system

Abstract

We provide the system. [Solution] Sensor means for detecting the worker's movements and the surrounding environment, A means for generating a work procedure using data from the aforementioned sensor means, Means for presenting the generated work procedures visually and audibly through an augmented reality or virtual reality device, A system that includes means for evaluating safety and issuing warnings.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a need to solve the problem that due to the decrease in skilled workers and communication barriers among workers, high-quality work cannot be carried out efficiently and safely. It is also necessary to reduce the burden for new workers and foreign workers to quickly acquire and utilize advanced technologies. Furthermore, real-time work monitoring and ensuring safety are also important issues.

Means for Solving the Problems

[0005] The present invention solves these problems by providing a system that includes sensor means for detecting the movements of a worker and the surrounding environment, means for generating work procedures using data from the sensor means, means for visually and audibly presenting the generated work procedures through an augmented reality or virtual reality device, and means for evaluating safety and issuing warnings. The system also includes functions for generating work procedures using artificial intelligence learned from data of past skilled workers, monitoring the progress and quality of work in real time, and analyzing the results. This enables highly accurate and safe work regardless of the user's skill level.

[0006] A "worker" is a person whose role is to perform a specific task on-site.

[0007] "Sensing means" refers to devices or technical means used to detect the movements of a worker or the surrounding environment.

[0008] "Data" refers to information collected through sensory means, and it represents specific numerical values ​​and conditions related to the worker's actions and the environment.

[0009] "Work procedures" refer to the steps and methods of operation that should be followed when performing a specific task.

[0010] "Means of generation" refers to processing methods and technologies for constructing work procedures based on data.

[0011] An "augmented reality or virtual reality device" is a device that provides digital content to users visually, and can overlay three-dimensional images and information onto the real environment.

[0012] "Means of visual and auditory presentation" refers to technologies or methods for providing generated work procedures to users in a visible or audible form.

[0013] "Means of assessing safety and issuing warnings" refers to a mechanism or system for analyzing current work conditions and issuing warnings in advance if potential hazards exist.

[0014] "Artificial intelligence" refers to the ability of computers to mimic human intellectual activity, and includes technologies such as machine learning and pattern recognition.

[0015] "Monitoring" refers to the process of monitoring and recording the progress of work and changes in the environment in real time. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for workers to receive real-time visual and audible work instructions. The system consists of multiple components, each with a specific role.

[0038] Server-based operation and management of AI agents

[0039] The server collects past operational data from skilled workers and uses it to train an AI model used to generate work procedures. The AI ​​uses machine learning algorithms to optimize the work and enhance the instruction content specific to each task. This AI continuously learns from new data and becomes flexible enough to adapt to changes in work equipment and the environment.

[0040] Instructions and status monitoring via terminals

[0041] The terminal is directly connected to an augmented reality (AR) or virtual reality (VR) device worn by the worker. Based on data received from the server, the terminal provides the worker with visual guidelines. For example, when performing pipework, the terminal supports the worker by displaying the correct connection procedure and the location of the tools to be used via AR. At the same time, the terminal monitors the worker's movements and performs safety assessments in real time. If an anomaly is detected, it issues a warning and prompts appropriate action.

[0042] User actions

[0043] Users perform tasks based on instructions using a headset-type AR / VR device. The device provides detailed information about the work site, supporting workers in following instructions step by step without making mistakes. Users can also notify the device of any problems or requests that arise during the work via voice or gestures, enabling an interactive experience.

[0044] Specifically, when a new machine needs to be installed at a construction site, the server analyzes the installation procedure and provides instructions to the user via a terminal. The user can then use augmented reality (AR) to overlay virtual installation markers onto the real-world work environment and follow the instructions. This enables even inexperienced workers to perform efficient and highly accurate work, accelerating the learning process.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server collects past work and behavioral data from skilled workers. This data includes information about the type of work performed, the tools used, and the environment in which the work was carried out.

[0048] Step 2:

[0049] The server trains an AI model based on the collected data. Through training, the AI ​​can learn effective work procedures and patterns of abnormal behavior. This model is enhanced using machine learning techniques such as time series analysis and pattern recognition.

[0050] Step 3:

[0051] The terminal receives trained AI models from the server and prepares them for use in the work field. The terminal connects to AR / VR devices and prepares visual and audio interfaces.

[0052] Step 4:

[0053] The user activates the system by putting on an AR / VR device before starting work. The terminal presents the user with work procedures and provides initial guidance via the device.

[0054] Step 5:

[0055] During operation, the terminal monitors the user's movements in real time. Sensors capture the user's hand movements and location information and transmit it to the server.

[0056] Step 6:

[0057] The server analyzes the received data and generates real-time feedback. This includes checking progress, evaluating accuracy, and performing safety checks, and sending correction instructions or warnings to the terminal as needed.

[0058] Step 7:

[0059] The terminal relays feedback received from the server to the user. For example, if the user is performing an incorrect procedure, it will show the correct procedure through visual guidelines or voice instructions.

[0060] Step 8:

[0061] The user follows the instructions on the device and completes the steps accurately. After completing the task, the user exits the AR / VR system and saves the day's work record.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In today's work environment, workers often find it difficult to understand and execute efficient and safe work processes. In particular, less experienced workers struggle to perform high-precision tasks without proper guidance and real-time support, which can impact productivity and safety.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, means for presenting the generated work process visually and audibly through an extended display or virtual display device, and means for using a generation model and instructions in generating the work process. This enables the worker to perform the work safely and efficiently in real time.

[0067] "Detection means" refers to a device or system that recognizes the worker's actions and the surrounding environment and acquires related information.

[0068] "Means for generating work processes" refers to a function that uses acquired information to create procedures for efficiently and safely accomplishing a specific task.

[0069] An "enhanced display or virtual display device" is a technology for providing digital information to workers visually and audibly, and is a device that overlays information onto the real environment or presents a completely virtual environment.

[0070] "Means for evaluating safety and issuing alarms" refers to a system that monitors the work environment and the actions of workers, and issues an alarm to warn of danger if it is present.

[0071] "Machine intelligence" is an artificial knowledge system that uses learning algorithms based on past work data to provide situation-appropriate instructions and advice.

[0072] A "generative model" is a pre-trained algorithm or framework used to generate work instructions tailored to a specific purpose or situation.

[0073] An "instruction document" is textual information that expresses specific guidance on how to operate or carry out a task, and it guides the worker's actions.

[0074] "Means of providing feedback" refers to a function that receives voice or motion input from workers, analyzes that information, and provides responses or guidance in real time.

[0075] The embodiments for carrying out this invention are shown below.

[0076] This system supports workers in performing their tasks efficiently and safely while receiving real-time work instructions. The system consists of three main components: a server, terminals, and users.

[0077] Server Role

[0078] The server collects worker motion data and surrounding environmental information through sensors and stores it in a database. Based on this data, the server trains an AI model using machine learning frameworks such as TENSORFLOW® and PyTorch. The server then utilizes the trained generative AI model to generate optimal instructions for a specific task based on the input of prompts. These generated instructions are then sent to the terminal.

[0079] Terminal role

[0080] The terminal is connected to an augmented reality (AR) or virtual reality (VR) device and presents instructions received from the server to the worker. This presentation is done via display and audio output to help the worker intuitively understand the instructions. For example, the terminal shows specific work procedures by displaying holograms within the worker's field of view or playing instructions aloud. At the same time, the terminal monitors the worker's movements using cameras and various sensors to ensure safety. If an abnormality is detected, an alarm is issued immediately.

[0081] User roles

[0082] Users operate a headset-type AR / VR device and follow instructions provided by the system. Users can request feedback and additional instructions from the device through voice input and gestures. For example, during a task, a user can ask a voice question such as, "What is the appropriate tool to pick up this part?" and receive real-time advice. This response is prepared by an AI model that uses past data to determine the best option.

[0083] Examples of specific cases and prompt statements

[0084] A concrete example is the installation of new machinery at a construction site. In this case, the server uses a prompt message such as "Optimize the machine installation procedure and generate instructions that allow beginners to work safely" to generate detailed instructions. The generated instructions are presented to the user via the terminal in an extended display format.

[0085] With a system configured in this way, workers can perform their tasks with high efficiency and safety, regardless of their experience level.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server collects worker motion data and sensor data. This data includes specific work procedures and information about the surrounding environment. This input data is stored in a database and then cleansed and preprocessed. As a result, it outputs a dataset in a format suitable for training an AI model.

[0089] Step 2:

[0090] The server trains an AI model using the collected data. This process utilizes machine learning tools such as TensorFlow and PyTorch. It receives a formatted dataset as input and performs training using a neural network. As a result, it outputs an AI model that generates optimization instructions to improve work efficiency and safety.

[0091] Step 3:

[0092] The server receives prompt text to generate specific work instructions using a generative AI model. Specifically, it provides text such as, "Optimize the procedure for efficient piping work and generate instructions that allow for safe work." Based on this prompt, the AI ​​outputs a specific set of instructions for the worker.

[0093] Step 4:

[0094] The server sends instructions generated by the AI ​​to the terminal. The instruction information is packed into a message format and sent in real time via a communication protocol (e.g., WebSocket). The terminal then forwards the received instruction data to the AR / VR device.

[0095] Step 5:

[0096] The terminal presents received instructions to the user visually and audibly. Here, an AR display is used to overlay holographic instructions onto the worker's field of view, and a speaker is used to play audio instructions. This allows the user to intuitively understand the task.

[0097] Step 6:

[0098] The terminal uses sensors and cameras to monitor the user's movements in real time. This monitoring allows for the evaluation of work progress and safety. If an anomaly is detected, an alarm is immediately issued to alert the user.

[0099] Step 7:

[0100] Users send feedback to the device via voice or gestures during their work, allowing them to receive additional instructions and support for problem-solving. This user input is then sent back to the server and used to improve the AI ​​model.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] In many factories, the introduction and maintenance of new machinery and equipment can be difficult, especially for less experienced workers, resulting in increased working hours, decreased quality, and safety issues. This invention aims to overcome these problems and improve efficiency and safety in factory operations.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes a detection device for detecting the movements of an operator and the surrounding environment; an information processing device for generating work procedures using data from the detection device; a display device for visually and audibly presenting the generated work procedures through an augmented reality or virtual reality display; a safety management device for evaluating safety and issuing warnings; and a support device for providing instructions to assist in setting up and maintaining machinery and equipment within the factory. This enables operators to efficiently and safely perform complex machinery setup and maintenance tasks.

[0106] A "detection device" is a device that senses the movements of a worker and the surrounding environment, and collects necessary data.

[0107] An "information processing device" is a device that generates work procedures using data from a detection device.

[0108] A "display device" is a device that presents generated work procedures to a worker visually and audibly in the form of augmented reality or virtual reality.

[0109] A "safety management device" is a device that evaluates safety during work and issues warnings as needed.

[0110] A "support device" is a device that provides instructions to workers to assist with the setup and maintenance of machinery and equipment within a factory.

[0111] The system that realizes this invention aims to improve efficiency and safety in factory operations and consists of multiple devices and programs. The system is operated as follows:

[0112] The server first collects information on past operations performed by skilled workers and uses it to train a machine learning model. This machine learning process utilizes machine learning frameworks such as TensorFlow to generate optimal work procedures based on the data. The information processing device continuously updates the generated procedures to adapt to the latest work environment and machine equipment specifications.

[0113] The terminal, specifically an augmented reality display such as Microsoft HoloLens®, overlays work procedures transmitted from the server onto the actual work environment. This display provides virtual guidance and voice instructions to make it easier for workers to understand visually. Furthermore, based on environmental data collected by detection devices, a safety management system issues real-time safety warnings to workers.

[0114] Users can efficiently and safely operate machinery and equipment within the factory by following these displays and instructions. For example, when a user introduces a new robotic arm, AR visually guides them through the installation process, ensuring they perform the setup correctly. Furthermore, if any new challenges arise, they can receive immediate prompts and instructions.

[0115] Examples of prompts to be input to the generated AI model include specific instructions such as, "Please describe the robot arm installation procedure in detail. List the necessary tools and precautions for each step." In this way, the entire system can work together to create an optimal working environment.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server collects data on the work actions of past skilled workers. This input data includes specific actions performed by the workers and information about the surrounding environment. After collection, the server preprocesses this data, performing tasks such as noise reduction and conversion to an appropriate format to generate a training dataset for machine learning models. This output dataset is then fed into subsequent training phases.

[0119] Step 2:

[0120] The server uses pre-processed data to train an AI model using a machine learning framework (e.g., TensorFlow). In this process, it learns patterns from the data and generates algorithms to optimize the work procedure. The output is a trained model capable of presenting the optimal procedure for each task.

[0121] Step 3:

[0122] The terminal receives a set of work instructions generated from a trained model sent from the server and displays them on the user's AR device (e.g., Microsoft HoloLens). The input data consists of the instructions inferred by the model, which the terminal displays as a visual and audio guide, overlaying information about the specific task the user is working on. In this process, the AR device uses input from environmental sensors to synchronize virtual information with the real world.

[0123] Step 4:

[0124] Users perform work procedures based on visual guides provided through AR devices. User input consists of data about the progress of the work and the environment, which is collected in real time by the device. This data then becomes the basis for the server to perform behavioral analysis.

[0125] Step 5:

[0126] The server analyzes real-time progress data collected from users and performs a safety assessment. Input data consists of progress logs and environmental data, which are used to determine whether the work is being performed safely and efficiently. If any anomalies are detected, a warning is issued, and appropriate guidance and corrective instructions are provided to the user. Output consists of warnings and improvement instructions communicated to the user as needed.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention provides a system that allows workers to receive work instructions visually and audibly in real time, while simultaneously monitoring their emotional state. The system consists of the following components:

[0129] Server-based management of AI agents and sentiment analysis

[0130] The server runs an AI trained with data from skilled workers, and combines the generated work procedures with other components. Furthermore, it uses an emotion engine to analyze the user's emotional state from the received data and determine what kind of feedback should be provided. For example, if an anxious state is detected, it will provide encouraging messages to boost motivation or offer simplified procedures.

[0131] Implementation and presentation via terminal

[0132] The terminal displays work instructions via an AR / VR device based on instructions and emotional information received from the server. This includes adjustments based on the worker's emotional state, changing the visual and audio guidance content as needed. For example, if the work is progressing smoothly, it displays normal instructions, but if stress is detected, it suggests instructions to regulate breathing or a short break.

[0133] User interaction and feedback reception

[0134] Users engage in tasks while wearing an AR / VR device. This device is equipped with a microphone and camera, which capture the user's voice and facial expressions and transmit them to an emotion engine. Users can also receive emotion-based feedback and take recommended actions. For example, if a task is too complex and confusing, they can accept simplified instructions from the device.

[0135] As a concrete example, if this system is implemented in the installation of a new conveyor belt in a factory, workers will accurately place the necessary parts following the AR guide. If stress or confusion is detected during the work, the emotion engine will intervene, switching to a friendly voice guide and providing reassuring support to the user. Such features are expected to improve not only the quality of work but also the mental satisfaction and safety of the users.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server collects past work data from skilled workers and uses it to train the AI. The AI ​​model uses this data to generate and optimize work procedures.

[0139] Step 2:

[0140] The server uses an emotion engine to receive facial expression data and voice information sent from the user and recognizes the user's emotional state. Based on this, it adjusts appropriate operation instructions.

[0141] Step 3:

[0142] The terminal receives instructions from the server and presents visual and audio work instructions to the user through an AR / VR device. Adjustments are made based on the user's emotional state during this process.

[0143] Step 4:

[0144] The user wears an AR / VR device and begins working according to the presented instructions. Sensors monitor the user's movements and environment during the work, providing real-time feedback.

[0145] Step 5:

[0146] The device continuously collects changes in the user's voice and facial expressions and sends this data to the emotion engine. This allows the system to understand changes in the user's emotions while they are working.

[0147] Step 6:

[0148] The server continuously analyzes user behavior and emotional data and generates feedback as needed. For example, if stress is detected, it generates and sends voice instructions to the device to encourage relaxation.

[0149] Step 7:

[0150] The terminal receives feedback from the server and makes adjustments according to the progress of the work. In addition to visual information, encouraging messages and audio are provided to the user.

[0151] Step 8:

[0152] The user receives feedback and adjusts their work pace or takes suggested breaks if necessary. After completing the task, they finish recording and save their work data and emotional changes.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] Conventional work support systems provided uniform instructions without considering the emotional state of workers, making it difficult to improve work efficiency or reduce workers' mental burden. Furthermore, real-time safety assessments and dynamic adjustments to work procedures based on emotions were not possible. Therefore, there is a need to improve work quality, safety, and workers' psychological satisfaction.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, analysis means for analyzing the user's emotional state, and adjustment means for providing emotion-based feedback. This makes it possible to dynamically adjust work procedures according to the worker's emotional state and work environment, thereby simultaneously improving work efficiency, safety, and the worker's mental satisfaction.

[0158] "Detection means" refers to devices or systems for collecting information about the worker's actions and the surrounding environment.

[0159] "Information processing means" refers to a computing device or software used to generate useful work procedures using collected data.

[0160] An "information presentation device" is a device or interface for providing generated information to users visually and audibly.

[0161] An "evaluation method" is a system that analyzes worker safety and issues warnings as needed.

[0162] An "analysis tool" is a system that analyzes the user's emotional state and adapts the work process based on the results.

[0163] A "adjustment mechanism" is a system for optimizing work procedures based on the results of an analysis.

[0164] An "intelligent device" is an artificial intelligence-based system that learns from data of past skilled workers and uses that data to create new work procedures and make adjustments.

[0165] A "data processing device" is a computer or system used to monitor the progress and quality of work in real time and analyze the results.

[0166] This invention is a system that grasps the actions and emotional state of a worker in real time and provides appropriate work instructions based on that information. The system mainly consists of three components: a server, a terminal, and a user.

[0167] server

[0168] The server runs AI and sentiment analysis engines, and is responsible for analyzing data obtained from the worker's actions and surrounding environment. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train generative AI models based on past data from skilled workers. This allows for the dynamic generation of work procedures and safety indicators. Furthermore, the sentiment analysis engine analyzes the user's voice and facial expressions to generate feedback that corresponds to their emotional state.

[0169] terminal

[0170] Based on work instructions and emotional information received from the server, the terminal provides visual and audible guidance to the user using HoloLens or similar information display devices. The terminal adjusts the guidance content as needed according to the user's emotional state, and if stress is detected, it provides instructions to encourage relaxation. The AR / VR devices used in this process incorporate sensors such as cameras and microphones to capture the user's emotional state.

[0171] User

[0172] Users wear AR / VR devices and perform tasks according to the instructions presented. Real-time feedback allows for smoother workflow and provides emotionally-based feedback as needed. For example, if a task is difficult or confusing, the device provides simplified instructions and user-friendly voice guidance.

[0173] As a concrete example, when installing conveyor belts in a factory, this system allows workers to quickly and accurately position the necessary parts using AR guidance. Furthermore, if stress is detected, the emotion analysis engine provides support to reassure the worker, ensuring safe and efficient work.

[0174] An example of a prompt would be, "Please provide details on developing an AI model to design a user-responsive feedback system suitable for complex factory tasks."

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server collects sensor data from the worker's movements and the environment. This data includes video information from cameras and audio information from microphones. The collected data is input into a machine learning model. Using this data, the server understands the work situation in real time and generates the optimal work procedure. The output is a set of work procedures.

[0178] Step 2:

[0179] The server uses an emotion analysis engine to analyze the user's emotional state from sensor data. Specifically, it analyzes voice tone through speech recognition technology and facial expressions using image processing technology. This analysis determines the user's stress level and concentration level. Based on the input data, parameters indicating the emotional state are output.

[0180] Step 3:

[0181] The terminal displays guidance to the user via an information display device, based on work procedures and emotion parameters received from the server. Using devices like HoloLens, it provides visual instructions through augmented reality (AR). Furthermore, it dynamically adjusts audio feedback and instructions according to the user's emotional state. Inputs include work procedures and emotion parameters from the server, while output is the specific guidance presented to the user.

[0182] Step 4:

[0183] The user proceeds with the task by following the instructions presented on the device. The user uses the device to confirm the steps while performing the actual work. During the task, changes in voice and facial expressions are detected again through the device and sent to the server. This allows for continuous monitoring of the task progress and emotional state. As output, the completion status of the task and any new emotional data are fed back to the server.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] At the worksite, there are problems such as a lack of instructions necessary for workers to carry out their tasks effectively and safely, and the negative impact of worker stress and anxiety on work efficiency and quality. Furthermore, a feedback function that takes into account the mental state of workers has the potential to improve the quality of work at the worksite, but the current system is insufficient, and this needs to be resolved.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes sensor means for detecting the worker's movements and the surrounding environment, means for monitoring and analyzing the worker's emotional state in real time, and means for providing appropriate feedback and adjustments to the worker based on the analysis results. This makes it possible for the worker to receive appropriate instructions and feedback in real time according to the work environment, improving work efficiency while ensuring physical and mental safety.

[0189] A "sensor device" is a device that monitors the worker's movements and the surrounding environment and collects necessary data.

[0190] "Means for generating work procedures" refers to methods or devices that construct optimal work procedures based on collected data and guide workers appropriately through them.

[0191] An "augmented reality or virtual reality device" is a device that presents digital information visually and audibly, and conveys information to a worker by combining a real work environment with virtual effects.

[0192] "Means for monitoring and analyzing emotional states in real time" refers to devices and methods for measuring the physiological and psychological states of workers and analyzing them without time delay.

[0193] "Means of providing feedback and adjustment" refers to a device or system for conveying information and instructions that are adapted to the worker's state based on their analyzed emotional state.

[0194] "Means for evaluating safety and issuing warnings" refers to devices or methods for continuously monitoring the work environment and the condition of workers, and for issuing warnings to workers when potential hazards are anticipated.

[0195] "Artificial intelligence" is a general term for algorithms and models designed to mimic human intellectual work, and it is a technology that processes data to learn and infer.

[0196] As a form for carrying out the invention, the system that realizes this application example is configured as follows.

[0197] The server plays a central role in processing data obtained from sensors that detect worker movements and the surrounding environment. This server analyzes the received data using AI models and generates work procedures. Furthermore, it uses a sentiment analysis engine to monitor the worker's emotional state in real time. Based on the results, it generates necessary feedback and adjusted instructions for the worker. Specifically, it utilizes cloud-based AI analytics services such as Microsoft Azure® Cognitive Services.

[0198] The terminal receives work instructions and sentiment analysis results transmitted from the server. Based on this information, it provides visual and audio instructions to the worker through augmented reality (AR) or virtual reality (VR) devices. This terminal is often implemented as smart glasses or a head-mounted display.

[0199] Users wear AR / VR devices and perform tasks while receiving real-time feedback. Built-in microphones and cameras in the devices detect the user's voice and facial expressions, and this data is sent back to the server, enabling two-way interaction.

[0200] A concrete example is its use as a system to assist in setting up new product lines when working collaboratively with robots in a factory. Based on the worker's psychological state during the task, it generates prompts such as "How can I make this task more efficient?" and uses AI to quickly provide feedback. This allows workers to reduce their workload and proceed with their work more efficiently.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The server receives data on worker movements and the surrounding environment acquired from sensors. This input data includes stream data from camera images and audio sensors. The server preprocesses this data, removing outliers and filtering noise. As a result, it outputs data that has been processed into a format suitable for analysis.

[0204] Step 2:

[0205] The server inputs pre-processed data into an AI model to generate work procedures. The AI ​​model is trained on data from past skilled workers and uses a neural network to infer the optimal work procedure. In this process, it calculates the workflow and necessary steps based on the input data and outputs work instruction data as a deliverable.

[0206] Step 3:

[0207] The server sends voice and facial expression data to an emotion analysis engine to monitor the worker's emotional state. The AI ​​agent performs emotion analysis and determines the worker's psychological state. This prompt is then temporarily input into a generation AI model to obtain a situation-appropriate feedback message.

[0208] Step 4:

[0209] The terminal receives work instruction data and feedback messages generated from the server and presents them to the worker visually and audibly via an augmented reality device. This includes highlighting work instructions and navigation through voice guidance.

[0210] Step 5:

[0211] Users perform tasks according to instructions from the terminal. During the task, the terminal's built-in camera and microphone continuously collect data and send it back to the server. This iterative process allows workers to receive continuously updated feedback, enabling them to work efficiently.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention is a system for workers to receive real-time visual and audible work instructions. The system consists of multiple components, each with a specific role.

[0229] Server-based operation and management of AI agents

[0230] The server collects past operational data from skilled workers and uses it to train an AI model used to generate work procedures. The AI ​​uses machine learning algorithms to optimize the work and enhance the instruction content specific to each task. This AI continuously learns from new data and becomes flexible enough to adapt to changes in work equipment and the environment.

[0231] Instructions and status monitoring via terminals

[0232] The terminal is directly connected to an augmented reality (AR) or virtual reality (VR) device worn by the worker. Based on data received from the server, the terminal provides the worker with visual guidelines. For example, when performing pipework, the terminal supports the worker by displaying the correct connection procedure and the location of the tools to be used via AR. At the same time, the terminal monitors the worker's movements and performs safety assessments in real time. If an anomaly is detected, it issues a warning and prompts appropriate action.

[0233] User actions

[0234] Users perform tasks based on instructions using a headset-type AR / VR device. The device provides detailed information about the work site, supporting workers in following instructions step by step without making mistakes. Users can also notify the device of any problems or requests that arise during the work via voice or gestures, enabling an interactive experience.

[0235] Specifically, when a new machine needs to be installed at a construction site, the server analyzes the installation procedure and provides instructions to the user via a terminal. The user can then use augmented reality (AR) to overlay virtual installation markers onto the real-world work environment and follow the instructions. This enables even inexperienced workers to perform efficient and highly accurate work, accelerating the learning process.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The server collects past work and behavioral data from skilled workers. This data includes information about the type of work performed, the tools used, and the environment in which the work was carried out.

[0239] Step 2:

[0240] The server trains an AI model based on the collected data. Through training, the AI ​​can learn effective work procedures and patterns of abnormal behavior. This model is enhanced using machine learning techniques such as time series analysis and pattern recognition.

[0241] Step 3:

[0242] The terminal receives trained AI models from the server and prepares them for use in the work field. The terminal connects to AR / VR devices and prepares visual and audio interfaces.

[0243] Step 4:

[0244] The user activates the system by putting on an AR / VR device before starting work. The terminal presents the user with work procedures and provides initial guidance via the device.

[0245] Step 5:

[0246] During operation, the terminal monitors the user's movements in real time. Sensors capture the user's hand movements and location information and transmit it to the server.

[0247] Step 6:

[0248] The server analyzes the received data and generates real-time feedback. This includes checking progress, evaluating accuracy, and performing safety checks, and sending correction instructions or warnings to the terminal as needed.

[0249] Step 7:

[0250] The terminal relays feedback received from the server to the user. For example, if the user is performing an incorrect procedure, it will show the correct procedure through visual guidelines or voice instructions.

[0251] Step 8:

[0252] The user follows the instructions on the device and completes the steps accurately. After completing the task, the user exits the AR / VR system and saves the day's work record.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] In today's work environment, workers often find it difficult to understand and execute efficient and safe work processes. In particular, less experienced workers struggle to perform high-precision tasks without proper guidance and real-time support, which can impact productivity and safety.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, means for presenting the generated work process visually and audibly through an extended display or virtual display device, and means for using a generation model and instructions in generating the work process. This enables the worker to perform the work safely and efficiently in real time.

[0258] "Detection means" refers to a device or system that recognizes the worker's actions and the surrounding environment and acquires related information.

[0259] "Means for generating work processes" refers to a function that uses acquired information to create procedures for efficiently and safely accomplishing a specific task.

[0260] An "enhanced display or virtual display device" is a technology for providing digital information to workers visually and audibly, and is a device that overlays information onto the real environment or presents a completely virtual environment.

[0261] "Means for evaluating safety and issuing alarms" refers to a system that monitors the work environment and the actions of workers, and issues an alarm to warn of danger if it is present.

[0262] "Machine intelligence" is an artificial knowledge system that uses learning algorithms based on past work data to provide situation-appropriate instructions and advice.

[0263] A "generative model" is a pre-trained algorithm or framework used to generate work instructions tailored to a specific purpose or situation.

[0264] An "instruction document" is textual information that expresses specific guidance on how to operate or carry out a task, and it guides the worker's actions.

[0265] "Means of providing feedback" refers to a function that receives voice or motion input from workers, analyzes that information, and provides responses or guidance in real time.

[0266] The embodiments for carrying out this invention are shown below.

[0267] This system supports workers in performing their tasks efficiently and safely while receiving real-time work instructions. The system consists of three main components: a server, terminals, and users.

[0268] Server Role

[0269] The server collects worker motion data and surrounding environmental information through sensors and stores it in a database. Based on this data, the server trains an AI model using machine learning frameworks such as TensorFlow and PyTorch. The server then utilizes the trained generative AI model to generate optimal instructions for a specific task based on the input of prompts. These generated instructions are then sent to the terminal.

[0270] Terminal role

[0271] The terminal is connected to an augmented reality (AR) or virtual reality (VR) device and presents instructions received from the server to the worker. This presentation is done via display and audio output to help the worker intuitively understand the instructions. For example, the terminal shows specific work procedures by displaying holograms within the worker's field of view or playing instructions aloud. At the same time, the terminal monitors the worker's movements using cameras and various sensors to ensure safety. If an abnormality is detected, an alarm is issued immediately.

[0272] User roles

[0273] Users operate a headset-type AR / VR device and follow instructions provided by the system. Users can request feedback and additional instructions from the device through voice input and gestures. For example, during a task, a user can ask a voice question such as, "What is the appropriate tool to pick up this part?" and receive real-time advice. This response is prepared by an AI model that uses past data to determine the best option.

[0274] Examples of specific cases and prompt statements

[0275] A concrete example is the installation of new machinery at a construction site. In this case, the server uses a prompt message such as "Optimize the machine installation procedure and generate instructions that allow beginners to work safely" to generate detailed instructions. The generated instructions are presented to the user via the terminal in an extended display format.

[0276] With a system configured in this way, workers can perform their tasks with high efficiency and safety, regardless of their experience level.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The server collects worker motion data and sensor data. This data includes specific work procedures and information about the surrounding environment. This input data is stored in a database and then cleansed and preprocessed. As a result, it outputs a dataset in a format suitable for training an AI model.

[0280] Step 2:

[0281] The server trains an AI model using the collected data. This process utilizes machine learning tools such as TensorFlow and PyTorch. It receives a formatted dataset as input and performs training using a neural network. As a result, it outputs an AI model that generates optimization instructions to improve work efficiency and safety.

[0282] Step 3:

[0283] The server receives prompt text to generate specific work instructions using a generative AI model. Specifically, it provides text such as, "Optimize the procedure for efficient piping work and generate instructions that allow for safe work." Based on this prompt, the AI ​​outputs a specific set of instructions for the worker.

[0284] Step 4:

[0285] The server sends the instructions generated by the AI to the terminal. The instruction information is packed in the message format and sent in real time through a communication protocol (e.g., WebSocket). The terminal transfers the received instruction data to the AR / VR device.

[0286] Step 5:

[0287] The terminal presents the received instructions to the user visually and audibly. Here, an AR display is used to overlay the hologram of the procedure on the operator's field of vision, and a speaker is used to play the voice instructions. This allows the user to intuitively understand the operation.

[0288] Step 6:

[0289] The terminal monitors the user's actions in real time using sensors and cameras. Based on this monitoring, the progress and safety of the operation are evaluated. If an abnormality is detected, an alarm is immediately issued to alert the user.

[0290] Step 7:

[0291] The user sends feedback to the terminal by voice or gesture during the operation. This allows the user to receive additional instructions and problem-solving support. The user input is sent back to the server and utilized to improve the AI model.

[0292] (Application Example 1)

[0293] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0294] In many factories, the introduction and maintenance of new machinery and equipment can be difficult, especially for less experienced workers, resulting in increased working hours, decreased quality, and safety issues. This invention aims to overcome these problems and improve efficiency and safety in factory operations.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes a detection device for detecting the movements of an operator and the surrounding environment; an information processing device for generating work procedures using data from the detection device; a display device for visually and audibly presenting the generated work procedures through an augmented reality or virtual reality display; a safety management device for evaluating safety and issuing warnings; and a support device for providing instructions to assist in setting up and maintaining machinery and equipment within the factory. This enables operators to efficiently and safely perform complex machinery setup and maintenance tasks.

[0297] A "detection device" is a device that senses the movements of a worker and the surrounding environment, and collects necessary data.

[0298] An "information processing device" is a device that generates work procedures using data from a detection device.

[0299] A "display device" is a device that presents generated work procedures to a worker visually and audibly in the form of augmented reality or virtual reality.

[0300] A "safety management device" is a device that evaluates safety during work and issues warnings as needed.

[0301] A "support device" is a device that provides instructions to workers to assist with the setup and maintenance of machinery and equipment within a factory.

[0302] The system for realizing this invention aims to improve the efficiency and safety of work in the factory and is composed of multiple devices and programs. The system operates as follows.

[0303] First, the server collects the operation information of past skilled workers and uses it to train a machine learning model. In this machine learning process, a machine learning framework such as TensorFlow is used to generate an optimal work procedure based on the data. The information processing device continuously updates the generated procedure to adapt to the latest working environment and the specifications of the mechanical devices.

[0304] The terminal, specifically an augmented reality display such as Microsoft HoloLens, overlays and displays the work procedure transmitted from the server in the actual work environment. This display device provides virtual guidance displays and voice instructions for easy visual understanding by the worker. Furthermore, based on the environmental data collected by the detection device, the safety management device issues real-time safety warnings to the worker.

[0305] The user can efficiently and safely operate the mechanical devices in the factory according to these displays and instructions. For example, when the user introduces a new robotic arm, the installation procedure is visually shown by AR, enabling error-free setup work. Furthermore, when a new problem occurs, instructions can be received immediately using prompts.

[0306] As an example of the prompt text input to the generation AI model, specific instructions such as "Please explain the installation procedure of the robotic arm in detail. List the necessary tools and precautions for each step." are given. In this way, the entire system can cooperate to build an optimal working environment.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server collects data on the work actions of past skilled workers. This input data includes specific actions performed by the workers and information about the surrounding environment. After collection, the server preprocesses this data, performing tasks such as noise reduction and conversion to an appropriate format to generate a training dataset for machine learning models. This output dataset is then fed into subsequent training phases.

[0310] Step 2:

[0311] The server uses pre-processed data to train an AI model using a machine learning framework (e.g., TensorFlow). In this process, it learns patterns from the data and generates algorithms to optimize the work procedure. The output is a trained model capable of presenting the optimal procedure for each task.

[0312] Step 3:

[0313] The terminal receives a set of work instructions generated from a trained model sent from the server and displays them on the user's AR device (e.g., Microsoft HoloLens). The input data consists of the instructions inferred by the model, which the terminal displays as a visual and audio guide, overlaying information about the specific task the user is working on. In this process, the AR device uses input from environmental sensors to synchronize virtual information with the real world.

[0314] Step 4:

[0315] Users perform work procedures based on visual guides provided through AR devices. User input consists of data about the progress of the work and the environment, which is collected in real time by the device. This data then becomes the basis for the server to perform behavioral analysis.

[0316] Step 5:

[0317] The server analyzes real-time progress data collected from users and performs a safety assessment. Input data consists of progress logs and environmental data, which are used to determine whether the work is being performed safely and efficiently. If any anomalies are detected, a warning is issued, and appropriate guidance and corrective instructions are provided to the user. Output consists of warnings and improvement instructions communicated to the user as needed.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] This invention provides a system that allows workers to receive work instructions visually and audibly in real time, while simultaneously monitoring their emotional state. The system consists of the following components:

[0320] Server-based management of AI agents and sentiment analysis

[0321] The server runs an AI trained with data from skilled workers, and combines the generated work procedures with other components. Furthermore, it uses an emotion engine to analyze the user's emotional state from the received data and determine what kind of feedback should be provided. For example, if an anxious state is detected, it will provide encouraging messages to boost motivation or offer simplified procedures.

[0322] Implementation and presentation via terminal

[0323] The terminal displays work instructions via an AR / VR device based on instructions and emotional information received from the server. This includes adjustments based on the worker's emotional state, changing the visual and audio guidance content as needed. For example, if the work is progressing smoothly, it displays normal instructions, but if stress is detected, it suggests instructions to regulate breathing or a short break.

[0324] User interaction and feedback reception

[0325] Users engage in tasks while wearing an AR / VR device. This device is equipped with a microphone and camera, which capture the user's voice and facial expressions and transmit them to an emotion engine. Users can also receive emotion-based feedback and take recommended actions. For example, if a task is too complex and confusing, they can accept simplified instructions from the device.

[0326] As a concrete example, if this system is implemented in the installation of a new conveyor belt in a factory, workers will accurately place the necessary parts following the AR guide. If stress or confusion is detected during the work, the emotion engine will intervene, switching to a friendly voice guide and providing reassuring support to the user. Such features are expected to improve not only the quality of work but also the mental satisfaction and safety of the users.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The server collects past work data from skilled workers and uses it to train the AI. The AI ​​model uses this data to generate and optimize work procedures.

[0330] Step 2:

[0331] The server uses an emotion engine to receive facial expression data and voice information sent from the user and recognizes the user's emotional state. Based on this, it adjusts appropriate operation instructions.

[0332] Step 3:

[0333] The terminal receives instructions from the server and presents visual and audio work instructions to the user through an AR / VR device. Adjustments are made based on the user's emotional state during this process.

[0334] Step 4:

[0335] The user wears an AR / VR device and begins working according to the presented instructions. Sensors monitor the user's movements and environment during the work, providing real-time feedback.

[0336] Step 5:

[0337] The device continuously collects changes in the user's voice and facial expressions and sends this data to the emotion engine. This allows the system to understand changes in the user's emotions while they are working.

[0338] Step 6:

[0339] The server continuously analyzes user behavior and emotional data and generates feedback as needed. For example, if stress is detected, it generates and sends voice instructions to the device to encourage relaxation.

[0340] Step 7:

[0341] The terminal receives feedback from the server and makes adjustments according to the progress of the work. In addition to visual information, encouraging messages and audio are provided to the user.

[0342] Step 8:

[0343] The user receives feedback and adjusts their work pace or takes suggested breaks if necessary. After completing the task, they finish recording and save their work data and emotional changes.

[0344] (Example 2)

[0345] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0346] Conventional work support systems provided uniform instructions without considering the emotional state of workers, making it difficult to improve work efficiency or reduce workers' mental burden. Furthermore, real-time safety assessments and dynamic adjustments to work procedures based on emotions were not possible. Therefore, there is a need to improve work quality, safety, and workers' psychological satisfaction.

[0347] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0348] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, analysis means for analyzing the user's emotional state, and adjustment means for providing emotion-based feedback. This makes it possible to dynamically adjust work procedures according to the worker's emotional state and work environment, thereby simultaneously improving work efficiency, safety, and the worker's mental satisfaction.

[0349] "Detection means" refers to devices or systems for collecting information about the worker's actions and the surrounding environment.

[0350] "Information processing means" refers to a computing device or software used to generate useful work procedures using collected data.

[0351] An "information presentation device" is a device or interface for providing generated information to users visually and audibly.

[0352] An "evaluation method" is a system that analyzes worker safety and issues warnings as needed.

[0353] An "analysis tool" is a system that analyzes the user's emotional state and adapts the work process based on the results.

[0354] A "adjustment mechanism" is a system for optimizing work procedures based on the results of an analysis.

[0355] An "intelligent device" is an artificial intelligence-based system that learns from data of past skilled workers and uses that data to create new work procedures and make adjustments.

[0356] A "data processing device" is a computer or system used to monitor the progress and quality of work in real time and analyze the results.

[0357] This invention is a system that grasps the actions and emotional state of a worker in real time and provides appropriate work instructions based on that information. The system mainly consists of three components: a server, a terminal, and a user.

[0358] server

[0359] The server runs AI and sentiment analysis engines, and is responsible for analyzing data obtained from the worker's actions and surrounding environment. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train generative AI models based on past data from skilled workers. This allows for the dynamic generation of work procedures and safety indicators. Furthermore, the sentiment analysis engine analyzes the user's voice and facial expressions to generate feedback that corresponds to their emotional state.

[0360] terminal

[0361] Based on work instructions and emotional information received from the server, the terminal provides visual and audible guidance to the user using HoloLens or similar information display devices. The terminal adjusts the guidance content as needed according to the user's emotional state, and if stress is detected, it provides instructions to encourage relaxation. The AR / VR devices used in this process incorporate sensors such as cameras and microphones to capture the user's emotional state.

[0362] User

[0363] Users wear AR / VR devices and perform tasks according to the instructions presented. Real-time feedback allows for smoother workflow and provides emotionally-based feedback as needed. For example, if a task is difficult or confusing, the device provides simplified instructions and user-friendly voice guidance.

[0364] As a concrete example, when installing conveyor belts in a factory, this system allows workers to quickly and accurately position the necessary parts using AR guidance. Furthermore, if stress is detected, the emotion analysis engine provides support to reassure the worker, ensuring safe and efficient work.

[0365] An example of a prompt would be, "Please provide details on developing an AI model to design a user-responsive feedback system suitable for complex factory tasks."

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The server collects sensor data from the worker's movements and the environment. This data includes video information from cameras and audio information from microphones. The collected data is input into a machine learning model. Using this data, the server understands the work situation in real time and generates the optimal work procedure. The output is a set of work procedures.

[0369] Step 2:

[0370] The server uses an emotion analysis engine to analyze the user's emotional state from sensor data. Specifically, it analyzes voice tone through speech recognition technology and facial expressions using image processing technology. This analysis determines the user's stress level and concentration level. Based on the input data, parameters indicating the emotional state are output.

[0371] Step 3:

[0372] The terminal displays guidance to the user via an information display device, based on work procedures and emotion parameters received from the server. Using devices like HoloLens, it provides visual instructions through augmented reality (AR). Furthermore, it dynamically adjusts audio feedback and instructions according to the user's emotional state. Inputs include work procedures and emotion parameters from the server, while output is the specific guidance presented to the user.

[0373] Step 4:

[0374] The user proceeds with the task by following the instructions presented on the device. The user uses the device to confirm the steps while performing the actual work. During the task, changes in voice and facial expressions are detected again through the device and sent to the server. This allows for continuous monitoring of the task progress and emotional state. As output, the completion status of the task and any new emotional data are fed back to the server.

[0375] (Application Example 2)

[0376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0377] At the worksite, there are problems such as a lack of instructions necessary for workers to carry out their tasks effectively and safely, and the negative impact of worker stress and anxiety on work efficiency and quality. Furthermore, a feedback function that takes into account the mental state of workers has the potential to improve the quality of work at the worksite, but the current system is insufficient, and this needs to be resolved.

[0378] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0379] In this invention, the server includes sensor means for detecting the worker's movements and the surrounding environment, means for monitoring and analyzing the worker's emotional state in real time, and means for providing appropriate feedback and adjustments to the worker based on the analysis results. This makes it possible for the worker to receive appropriate instructions and feedback in real time according to the work environment, improving work efficiency while ensuring physical and mental safety.

[0380] A "sensor device" is a device that monitors the worker's movements and the surrounding environment and collects necessary data.

[0381] "Means for generating work procedures" refers to methods or devices that construct optimal work procedures based on collected data and guide workers appropriately through them.

[0382] An "augmented reality or virtual reality device" is a device that presents digital information visually and audibly, and conveys information to a worker by combining a real work environment with virtual effects.

[0383] "Means for monitoring and analyzing emotional states in real time" refers to devices and methods for measuring the physiological and psychological states of workers and analyzing them without time delay.

[0384] "Means of providing feedback and adjustment" refers to a device or system for conveying information and instructions that are adapted to the worker's state based on their analyzed emotional state.

[0385] "Means for evaluating safety and issuing warnings" refers to devices or methods for continuously monitoring the work environment and the condition of workers, and for issuing warnings to workers when potential hazards are anticipated.

[0386] "Artificial intelligence" is a general term for algorithms and models designed to mimic human intellectual work, and it is a technology that processes data to learn and infer.

[0387] As a form for carrying out the invention, the system that realizes this application example is configured as follows.

[0388] The server plays a central role in processing data obtained from sensors that detect worker movements and the surrounding environment. This server analyzes the incoming data using AI models and generates work procedures. Furthermore, it uses a sentiment analysis engine to monitor the worker's emotional state in real time. Based on the results, it generates necessary feedback and adjusted instructions for the worker. Specifically, it utilizes cloud-based AI analytics services such as Microsoft Azure Cognitive Services.

[0389] The terminal receives work instructions and sentiment analysis results transmitted from the server. Based on this information, it provides visual and audio instructions to the worker through augmented reality (AR) or virtual reality (VR) devices. This terminal is often implemented as smart glasses or a head-mounted display.

[0390] Users wear AR / VR devices and perform tasks while receiving real-time feedback. Built-in microphones and cameras in the devices detect the user's voice and facial expressions, and this data is sent back to the server, enabling two-way interaction.

[0391] A concrete example is its use as a system to assist in setting up new product lines when working collaboratively with robots in a factory. Based on the worker's psychological state during the task, it generates prompts such as "How can I make this task more efficient?" and uses AI to quickly provide feedback. This allows workers to reduce their workload and proceed with their work more efficiently.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server receives data on worker movements and the surrounding environment acquired from sensors. This input data includes stream data from camera images and audio sensors. The server preprocesses this data, removing outliers and filtering noise. As a result, it outputs data that has been processed into a format suitable for analysis.

[0395] Step 2:

[0396] The server inputs pre-processed data into an AI model to generate work procedures. The AI ​​model is trained on data from past skilled workers and uses a neural network to infer the optimal work procedure. In this process, it calculates the workflow and necessary steps based on the input data and outputs work instruction data as a deliverable.

[0397] Step 3:

[0398] The server sends voice and facial expression data to an emotion analysis engine to monitor the worker's emotional state. The AI ​​agent performs emotion analysis and determines the worker's psychological state. This prompt is then temporarily input into a generation AI model to obtain a situation-appropriate feedback message.

[0399] Step 4:

[0400] The terminal receives work instruction data and feedback messages generated from the server and presents them to the worker visually and audibly via an augmented reality device. This includes highlighting work instructions and navigation through voice guidance.

[0401] Step 5:

[0402] Users perform tasks according to instructions from the terminal. During the task, the terminal's built-in camera and microphone continuously collect data and send it back to the server. This iterative process allows workers to receive continuously updated feedback, enabling them to work efficiently.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a system for workers to receive real-time visual and audible work instructions. The system consists of multiple components, each with a specific role.

[0420] Server-based operation and management of AI agents

[0421] The server collects past operational data from skilled workers and uses it to train an AI model used to generate work procedures. The AI ​​uses machine learning algorithms to optimize the work and enhance the instruction content specific to each task. This AI continuously learns from new data and becomes flexible enough to adapt to changes in work equipment and the environment.

[0422] Instructions and status monitoring via terminals

[0423] The terminal is directly connected to an augmented reality (AR) or virtual reality (VR) device worn by the worker. Based on data received from the server, the terminal provides the worker with visual guidelines. For example, when performing pipework, the terminal supports the worker by displaying the correct connection procedure and the location of the tools to be used via AR. At the same time, the terminal monitors the worker's movements and performs safety assessments in real time. If an anomaly is detected, it issues a warning and prompts appropriate action.

[0424] User actions

[0425] Users perform tasks based on instructions using a headset-type AR / VR device. The device provides detailed information about the work site, supporting workers in following instructions step by step without making mistakes. Users can also notify the device of any problems or requests that arise during the work via voice or gestures, enabling an interactive experience.

[0426] Specifically, when a new machine needs to be installed at a construction site, the server analyzes the installation procedure and provides instructions to the user via a terminal. The user can then use augmented reality (AR) to overlay virtual installation markers onto the real-world work environment and follow the instructions. This enables even inexperienced workers to perform efficient and highly accurate work, accelerating the learning process.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The server collects past work and behavioral data from skilled workers. This data includes information about the type of work performed, the tools used, and the environment in which the work was carried out.

[0430] Step 2:

[0431] The server trains an AI model based on the collected data. Through training, the AI ​​can learn effective work procedures and patterns of abnormal behavior. This model is enhanced using machine learning techniques such as time series analysis and pattern recognition.

[0432] Step 3:

[0433] The terminal receives trained AI models from the server and prepares them for use in the work field. The terminal connects to AR / VR devices and prepares visual and audio interfaces.

[0434] Step 4:

[0435] The user activates the system by putting on an AR / VR device before starting work. The terminal presents the user with work procedures and provides initial guidance via the device.

[0436] Step 5:

[0437] During operation, the terminal monitors the user's movements in real time. Sensors capture the user's hand movements and location information and transmit it to the server.

[0438] Step 6:

[0439] The server analyzes the received data and generates real-time feedback. This includes checking progress, evaluating accuracy, and performing safety checks, and sending correction instructions or warnings to the terminal as needed.

[0440] Step 7:

[0441] The terminal relays feedback received from the server to the user. For example, if the user is performing an incorrect procedure, it will show the correct procedure through visual guidelines or voice instructions.

[0442] Step 8:

[0443] The user follows the instructions on the device and completes the steps accurately. After completing the task, the user exits the AR / VR system and saves the day's work record.

[0444] (Example 1)

[0445] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0446] In today's work environment, workers often find it difficult to understand and execute efficient and safe work processes. In particular, less experienced workers struggle to perform high-precision tasks without proper guidance and real-time support, which can impact productivity and safety.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, means for presenting the generated work process visually and audibly through an extended display or virtual display device, and means for using a generation model and instructions in generating the work process. This enables the worker to perform the work safely and efficiently in real time.

[0449] "Detection means" refers to a device or system that recognizes the worker's actions and the surrounding environment and acquires related information.

[0450] "Means for generating work processes" refers to a function that uses acquired information to create procedures for efficiently and safely accomplishing a specific task.

[0451] An "enhanced display or virtual display device" is a technology for providing digital information to workers visually and audibly, and is a device that overlays information onto the real environment or presents a completely virtual environment.

[0452] "Means for evaluating safety and issuing alarms" refers to a system that monitors the work environment and the actions of workers, and issues an alarm to warn of danger if it is present.

[0453] "Machine intelligence" is an artificial knowledge system that uses learning algorithms based on past work data to provide situation-appropriate instructions and advice.

[0454] A "generative model" is a pre-trained algorithm or framework used to generate work instructions tailored to a specific purpose or situation.

[0455] An "instruction document" is textual information that expresses specific guidance on how to operate or carry out a task, and it guides the worker's actions.

[0456] "Means of providing feedback" refers to a function that receives voice or motion input from workers, analyzes that information, and provides responses or guidance in real time.

[0457] The embodiments for carrying out this invention are shown below.

[0458] This system supports workers in performing their tasks efficiently and safely while receiving real-time work instructions. The system consists of three main components: a server, terminals, and users.

[0459] Server Role

[0460] The server collects worker motion data and surrounding environmental information through sensors and stores it in a database. Based on this data, the server trains an AI model using machine learning frameworks such as TensorFlow and PyTorch. The server then utilizes the trained generative AI model to generate optimal instructions for a specific task based on the input of prompts. These generated instructions are then sent to the terminal.

[0461] Terminal role

[0462] The terminal is connected to an augmented reality (AR) or virtual reality (VR) device and presents instructions received from the server to the worker. This presentation is done via display and audio output to help the worker intuitively understand the instructions. For example, the terminal shows specific work procedures by displaying holograms within the worker's field of view or playing instructions aloud. At the same time, the terminal monitors the worker's movements using cameras and various sensors to ensure safety. If an abnormality is detected, an alarm is issued immediately.

[0463] User roles

[0464] Users operate a headset-type AR / VR device and follow instructions provided by the system. Users can request feedback and additional instructions from the device through voice input and gestures. For example, during a task, a user can ask a voice question such as, "What is the appropriate tool to pick up this part?" and receive real-time advice. This response is prepared by an AI model that uses past data to determine the best option.

[0465] Examples of specific cases and prompt statements

[0466] A concrete example is the installation of new machinery at a construction site. In this case, the server uses a prompt message such as "Optimize the machine installation procedure and generate instructions that allow beginners to work safely" to generate detailed instructions. The generated instructions are presented to the user via the terminal in an extended display format.

[0467] With a system configured in this way, workers can perform their tasks with high efficiency and safety, regardless of their experience level.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The server collects worker motion data and sensor data. This data includes specific work procedures and information about the surrounding environment. This input data is stored in a database and then cleansed and preprocessed. As a result, it outputs a dataset in a format suitable for training an AI model.

[0471] Step 2:

[0472] The server trains an AI model using the collected data. This process utilizes machine learning tools such as TensorFlow and PyTorch. It receives a formatted dataset as input and performs training using a neural network. As a result, it outputs an AI model that generates optimization instructions to improve work efficiency and safety.

[0473] Step 3:

[0474] The server receives prompt text to generate specific work instructions using a generative AI model. Specifically, it provides text such as, "Optimize the procedure for efficient piping work and generate instructions that allow for safe work." Based on this prompt, the AI ​​outputs a specific set of instructions for the worker.

[0475] Step 4:

[0476] The server sends instructions generated by the AI ​​to the terminal. The instruction information is packed into a message format and sent in real time via a communication protocol (e.g., WebSocket). The terminal then forwards the received instruction data to the AR / VR device.

[0477] Step 5:

[0478] The terminal presents received instructions to the user visually and audibly. Here, an AR display is used to overlay holographic instructions onto the worker's field of view, and a speaker is used to play audio instructions. This allows the user to intuitively understand the task.

[0479] Step 6:

[0480] The terminal uses sensors and cameras to monitor the user's movements in real time. This monitoring allows for the evaluation of work progress and safety. If an anomaly is detected, an alarm is immediately issued to alert the user.

[0481] Step 7:

[0482] Users send feedback to the device via voice or gestures during their work, allowing them to receive additional instructions and support for problem-solving. This user input is then sent back to the server and used to improve the AI ​​model.

[0483] (Application Example 1)

[0484] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] In many factories, the introduction and maintenance of new machinery and equipment can be difficult, especially for less experienced workers, resulting in increased working hours, decreased quality, and safety issues. This invention aims to overcome these problems and improve efficiency and safety in factory operations.

[0486] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0487] In this invention, the server includes a detection device for detecting the movements of an operator and the surrounding environment; an information processing device for generating work procedures using data from the detection device; a display device for visually and audibly presenting the generated work procedures through an augmented reality or virtual reality display; a safety management device for evaluating safety and issuing warnings; and a support device for providing instructions to assist in setting up and maintaining machinery and equipment within the factory. This enables operators to efficiently and safely perform complex machinery setup and maintenance tasks.

[0488] A "detection device" is a device that senses the movements of a worker and the surrounding environment, and collects necessary data.

[0489] An "information processing device" is a device that generates work procedures using data from a detection device.

[0490] A "display device" is a device that presents generated work procedures to a worker visually and audibly in the form of augmented reality or virtual reality.

[0491] A "safety management device" is a device that evaluates safety during work and issues warnings as needed.

[0492] A "support device" is a device that provides instructions to workers to assist with the setup and maintenance of machinery and equipment within a factory.

[0493] The system that realizes this invention aims to improve efficiency and safety in factory operations and consists of multiple devices and programs. The system is operated as follows:

[0494] The server first collects information on past operations performed by skilled workers and uses it to train a machine learning model. This machine learning process utilizes machine learning frameworks such as TensorFlow to generate optimal work procedures based on the data. The information processing device continuously updates the generated procedures to adapt to the latest work environment and machine equipment specifications.

[0495] The terminal, specifically an augmented reality display like Microsoft HoloLens, overlays work procedures transmitted from the server onto the actual work environment. This display provides virtual guidance and voice instructions to make it easier for workers to understand visually. Furthermore, based on environmental data collected by detection devices, a safety management system issues real-time safety warnings to workers.

[0496] Users can efficiently and safely operate machinery and equipment within the factory by following these displays and instructions. For example, when a user introduces a new robotic arm, AR visually guides them through the installation process, ensuring they perform the setup correctly. Furthermore, if any new challenges arise, they can receive immediate prompts and instructions.

[0497] Examples of prompts to be input to the generated AI model include specific instructions such as, "Please describe the robot arm installation procedure in detail. List the necessary tools and precautions for each step." In this way, the entire system can work together to create an optimal working environment.

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] The server collects data on the work actions of past skilled workers. This input data includes specific actions performed by the workers and information about the surrounding environment. After collection, the server preprocesses this data, performing tasks such as noise reduction and conversion to an appropriate format to generate a training dataset for machine learning models. This output dataset is then fed into subsequent training phases.

[0501] Step 2:

[0502] The server uses pre-processed data to train an AI model using a machine learning framework (e.g., TensorFlow). In this process, it learns patterns from the data and generates algorithms to optimize the work procedure. The output is a trained model capable of presenting the optimal procedure for each task.

[0503] Step 3:

[0504] The terminal receives a set of work instructions generated from a trained model sent from the server and displays them on the user's AR device (e.g., Microsoft HoloLens). The input data consists of the instructions inferred by the model, which the terminal displays as a visual and audio guide, overlaying information about the specific task the user is working on. In this process, the AR device uses input from environmental sensors to synchronize virtual information with the real world.

[0505] Step 4:

[0506] Users perform work procedures based on visual guides provided through AR devices. User input consists of data about the progress of the work and the environment, which is collected in real time by the device. This data then becomes the basis for the server to perform behavioral analysis.

[0507] Step 5:

[0508] The server analyzes real-time progress data collected from users and performs a safety assessment. Input data consists of progress logs and environmental data, which are used to determine whether the work is being performed safely and efficiently. If any anomalies are detected, a warning is issued, and appropriate guidance and corrective instructions are provided to the user. Output consists of warnings and improvement instructions communicated to the user as needed.

[0509] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0510] This invention provides a system that allows workers to receive work instructions visually and audibly in real time, while simultaneously monitoring their emotional state. The system consists of the following components:

[0511] Server-based management of AI agents and sentiment analysis

[0512] The server runs an AI trained with data from skilled workers, and combines the generated work procedures with other components. Furthermore, it uses an emotion engine to analyze the user's emotional state from the received data and determine what kind of feedback should be provided. For example, if an anxious state is detected, it will provide encouraging messages to boost motivation or offer simplified procedures.

[0513] Implementation and presentation via terminal

[0514] The terminal displays work instructions via an AR / VR device based on instructions and emotional information received from the server. This includes adjustments based on the worker's emotional state, changing the visual and audio guidance content as needed. For example, if the work is progressing smoothly, it displays normal instructions, but if stress is detected, it suggests instructions to regulate breathing or a short break.

[0515] User interaction and feedback reception

[0516] Users engage in tasks while wearing an AR / VR device. This device is equipped with a microphone and camera, which capture the user's voice and facial expressions and transmit them to an emotion engine. Users can also receive emotion-based feedback and take recommended actions. For example, if a task is too complex and confusing, they can accept simplified instructions from the device.

[0517] As a concrete example, if this system is implemented in the installation of a new conveyor belt in a factory, workers will accurately place the necessary parts following the AR guide. If stress or confusion is detected during the work, the emotion engine will intervene, switching to a friendly voice guide and providing reassuring support to the user. Such features are expected to improve not only the quality of work but also the mental satisfaction and safety of the users.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The server collects past work data from skilled workers and uses it to train the AI. The AI ​​model uses this data to generate and optimize work procedures.

[0521] Step 2:

[0522] The server uses an emotion engine to receive facial expression data and voice information sent from the user and recognizes the user's emotional state. Based on this, it adjusts appropriate operation instructions.

[0523] Step 3:

[0524] The terminal receives instructions from the server and presents visual and audio work instructions to the user through an AR / VR device. Adjustments are made based on the user's emotional state during this process.

[0525] Step 4:

[0526] The user wears an AR / VR device and begins working according to the presented instructions. Sensors monitor the user's movements and environment during the work, providing real-time feedback.

[0527] Step 5:

[0528] The device continuously collects changes in the user's voice and facial expressions and sends this data to the emotion engine. This allows the system to understand changes in the user's emotions while they are working.

[0529] Step 6:

[0530] The server continuously analyzes user behavior and emotional data and generates feedback as needed. For example, if stress is detected, it generates and sends voice instructions to the device to encourage relaxation.

[0531] Step 7:

[0532] The terminal receives feedback from the server and makes adjustments according to the progress of the work. In addition to visual information, encouraging messages and audio are provided to the user.

[0533] Step 8:

[0534] The user receives feedback and adjusts their work pace or takes suggested breaks if necessary. After completing the task, they finish recording and save their work data and emotional changes.

[0535] (Example 2)

[0536] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] Conventional work support systems provided uniform instructions without considering the emotional state of workers, making it difficult to improve work efficiency or reduce workers' mental burden. Furthermore, real-time safety assessments and dynamic adjustments to work procedures based on emotions were not possible. Therefore, there is a need to improve work quality, safety, and workers' psychological satisfaction.

[0538] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0539] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, analysis means for analyzing the user's emotional state, and adjustment means for providing emotion-based feedback. This makes it possible to dynamically adjust work procedures according to the worker's emotional state and work environment, thereby simultaneously improving work efficiency, safety, and the worker's mental satisfaction.

[0540] "Detection means" refers to devices or systems for collecting information about the worker's actions and the surrounding environment.

[0541] "Information processing means" refers to a computing device or software used to generate useful work procedures using collected data.

[0542] An "information presentation device" is a device or interface for providing generated information to users visually and audibly.

[0543] An "evaluation method" is a system that analyzes worker safety and issues warnings as needed.

[0544] An "analysis tool" is a system that analyzes the user's emotional state and adapts the work process based on the results.

[0545] A "adjustment mechanism" is a system for optimizing work procedures based on the results of an analysis.

[0546] An "intelligent device" is an artificial intelligence-based system that learns from data of past skilled workers and uses that data to create new work procedures and make adjustments.

[0547] A "data processing device" is a computer or system used to monitor the progress and quality of work in real time and analyze the results.

[0548] This invention is a system that grasps the actions and emotional state of a worker in real time and provides appropriate work instructions based on that information. The system mainly consists of three components: a server, a terminal, and a user.

[0549] server

[0550] The server runs AI and sentiment analysis engines, and is responsible for analyzing data obtained from the worker's actions and surrounding environment. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train generative AI models based on past data from skilled workers. This allows for the dynamic generation of work procedures and safety indicators. Furthermore, the sentiment analysis engine analyzes the user's voice and facial expressions to generate feedback that corresponds to their emotional state.

[0551] terminal

[0552] Based on work instructions and emotional information received from the server, the terminal provides visual and audible guidance to the user using HoloLens or similar information display devices. The terminal adjusts the guidance content as needed according to the user's emotional state, and if stress is detected, it provides instructions to encourage relaxation. The AR / VR devices used in this process incorporate sensors such as cameras and microphones to capture the user's emotional state.

[0553] User

[0554] Users wear AR / VR devices and perform tasks according to the instructions presented. Real-time feedback allows for smoother workflow and provides emotionally-based feedback as needed. For example, if a task is difficult or confusing, the device provides simplified instructions and user-friendly voice guidance.

[0555] As a concrete example, when installing conveyor belts in a factory, this system allows workers to quickly and accurately position the necessary parts using AR guidance. Furthermore, if stress is detected, the emotion analysis engine provides support to reassure the worker, ensuring safe and efficient work.

[0556] An example of a prompt would be, "Please provide details on developing an AI model to design a user-responsive feedback system suitable for complex factory tasks."

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The server collects sensor data from the worker's movements and the environment. This data includes video information from cameras and audio information from microphones. The collected data is input into a machine learning model. Using this data, the server understands the work situation in real time and generates the optimal work procedure. The output is a set of work procedures.

[0560] Step 2:

[0561] The server uses an emotion analysis engine to analyze the user's emotional state from sensor data. Specifically, it analyzes voice tone through speech recognition technology and facial expressions using image processing technology. This analysis determines the user's stress level and concentration level. Based on the input data, parameters indicating the emotional state are output.

[0562] Step 3:

[0563] The terminal displays guidance to the user via an information display device, based on work procedures and emotion parameters received from the server. Using devices like HoloLens, it provides visual instructions through augmented reality (AR). Furthermore, it dynamically adjusts audio feedback and instructions according to the user's emotional state. Inputs include work procedures and emotion parameters from the server, while output is the specific guidance presented to the user.

[0564] Step 4:

[0565] The user proceeds with the task by following the instructions presented on the device. The user uses the device to confirm the steps while performing the actual work. During the task, changes in voice and facial expressions are detected again through the device and sent to the server. This allows for continuous monitoring of the task progress and emotional state. As output, the completion status of the task and any new emotional data are fed back to the server.

[0566] (Application Example 2)

[0567] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0568] At the worksite, there are problems such as a lack of instructions necessary for workers to carry out their tasks effectively and safely, and the negative impact of worker stress and anxiety on work efficiency and quality. Furthermore, a feedback function that takes into account the mental state of workers has the potential to improve the quality of work at the worksite, but the current system is insufficient, and this needs to be resolved.

[0569] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0570] In this invention, the server includes sensor means for detecting the worker's movements and the surrounding environment, means for monitoring and analyzing the worker's emotional state in real time, and means for providing appropriate feedback and adjustments to the worker based on the analysis results. This makes it possible for the worker to receive appropriate instructions and feedback in real time according to the work environment, improving work efficiency while ensuring physical and mental safety.

[0571] A "sensor device" is a device that monitors the worker's movements and the surrounding environment and collects necessary data.

[0572] "Means for generating work procedures" refers to methods or devices that construct optimal work procedures based on collected data and guide workers appropriately through them.

[0573] An "augmented reality or virtual reality device" is a device that presents digital information visually and audibly, and conveys information to a worker by combining a real work environment with virtual effects.

[0574] "Means for monitoring and analyzing emotional states in real time" refers to devices and methods for measuring the physiological and psychological states of workers and analyzing them without time delay.

[0575] "Means of providing feedback and adjustment" refers to a device or system for conveying information and instructions that are adapted to the worker's state based on their analyzed emotional state.

[0576] "Means for evaluating safety and issuing warnings" refers to devices or methods for continuously monitoring the work environment and the condition of workers, and for issuing warnings to workers when potential hazards are anticipated.

[0577] "Artificial intelligence" is a general term for algorithms and models designed to mimic human intellectual work, and it is a technology that processes data to learn and infer.

[0578] As a form for carrying out the invention, the system that realizes this application example is configured as follows.

[0579] The server plays a central role in processing data obtained from sensors that detect worker movements and the surrounding environment. This server analyzes the incoming data using AI models and generates work procedures. Furthermore, it uses a sentiment analysis engine to monitor the worker's emotional state in real time. Based on the results, it generates necessary feedback and adjusted instructions for the worker. Specifically, it utilizes cloud-based AI analytics services such as Microsoft Azure Cognitive Services.

[0580] The terminal receives work instructions and sentiment analysis results transmitted from the server. Based on this information, it provides visual and audio instructions to the worker through augmented reality (AR) or virtual reality (VR) devices. This terminal is often implemented as smart glasses or a head-mounted display.

[0581] Users wear AR / VR devices and perform tasks while receiving real-time feedback. Built-in microphones and cameras in the devices detect the user's voice and facial expressions, and this data is sent back to the server, enabling two-way interaction.

[0582] A concrete example is its use as a system to assist in setting up new product lines when working collaboratively with robots in a factory. Based on the worker's psychological state during the task, it generates prompts such as "How can I make this task more efficient?" and uses AI to quickly provide feedback. This allows workers to reduce their workload and proceed with their work more efficiently.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The server receives data on worker movements and the surrounding environment acquired from sensors. This input data includes stream data from camera images and audio sensors. The server preprocesses this data, removing outliers and filtering noise. As a result, it outputs data that has been processed into a format suitable for analysis.

[0586] Step 2:

[0587] The server inputs pre-processed data into an AI model to generate work procedures. The AI ​​model is trained on data from past skilled workers and uses a neural network to infer the optimal work procedure. In this process, it calculates the workflow and necessary steps based on the input data and outputs work instruction data as a deliverable.

[0588] Step 3:

[0589] The server sends voice and facial expression data to an emotion analysis engine to monitor the worker's emotional state. The AI ​​agent performs emotion analysis and determines the worker's psychological state. This prompt is then temporarily input into a generation AI model to obtain a situation-appropriate feedback message.

[0590] Step 4:

[0591] The terminal receives work instruction data and feedback messages generated from the server and presents them to the worker visually and audibly via an augmented reality device. This includes highlighting work instructions and navigation through voice guidance.

[0592] Step 5:

[0593] Users perform tasks according to instructions from the terminal. During the task, the terminal's built-in camera and microphone continuously collect data and send it back to the server. This iterative process allows workers to receive continuously updated feedback, enabling them to work efficiently.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention is a system for workers to receive real-time visual and audible work instructions. The system consists of multiple components, each with a specific role.

[0612] Server-based operation and management of AI agents

[0613] The server collects past operational data from skilled workers and uses it to train an AI model used to generate work procedures. The AI ​​uses machine learning algorithms to optimize the work and enhance the instruction content specific to each task. This AI continuously learns from new data and becomes flexible enough to adapt to changes in work equipment and the environment.

[0614] Instructions and status monitoring via terminals

[0615] The terminal is directly connected to an augmented reality (AR) or virtual reality (VR) device worn by the worker. Based on data received from the server, the terminal provides the worker with visual guidelines. For example, when performing pipework, the terminal supports the worker by displaying the correct connection procedure and the location of the tools to be used via AR. At the same time, the terminal monitors the worker's movements and performs safety assessments in real time. If an anomaly is detected, it issues a warning and prompts appropriate action.

[0616] User actions

[0617] Users perform tasks based on instructions using a headset-type AR / VR device. The device provides detailed information about the work site, supporting workers in following instructions step by step without making mistakes. Users can also notify the device of any problems or requests that arise during the work via voice or gestures, enabling an interactive experience.

[0618] Specifically, when a new machine needs to be installed at a construction site, the server analyzes the installation procedure and provides instructions to the user via a terminal. The user can then use augmented reality (AR) to overlay virtual installation markers onto the real-world work environment and follow the instructions. This enables even inexperienced workers to perform efficient and highly accurate work, accelerating the learning process.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The server collects past work and behavioral data from skilled workers. This data includes information about the type of work performed, the tools used, and the environment in which the work was carried out.

[0622] Step 2:

[0623] The server trains an AI model based on the collected data. Through training, the AI ​​can learn effective work procedures and patterns of abnormal behavior. This model is enhanced using machine learning techniques such as time series analysis and pattern recognition.

[0624] Step 3:

[0625] The terminal receives trained AI models from the server and prepares them for use in the work field. The terminal connects to AR / VR devices and prepares visual and audio interfaces.

[0626] Step 4:

[0627] The user activates the system by putting on an AR / VR device before starting work. The terminal presents the user with work procedures and provides initial guidance via the device.

[0628] Step 5:

[0629] During operation, the terminal monitors the user's movements in real time. Sensors capture the user's hand movements and location information and transmit it to the server.

[0630] Step 6:

[0631] The server analyzes the received data and generates real-time feedback. This includes checking progress, evaluating accuracy, and performing safety checks, and sending correction instructions or warnings to the terminal as needed.

[0632] Step 7:

[0633] The terminal relays feedback received from the server to the user. For example, if the user is performing an incorrect procedure, it will show the correct procedure through visual guidelines or voice instructions.

[0634] Step 8:

[0635] The user follows the instructions on the device and completes the steps accurately. After completing the task, the user exits the AR / VR system and saves the day's work record.

[0636] (Example 1)

[0637] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] In today's work environment, workers often find it difficult to understand and execute efficient and safe work processes. In particular, less experienced workers struggle to perform high-precision tasks without proper guidance and real-time support, which can impact productivity and safety.

[0639] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0640] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, means for presenting the generated work process visually and audibly through an extended display or virtual display device, and means for using a generation model and instructions in generating the work process. This enables the worker to perform the work safely and efficiently in real time.

[0641] "Detection means" refers to a device or system that recognizes the worker's actions and the surrounding environment and acquires related information.

[0642] "Means for generating work processes" refers to a function that uses acquired information to create procedures for efficiently and safely accomplishing a specific task.

[0643] An "enhanced display or virtual display device" is a technology for providing digital information to workers visually and audibly, and is a device that overlays information onto the real environment or presents a completely virtual environment.

[0644] "Means for evaluating safety and issuing alarms" refers to a system that monitors the work environment and the actions of workers, and issues an alarm to warn of danger if it is present.

[0645] "Machine intelligence" is an artificial knowledge system that uses learning algorithms based on past work data to provide situation-appropriate instructions and advice.

[0646] A "generative model" is a pre-trained algorithm or framework used to generate work instructions tailored to a specific purpose or situation.

[0647] An "instruction document" is textual information that expresses specific guidance on how to operate or carry out a task, and it guides the worker's actions.

[0648] "Means of providing feedback" refers to a function that receives voice or motion input from workers, analyzes that information, and provides responses or guidance in real time.

[0649] The embodiments for carrying out this invention are shown below.

[0650] This system supports workers in performing their tasks efficiently and safely while receiving real-time work instructions. The system consists of three main components: a server, terminals, and users.

[0651] Server Role

[0652] The server collects worker motion data and surrounding environmental information through sensors and stores it in a database. Based on this data, the server trains an AI model using machine learning frameworks such as TensorFlow and PyTorch. The server then utilizes the trained generative AI model to generate optimal instructions for a specific task based on the input of prompts. These generated instructions are then sent to the terminal.

[0653] Terminal role

[0654] The terminal is connected to an augmented reality (AR) or virtual reality (VR) device and presents instructions received from the server to the worker. This presentation is done via display and audio output to help the worker intuitively understand the instructions. For example, the terminal shows specific work procedures by displaying holograms within the worker's field of view or playing instructions aloud. At the same time, the terminal monitors the worker's movements using cameras and various sensors to ensure safety. If an abnormality is detected, an alarm is issued immediately.

[0655] User roles

[0656] Users operate a headset-type AR / VR device and follow instructions provided by the system. Users can request feedback and additional instructions from the device through voice input and gestures. For example, during a task, a user can ask a voice question such as, "What is the appropriate tool to pick up this part?" and receive real-time advice. This response is prepared by an AI model that uses past data to determine the best option.

[0657] Examples of specific cases and prompt statements

[0658] A concrete example is the installation of new machinery at a construction site. In this case, the server uses a prompt message such as "Optimize the machine installation procedure and generate instructions that allow beginners to work safely" to generate detailed instructions. The generated instructions are presented to the user via the terminal in an extended display format.

[0659] With a system configured in this way, workers can perform their tasks with high efficiency and safety, regardless of their experience level.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] The server collects worker motion data and sensor data. This data includes specific work procedures and information about the surrounding environment. This input data is stored in a database and then cleansed and preprocessed. As a result, it outputs a dataset in a format suitable for training an AI model.

[0663] Step 2:

[0664] The server trains an AI model using the collected data. This process utilizes machine learning tools such as TensorFlow and PyTorch. It receives a formatted dataset as input and performs training using a neural network. As a result, it outputs an AI model that generates optimization instructions to improve work efficiency and safety.

[0665] Step 3:

[0666] The server receives prompt text to generate specific work instructions using a generative AI model. Specifically, it provides text such as, "Optimize the procedure for efficient piping work and generate instructions that allow for safe work." Based on this prompt, the AI ​​outputs a specific set of instructions for the worker.

[0667] Step 4:

[0668] The server sends instructions generated by the AI ​​to the terminal. The instruction information is packed into a message format and sent in real time via a communication protocol (e.g., WebSocket). The terminal then forwards the received instruction data to the AR / VR device.

[0669] Step 5:

[0670] The terminal presents received instructions to the user visually and audibly. Here, an AR display is used to overlay holographic instructions onto the worker's field of view, and a speaker is used to play audio instructions. This allows the user to intuitively understand the task.

[0671] Step 6:

[0672] The terminal uses sensors and cameras to monitor the user's movements in real time. This monitoring allows for the evaluation of work progress and safety. If an anomaly is detected, an alarm is immediately issued to alert the user.

[0673] Step 7:

[0674] Users send feedback to the device via voice or gestures during their work, allowing them to receive additional instructions and support for problem-solving. This user input is then sent back to the server and used to improve the AI ​​model.

[0675] (Application Example 1)

[0676] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] In many factories, the introduction and maintenance of new machinery and equipment can be difficult, especially for less experienced workers, resulting in increased working hours, decreased quality, and safety issues. This invention aims to overcome these problems and improve efficiency and safety in factory operations.

[0678] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0679] In this invention, the server includes a detection device for detecting the movements of an operator and the surrounding environment; an information processing device for generating work procedures using data from the detection device; a display device for visually and audibly presenting the generated work procedures through an augmented reality or virtual reality display; a safety management device for evaluating safety and issuing warnings; and a support device for providing instructions to assist in setting up and maintaining machinery and equipment within the factory. This enables operators to efficiently and safely perform complex machinery setup and maintenance tasks.

[0680] A "detection device" is a device that senses the movements of a worker and the surrounding environment, and collects necessary data.

[0681] An "information processing device" is a device that generates work procedures using data from a detection device.

[0682] A "display device" is a device that presents generated work procedures to a worker visually and audibly in the form of augmented reality or virtual reality.

[0683] A "safety management device" is a device that evaluates safety during work and issues warnings as needed.

[0684] A "support device" is a device that provides instructions to workers to assist with the setup and maintenance of machinery and equipment within a factory.

[0685] The system that realizes this invention aims to improve efficiency and safety in factory operations and consists of multiple devices and programs. The system is operated as follows:

[0686] The server first collects information on past operations performed by skilled workers and uses it to train a machine learning model. This machine learning process utilizes machine learning frameworks such as TensorFlow to generate optimal work procedures based on the data. The information processing device continuously updates the generated procedures to adapt to the latest work environment and machine equipment specifications.

[0687] The terminal, specifically an augmented reality display like Microsoft HoloLens, overlays work procedures transmitted from the server onto the actual work environment. This display provides virtual guidance and voice instructions to make it easier for workers to understand visually. Furthermore, based on environmental data collected by detection devices, a safety management system issues real-time safety warnings to workers.

[0688] Users can efficiently and safely operate machinery and equipment within the factory by following these displays and instructions. For example, when a user introduces a new robotic arm, AR visually guides them through the installation process, ensuring they perform the setup correctly. Furthermore, if any new challenges arise, they can receive immediate prompts and instructions.

[0689] Examples of prompts to be input to the generated AI model include specific instructions such as, "Please describe the robot arm installation procedure in detail. List the necessary tools and precautions for each step." In this way, the entire system can work together to create an optimal working environment.

[0690] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0691] Step 1:

[0692] The server collects data on the work actions of past skilled workers. This input data includes specific actions performed by the workers and information about the surrounding environment. After collection, the server preprocesses this data, performing tasks such as noise reduction and conversion to an appropriate format to generate a training dataset for machine learning models. This output dataset is then fed into subsequent training phases.

[0693] Step 2:

[0694] The server uses pre-processed data to train an AI model using a machine learning framework (e.g., TensorFlow). In this process, it learns patterns from the data and generates algorithms to optimize the work procedure. The output is a trained model capable of presenting the optimal procedure for each task.

[0695] Step 3:

[0696] The terminal receives a set of work instructions generated from a trained model sent from the server and displays them on the user's AR device (e.g., Microsoft HoloLens). The input data consists of the instructions inferred by the model, which the terminal displays as a visual and audio guide, overlaying information about the specific task the user is working on. In this process, the AR device uses input from environmental sensors to synchronize virtual information with the real world.

[0697] Step 4:

[0698] Users perform work procedures based on visual guides provided through AR devices. User input consists of data about the progress of the work and the environment, which is collected in real time by the device. This data then becomes the basis for the server to perform behavioral analysis.

[0699] Step 5:

[0700] The server analyzes real-time progress data collected from users and performs a safety assessment. Input data consists of progress logs and environmental data, which are used to determine whether the work is being performed safely and efficiently. If any anomalies are detected, a warning is issued, and appropriate guidance and corrective instructions are provided to the user. Output consists of warnings and improvement instructions communicated to the user as needed.

[0701] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0702] This invention provides a system that allows workers to receive work instructions visually and audibly in real time, while simultaneously monitoring their emotional state. The system consists of the following components:

[0703] Server-based management of AI agents and sentiment analysis

[0704] The server runs an AI trained with data from skilled workers, and combines the generated work procedures with other components. Furthermore, it uses an emotion engine to analyze the user's emotional state from the received data and determine what kind of feedback should be provided. For example, if an anxious state is detected, it will provide encouraging messages to boost motivation or offer simplified procedures.

[0705] Implementation and presentation via terminal

[0706] The terminal displays work instructions via an AR / VR device based on instructions and emotional information received from the server. This includes adjustments based on the worker's emotional state, changing the visual and audio guidance content as needed. For example, if the work is progressing smoothly, it displays normal instructions, but if stress is detected, it suggests instructions to regulate breathing or a short break.

[0707] User interaction and feedback reception

[0708] Users engage in tasks while wearing an AR / VR device. This device is equipped with a microphone and camera, which capture the user's voice and facial expressions and transmit them to an emotion engine. Users can also receive emotion-based feedback and take recommended actions. For example, if a task is too complex and confusing, they can accept simplified instructions from the device.

[0709] As a concrete example, if this system is implemented in the installation of a new conveyor belt in a factory, workers will accurately place the necessary parts following the AR guide. If stress or confusion is detected during the work, the emotion engine will intervene, switching to a friendly voice guide and providing reassuring support to the user. Such features are expected to improve not only the quality of work but also the mental satisfaction and safety of the users.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] The server collects past work data from skilled workers and uses it to train the AI. The AI ​​model uses this data to generate and optimize work procedures.

[0713] Step 2:

[0714] The server uses an emotion engine to receive facial expression data and voice information sent from the user and recognizes the user's emotional state. Based on this, it adjusts appropriate operation instructions.

[0715] Step 3:

[0716] The terminal receives instructions from the server and presents visual and audio work instructions to the user through an AR / VR device. Adjustments are made based on the user's emotional state during this process.

[0717] Step 4:

[0718] The user wears an AR / VR device and begins working according to the presented instructions. Sensors monitor the user's movements and environment during the work, providing real-time feedback.

[0719] Step 5:

[0720] The device continuously collects changes in the user's voice and facial expressions and sends this data to the emotion engine. This allows the system to understand changes in the user's emotions while they are working.

[0721] Step 6:

[0722] The server continuously analyzes user behavior and emotional data and generates feedback as needed. For example, if stress is detected, it generates and sends voice instructions to the device to encourage relaxation.

[0723] Step 7:

[0724] The terminal receives feedback from the server and makes adjustments according to the progress of the work. In addition to visual information, encouraging messages and audio are provided to the user.

[0725] Step 8:

[0726] The user receives feedback and adjusts their work pace or takes suggested breaks if necessary. After completing the task, they finish recording and save their work data and emotional changes.

[0727] (Example 2)

[0728] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] Conventional work support systems provided uniform instructions without considering the emotional state of workers, making it difficult to improve work efficiency or reduce workers' mental burden. Furthermore, real-time safety assessments and dynamic adjustments to work procedures based on emotions were not possible. Therefore, there is a need to improve work quality, safety, and workers' psychological satisfaction.

[0730] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0731] In this invention, the server includes detection means for detecting the worker's movements and the surrounding environment, analysis means for analyzing the user's emotional state, and adjustment means for providing emotion-based feedback. This makes it possible to dynamically adjust work procedures according to the worker's emotional state and work environment, thereby simultaneously improving work efficiency, safety, and the worker's mental satisfaction.

[0732] "Detection means" refers to devices or systems for collecting information about the worker's actions and the surrounding environment.

[0733] "Information processing means" refers to a computing device or software used to generate useful work procedures using collected data.

[0734] An "information presentation device" is a device or interface for providing generated information to users visually and audibly.

[0735] An "evaluation method" is a system that analyzes worker safety and issues warnings as needed.

[0736] An "analysis tool" is a system that analyzes the user's emotional state and adapts the work process based on the results.

[0737] A "adjustment mechanism" is a system for optimizing work procedures based on the results of an analysis.

[0738] An "intelligent device" is an artificial intelligence-based system that learns from data of past skilled workers and uses that data to create new work procedures and make adjustments.

[0739] A "data processing device" is a computer or system used to monitor the progress and quality of work in real time and analyze the results.

[0740] This invention is a system that grasps the actions and emotional state of a worker in real time and provides appropriate work instructions based on that information. The system mainly consists of three components: a server, a terminal, and a user.

[0741] server

[0742] The server runs AI and sentiment analysis engines, and is responsible for analyzing data obtained from the worker's actions and surrounding environment. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train generative AI models based on past data from skilled workers. This allows for the dynamic generation of work procedures and safety indicators. Furthermore, the sentiment analysis engine analyzes the user's voice and facial expressions to generate feedback that corresponds to their emotional state.

[0743] terminal

[0744] Based on work instructions and emotional information received from the server, the terminal provides visual and audible guidance to the user using HoloLens or similar information display devices. The terminal adjusts the guidance content as needed according to the user's emotional state, and if stress is detected, it provides instructions to encourage relaxation. The AR / VR devices used in this process incorporate sensors such as cameras and microphones to capture the user's emotional state.

[0745] User

[0746] Users wear AR / VR devices and perform tasks according to the instructions presented. Real-time feedback allows for smoother workflow and provides emotionally-based feedback as needed. For example, if a task is difficult or confusing, the device provides simplified instructions and user-friendly voice guidance.

[0747] As a concrete example, when installing conveyor belts in a factory, this system allows workers to quickly and accurately position the necessary parts using AR guidance. Furthermore, if stress is detected, the emotion analysis engine provides support to reassure the worker, ensuring safe and efficient work.

[0748] An example of a prompt would be, "Please provide details on developing an AI model to design a user-responsive feedback system suitable for complex factory tasks."

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The server collects sensor data from the worker's movements and the environment. This data includes video information from cameras and audio information from microphones. The collected data is input into a machine learning model. Using this data, the server understands the work situation in real time and generates the optimal work procedure. The output is a set of work procedures.

[0752] Step 2:

[0753] The server uses an emotion analysis engine to analyze the user's emotional state from sensor data. Specifically, it analyzes voice tone through speech recognition technology and facial expressions using image processing technology. This analysis determines the user's stress level and concentration level. Based on the input data, parameters indicating the emotional state are output.

[0754] Step 3:

[0755] The terminal displays guidance to the user via an information display device, based on work procedures and emotion parameters received from the server. Using devices like HoloLens, it provides visual instructions through augmented reality (AR). Furthermore, it dynamically adjusts audio feedback and instructions according to the user's emotional state. Inputs include work procedures and emotion parameters from the server, while output is the specific guidance presented to the user.

[0756] Step 4:

[0757] The user proceeds with the task by following the instructions presented on the device. The user uses the device to confirm the steps while performing the actual work. During the task, changes in voice and facial expressions are detected again through the device and sent to the server. This allows for continuous monitoring of the task progress and emotional state. As output, the completion status of the task and any new emotional data are fed back to the server.

[0758] (Application Example 2)

[0759] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] At the worksite, there are problems such as a lack of instructions necessary for workers to carry out their tasks effectively and safely, and the negative impact of worker stress and anxiety on work efficiency and quality. Furthermore, a feedback function that takes into account the mental state of workers has the potential to improve the quality of work at the worksite, but the current system is insufficient, and this needs to be resolved.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0762] In this invention, the server includes sensor means for detecting the worker's movements and the surrounding environment, means for monitoring and analyzing the worker's emotional state in real time, and means for providing appropriate feedback and adjustments to the worker based on the analysis results. This makes it possible for the worker to receive appropriate instructions and feedback in real time according to the work environment, improving work efficiency while ensuring physical and mental safety.

[0763] A "sensor device" is a device that monitors the worker's movements and the surrounding environment and collects necessary data.

[0764] "Means for generating work procedures" refers to methods or devices that construct optimal work procedures based on collected data and guide workers appropriately through them.

[0765] An "augmented reality or virtual reality device" is a device that presents digital information visually and audibly, and conveys information to a worker by combining a real work environment with virtual effects.

[0766] "Means for monitoring and analyzing emotional states in real time" refers to devices and methods for measuring the physiological and psychological states of workers and analyzing them without time delay.

[0767] "Means of providing feedback and adjustment" refers to a device or system for conveying information and instructions that are adapted to the worker's state based on their analyzed emotional state.

[0768] "Means for evaluating safety and issuing warnings" refers to devices or methods for continuously monitoring the work environment and the condition of workers, and for issuing warnings to workers when potential hazards are anticipated.

[0769] "Artificial intelligence" is a general term for algorithms and models designed to mimic human intellectual work, and it is a technology that processes data to learn and infer.

[0770] As a form for carrying out the invention, the system that realizes this application example is configured as follows.

[0771] The server plays a central role in processing data obtained from sensors that detect worker movements and the surrounding environment. This server analyzes the incoming data using AI models and generates work procedures. Furthermore, it uses a sentiment analysis engine to monitor the worker's emotional state in real time. Based on the results, it generates necessary feedback and adjusted instructions for the worker. Specifically, it utilizes cloud-based AI analytics services such as Microsoft Azure Cognitive Services.

[0772] The terminal receives work instructions and sentiment analysis results transmitted from the server. Based on this information, it provides visual and audio instructions to the worker through augmented reality (AR) or virtual reality (VR) devices. This terminal is often implemented as smart glasses or a head-mounted display.

[0773] Users wear AR / VR devices and perform tasks while receiving real-time feedback. Built-in microphones and cameras in the devices detect the user's voice and facial expressions, and this data is sent back to the server, enabling two-way interaction.

[0774] A concrete example is its use as a system to assist in setting up new product lines when working collaboratively with robots in a factory. Based on the worker's psychological state during the task, it generates prompts such as "How can I make this task more efficient?" and uses AI to quickly provide feedback. This allows workers to reduce their workload and proceed with their work more efficiently.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The server receives data on worker movements and the surrounding environment acquired from sensors. This input data includes stream data from camera images and audio sensors. The server preprocesses this data, removing outliers and filtering noise. As a result, it outputs data that has been processed into a format suitable for analysis.

[0778] Step 2:

[0779] The server inputs pre-processed data into an AI model to generate work procedures. The AI ​​model is trained on data from past skilled workers and uses a neural network to infer the optimal work procedure. In this process, it calculates the workflow and necessary steps based on the input data and outputs work instruction data as a deliverable.

[0780] Step 3:

[0781] The server sends voice and facial expression data to an emotion analysis engine to monitor the worker's emotional state. The AI ​​agent performs emotion analysis and determines the worker's psychological state. This prompt is then temporarily input into a generation AI model to obtain a situation-appropriate feedback message.

[0782] Step 4:

[0783] The terminal receives work instruction data and feedback messages generated from the server and presents them to the worker visually and audibly via an augmented reality device. This includes highlighting work instructions and navigation through voice guidance.

[0784] Step 5:

[0785] Users perform tasks according to instructions from the terminal. During the task, the terminal's built-in camera and microphone continuously collect data and send it back to the server. This iterative process allows workers to receive continuously updated feedback, enabling them to work efficiently.

[0786] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0787] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0788] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0789] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0790] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0791] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0792] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0793] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0794] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0795] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0796] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0797] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0798] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0799] 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.

[0800] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0801] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0802] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0803] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0804] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0805] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0806] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0807] The following is further disclosed regarding the embodiments described above.

[0808] (Claim 1)

[0809] Sensor means for detecting the worker's movements and the surrounding environment,

[0810] A means for generating a work procedure using data from the aforementioned sensor means,

[0811] Means for presenting the generated work procedures visually and audibly through an augmented reality or virtual reality device,

[0812] A system that includes means for evaluating safety and issuing warnings.

[0813] (Claim 2)

[0814] The system according to claim 1, which uses artificial intelligence trained on past data of skilled workers to generate work procedures.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising means for monitoring the progress and quality of work in real time and analyzing the results.

[0817] "Example 1"

[0818] (Claim 1)

[0819] Detection means for detecting the worker's movements and the surrounding environment,

[0820] A means for generating a work process using information from the aforementioned detection means,

[0821] Means for presenting the generated work process visually and audibly through an extended display or virtual display device,

[0822] A means of evaluating safety and issuing alarms,

[0823] A means of generating work instructions using machine intelligence learned from past work experience,

[0824] A means of receiving voice or motion input from the worker and providing feedback,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, which uses a generation model and instructions in generating work processes.

[0828] (Claim 3)

[0829] The system according to claim 1, further comprising means for inspecting the progress and quality of work in real time and analyzing the results.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] A detection device for detecting the worker's movements and the surrounding environment,

[0833] An information processing device that generates a work procedure using data from the aforementioned detection device,

[0834] A display device that visually and audibly presents the generated work procedures through an augmented reality or virtual reality display,

[0835] A safety management device that evaluates safety and issues warnings,

[0836] A system including support devices that provide instructions to assist with the setup and maintenance of machinery and equipment within a factory.

[0837] (Claim 2)

[0838] The system according to claim 1, which uses machine learning learned from past operation information of skilled workers to generate work procedures.

[0839] (Claim 3)

[0840] The system according to claim 1, further comprising means for monitoring operational data of machinery and equipment within a factory in real time and analyzing the results.

[0841] "Example 2 of combining an emotion engine"

[0842] (Claim 1)

[0843] Detection means for detecting the worker's movements and the surrounding environment,

[0844] An information processing means for generating a work procedure using data from the aforementioned detection means,

[0845] A presentation means for presenting the generated work procedure visually and audibly through an information presentation device,

[0846] An evaluation method for assessing safety and issuing warnings,

[0847] An analytical tool that analyzes the user's emotional state and provides emotion-based feedback,

[0848] A system including an adjustment means for adjusting work procedures based on the results of the analysis means.

[0849] (Claim 2)

[0850] The system according to claim 1, which uses an intelligent device that has learned from past data of skilled workers to generate and adjust work procedures.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising a function for monitoring the progress and quality of work in real time and analyzing the results with a data processing device.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] Sensor means for detecting the worker's movements and the surrounding environment,

[0856] A means for generating a work procedure using data from the aforementioned sensor means,

[0857] Means for presenting the generated work procedures visually and audibly through an augmented reality or virtual reality device,

[0858] A means to monitor and analyze the emotional state of workers in real time,

[0859] A means of providing appropriate feedback and adjustments to workers based on the analysis results,

[0860] A system that includes means for evaluating safety and issuing warnings.

[0861] (Claim 2)

[0862] The system according to claim 1, which uses artificial intelligence trained on past data of skilled workers to generate work procedures and analyze emotional states.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for monitoring the progress and quality of work in real time, analyzing the results, and providing feedback based on emotional state. [Explanation of Symbols]

[0865] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Sensor means for detecting the worker's movements and the surrounding environment, A means for generating a work procedure using data from the aforementioned sensor means, Means for presenting the generated work procedures visually and audibly through an augmented reality or virtual reality device, A system that includes means for evaluating safety and issuing warnings.

2. The system according to claim 1, which uses artificial intelligence trained on past data of skilled workers to generate work procedures.

3. The system according to claim 1, further comprising means for monitoring the progress and quality of work in real time and analyzing the results.

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