917] "Mixed reality technology" is a technology that provides users with visual and auditory experiences based on generated scenarios.

[0918] "Emotion recognition means" is a technology that recognizes the user's emotional state in real time and adjusts the feedback content based on that.

[0919] "Operation data" refers to information about operations recorded when a user performs a business simulation.

[0920] "Feedback" refers to evaluation and instruction information provided after the simulation is completed, which is generated by analyzing the user's operation data and emotional data, in order to improve the user's learning and understanding of the task.

[0921] A "scenario" refers to a virtual business procedure that includes specific tasks and procedures that users must perform in a business simulation.

[0922] "Visual experience" refers to the information that users see and feel through mixed reality technology.

[0923] "Auditory experience" refers to the information that users hear and feel through mixed reality technology.

[0924] This invention relates to a system that allows users to experience real-life work from remote locations. This system is realized by combining generative artificial intelligence, mixed reality technology, and emotion recognition means.

[0925] The main components of this system are as follows:

[0926] 1. User devices (personal computers, tablets, smartphones, etc.)

[0927] 2. Server

[0928] 3. Users

[0929] (Communication between user terminal and server)

[0930] Users access the system using their own terminal. The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu from which users can select a work simulation. When the user selects the work simulation they want to experience (e.g., factory line work, office work, safety training, etc.), the information is sent from the terminal to the server.

[0931] (Generating scenarios for business simulation)

[0932] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "robot maintenance," "parts replacement," "error code confirmation," and "system reboot procedure" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[0933] (Providing visual and auditory experiences using MR technology)

[0934] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0935] (Real-time action detection and emotion recognition)

[0936] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates emotion recognition means that recognize the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[0937] (Feedback after the simulation)

[0938] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with their emotional state.

[0939] Examples:

[0940] The user logs into the application and selects a robot maintenance task. The server generates a scenario and guides the user through the headset to actually perform the robot maintenance. The system monitors the user's actions and emotional state, providing feedback on correct operations and suggesting a break if the user feels stressed.

[0941] Example prompts to input to a generative AI model:

[0942] "Generate a simulation scenario in which users can practice robot maintenance in a factory. Include specific steps (e.g., robot arm part replacement, error code checking, system reboot procedure, etc.) along with emotion-aware feedback."

[0943] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0944] Step 1:

[0945] The user accesses the login screen using their own device, enters authentication information (ID and password), and sends it to the server. The server receives this authentication information and checks it against a database. If authentication is successful, the server displays a menu on the user's device for selecting a business simulation. The input is the user's authentication information, and the output is the business simulation selection menu.

[0946] Step 2:

[0947] The user selects the business simulation they wish to experience from a menu. This selection information is sent from the terminal to the server. The server generates the initial setting data required for the selected business simulation and sends it to the terminal. The input is the selected business simulation information, and the output is the initial setting data.

[0948] Step 3:

[0949] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. The generated scenarios include specific tasks and procedures, and use appropriate prompt statements as input. The output is scenario data.

[0950] Step 4:

[0951] The server uses mixed reality technology to generate 3D models, audio guides, and text data for task guides based on the generated scenario data. These data are then sent to the user's device, where they can experience them visually and audibly through an MR headset or display. The input is the scenario data, and the output is the 3D models, audio guides, and text data for task guides.

[0952] Step 5:

[0953] When a user starts a work simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data in real time, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The input is the user's operation data, and the output is instructions and feedback for proceeding to the next step.

[0954] Step 6:

[0955] The server uses emotion recognition to recognize the user's emotional state in real time, and adjusts the feedback content based on the recognized emotional data. For example, if the user is feeling stressed, it may suggest a temporary break or a simple task. The input is the emotional data, and the output is the adjusted feedback content.

[0956] Step 7:

[0957] When the simulation is finished, the server analyzes the collected operation data and emotional data to generate comprehensive feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user. The input is the operation data and emotional data, and the output is the feedback evaluation results.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] [Fourth embodiment]

[0962] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0963] 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.

[0964] 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).

[0965] 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.

[0966] 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.

[0967] 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).

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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."

[0975] This invention relates to a system that combines generative artificial intelligence and mixed reality technology to enable users to have a realistic work experience even when they are not at the actual work site. This system is composed of three main entities: a server, a user's terminal, and the user.

[0976] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[0977] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[0978] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[0979] The server also uses mixed reality technology to generate 3D models, audio guides, and text data for task guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[0980] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. The device displays this to the user, allowing the simulation to proceed.

[0981] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[0982] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their performance and understand areas for improvement.

[0983] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved educational effectiveness, and reduced early employee turnover. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[0984] The processing flow will be explained below.

[0985] Step 1:

[0986] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[0987] Step 2:

[0988] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[0989] Step 3:

[0990] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[0991] Step 4:

[0992] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[0993] Step 5:

[0994] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[0995] Step 6:

[0996] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[0997] Step 7:

[0998] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[0999] Step 8:

[1000] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[1001] Step 9:

[1002] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. Based on the analysis results, feedback evaluating the user's work accuracy, speed, and response accuracy is sent to the device.

[1003] Step 10:

[1004] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[1005] Example 1

[1006] 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."

[1007] With conventional work simulation systems, it was difficult for users to gain realistic work experience when they were not present at the site, and the lack of real-time feedback and evaluation based on user operations made it difficult to achieve effective work training.

[1008] 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.

[1009] In this invention, the server includes: a means for a user to transmit authentication information from a terminal to the server and for the server to verify the information; a means for a user to select a business simulation and transmit the selection information to the server; and a means for the server to generate initial setting data for the selected business simulation and transmit the initial setting data to the terminal. This allows the user to experience a realistic business operation even when not at the site. The server also includes a means for generating a specific scenario using generative artificial intelligence, a means for providing a visual and auditory experience using mixed reality technology, a means for the terminal to detect user operations in real time and transmit the data to the server, and a means for the server to analyze the operation data, generate next steps and feedback, and transmit the data to the terminal. This enables real-time feedback and evaluation based on the user's operations, resulting in more effective business training.

[1010] "User authentication" is the process in which a user provides authentication information, such as an ID and password, required to prove his or her identity to a server, which then verifies the information.

[1011] "Business simulation" is a system that virtually recreates a specific business environment and procedures, allowing users to experience simulated business operations within that environment.

[1012] "Initial setting data" refers to data that includes basic information and conditions necessary to start a business simulation, such as a line layout diagram and the types of tools to be used.

[1013] "Generative AI" is an AI technology that has the ability to automatically generate scenarios and data according to specific purposes.

[1014] A "specific scenario" is a plan that describes in detail the series of tasks and procedures that a user will experience in a business simulation.

[1015] "Mixed reality technology" is a technology that provides an environment that combines the real world and the virtual world, allowing users to experience 3D models and audio guides visually and aurally in real space.

[1016] "Operation data" refers to data related to specific operations (for example, hand movements or actions following instructions) performed by the user during the task simulation.

[1017] "Feedback" is information that includes evaluations and instructions on a user's actions and behaviors, allowing the user to check their performance and understand areas for improvement.

[1018] This invention relates to a system that allows users to gain a realistic work experience even when they are not at the site. This system is composed of three main components: a server, a user's terminal, and the user.

[1019] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends this information to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[1020] The user selects the task they wish to simulate from the menu (e.g., factory line work, office work, safety training, etc.) and sends the selection information from the terminal to the server. The server generates initial setting data for the selected task simulation (e.g., line layout diagram, types of tools to be used, etc.) and sends it to the terminal.

[1021] Next, the server uses generative artificial intelligence to generate specific scenarios (e.g., "product check," "packaging," "response when the line stops," etc.). This scenario data is sent to the device and displayed on the user's display screen. The server then uses mixed reality technology to generate 3D models, audio guides, and text data for work guides corresponding to the scenario. These data are then delivered to the user's device, allowing them to experience them visually and audibly through an MR headset or display.

[1022] When a user starts a business simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The terminal displays this to the user, allowing the simulation to proceed.

[1023] For example, when a user simulates factory line work, they put on an MR headset and perform actions such as checking products. The device detects the user's hand movements and sends them to the server. The server then generates the next step based on the user's actions and sends that information to the device. The user can refer to this information to continue the simulation.

[1024] Once the simulation is complete, the server analyzes the collected operation data and generates feedback. This feedback evaluates the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user. The user can check their own performance and understand areas for improvement.

[1025] This allows users to experience work that is close to the actual work even when they are not on-site, which is expected to have the effects of improving work understanding, improving educational effectiveness, and even preventing early turnover. Specific examples include simulations of factory line work, training in work procedures in the office, and safety management scenarios. The present invention can be applied to these wide-ranging work simulations.

[1026] Examples of prompts include:

[1027] "Please start the factory line work simulation training."

[1028] "Please explain the packaging steps in this business scenario."

[1029] "Show emergency shutdown procedures in safety training scenarios."

[1030] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1031] Step 1: User authentication

[1032] Input: Authentication information (ID and password) entered by the user on the device

[1033] Processing: The terminal sends the entered authentication information to the server, which checks the information against a database to authenticate the user.

[1034] Output: If authentication is successful, the server sends a business simulation menu to the terminal, which displays it to the user.

[1035] Specific operation: The user enters "user123" and "password1234" on the login screen, and the terminal sends that information to the server. The server checks the database, and if authentication is successful, it sends the message "Authentication successful" along with a business simulation menu to the terminal. The terminal displays it to the user.

[1036] Step 2: Simulation Selection

[1037] Input: User-selected business simulation information

[1038] Processing: The device sends information about the selected simulation to the server, which then generates initial setting data based on that information.

[1039] Output: The server sends the generated initialization data to the terminal, which displays it to the user.

[1040] Specific operation: The user selects "Line Work Simulation" from the menu, and the terminal sends the selection result to the server. The server generates initial setting data such as "Line Layout Diagram" and "Tool List" and sends it back to the terminal. The terminal displays the initial setting data on the screen.

[1041] Step 3: Scenario generation

[1042] Input: Initial setting data and business simulation selection information sent to the server

[1043] Processing: The server uses generative artificial intelligence (generative AI model) to generate specific scenarios.

[1044] Output: The server sends the generated scenario data to the terminal, which displays it to the user.

[1045] Specific operation: The server generates scenarios such as "product check" and "response when line is stopped" based on the initial setting data and sends them to the terminal. The terminal displays "Next step: Please check the product."

[1046] Step 4: Guide Data Generation

[1047] Input: Scenario data sent to the server

[1048] Processing: The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the task guide corresponding to the scenario.

[1049] Output: The generated data is sent to the terminal, which presents it to the user visually and audibly.

[1050] Specific operation: The server generates a 3D model of how to check product A and an audio guide, and sends them to the device. The device displays the 3D model on the headset and plays the audio guide.

[1051] Step 5: Run the simulation and send and receive data

[1052] Input: User operation information (hand movements, gaze, etc.) and scenario data

[1053] Processing: The device detects user actions in real time and sends the information to the server, which analyzes the action data and generates next steps and feedback.

[1054] Output: Instructions and feedback data from the server are sent to the device, which displays them to the user.

[1055] Specific operation: The user puts on the headset and performs the action of "checking product A." The device detects this movement and sends it to the server. The server analyzes the operation data and sends an instruction to the device saying, "Next, check product B." The device displays this on the screen.

[1056] Step 6: Generate feedback upon completion

[1057] Input: All operational data collected during the simulation

[1058] Processing: At the end of the simulation, the device sends all operation data to the server, which analyzes the data and evaluates the accuracy, efficiency, and speed of the operation.

[1059] Output: The server generates the evaluation result and sends it to the terminal, which displays it to the user.

[1060] Specific operation: At the end of the simulation, the device sends all operation data to the server. The server generates feedback such as "Product A check: successful" or "Product B check: failed (details of the error)" and sends it back to the device. The device displays the evaluation results to the user.

[1061] (Application example 1)

[1062] 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."

[1063] There is a need for a method that allows users to learn factory robot operation and maintenance work in a realistic way without visiting the site. There is also a need to improve the effectiveness of learning by capturing user operations in real time and providing appropriate feedback.

[1064] 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.

[1065] In this invention, the server includes means for generating a specific scenario for a selected business simulation using generative artificial intelligence, means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting the user's operations in real time and progressing the simulation based on the detected operations, means for analyzing the user's operation data after the simulation is completed and generating feedback, means for learning factory robot operation and maintenance work, and means for capturing the user's operations and transmitting them to the server. This allows the user to realistically experience actual factory work even when they are not on-site, thereby improving the learning effect.

[1066] "Generative AI" is a technology that generates specific scenarios and instructions for a selected business simulation.

[1067] "Mixed reality technology" is a technology that provides users with a visual and auditory experience, displaying a combination of the real world and the virtual world.

[1068] A "scenario" is a storyline that includes a series of tasks and instructions that a user experiences in a business simulation.

[1069] A "3D model" is a virtual object displayed in three-dimensional space using computer graphics, and is intended for users to visually recognize.

[1070] "Audio guide" is a system that provides audio guidance to users on tasks and operation methods in business simulations.

[1071] "User operations" refer to inputs and actions that a user performs to progress the simulation.

[1072] "Real-time detection" means that the system recognizes user actions immediately and on the spot.

[1073] "Feedback" refers to evaluations and instructions generated based on a user's operations, including the accuracy and efficiency of the operations.

[1074] A "factory robot" is an automated machine used to perform manufacturing and maintenance work within a factory.

[1075] "Capture" means recording a user's actions and behavior.

[1076] A "server" is a computer system that controls the entire business simulation system and generates and analyzes data.

[1077] This invention is a system for learning factory robot operation and maintenance work that combines generative artificial intelligence and mixed reality technology. This system is composed of three main entities: a server, a user's terminal, and the user.

[1078] The server uses generative artificial intelligence to generate a specific scenario for the selected task simulation. Specifically, a scenario is generated that includes tasks such as "robot oil change," "oil filter removal," and "emergency shutdown procedure." During this generation process, the server sets a prompt sentence based on the user's selection. For example, a prompt sentence such as "The user is changing the oil on a factory robot. Please instruct the next step, how to remove the oil filter." is used.

[1079] The user's device uses mixed reality technology to display a 3D model based on the generated scenario, along with audio and text guides. By wearing an optical head-mounted display (HMD), the user can experience the realistic operating environment of an actual factory robot.

[1080] When a user starts a work simulation, the device detects the user's actions in real time and sends them to the server. For example, when a user removes an oil filter from a factory robot, a sensor on the device detects hand movements and sends the data to the server. The server analyzes this data, generates instructions and feedback for proceeding to the next step, and sends it to the device. The user can refer to this to continue the simulation.

[1081] After the simulation is complete, the server analyzes the collected operation data and provides the results as feedback. This feedback includes an evaluation of the accuracy, efficiency, and speed of response of the operation. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand areas for improvement.

[1082] As a concrete example, a user wearing a mixed reality headset simulates an oil change operation by a factory robot, and the system captures and analyzes the operation and provides appropriate feedback. This process contributes to improving the user's skills, enabling efficient training without going to the site.

[1083] As described above, the present invention provides an effective means for virtually experiencing the operation and maintenance of factory robots and acquiring skills.

[1084] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1085] Step 1:

[1086] The user accesses the system using a device (smartphone, tablet, PC, etc.) and logs in. The user enters authentication information (ID and password) and sends it to the server. The server verifies this authentication information, and if authentication is successful, displays a business simulation menu on the device.

[1087] Input: User authentication information (ID, password)

[1088] Output: Authentication results, business simulation menu display

[1089] Step 2:

[1090] The user selects a simulation from the business simulation menu. The selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The terminal prepares the received initial setting data and prepares for the simulation.

[1091] Input: User's simulation selection information

[1092] Output: Initial setting data, simulation ready

[1093] Step 3:

[1094] The server uses generative AI to generate specific scenarios required for the selected task simulation. For example, it generates a scenario for the task of "changing oil for a robot." In this case, it uses prompt statements to input the generative AI model and generates an appropriate scenario.

[1095] Input: Simulation selection information, prompt text

[1096] Output: Scenario data (specific task flow)

[1097] Step 4:

[1098] The server uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, and sends them to the user's device, which receives the data and provides the user with a visual and auditory experience through an MR headset or display.

[1099] Input: Scenario data

[1100] Output: 3D model, audio guide, text data of operation guide

[1101] Step 5:

[1102] The user starts the task simulation, and the device detects the user's operations in real time. For example, when a user wears an MR headset and performs an action to remove an oil filter from a robot, the device's sensors capture the user's hand movements. The device then transmits the captured operation data to the server.

[1103] Input: User operation (hand movement)

[1104] Output: Captured operation data

[1105] Step 6:

[1106] The server receives and analyzes the user's operation data. Based on the analyzed data, it generates instructions for the next step and real-time feedback and sends them to the device. The device then displays the instructions and feedback to the user, progressing through the simulation.

[1107] Input: Captured operation data

[1108] Output: Next step instructions, feedback

[1109] Step 7:

[1110] After the simulation is over, the server analyzes the collected operation data and generates feedback, including the accuracy, efficiency, and speed of response. The evaluation results are sent to the terminal and displayed to the user, allowing them to check their performance and understand areas for improvement.

[1111] Input: All operation data

[1112] Output: Evaluation results, feedback

[1113] These steps allow users to experience and learn factory robot operation and maintenance tasks in a realistic way, even when they are not on-site, thereby efficiently improving their skills.

[1114] 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.

[1115] This invention relates to a system that combines generative artificial intelligence, mixed reality technology, and an emotion engine to enable users to have a realistic work experience even when they are not at the actual work site. This system consists of three entities: a server, a user terminal, and the user.

[1116] First, the user accesses the system using their own device (personal computer, tablet, smartphone, etc.). The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a business simulation.

[1117] From this menu, the user selects the work simulation they wish to experience (e.g., factory line work, office work, safety training, etc.). The user's selection information is sent from the terminal to the server. The server generates initial setting data for the selected work simulation and sends it to the terminal.

[1118] Next, the server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's screen.

[1119] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[1120] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and adjusts the progress of the simulation and the content of the feedback according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or a simple task.

[1121] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's actions and sends that information to the device. The user continues the simulation while referring to this. The device also detects emotions from the user's facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that the user is tired.

[1122] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[1123] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[1124] The processing flow will be explained below.

[1125] Step 1:

[1126] The terminal displays a login screen and prompts the user to enter their ID and password. The user enters their authentication information.

[1127] Step 2:

[1128] The terminal sends the entered authentication information to the server. The server looks up the user information in the database, and if it matches, it sends a response indicating authentication success, and if it does not match, it sends a message indicating authentication failure to the terminal.

[1129] Step 3:

[1130] If the terminal is successfully authenticated, a business simulation selection screen is displayed, allowing the user to select the business simulation they wish to experience.

[1131] Step 4:

[1132] The terminal transmits the user's selection information to the server. The server generates initial setting data for the selected business simulation and transmits it to the terminal. The terminal receives the initial setting data and updates the display.

[1133] Step 5:

[1134] The server uses artificial intelligence to generate specific scenarios for the selected business simulation, and sends the generated scenarios (e.g., task flow, trouble scenarios, etc.) to the terminal.

[1135] Step 6:

[1136] The server uses mixed reality technology to generate a scenario-based 3D model, audio guide, and text data for task guides, and sends them to the device, which receives the data and displays it on the MR headset or display.

[1137] Step 7:

[1138] The user puts on the MR headset and starts the work simulation. The user performs operations based on the scenario. The device detects the user's operations (e.g., gestures, eye movements, voice commands, etc.) in real time and sends them to the server.

[1139] Step 8:

[1140] The server analyzes the user's actions, generates instructions for the next step and feedback, and sends the generated feedback to the device, which displays the feedback to the user and continues the simulation.

[1141] Step 9:

[1142] The emotion engine detects emotions from the user's facial expressions and voice and sends the emotion data to the server, which analyzes the emotion data and adjusts the scenario and feedback content based on the user's emotional state.

[1143] Step 10:

[1144] If the user feels stressed during the simulation, the server generates instructions suggesting a temporary break or a simple task and sends them to the device, which then displays the suggested instructions to the user.

[1145] Step 11:

[1146] Once the simulation is complete, the server analyzes the collected operation and emotion data and generates feedback. Based on the analysis results, feedback evaluating the user's task accuracy, speed, and response accuracy, as well as their emotional state, is sent to the device.

[1147] Step 12:

[1148] The device displays the feedback results to the user, allowing them to review their performance and understand areas for improvement if necessary. They can then choose to proceed to the next simulation or try again.

[1149] Example 2

[1150] 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."

[1151] Conventional business simulation systems have the problem that it is difficult for users to experience realistic business operations when they are not present at the site, and it is also difficult to provide appropriate feedback based on the user's operations and emotional state. Furthermore, conventional systems cannot provide feedback that takes into account the user's emotional state, which limits the effectiveness of user learning and the improvement of business understanding.

[1152] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for transmitting authentication information via an information processing device operated by a user and displaying a menu of a business simulation if the authentication is successful; means for generating a specific scenario for the selected business simulation using a generative artificial intelligence; means for providing the user with a visual and auditory experience based on the generated scenario using mixed reality technology; means for detecting the user's operations in real time and progressing the simulation based on the detected operations; means for analyzing the user's emotional state using an emotion engine and generating feedback accordingly; and means for analyzing the user's operation data and emotion data after the simulation is completed and generating feedback. This allows the user to have a realistic business experience even when they are not present at the site, and makes it possible to provide appropriate feedback based on the operation data and emotion data.

[1153] "Information processing device" is a general term for devices and systems that are operated by a user to input, display, and transmit data.

[1154] "Authentication Information" refers to personal identification information such as ID and password used by a User to access the System.

[1155] "Generative AI" refers to AI technology that has the ability to analyze data and generate scenarios and feedback for specific tasks or purposes.

[1156] "Business simulation" refers to a simulated business process that allows users to experience real business processes in a virtual environment for practice and training.

[1157] "Mixed reality technology" refers to technology that blends the real world with the virtual world, providing users with a real-time visual and auditory experience.

[1158] An "emotion engine" refers to a technology that analyzes a user's emotional state from their facial expressions and voice, and generates appropriate feedback based on the analysis results.

[1159] A "three-dimensional model" refers to a visual object constructed in three-dimensional space using computer graphics (CG).

[1160] "Voice guide" refers to a function that provides instructions and guidance to the user by voice.

[1161] "Work guide text data" refers to data that explains work procedures to the user in text and illustrations.

[1162] "Operation data" refers to data related to a series of operations performed by a user during a business simulation.

[1163] "Emotion data" refers to data relating to the emotional state detected by the user's facial expressions and voice.

[1164] This invention relates to a system that allows users to have a realistic work experience even when they are not at the site, and achieves this by combining generative artificial intelligence, mixed reality technology, and an emotion engine. The system consists of three entities: a server, a user's terminal, and the user.

[1165] The server, as the central control device of the present invention, utilizes the following hardware and software: the hardware includes a CPU, memory, storage device, and network interface, while the software includes a live artificial intelligence algorithm, mixed reality (MR) technology, an emotion engine, and a database system for managing user data.

[1166] The user's device may be a personal computer (PC), tablet, smartphone, etc., and provides an interface for the user to operate, including a keyboard, mouse, touchscreen, and MR headset.

[1167] Users access the system using their own terminal, enter their authentication information (ID and password) on the login screen, and send it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu for selecting a work simulation. The user selects the work simulation they want to experience from this menu (e.g., factory line work, office work, safety training, etc.).

[1168] The user's selection information is sent from the terminal to the server, and the server generates initial setting data for the selected business simulation and sends it to the terminal. The server then uses the generative AI model to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "product check," "packaging," and "response when the line stops" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[1169] The server also uses mixed reality technology to generate a three-dimensional model, audio guide, and text data for operation guides corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[1170] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates an emotion engine that recognizes the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[1171] For example, when a user simulates factory line work, they put on an MR headset and perform the actions of checking products. The device detects the user's hand movements and sends them to the server. The server generates the next step based on the user's operation and sends that information to the device. The user continues the simulation while referring to this. The device also detects the user's emotions from their facial expressions and voice while working, and generates instructions to encourage them to take a break if it determines that they are tired.

[1172] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with the emotional state.

[1173] As a specific example, the following prompt sentence can be used as input to a generative AI model: "Generate a simulation scenario of factory line work. Specific tasks should include 'product inspection,' 'packaging,' and 'response when the line stops.' Also, add a function to suggest breaks based on the user's emotional state."

[1174] This invention allows users to experience work that is close to the actual work even when they are not on-site, which is expected to result in improved work understanding, improved training effectiveness, and reduced early employee turnover. Furthermore, by combining it with an emotion engine, effective feedback that takes into account the user's emotional state can be provided. Specific examples include simulations of factory line work, training in office work procedures, and safety management scenarios. In this way, this invention can be applied to a wide range of work simulations.

[1175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] Authentication Process

[1178] User: Enter your ID and password on the device login screen.

[1179] Terminal: Sends the entered authentication information to the server. Specifically, it obtains the "ID" and "password" from the text field and sends an HTTP request to the server.

[1180] Server: Receives authentication information and checks it against a database. If authentication is successful, it generates an authentication success message and sends a business simulation menu to the terminal. For example, it checks user information using a database query and returns the results to the terminal in JSON format.

[1181] Input: User ID and password.

[1182] Output: Authentication success message and menu screen.

[1183] Step 2:

[1184] Selection of business simulations

[1185] User: Select the simulation they want to experience from the business simulation menu displayed on their device. For example, click on "Product Check Simulation."

[1186] Terminal: Sends the selection information to the server. Gets the ID of the selected simulation and sends an HTTP request to the server.

[1187] Server: Receives the selection information and generates initial setup data. It sends the generated initial setup data to the device. Specifically, it invokes the built-in algorithm that generates the initial setup data and returns the result to the device in JSON format.

[1188] Input: The ID of the selected business simulation.

[1189] Output: Initial setting data.

[1190] Step 3:

[1191] Simulation scenario generation

[1192] Server: Using the generative AI model, it generates the scenarios required for the selected business simulation. For example, it creates detailed scenarios including "product checks," "packaging," and "responses when the line stops."

[1193] Server: Sends the generated scenario data to the user terminal. The scenario data is sent to the terminal as an HTTP response.

[1194] Input: Initial setting data

[1195] Output: Scenario data

[1196] Step 4:

[1197] Mixed reality data generation

[1198] Server: Using mixed reality technology, it generates a 3D model, audio guide, and text data for the operation guide corresponding to the scenario.

[1199] Server: Sends the generated 3D model, audio guide, and text data of the operation guide to the terminal.

[1200] Terminal: displays this data and provides the user with a visual and auditory experience.

[1201] Input: Scenario data

[1202] Output: 3D model, audio guide, text data of operation guide

[1203] Step 5:

[1204] Operation detection during simulation

[1205] User: Puts on the MR headset and starts the simulation. For example, performs a simulated action of "checking the product."

[1206] Terminal: Detects user operations in real time and sends that information to the server. For example, a camera sensor can be used to track hand movements, acquire coordinate data, and send it to the server.

[1207] Server: Analyzes the received operation data and generates instructions and feedback for the next step.

[1208] Input: User operation data

[1209] Output: Next step instructions and feedback

[1210] Step 6:

[1211] Emotional state analysis and response

[1212] Device: Detects the user's facial expressions and voice in real time and sends the data to a server. For example, it uses a camera and microphone to perform facial expression recognition and voice analysis.

[1213] Server: Analyzes the received emotion data using the emotion engine and evaluates the user's emotional state. If stress or fatigue is detected, it generates a suggestion for a break or an instruction for a simple task and sends it to the device.

[1214] Input: User emotion data

[1215] Output: Emotion-based feedback

[1216] Step 7:

[1217] Feedback after the simulation

[1218] Server: Analyzes the operation and emotion data collected during the simulation and generates final feedback. Evaluations include evaluations based on operation accuracy, efficiency, response speed, and emotional state.

[1219] Server: Sends the generated feedback to the device.

[1220] Terminal: Displays received feedback to the user.

[1221] User: See the results of their evaluation based on their performance and emotional state. For example, "Your product inspection was accurate, but you need to improve your efficiency. Your emotional state showed positive fluctuations."

[1222] Input: Operation data and emotion data

[1223] Output: Final feedback

[1224] (Application example 2)

[1225] 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."

[1226] Conventional work simulation systems lack the ability to provide feedback that takes into account both realism and the user's emotional state, limiting the effectiveness of user learning and the improvement of their work comprehension. Furthermore, providing training without on-site experience carries risks in terms of safety and work efficiency. In response to these issues, there is a need to introduce emotion recognition technology to provide appropriate feedback based on the user's emotional state, enabling more practical and effective training.

[1227] 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.

[1228] In this invention, the server includes means for generating a specific scenario for a selected business simulation using a generative artificial intelligence, means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology, means for detecting user operations in real time and progressing the simulation based on the detected operations, means for analyzing user operation data after the simulation is completed and generating feedback, and means for adjusting the feedback content using emotion recognition means for recognizing the user's emotional state. This provides appropriate feedback according to the user's emotional state, enabling a more practical and effective business simulation experience.

[1229] "Generative AI" refers to artificial intelligence techniques used to generate specific scenarios for selected business simulations.

[1230] "Mixed reality technology" is a technology that provides users with visual and auditory experiences based on generated scenarios.

[1231] "Emotion recognition means" is a technology that recognizes the user's emotional state in real time and adjusts the feedback content based on that.

[1232] "Operation data" refers to information about operations recorded when a user performs a business simulation.

[1233] "Feedback" refers to evaluation and instruction information provided after the simulation is completed, which is generated by analyzing the user's operation data and emotional data, in order to improve the user's learning and understanding of the task.

[1234] A "scenario" refers to a virtual business procedure that includes specific tasks and procedures that users must perform in a business simulation.

[1235] "Visual experience" refers to the information that users see and feel through mixed reality technology.

[1236] "Auditory experience" refers to the information that users hear and feel through mixed reality technology.

[1237] This invention relates to a system that allows users to experience real-life work from remote locations. This system is realized by combining generative artificial intelligence, mixed reality technology, and emotion recognition means.

[1238] The main components of this system are as follows:

[1239] 1. User devices (personal computers, tablets, smartphones, etc.)

[1240] 2. Server

[1241] 3. Users

[1242] (Communication between user terminal and server)

[1243] Users access the system using their own terminal. The user enters authentication information (ID and password) on the login screen and sends it to the server. The server verifies the authentication information, and if authentication is successful, displays a menu from which users can select a work simulation. When the user selects the work simulation they want to experience (e.g., factory line work, office work, safety training, etc.), the information is sent from the terminal to the server.

[1244] (Generating scenarios for business simulation)

[1245] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. For example, a scenario including tasks such as "robot maintenance," "parts replacement," "error code confirmation," and "system reboot procedure" is generated. This scenario data is sent to the terminal and displayed on the user's display screen.

[1246] (Providing visual and auditory experiences using MR technology)

[1247] The server then uses mixed reality technology to generate a 3D model, audio guide, and text data for the operation guide corresponding to the scenario, which are then distributed to the user's device, allowing the user to experience the data visually and audibly through an MR headset or display.

[1248] (Real-time action detection and emotion recognition)

[1249] When a user starts a work simulation, the device detects the user's actions in real time and sends that information to the server. The server analyzes the user's actions, generates instructions and feedback for the next step, and sends them to the device. This feedback incorporates emotion recognition means that recognize the user's emotional state, and the progress of the simulation and the content of the feedback are adjusted according to the user's emotions. For example, if the user is feeling stressed, the server can suggest a temporary break or an easy task.

[1250] (Feedback after the simulation)

[1251] Once the simulation is complete, the server analyzes the collected operation data and emotional data and generates feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user, allowing the user to check their own performance and understand the correlation with their emotional state.

[1252] Examples:

[1253] The user logs into the application and selects a robot maintenance task. The server generates a scenario and guides the user through the headset to actually perform the robot maintenance. The system monitors the user's actions and emotional state, providing feedback on correct operations and suggesting a break if the user feels stressed.

[1254] Example prompts to input to a generative AI model:

[1255] "Generate a simulation scenario in which users can practice robot maintenance in a factory. Include specific steps (e.g., robot arm part replacement, error code checking, system reboot procedure, etc.) along with emotion-aware feedback."

[1256] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1257] Step 1:

[1258] The user accesses the login screen using their own device, enters authentication information (ID and password), and sends it to the server. The server receives this authentication information and checks it against a database. If authentication is successful, the server displays a menu on the user's device for selecting a business simulation. The input is the user's authentication information, and the output is the business simulation selection menu.

[1259] Step 2:

[1260] The user selects the business simulation they wish to experience from a menu. This selection information is sent from the terminal to the server. The server generates the initial setting data required for the selected business simulation and sends it to the terminal. The input is the selected business simulation information, and the output is the initial setting data.

[1261] Step 3:

[1262] The server uses generative artificial intelligence to generate specific scenarios required for the selected business simulation. The generated scenarios include specific tasks and procedures, and use appropriate prompt statements as input. The output is scenario data.

[1263] Step 4:

[1264] The server uses mixed reality technology to generate 3D models, audio guides, and text data for task guides based on the generated scenario data. These data are then sent to the user's device, where they can experience them visually and audibly through an MR headset or display. The input is the scenario data, and the output is the 3D models, audio guides, and text data for task guides.

[1265] Step 5:

[1266] When a user starts a work simulation, the terminal detects the user's operations in real time and sends that information to the server. The server analyzes the user's operation data in real time, generates instructions and feedback for proceeding to the next step, and sends them to the terminal. The input is the user's operation data, and the output is instructions and feedback for proceeding to the next step.

[1267] Step 6:

[1268] The server uses emotion recognition to recognize the user's emotional state in real time, and adjusts the feedback content based on the recognized emotional data. For example, if the user is feeling stressed, it may suggest a temporary break or a simple task. The input is the emotional data, and the output is the adjusted feedback content.

[1269] Step 7:

[1270] When the simulation is finished, the server analyzes the collected operation data and emotional data to generate comprehensive feedback. This feedback includes an evaluation based on the accuracy, efficiency, and speed of response of the operation, as well as the emotional state. The evaluation results are sent to the terminal and displayed to the user. The input is the operation data and emotional data, and the output is the feedback evaluation results.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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."

[1280] 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.

[1281] 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).

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] The following is further disclosed regarding the above embodiment.

[1293] (Claim 1)

[1294] A means for generating a specific scenario for the selected business simulation using a generative artificial intelligence;

[1295] A means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology;

[1296] A means of detecting user actions in real time and progressing the simulation based on them;

[1297] a means for analyzing user operation data and generating feedback after the simulation is completed;

[1298] A system including:

[1299] (Claim 2)

[1300] 2. The system according to claim 1, further comprising means for transmitting, when a user selects a business simulation, information about the selection to a server, and for the server to transmit initial setting data for the selected business simulation to the user's terminal.

[1301] (Claim 3)

[1302] The system according to claim 1, further comprising means for generating text data of a 3D model, an audio guide, and a task guide using mixed reality technology and displaying them on a user's terminal.

[1303] "Example 1"

[1304] (Claim 1)

[1305] A means for the user to send authentication information from the device to the server, which then verifies it;

[1306] A means for a user to select a business simulation and transmit the selection information to a server;

[1307] A means for the server to generate initial setting data for the selected business simulation and transmit the data to the terminal;

[1308] A means for generating a specific scenario for the selected business simulation using a generative artificial intelligence;

[1309] A means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology;

[1310] A means for the terminal to detect user operations in real time and transmit the data to a server;

[1311] A means for the server to analyze the user's operation data, generate next steps and feedback, and send the data to the terminal;

[1312] a means for the server to analyze the user's operation data and generate feedback after the simulation is completed;

[1313] A system including:

[1314] (Claim 2)

[1315] The system according to claim 1, further comprising means for generating text data of a 3D model, an audio guide, and a task guide using mixed reality technology and displaying them on a user's terminal.

[1316] (Claim 3)

[1317] 10. The system of claim 1, further comprising means for displaying the initial setting data, scenario data, and feedback data received from the server at the user's terminal, and providing visual and audio information to the user.

[1318] "Application Example 1"

[1319] (Claim 1)

[1320] A means for generating a specific scenario for the selected business simulation using a generative artificial intelligence;

[1321] A means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology;

[1322] A means of detecting user actions in real time and progressing the simulation based on them;

[1323] a means for analyzing user operation data and generating feedback after the simulation is completed;

[1324] A means to learn how to operate and maintain factory robots,

[1325] A means for capturing user actions and sending them to a server;

[1326] A system including:

[1327] (Claim 2)

[1328] 2. The system according to claim 1, further comprising means for transmitting, when a user selects a business simulation, information about the selection to a server, and for the server to transmit initial setting data for the selected business simulation to the user's terminal.

[1329] (Claim 3)

[1330] The system according to claim 1, further comprising means for generating text data of a 3D model, an audio guide, and a task guide using mixed reality technology and displaying them on a user's terminal.

[1331] "Example 2: Combining Emotion Engines"

[1332] (Claim 1)

[1333] means for transmitting authentication information via an information processing device operated by a user and displaying a business simulation menu when authentication is successful;

[1334] A means for generating a specific scenario for the selected business simulation using a generative artificial intelligence;

[1335] A means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology;

[1336] a means for detecting user operations in real time and progressing the simulation based on the operations;

[1337] means for analyzing the user's emotional state using an emotion engine and generating feedback accordingly;

[1338] means for analyzing the user's operation data and emotion data after the simulation is completed and generating feedback;

[1339] A system including:

[1340] (Claim 2)

[1341] 2. The system according to claim 1, further comprising means for, when a user selects a business simulation, transmitting the selection information to a server, and the server transmitting initial setting data of the selected business simulation to the user's terminal.

[1342] (Claim 3)

[1343] 2. The system according to claim 1, further comprising means for generating text data of a three-dimensional model, an audio guide, and a task guide using mixed reality technology and displaying them on a user's terminal.

[1344] "Application example 2 when combining emotion engines"

[1345] (Claim 1)

[1346] A means for generating a specific scenario for the selected business simulation using a generative artificial intelligence;

[1347] A means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology;

[1348] A means of detecting user actions in real time and progressing the simulation based on them;

[1349] a means for analyzing user operation data and generating feedback after the simulation is completed;

[1350] a means for adjusting the feedback content using an emotion recognition means for recognizing the user's emotional state;

[1351] A system including:

[1352] (Claim 2)

[1353] 2. The system according to claim 1, further comprising means for transmitting, when a user selects a business simulation, information about the selection to a server, and for the server to transmit initial setting data for the selected business simulation to the user's terminal.

[1354] (Claim 3)

[1355] The system according to claim 1, further comprising means for generating text data of a 3D model, an audio guide, and a task guide using mixed reality technology and displaying them on a user's terminal. [Explanation of symbols]

[1356] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating a specific scenario for the selected business simulation using a generative artificial intelligence; A means for providing a user with a visual and auditory experience based on the generated scenario using mixed reality technology; A means of detecting user actions in real time and progressing the simulation based on them; a means for analyzing user operation data and generating feedback after the simulation is completed; A system including:

2. 2. The system according to claim 1, further comprising means for transmitting, when a user selects a business simulation, information about the selection to the server, and for the server to transmit initial setting data for the selected business simulation to the user's terminal.

3. The system according to claim 1, further comprising means for generating text data of a 3D model, an audio guide, and a work guide using mixed reality technology, and displaying them on a user's terminal.

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