system

Abstract

We provide the system. [Solution] A means of visually providing operating procedures for information devices using virtual display technology, A means of analyzing operational data in real time using machine learning algorithms to identify operational errors, A means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, A means of monitoring physiological data obtained from a wearable device using a health information processing module and notifying when abnormal values ​​are detected, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For users who are not used to operating information devices, especially the elderly, it is difficult to acquire the operation methods of information devices and appropriately manage health information. The operation procedures cannot be intuitively understood, and the anxiety and stress caused by repeated incorrect operations have become problems. In addition, as the digitalization of health information progresses, it is also a problem to monitor these information in real time and respond quickly when an abnormality occurs.

Means for Solving the Problems

[0005] This invention provides users with an intuitive way to learn how to operate information devices by visually presenting operating procedures using virtual display technology. Furthermore, it analyzes operation data in real time using machine learning algorithms, quickly identifies operating errors, and provides corrective feedback. It also uses an emotion recognition module to understand the user's emotional state and adjust feedback appropriately to provide emotional support. Finally, it uses a health information processing module to monitor physiological data from wearable devices, providing immediate notifications in case of abnormalities, thereby enhancing the reliability of health management.

[0006] "Virtual display technology" is a technology that overlays digital information onto real-world vision, enabling users to intuitively understand the information.

[0007] "Information equipment" refers to devices that have communication functions and data processing capabilities, enabling the management and manipulation of information for individuals and businesses.

[0008] A "machine learning algorithm" is an algorithm that allows a computer to learn from data and use that data to make predictions and decisions.

[0009] The "emotion recognition module" is part of a system that analyzes the user's emotional state from their facial expressions and voice, and provides feedback based on that analysis.

[0010] A "health information processing module" is a system that acquires and analyzes physiological data of the body, and has the function of monitoring health status and notifying of abnormal values.

[0011] A "wearable device" is an electronic device worn on the body that is equipped with functions for collecting health data and communication. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple 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.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is an information device operation support system that combines virtual display technology and AI technology, with the aim of enabling users to intuitively and confidently acquire digital technology and manage their health.

[0034] In this system, the server first generates data for virtual display and distributes it to the terminal. The terminal then uses this data to display a virtual version overlaid on the user's screen. For example, it can visually show the installation procedure for a new application. This allows users to learn how to operate the application while viewing a visual guide.

[0035] The terminal also monitors each step of the user's operation and sends it to the server. The server analyzes this operation data and uses machine learning algorithms to identify incorrect or unfamiliar operations. For identified errors, it generates feedback including specific corrective methods and sends it back to the terminal. The terminal supports the user by displaying hints for improving their operation, thereby boosting their confidence.

[0036] In addition, an emotion recognition module installed in the device acquires the user's facial expressions and voice data, and the server analyzes this information. The server uses emotion recognition algorithms to determine the user's emotional state and adjusts the tone and content of the feedback as needed. This makes it possible to provide support that is always considerate of the user's mental state.

[0037] Furthermore, the health information processing module acquires physiological data from the wearable device and periodically sends it to the server. The server monitors this data in real time, and if it deviates from the standard values ​​(e.g., a sudden increase in blood pressure), it immediately issues a warning to the user through the terminal. The user can then receive this warning and take appropriate action quickly.

[0038] For example, if a user is wearing a smartwatch, the terminal acquires heart rate data from this device. The server monitors the heart rate data in real time, and if an abnormality is detected, it immediately sends a notification to the terminal. The terminal can then display a warning message to the user on its screen and suggest necessary countermeasures.

[0039] In this way, this system, which skillfully integrates virtual display technology and AI technology, makes it easier for users to access digital technology and allows them to manage their health with greater peace of mind.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user starts up the device and accesses the initial setup screen. At this time, the device establishes an internet connection and begins communicating with the server.

[0043] Step 2:

[0044] The device displays a screen where the user can input their skill level and desired learning content. The user enters their technical level and learning objectives, and this information is sent to the server via the device.

[0045] Step 3:

[0046] Based on the data received by the server, it generates an operation lesson plan tailored to the user. The generated plan is sent to the terminal as virtual display data.

[0047] Step 4:

[0048] The device uses virtual display technology to overlay operating procedures and guides onto the user's screen. For example, the icon of a specific application might be highlighted.

[0049] Step 5:

[0050] The user begins operating the device. The device records the user's operation data in real time and sends it to the server.

[0051] Step 6:

[0052] The server analyzes the received operation data to identify errors and operations that require improvement. Based on the results, specific feedback is generated and sent to the terminal.

[0053] Step 7:

[0054] The device notifies the user of feedback from the server. Based on the notification, the user corrects errors or learns new ways of operating.

[0055] Step 8:

[0056] The device monitors the user's emotional state using an emotion recognition module. The device then sends the acquired data to the server.

[0057] Step 9:

[0058] The server analyzes the emotional data it receives and, if it determines that the user is feeling anxious or stressed, it flexibly adjusts the tone and content of the feedback.

[0059] Step 10:

[0060] The device connects with a wearable device and periodically sends physiological data to a server.

[0061] Step 11:

[0062] The server monitors physiological data in real time, and if an abnormal value is detected, it generates an abnormality notification and sends it to the terminal.

[0063] Step 12:

[0064] The device immediately reports any abnormalities to the user, who can then take appropriate action as needed.

[0065] (Example 1)

[0066] 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."

[0067] Modern information processing devices have become more complex to operate due to their increased functionality, often making it difficult for new users and the elderly to learn how to use them. Furthermore, the increasing number of problems and stresses caused by user errors, coupled with the growing need for immediate responses in health management, presents challenges that conventional technologies struggle to adequately address.

[0068] 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.

[0069] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing user operation information in real time using a machine learning method and identifying operational errors, and means for detecting the user's emotional state using an emotion recognition device and adjusting the feedback provision method. As a result, users can more easily learn functions intuitively, reduce operational errors, and receive support tailored to their emotional state.

[0070] "Virtual display technology" is a technology that overlays visual guides and information onto the actual display on an information processing device, making it easier for users to visually understand the operating procedures.

[0071] A "machine learning method" is a method that uses operational information accumulated in a device to perform pattern recognition and data analysis, automatically identifying user errors and trends.

[0072] An "emotion recognition device" is a device that detects the user's emotional state through facial expressions, voice data, etc., and adjusts the feedback based on that.

[0073] The "biometric information processing function" is a function that monitors physiological data acquired from portable devices and other sources, and responds appropriately when abnormal values ​​are detected, thereby managing the user's health status.

[0074] "User skill level" is a concept that indicates the user's proficiency and understanding when operating information processing equipment, and appropriate guidance and support should be adjusted accordingly.

[0075] A "voice output device" is a device that transmits instructions and guidance from an information processing device to the user via voice, providing operational support not only through visual information but also through auditory means.

[0076] This invention is an operation support system for an information processing device that integrates virtual display technology and AI technology. Using this system, users can intuitively learn digital technologies while managing their health status.

[0077] The server first utilizes virtual display technology to generate virtual display data corresponding to the application the user is using. This data includes operating procedures and visual guides, allowing users to easily learn new operations. Specifically, it can visually guide users through the installation procedure of a new application.

[0078] The terminal overlays virtual display data received from the server onto the user's screen. This visually provides instructions and helps the user follow the operation flow. The terminal is also equipped with an emotion recognition device that detects the user's facial expressions and voice and sends this information to the server. The server analyzes this information and provides feedback tailored to the user's emotional state. For example, if the user has a confused expression, the feedback is adjusted to a more helpful tone.

[0079] Furthermore, the terminal transmits physiological data acquired from the wearable device to a server, which monitors for abnormal values ​​in real time. If an abnormality is detected, the system immediately alerts the user, enabling a rapid response. For example, the user acquires heart rate data via a smartwatch, and if an abnormality is detected, the system immediately notifies the user.

[0080] Example of a prompt:

[0081] "Design a system that provides a virtual installation guide for the new smartwatch and monitors sudden increases in heart rate to alert the user."

[0082] Thus, the system of the present invention enables seamless information sharing between servers and terminals, provides individualized operational support to users, and also allows for integrated health management.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server uses virtual display technology to generate virtual display data for the application being used by the user. It receives application specifications and user operation history as input, applies a data analysis algorithm based on this data, and generates a visual guide regarding the operation procedure. As output, it sends the generated virtual display data to the terminal.

[0086] Step 2:

[0087] The terminal uses virtual display data received from the server and overlays it onto the user's screen. Specifically, it displays arrows and highlights on the user interface to visually indicate the next step to be performed. The input is virtual display data from the server, and the output is a visual guide presented to the user.

[0088] Step 3:

[0089] The user operates the device while referring to the visual guide provided on the terminal. User input consists of specific operation steps (e.g., button clicks or menu selections), and the terminal checks the conditions for proceeding to the next step based on this input. The output is recorded as an operation log.

[0090] Step 4:

[0091] The terminal monitors user actions and sends that action data to the server. Specifically, it collects detailed operation history, such as user action steps, time, and frequency, as input data. The output is the data sent to the server.

[0092] Step 5:

[0093] The server analyzes the received operation data and uses machine learning algorithms to identify errors and unfamiliar operations. The input data is the operation history from step 4, and data analysis extracts examples of errors and generates necessary corrective feedback. The output is feedback data sent to the terminal.

[0094] Step 6:

[0095] The terminal receives feedback data from the server and provides the user with hints for improving operation. Specifically, it displays markers to draw attention to certain buttons or menus and provides instructions on how to operate them in text or voice. The input is feedback data from the server, and the output is what is presented to the user.

[0096] Step 7:

[0097] The emotion recognition device installed in the terminal acquires the user's facial expressions and voice data and sends it to the server. The input data is emotion-related information acquired from the camera and microphone, and the server adjusts the feedback tone based on this. The output is emotion information sent to the server.

[0098] Step 8:

[0099] The server analyzes the received emotional information and selects a feedback tone appropriate to the user's emotional state. The input is the emotional information from step 7, and the output determines the tone of the feedback message, which is then delivered to the terminal.

[0100] Step 9:

[0101] The terminal acquires physiological data from a wearable device and sends it to a server. The input data includes heart rate and blood pressure information, and the output is data for monitoring on the server.

[0102] Step 10:

[0103] The server monitors physiological data in real time and immediately notifies the user of any deviations from the baseline values. The input data is the physiological information from step 9, and the output is the data that displays the warning message to the user.

[0104] (Application Example 1)

[0105] 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."

[0106] In recent years, with the widespread adoption of information processing devices, the importance of support systems for users to efficiently learn how to operate digital technologies has increased. In particular, in delivery operations, there is a need to cultivate the ability of delivery personnel to deliver quickly and accurately in unfamiliar areas. However, current navigation systems lack intuitive guidance, potentially leading users to choose incorrect routes. Furthermore, there is a lack of technology to monitor the health and mental state of delivery personnel in real time and provide safe and effective support. A system is needed to address these issues and ensure that delivery personnel can reliably perform their duties.

[0107] 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.

[0108] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing operation information in real time using a machine learning algorithm and identifying errors in operation, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological information obtained from a wearable terminal using a health information processing module and notifying when abnormal values ​​are detected, and means for obtaining the delivery person's location using a location measuring device and visually providing virtual guidance. As a result, delivery people can efficiently perform their duties even in new areas through visual guidance, and the associated mental and physical stress can be reduced.

[0109] "Virtual display technology" is a technology that displays information visually overlaid on the user's field of vision, and is a means of providing intuitive operating procedures and guidance.

[0110] "Information processing equipment" is a general term for electronic devices used by users to manipulate and manage digital information.

[0111] A "machine learning algorithm" is a mathematical method used to analyze data, identify patterns, and make predictions and decisions.

[0112] "Operation information" refers to the history of inputs and instructions given by a user to an information processing device.

[0113] An "emotion recognition module" is a general term for hardware or software that detects and evaluates emotions from a user's facial expressions, voice, etc.

[0114] "User" refers to a person who uses this system.

[0115] "Feedback" refers to information and instructions provided by a system to a user, and is offered as a response to the user's actions and behavior.

[0116] A "health information processing module" is a device or program that processes physiological information acquired from wearable devices and other sources to evaluate a person's health status.

[0117] A "wearable device" is a general term for electronic devices that can be worn on the body and used to acquire health information.

[0118] "Physiological information" refers to data that indicates the state of the body, such as heart rate, blood pressure, and body temperature.

[0119] A "location measuring device" is a device used to detect and measure geographical location, and is used for route guidance and tracking.

[0120] A "delivery person" is a general term for a person whose job is to transport specified goods to a specified location.

[0121] "Virtual guidance" refers to providing instructions and directions to users visually using digital information, and is delivered in real time.

[0122] This invention provides an operation support system for an information processing device, specifically a system that combines virtual display technology and AI technology. The server first acquires the user's location information and operation information. In this process, GPS and operation data are used from smart glasses or other wearable devices. Based on this, the server identifies the user's current location and uses virtual display technology to overlay and display the delivery route and information operation steps on the field of view of the smart glasses.

[0123] On the device side, an emotion recognition module is used to acquire the user's facial expressions and voice data, and this information is sent to the server. The server uses an emotion recognition algorithm to analyze the user's emotional state and appropriately adjust the tone and content of the feedback. In addition, a health information processing module obtains physiological information from the wearable device, and if this exceeds a certain threshold, a warning is issued immediately.

[0124] Specifically, delivery drivers who receive guidance through smart glasses can efficiently navigate routes even in unfamiliar areas. This guidance is constantly updated in real time, allowing drivers to reach their destinations without getting lost.

[0125] For example, the system displays information such as, "Turn right at the next intersection and you will reach your delivery destination 200 meters ahead." This eliminates the need for delivery personnel to consult a map while on the move, allowing them to perform their duties while being visually guided.

[0126] Examples of prompt statements generated by the AI ​​model are as follows:

[0127] "Design a system that uses smart glasses to visually display navigation guides for new food delivery drivers venturing into unfamiliar areas, helping them understand the optimal route."

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The server acquires the user's location and operation information from wearable devices and smart glasses. GPS data acquired from location measurement devices serves as input. The server analyzes this data to determine the user's current location. The output is the identified geographic location information.

[0131] Step 2:

[0132] The terminal receives delivery route data from the server, which is necessary for real-time virtual display via smart glasses. The inputs used are geographic location information and optimal delivery route information identified by the server. The terminal uses virtual display technology to overlay these routes onto the user's field of view. The output is the delivery route displayed in the user's field of view.

[0133] Step 3:

[0134] The emotion recognition module installed in the device continuously acquires the user's facial expressions and voice data and sends it to the server. Raw data acquired from the camera and microphone is used as input. The server analyzes this data using an emotion recognition algorithm to determine the user's emotional state. The output is analyzed data indicating the user's emotional state.

[0135] Step 4:

[0136] The server adjusts the tone and content of the feedback as needed, based on the analyzed emotional data. Input includes user action data and emotional state data. The server generates feedback content based on this data and sends it to the terminal. The output is the adjusted feedback message.

[0137] Step 5:

[0138] A health information processing module receives physiological data such as heart rate and blood pressure from a wearable device and sends it to a server. The input is physiological data acquired from the wearable device. The server compares the data to reference values ​​and immediately generates a warning if abnormal values ​​are found. The output is a warning message or alert.

[0139] Step 6:

[0140] The user reviews the feedback and warning messages received from the device and takes the necessary actions. The input consists of the feedback and warning messages displayed by the device. The output consists of the specific actions taken by the user.

[0141] 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.

[0142] This invention relates to a system that recognizes a user's emotions, adaptively modifies the operation guide of an information device based on those emotions, and supports health management. The emotion engine is used to understand the user's emotional state by analyzing the user's facial expressions and voice data. Based on this information, the system dynamically adjusts the feedback and guide content to provide more flexible support.

[0143] The server analyzes the emotional data provided by the emotion engine and adaptively adjusts the virtual display content and voice guidance shown on the terminal based on this data. For example, if the user is feeling anxious, the server makes the operation guide clearer and more detailed and communicates it to the terminal. Also, if the terminal determines that the user's stress level is high, it can display relaxation suggestions on the screen.

[0144] The device collects operation data when the user performs an action and sends it to the server. The server analyzes this data using machine learning algorithms and, if it detects an incorrect operation, generates feedback suggesting specific corrective steps. This feedback is displayed on the device in an appropriate manner, taking into account the user's emotional state.

[0145] Furthermore, the device receives health data from wearable devices in real time and periodically sends it to a server. The server monitors this data and immediately notifies the device if any abnormal values ​​deviating from the standard range are detected. The user receives this notification and can promptly take action if health consultation or medical treatment is necessary.

[0146] As a concrete example, consider a scenario where a user's smartwatch detects a high stress level while they are using an application on their device. In this case, the server receives the result from the emotion engine and displays an encouraging message on the device that corresponds to the user's emotional state. Furthermore, if a heart rate exceeding a healthy range is detected, it is possible to immediately suggest relaxation exercises or hydration.

[0147] In this way, the present invention provides a system that comprehensively supports both the acquisition of digital technology and health management while providing flexible feedback that responds to the user's emotions.

[0148] The following describes the processing flow.

[0149] Step 1:

[0150] The user starts up the terminal and begins the operation guide using a virtual display. At this time, the terminal starts communicating with the server via the internet connection.

[0151] Step 2:

[0152] The device collects the user's facial expressions and voice through an emotion recognition module. This allows for the acquisition of user emotion data in real time.

[0153] Step 3:

[0154] The device sends collected emotional data to the server. The server uses an emotion engine to analyze the data and evaluate the user's emotional state.

[0155] Step 4:

[0156] The server generates feedback that responds to the user's emotional state. Specifically, if the user is feeling anxious, the user guide will be adjusted to be more detailed and gentler in tone.

[0157] Step 5:

[0158] The terminal updates the guidance content displayed virtually based on feedback from the server. For example, it visually highlights the operating procedures on the screen.

[0159] Step 6:

[0160] The user operates the device. The operation data is recorded in real time by the device and sent to the server.

[0161] Step 7:

[0162] The server uses machine learning algorithms to analyze user data and identify incorrect operations or areas requiring improvement. Based on these results, it generates feedback including detailed improvement steps.

[0163] Step 8:

[0164] The device displays feedback sent from the server to the user. The user uses this feedback to correct their actions and continue learning.

[0165] Step 9:

[0166] A wearable device transmits physiological data to a terminal in real time. The terminal periodically sends this data to a server.

[0167] Step 10:

[0168] The server monitors the health data it receives and immediately generates an anomaly notification if any abnormal values ​​are detected.

[0169] Step 11:

[0170] The device reports any abnormalities to the user. If necessary, the device offers the user relaxation suggestions or medical consultation options.

[0171] Step 12:

[0172] Users can check notifications from their devices and take appropriate action for maintaining their health and learning how to use their devices.

[0173] (Example 2)

[0174] 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".

[0175] In modern society, users need to become proficient in operating information devices, and in the process, they often experience errors and health problems. While conventional systems have technologies to address these issues individually, they lack mechanisms to adjust information output and provide flexible feedback by comprehensively considering the user's emotions and physiological state. Therefore, the challenge is to provide a system that adapts to the user's emotions and health condition and supports device operation more effectively and safely.

[0176] 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.

[0177] In this invention, the server includes means for using an emotion engine to analyze the user's emotional state, means for dynamically adjusting information output by utilizing the emotion analysis results based on a generative model, and means for processing operation data in real time using a machine learning algorithm to detect operation errors. This makes it possible to provide customized feedback according to the user's emotions and health condition, and to support the operation of information devices effectively and safely.

[0178] The "emotion engine" is a mechanism that analyzes the user's facial expression data and voice data to identify their emotional state.

[0179] A "generative model" refers to an algorithm or program that uses artificial intelligence technology to generate output under specific conditions.

[0180] A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and makes decisions such as predictions and classifications.

[0181] A "biometric monitoring device" is a device used to measure and record physiological indicators of the body in real time.

[0182] "Information output" refers to visual and auditory instructions and notifications presented to the user.

[0183] "User feedback" refers to the real-time responses and advice that a system provides to its users.

[0184] This invention is a system that supports the operation of information devices based on the user's emotional state and health data. This system includes a server, terminals, and wearable devices.

[0185] The server plays a central role in comprehensively processing the data collected from users. Using an emotion engine, the server analyzes user facial and voice data received from terminals and wearable devices to identify emotional states. This process incorporates a generative AI model that analyzes emotional changes in real time.

[0186] The device collects the user's facial expressions and voice through its camera and microphone. It then sends this data to a server and displays information output based on instructions from the server. Periodically, physiological data from the wearable device is also sent to the server via the device, providing the user with adaptive feedback based on their health status.

[0187] For example, if the camera detects a user's smile while they are using an application on their device, the server will interpret this as joy through its emotion engine and display a message of praise on the device. Conversely, if the heart rate is higher than normal, the system will immediately suggest taking a break to monitor the user's health.

[0188] As an example of a generative AI model, it is possible to input the instruction "Generate appropriate feedback when the user is confused about the operation and their heart rate increases" into the prompt text. Through this system, users can operate information devices in a better way while being mindful of their own emotions and health.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The device collects user facial expression and voice data. This data is acquired through the camera and microphone. Specifically, the device's camera continuously captures the user's facial expressions, and the microphone records their voice. This data is digitized on the spot and sent to the server as input data.

[0192] Step 2:

[0193] The server receives facial expression and audio data transmitted from the terminal and inputs them into the emotion engine. The emotion engine uses a generative AI model to analyze the input data and identify the emotional state. Specifically, a video analysis algorithm determines the movement of facial muscles, and audio analysis technology evaluates tone and speech speed. The output is the user's specific emotional state (joy, anxiety, etc.).

[0194] Step 3:

[0195] The server generates information output based on the emotion analysis results and sends it to the terminal. In this process, a generative AI model is used to select a message appropriate to the situation. For example, if the emotion is identified as "anxiety," the server generates a message such as "Please relax." Specifically, it extracts the appropriate message from the database and sends it to the terminal.

[0196] Step 4:

[0197] The terminal displays the information output received from the server. The user's screen displays text messages and graphical feedback tailored to their emotional state. Specifically, the terminal UI is updated to provide the user with visual or audio guidance. This allows the user to instantly understand the situation.

[0198] Step 5:

[0199] The terminal periodically collects physiological data (e.g., heart rate) from wearable devices and sends it to a server. This information is used to assess health status. Specifically, it uses communication technologies such as Bluetooth to acquire data from the device and transmit that data to the server.

[0200] Step 6:

[0201] The server analyzes physiological data and immediately generates a notification if an abnormal value is detected. Specifically, it performs calculations to compare the value with a baseline, and if a deviation is recognized, it creates an alert and sends it to the terminal. The user receives the alert and can quickly check health-related precautions.

[0202] Through these steps, the system comprehensively supports the user's operation of information devices in accordance with their emotions and health condition.

[0203] (Application Example 2)

[0204] 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".

[0205] In modern work environments, reducing the emotional and physical burden on workers and achieving efficient and safe work practices are crucial challenges. However, conventional machinery and systems often struggle to adjust their operation to accommodate user emotions and health conditions, potentially impacting work quality and safety. Therefore, there is a need to develop systems that can appropriately adapt their operation and guidance based on the user's emotional and health state.

[0206] 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.

[0207] In this invention, the server includes means for visually providing operating procedures for information devices using virtual display technology, means for analyzing operation data in real time using machine learning algorithms and identifying operational errors, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological data obtained from wearable devices using a health information processing module and notifying when abnormal values ​​are detected, and means for adaptively adjusting the operation and guidance of mechanical devices based on the user's emotional state and health state in the work environment. This makes it possible to perform work safely and efficiently while taking into account the user's emotional state and health state.

[0208] "Virtual display technology" is a technology that visually reproduces physical operation methods, allowing users to intuitively understand how to operate them.

[0209] A "machine learning algorithm" is a method that learns patterns based on past data and automatically makes specific predictions or judgments on new data.

[0210] An "emotion recognition module" is a device or software that analyzes data such as a user's facial expressions and voice to estimate their emotional state.

[0211] A "health information processing module" is a system component that monitors and processes physiological data collected from wearable devices to evaluate health status.

[0212] A "wearable device" is an electronic device that can be worn on the body and measures and records data such as health status and activity levels in real time.

[0213] "An adaptive adjustment mechanism" refers to a method for dynamically changing the operation of a machine or device and the content of its operating guide according to the user's situation and condition, thereby providing optimal feedback.

[0214] The term "work environment" refers to the physical or virtual space where specific tasks are performed, and is the space in which users interact with machinery, equipment, and information systems.

[0215] In the system that realizes this invention, various hardware and software components work together to optimize the work environment based on the user's emotions and health condition.

[0216] The server uses an emotion recognition module to analyze the user's emotional state from facial and voice data. For this purpose, it utilizes cloud-based emotion analysis tools such as Microsoft® Azure® Cognitive Services. Furthermore, for collecting physiological data from wearable devices, smartwatches and other devices are used, leveraging Apple HealthKit and Google Fit API.

[0217] The device performs real-time analysis using machine learning algorithms (such as TENSORFLOW®) based on collected emotional state and physiological data. This allows it to detect operational errors and dynamically adjust feedback. Specifically, it provides intuitive operation guides using virtual display technology. In addition, an audio output module provides voice guidance to the user regarding the operation procedure.

[0218] This allows users to receive work support tailored to their emotions and health condition. For example, if they are feeling stressed, the robot's movements will slow down and it will be able to provide more detailed explanations. Furthermore, if there is an abnormality in their health condition, they will receive an instant notification, allowing them to take breaks and manage their health as needed.

[0219] For example, if a facial recognition camera detects signs of fatigue in a worker, it can display a prompt to the user asking, "Should you take a short break?" The generating AI model would utilize prompts such as: "Do you feel tired? If you are feeling tired or stressed, what are some effective ways to relax?"

[0220] These features allow the system to provide detailed feedback based on emotions and health status, regardless of whether the user is an experienced worker or a novice.

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The server receives the user's facial and voice data as input to the emotion recognition module. The input data is analyzed as an emotional state using Microsoft Azure Cognitive Services. The output is the user's current emotional state (e.g., stress, exhilaration, relief, etc.). Specifically, the server analyzes facial features and voice patterns and assigns an emotion label.

[0224] Step 2:

[0225] The device acquires physiological data (e.g., heart rate, body temperature) from a wearable device and inputs it into a health information processing module. For data processing, the data is standardized using Apple HealthKit or Google Fit® API. The output provides an assessment of the current health status (e.g., normal, abnormal). Specifically, the device continuously collects data and prepares to send real-time notifications when abnormal values ​​are detected.

[0226] Step 3:

[0227] The device receives acquired emotional and health status data as input to a machine learning algorithm and analyzes the operation data in real time. TensorFlow is used for data processing to detect errors in the current operation based on past training data. The output includes operation steps that need correction and improvement suggestions. Specifically, the device analyzes the operation history and generates optimal feedback.

[0228] Step 4:

[0229] The device provides operation guidance using virtual display technology based on the user's emotions and health status. Feedback data from the previous step is used as input. The output is a visual guide optimized for the user displayed on the screen. Specifically, the guide content is dynamically adjusted to be easily understood by the user.

[0230] Step 5:

[0231] The user receives feedback from the device, performs the necessary actions, and continues working based on the provided guide. As an output, an efficient and safe work environment is maintained. Specifically, the user improves productivity by following the presented instructions and implementing improvement suggestions.

[0232] Step 6:

[0233] The terminal uses a voice output module to provide voice guidance to the user. Feedback content is used as input. As output, an easy-to-understand voice guide is provided. Specifically, the voice tone and pace are adjusted to convey instructions that are appropriate to the user's situation.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] This invention is an information device operation support system that combines virtual display technology and AI technology, with the aim of enabling users to intuitively and confidently acquire digital technology and manage their health.

[0251] In this system, the server first generates data for virtual display and distributes it to the terminal. The terminal then uses this data to display a virtual version overlaid on the user's screen. For example, it can visually show the installation procedure for a new application. This allows users to learn how to operate the application while viewing a visual guide.

[0252] The terminal also monitors each step of the user's operation and sends it to the server. The server analyzes this operation data and uses machine learning algorithms to identify incorrect or unfamiliar operations. For identified errors, it generates feedback including specific corrective methods and sends it back to the terminal. The terminal supports the user by displaying hints for improving their operation, thereby boosting their confidence.

[0253] In addition, an emotion recognition module installed in the device acquires the user's facial expressions and voice data, and the server analyzes this information. The server uses emotion recognition algorithms to determine the user's emotional state and adjusts the tone and content of the feedback as needed. This makes it possible to provide support that is always considerate of the user's mental state.

[0254] Furthermore, the health information processing module acquires physiological data from the wearable device and periodically sends it to the server. The server monitors this data in real time, and if it deviates from the standard values ​​(e.g., a sudden increase in blood pressure), it immediately issues a warning to the user through the terminal. The user can then receive this warning and take appropriate action quickly.

[0255] For example, if a user is wearing a smartwatch, the terminal acquires heart rate data from this device. The server monitors the heart rate data in real time, and if an abnormality is detected, it immediately sends a notification to the terminal. The terminal can then display a warning message to the user on its screen and suggest necessary countermeasures.

[0256] In this way, this system, which skillfully integrates virtual display technology and AI technology, makes it easier for users to access digital technology and allows them to manage their health with greater peace of mind.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user starts up the device and accesses the initial setup screen. At this time, the device establishes an internet connection and begins communicating with the server.

[0260] Step 2:

[0261] The device displays a screen where the user can input their skill level and desired learning content. The user enters their technical level and learning objectives, and this information is sent to the server via the device.

[0262] Step 3:

[0263] Based on the data received by the server, it generates an operation lesson plan tailored to the user. The generated plan is sent to the terminal as virtual display data.

[0264] Step 4:

[0265] The device uses virtual display technology to overlay operating procedures and guides onto the user's screen. For example, the icon of a specific application might be highlighted.

[0266] Step 5:

[0267] The user begins operating the device. The device records the user's operation data in real time and sends it to the server.

[0268] Step 6:

[0269] The server analyzes the received operation data to identify errors and operations that require improvement. Based on the results, specific feedback is generated and sent to the terminal.

[0270] Step 7:

[0271] The device notifies the user of feedback from the server. Based on the notification, the user corrects errors or learns new ways of operating.

[0272] Step 8:

[0273] The terminal monitors the user's emotional state using an emotion recognition module. The terminal transmits the acquired data to the server.

[0274] Step 9:

[0275] The server analyzes the received emotion data. If it determines that the user is feeling anxious or stressed, it flexibly adjusts the tone and content of the feedback.

[0276] Step 10:

[0277] The terminal cooperates with the wearable device and periodically transmits physiological data to the server.

[0278] Step 11:

[0279] The server monitors the physiological data in real time. If an abnormal value is detected, it generates an abnormality notification and transmits it to the terminal.

[0280] Step 12:

[0281] The terminal immediately reports the abnormality notification to the user, and the user takes appropriate actions as needed.

[0282] (Example 1)

[0283] Next, 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".

[0284] In modern information processing devices, as the functions increase, the operations of users have become more complex. Therefore, new users and the elderly often have difficulty in acquiring the operations. In addition, there are more situations where users feel troubles and stress due to incorrect operations, and furthermore, in the context of the need for immediate response in health management, there is a problem that it is difficult for the conventional technology to sufficiently solve these problems.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0286] In this invention, the server includes means for visually providing the operation procedure of the information processing device using virtual display technology, means for analyzing the operation information of the user in real time using a machine learning method to identify operation errors, and means for detecting the emotional state of the user using an emotion recognition device and adjusting the feedback providing method. As a result, the user can easily acquire functions intuitively, reduce incorrect operations, and receive support according to the emotional state.

[0287] "Virtual display technology" is a technology for superimposing visual guides and information on the actual display on the information processing device, making it easier for the user to visually understand the operation procedure.

[0288] "Machine learning method" is a method for performing pattern recognition and data analysis using the operation information stored in the device, automatically identifying operation errors and tendencies of the user.

[0289] "Emotion recognition device" is a device for detecting the emotional state of the user through expressions, voice data, etc., and adjusting the feedback based on it.

[0290] "Biological information processing function" is a function for monitoring physiological data obtained from a portable device or the like and appropriately reacting when abnormal values are detected, managing the health state of the user.

[0291] "Skill level of the user" is a concept indicating the proficiency and understanding of the user when operating the information processing device, and adjusting appropriate guides and support contents according to it.

[0292] "Voice output device" is a device for transmitting instructions and guides from the information processing device to the user by voice, providing operation support not only visually but also aurally.

[0293] This invention is an operation support system for an information processing device that integrates virtual display technology and AI technology. Using this system, users can intuitively learn digital technologies while managing their health status.

[0294] The server first utilizes virtual display technology to generate virtual display data corresponding to the application the user is using. This data includes operating procedures and visual guides, allowing users to easily learn new operations. Specifically, it can visually guide users through the installation procedure of a new application.

[0295] The terminal overlays virtual display data received from the server onto the user's screen. This visually provides instructions and helps the user follow the operation flow. The terminal is also equipped with an emotion recognition device that detects the user's facial expressions and voice and sends this information to the server. The server analyzes this information and provides feedback tailored to the user's emotional state. For example, if the user has a confused expression, the feedback is adjusted to a more helpful tone.

[0296] Furthermore, the terminal transmits physiological data acquired from the wearable device to a server, which monitors for abnormal values ​​in real time. If an abnormality is detected, the system immediately alerts the user, enabling a rapid response. For example, the user acquires heart rate data via a smartwatch, and if an abnormality is detected, the system immediately notifies the user.

[0297] Example of a prompt:

[0298] "Design a system that provides a virtual installation guide for the new smartwatch and monitors sudden increases in heart rate to alert the user."

[0299] Thus, the system of the present invention is for seamlessly sharing information between a server and a terminal, providing individual operation support to a user, and enabling integrated health management.

[0300] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0301] Step 1:

[0302] The server uses virtual display technology to generate virtual display data of the application used by the user. As input, it receives the specifications of the application and the user's operation history, applies a data analysis algorithm based on this, and generates a visual guide regarding the operation procedure. As output, it transmits the generated virtual display data to the terminal.

[0303] Step 2:

[0304] The terminal uses the virtual display data received from the server to superimpose and display it on the user's operation screen. As a specific operation, it displays arrows and highlights on the operation interface to visually indicate the next step to be operated. The input is the virtual display data from the server, and the output is the visual guide presented to the user.

[0305] Step 3:

[0306] The user performs operations while referring to the visual guide provided by the terminal. The user's input is a specific operation procedure (for example, clicking a button or selecting a menu), and based on this, the terminal checks the conditions for proceeding to the next step. The output is recorded as an operation log.

[0307] Step 4:

[0308] The terminal monitors the user's operations and transmits the operation data to the server. Specifically, it collects detailed operation history such as the user's operation steps, time, and frequency as input data. The output is the data transmitted to the server.

[0309] Step 5:

[0310] The server analyzes the received operation data and uses machine learning algorithms to identify errors and unfamiliar operations. The input data is the operation history from step 4, and data analysis extracts examples of errors and generates necessary corrective feedback. The output is feedback data sent to the terminal.

[0311] Step 6:

[0312] The terminal receives feedback data from the server and provides the user with hints for improving operation. Specifically, it displays markers to draw attention to certain buttons or menus and provides instructions on how to operate them in text or voice. The input is feedback data from the server, and the output is what is presented to the user.

[0313] Step 7:

[0314] The emotion recognition device installed in the terminal acquires the user's facial expressions and voice data and sends it to the server. The input data is emotion-related information acquired from the camera and microphone, and the server adjusts the feedback tone based on this. The output is emotion information sent to the server.

[0315] Step 8:

[0316] The server analyzes the received emotional information and selects a feedback tone appropriate to the user's emotional state. The input is the emotional information from step 7, and the output determines the tone of the feedback message, which is then delivered to the terminal.

[0317] Step 9:

[0318] The terminal acquires physiological data from a wearable device and sends it to a server. The input data includes heart rate and blood pressure information, and the output is data for monitoring on the server.

[0319] Step 10:

[0320] The server monitors physiological data in real time and immediately notifies the user of any deviations from the baseline values. The input data is the physiological information from step 9, and the output is the data that displays the warning message to the user.

[0321] (Application Example 1)

[0322] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0323] In recent years, with the widespread adoption of information processing devices, the importance of support systems for users to efficiently learn how to operate digital technologies has increased. In particular, in delivery operations, there is a need to cultivate the ability of delivery personnel to deliver quickly and accurately in unfamiliar areas. However, current navigation systems lack intuitive guidance, potentially leading users to choose incorrect routes. Furthermore, there is a lack of technology to monitor the health and mental state of delivery personnel in real time and provide safe and effective support. A system is needed to address these issues and ensure that delivery personnel can reliably perform their duties.

[0324] 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.

[0325] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing operation information in real time using a machine learning algorithm and identifying errors in operation, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological information obtained from a wearable terminal using a health information processing module and notifying when abnormal values ​​are detected, and means for obtaining the delivery person's location using a location measuring device and visually providing virtual guidance. As a result, delivery people can efficiently perform their duties even in new areas through visual guidance, and the associated mental and physical stress can be reduced.

[0326] "Virtual display technology" is a technology that displays information visually overlaid on the user's field of vision, and is a means of providing intuitive operating procedures and guidance.

[0327] "Information processing equipment" is a general term for electronic devices used by users to manipulate and manage digital information.

[0328] A "machine learning algorithm" is a mathematical method used to analyze data, identify patterns, and make predictions and decisions.

[0329] "Operation information" refers to the history of inputs and instructions given by a user to an information processing device.

[0330] An "emotion recognition module" is a general term for hardware or software that detects and evaluates emotions from a user's facial expressions, voice, etc.

[0331] "User" refers to a person who uses this system.

[0332] "Feedback" refers to information and instructions provided by a system to a user, and is offered as a response to the user's actions and behavior.

[0333] A "health information processing module" is a device or program that processes physiological information acquired from wearable devices and other sources to evaluate a person's health status.

[0334] A "wearable device" is a general term for electronic devices that can be worn on the body and used to acquire health information.

[0335] "Physiological information" refers to data that indicates the state of the body, such as heart rate, blood pressure, and body temperature.

[0336] A "location measuring device" is a device used to detect and measure geographical location, and is used for route guidance and tracking.

[0337] A "delivery person" is a general term for a person whose job is to transport specified goods to a specified location.

[0338] "Virtual guidance" refers to providing instructions and directions to users visually using digital information, and is delivered in real time.

[0339] This invention provides an operation support system for an information processing device, specifically a system that combines virtual display technology and AI technology. The server first acquires the user's location information and operation information. In this process, GPS and operation data are used from smart glasses or other wearable devices. Based on this, the server identifies the user's current location and uses virtual display technology to overlay and display the delivery route and information operation steps on the field of view of the smart glasses.

[0340] On the device side, an emotion recognition module is used to acquire the user's facial expressions and voice data, and this information is sent to the server. The server uses an emotion recognition algorithm to analyze the user's emotional state and appropriately adjust the tone and content of the feedback. In addition, a health information processing module obtains physiological information from the wearable device, and if this exceeds a certain threshold, a warning is issued immediately.

[0341] Specifically, delivery drivers who receive guidance through smart glasses can efficiently navigate routes even in unfamiliar areas. This guidance is constantly updated in real time, allowing drivers to reach their destinations without getting lost.

[0342] For example, the system displays information such as, "Turn right at the next intersection and you will reach your delivery destination 200 meters ahead." This eliminates the need for delivery personnel to consult a map while on the move, allowing them to perform their duties while being visually guided.

[0343] Examples of prompt statements generated by the AI ​​model are as follows:

[0344] "Design a system that uses smart glasses to visually display navigation guides for new food delivery drivers venturing into unfamiliar areas, helping them understand the optimal route."

[0345] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0346] Step 1:

[0347] The server acquires the user's location and operation information from wearable devices and smart glasses. GPS data acquired from location measurement devices serves as input. The server analyzes this data to determine the user's current location. The output is the identified geographic location information.

[0348] Step 2:

[0349] The terminal receives delivery route data from the server, which is necessary for real-time virtual display via smart glasses. The inputs used are geographic location information and optimal delivery route information identified by the server. The terminal uses virtual display technology to overlay these routes onto the user's field of view. The output is the delivery route displayed in the user's field of view.

[0350] Step 3:

[0351] The emotion recognition module installed in the device continuously acquires the user's facial expressions and voice data and sends it to the server. Raw data acquired from the camera and microphone is used as input. The server analyzes this data using an emotion recognition algorithm to determine the user's emotional state. The output is analyzed data indicating the user's emotional state.

[0352] Step 4:

[0353] The server adjusts the tone and content of the feedback as needed, based on the analyzed emotional data. Input includes user action data and emotional state data. The server generates feedback content based on this data and sends it to the terminal. The output is the adjusted feedback message.

[0354] Step 5:

[0355] A health information processing module receives physiological data such as heart rate and blood pressure from a wearable device and sends it to a server. The input is physiological data acquired from the wearable device. The server compares the data to reference values ​​and immediately generates a warning if abnormal values ​​are found. The output is a warning message or alert.

[0356] Step 6:

[0357] The user reviews the feedback and warning messages received from the device and takes the necessary actions. The input consists of the feedback and warning messages displayed by the device. The output consists of the specific actions taken by the user.

[0358] 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.

[0359] This invention relates to a system that recognizes a user's emotions, adaptively modifies the operation guide of an information device based on those emotions, and supports health management. The emotion engine is used to understand the user's emotional state by analyzing the user's facial expressions and voice data. Based on this information, the system dynamically adjusts the feedback and guide content to provide more flexible support.

[0360] The server analyzes the emotional data provided by the emotion engine and adaptively adjusts the virtual display content and voice guidance shown on the terminal based on this data. For example, if the user is feeling anxious, the server makes the operation guide clearer and more detailed and communicates it to the terminal. Also, if the terminal determines that the user's stress level is high, it can display relaxation suggestions on the screen.

[0361] The device collects operation data when the user performs an action and sends it to the server. The server analyzes this data using machine learning algorithms and, if it detects an incorrect operation, generates feedback suggesting specific corrective steps. This feedback is displayed on the device in an appropriate manner, taking into account the user's emotional state.

[0362] Furthermore, the device receives health data from wearable devices in real time and periodically sends it to a server. The server monitors this data and immediately notifies the device if any abnormal values ​​deviating from the standard range are detected. The user receives this notification and can promptly take action if health consultation or medical treatment is necessary.

[0363] As a concrete example, consider a scenario where a user's smartwatch detects a high stress level while they are using an application on their device. In this case, the server receives the result from the emotion engine and displays an encouraging message on the device that corresponds to the user's emotional state. Furthermore, if a heart rate exceeding a healthy range is detected, it is possible to immediately suggest relaxation exercises or hydration.

[0364] In this way, the present invention provides a system that comprehensively supports both the acquisition of digital technology and health management while providing flexible feedback that responds to the user's emotions.

[0365] The following describes the processing flow.

[0366] Step 1:

[0367] The user starts up the terminal and begins the operation guide using a virtual display. At this time, the terminal starts communicating with the server via the internet connection.

[0368] Step 2:

[0369] The device collects the user's facial expressions and voice through an emotion recognition module. This allows for the acquisition of user emotion data in real time.

[0370] Step 3:

[0371] The device sends collected emotional data to the server. The server uses an emotion engine to analyze the data and evaluate the user's emotional state.

[0372] Step 4:

[0373] The server generates feedback that responds to the user's emotional state. Specifically, if the user is feeling anxious, the user guide will be adjusted to be more detailed and gentler in tone.

[0374] Step 5:

[0375] The terminal updates the guidance content displayed virtually based on feedback from the server. For example, it visually highlights the operating procedures on the screen.

[0376] Step 6:

[0377] The user operates the device. The operation data is recorded in real time by the device and sent to the server.

[0378] Step 7:

[0379] The server uses machine learning algorithms to analyze user data and identify incorrect operations or areas requiring improvement. Based on these results, it generates feedback including detailed improvement steps.

[0380] Step 8:

[0381] The device displays feedback sent from the server to the user. The user uses this feedback to correct their actions and continue learning.

[0382] Step 9:

[0383] A wearable device transmits physiological data to a terminal in real time. The terminal periodically sends this data to a server.

[0384] Step 10:

[0385] The server monitors the health data it receives and immediately generates an anomaly notification if any abnormal values ​​are detected.

[0386] Step 11:

[0387] The device reports any abnormalities to the user. If necessary, the device offers the user relaxation suggestions or medical consultation options.

[0388] Step 12:

[0389] Users can check notifications from their devices and take appropriate action for maintaining their health and learning how to use their devices.

[0390] (Example 2)

[0391] 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".

[0392] In modern society, users need to become proficient in operating information devices, and in the process, they often experience errors and health problems. While conventional systems have technologies to address these issues individually, they lack mechanisms to adjust information output and provide flexible feedback by comprehensively considering the user's emotions and physiological state. Therefore, the challenge is to provide a system that adapts to the user's emotions and health condition and supports device operation more effectively and safely.

[0393] 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.

[0394] In this invention, the server includes means for using an emotion engine to analyze the user's emotional state, means for dynamically adjusting information output by utilizing the emotion analysis results based on a generative model, and means for processing operation data in real time using a machine learning algorithm to detect operation errors. This makes it possible to provide customized feedback according to the user's emotions and health condition, and to support the operation of information devices effectively and safely.

[0395] The "emotion engine" is a mechanism that analyzes the user's facial expression data and voice data to identify their emotional state.

[0396] A "generative model" refers to an algorithm or program that uses artificial intelligence technology to generate output under specific conditions.

[0397] A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and makes decisions such as predictions and classifications.

[0398] A "biometric monitoring device" is a device used to measure and record physiological indicators of the body in real time.

[0399] "Information output" refers to visual and auditory instructions and notifications presented to the user.

[0400] "User feedback" refers to the real-time responses and advice that a system provides to its users.

[0401] This invention is a system that supports the operation of information devices based on the user's emotional state and health data. This system includes a server, terminals, and wearable devices.

[0402] The server plays a central role in comprehensively processing the data collected from users. Using an emotion engine, the server analyzes user facial and voice data received from terminals and wearable devices to identify emotional states. This process incorporates a generative AI model that analyzes emotional changes in real time.

[0403] The device collects the user's facial expressions and voice through its camera and microphone. It then sends this data to a server and displays information output based on instructions from the server. Periodically, physiological data from the wearable device is also sent to the server via the device, providing the user with adaptive feedback based on their health status.

[0404] For example, if the camera detects a user's smile while they are using an application on their device, the server will interpret this as joy through its emotion engine and display a message of praise on the device. Conversely, if the heart rate is higher than normal, the system will immediately suggest taking a break to monitor the user's health.

[0405] As an example of a generative AI model, it is possible to input the instruction "Generate appropriate feedback when the user is confused about the operation and their heart rate increases" into the prompt text. Through this system, users can operate information devices in a better way while being mindful of their own emotions and health.

[0406] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0407] Step 1:

[0408] The device collects user facial expression and voice data. This data is acquired through the camera and microphone. Specifically, the device's camera continuously captures the user's facial expressions, and the microphone records their voice. This data is digitized on the spot and sent to the server as input data.

[0409] Step 2:

[0410] The server receives facial expression and audio data transmitted from the terminal and inputs them into the emotion engine. The emotion engine uses a generative AI model to analyze the input data and identify the emotional state. Specifically, a video analysis algorithm determines the movement of facial muscles, and audio analysis technology evaluates tone and speech speed. The output is the user's specific emotional state (joy, anxiety, etc.).

[0411] Step 3:

[0412] The server generates information output based on the emotion analysis results and sends it to the terminal. In this process, a generative AI model is used to select a message appropriate to the situation. For example, if the emotion is identified as "anxiety," the server generates a message such as "Please relax." Specifically, it extracts the appropriate message from the database and sends it to the terminal.

[0413] Step 4:

[0414] The terminal displays the information output received from the server. The user's screen displays text messages and graphical feedback tailored to their emotional state. Specifically, the terminal UI is updated to provide the user with visual or audio guidance. This allows the user to instantly understand the situation.

[0415] Step 5:

[0416] The terminal periodically collects physiological data (e.g., heart rate) from wearable devices and sends it to a server. This information is used to assess health status. Specifically, it uses communication technologies such as Bluetooth to acquire data from the device and transmit that data to the server.

[0417] Step 6:

[0418] The server analyzes physiological data and immediately generates a notification if an abnormal value is detected. Specifically, it performs calculations to compare the value with a baseline, and if a deviation is recognized, it creates an alert and sends it to the terminal. The user receives the alert and can quickly check health-related precautions.

[0419] Through these steps, the system comprehensively supports the user's operation of information devices in accordance with their emotions and health condition.

[0420] (Application Example 2)

[0421] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0422] In modern work environments, reducing the emotional and physical burden on workers and achieving efficient and safe work practices are crucial challenges. However, conventional machinery and systems often struggle to adjust their operation to accommodate user emotions and health conditions, potentially impacting work quality and safety. Therefore, there is a need to develop systems that can appropriately adapt their operation and guidance based on the user's emotional and health state.

[0423] 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.

[0424] In this invention, the server includes means for visually providing operating procedures for information devices using virtual display technology, means for analyzing operation data in real time using machine learning algorithms and identifying operational errors, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological data obtained from wearable devices using a health information processing module and notifying when abnormal values ​​are detected, and means for adaptively adjusting the operation and guidance of mechanical devices based on the user's emotional state and health state in the work environment. This makes it possible to perform work safely and efficiently while taking into account the user's emotional state and health state.

[0425] "Virtual display technology" is a technology that visually reproduces physical operation methods, allowing users to intuitively understand how to operate them.

[0426] A "machine learning algorithm" is a method that learns patterns based on past data and automatically makes specific predictions or judgments on new data.

[0427] An "emotion recognition module" is a device or software that analyzes data such as a user's facial expressions and voice to estimate their emotional state.

[0428] A "health information processing module" is a system component that monitors and processes physiological data collected from wearable devices to evaluate health status.

[0429] A "wearable device" is an electronic device that can be worn on the body and measures and records data such as health status and activity levels in real time.

[0430] "An adaptive adjustment mechanism" refers to a method for dynamically changing the operation of a machine or device and the content of its operating guide according to the user's situation and condition, thereby providing optimal feedback.

[0431] The term "work environment" refers to the physical or virtual space where specific tasks are performed, and is the space in which users interact with machinery, equipment, and information systems.

[0432] In the system that realizes this invention, various hardware and software components work together to optimize the work environment based on the user's emotions and health condition.

[0433] The server uses an emotion recognition module to analyze the user's emotional state from facial and voice data. For this purpose, it utilizes cloud-based emotion analysis tools such as Microsoft Azure Cognitive Services. Furthermore, for collecting physiological data from wearable devices, smartwatches and other devices are used, leveraging Apple HealthKit and Google® Fit API.

[0434] The device performs real-time analysis using machine learning algorithms (such as TensorFlow) based on collected emotional state and physiological data. This allows it to detect operational errors and dynamically adjust feedback. Specifically, it provides intuitive operation guides using virtual display technology. In addition, an audio output module provides voice guidance to the user regarding the operation procedure.

[0435] This allows users to receive work support tailored to their emotions and health condition. For example, if they are feeling stressed, the robot's movements will slow down and it will be able to provide more detailed explanations. Furthermore, if there is an abnormality in their health condition, they will receive an instant notification, allowing them to take breaks and manage their health as needed.

[0436] For example, if a facial recognition camera detects signs of fatigue in a worker, it can display a prompt to the user asking, "Should you take a short break?" The generating AI model would utilize prompts such as: "Do you feel tired? If you are feeling tired or stressed, what are some effective ways to relax?"

[0437] These features allow the system to provide detailed feedback based on emotions and health status, regardless of whether the user is an experienced worker or a novice.

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The server receives the user's facial and voice data as input to the emotion recognition module. The input data is analyzed as an emotional state using Microsoft Azure Cognitive Services. The output is the user's current emotional state (e.g., stress, exhilaration, relief, etc.). Specifically, the server analyzes facial features and voice patterns and assigns an emotion label.

[0441] Step 2:

[0442] The device acquires physiological data (e.g., heart rate, body temperature) from a wearable device and inputs it into a health information processing module. For data processing, the data is standardized using Apple HealthKit or Google Fit API. The output provides an assessment of the current health status (e.g., normal, abnormal). Specifically, the device continuously collects data and prepares real-time notifications when abnormal values ​​are detected.

[0443] Step 3:

[0444] The device receives acquired emotional and health status data as input to a machine learning algorithm and analyzes the operation data in real time. TensorFlow is used for data processing to detect errors in the current operation based on past training data. The output includes operation steps that need correction and improvement suggestions. Specifically, the device analyzes the operation history and generates optimal feedback.

[0445] Step 4:

[0446] The device provides operation guidance using virtual display technology based on the user's emotions and health status. Feedback data from the previous step is used as input. The output is a visual guide optimized for the user displayed on the screen. Specifically, the guide content is dynamically adjusted to be easily understood by the user.

[0447] Step 5:

[0448] The user receives feedback from the device, performs the necessary actions, and continues working based on the provided guide. As an output, an efficient and safe work environment is maintained. Specifically, the user improves productivity by following the presented instructions and implementing improvement suggestions.

[0449] Step 6:

[0450] The terminal uses a voice output module to provide voice guidance to the user. Feedback content is used as input. As output, an easy-to-understand voice guide is provided. Specifically, the voice tone and pace are adjusted to convey instructions that are appropriate to the user's situation.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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".

[0467] This invention is an information device operation support system that combines virtual display technology and AI technology, with the aim of enabling users to intuitively and confidently acquire digital technology and manage their health.

[0468] In this system, the server first generates data for virtual display and distributes it to the terminal. The terminal then uses this data to display a virtual version overlaid on the user's screen. For example, it can visually show the installation procedure for a new application. This allows users to learn how to operate the application while viewing a visual guide.

[0469] The terminal also monitors each step of the user's operation and sends it to the server. The server analyzes this operation data and uses machine learning algorithms to identify incorrect or unfamiliar operations. For identified errors, it generates feedback including specific corrective methods and sends it back to the terminal. The terminal supports the user by displaying hints for improving their operation, thereby boosting their confidence.

[0470] In addition, an emotion recognition module installed in the device acquires the user's facial expressions and voice data, and the server analyzes this information. The server uses emotion recognition algorithms to determine the user's emotional state and adjusts the tone and content of the feedback as needed. This makes it possible to provide support that is always considerate of the user's mental state.

[0471] Furthermore, the health information processing module acquires physiological data from the wearable device and periodically sends it to the server. The server monitors this data in real time, and if it deviates from the standard values ​​(e.g., a sudden increase in blood pressure), it immediately issues a warning to the user through the terminal. The user can then receive this warning and take appropriate action quickly.

[0472] For example, if a user is wearing a smartwatch, the terminal acquires heart rate data from this device. The server monitors the heart rate data in real time, and if an abnormality is detected, it immediately sends a notification to the terminal. The terminal can then display a warning message to the user on its screen and suggest necessary countermeasures.

[0473] In this way, this system, which skillfully integrates virtual display technology and AI technology, makes it easier for users to access digital technology and allows them to manage their health with greater peace of mind.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The user starts up the device and accesses the initial setup screen. At this time, the device establishes an internet connection and begins communicating with the server.

[0477] Step 2:

[0478] The device displays a screen where the user can input their skill level and desired learning content. The user enters their technical level and learning objectives, and this information is sent to the server via the device.

[0479] Step 3:

[0480] Based on the data received by the server, it generates an operation lesson plan tailored to the user. The generated plan is sent to the terminal as virtual display data.

[0481] Step 4:

[0482] The device uses virtual display technology to overlay operating procedures and guides onto the user's screen. For example, the icon of a specific application might be highlighted.

[0483] Step 5:

[0484] The user begins operating the device. The device records the user's operation data in real time and sends it to the server.

[0485] Step 6:

[0486] The server analyzes the received operation data to identify errors and operations that require improvement. Based on the results, specific feedback is generated and sent to the terminal.

[0487] Step 7:

[0488] The device notifies the user of feedback from the server. Based on the notification, the user corrects errors or learns new ways of operating.

[0489] Step 8:

[0490] The device monitors the user's emotional state using an emotion recognition module. The device then sends the acquired data to the server.

[0491] Step 9:

[0492] The server analyzes the emotional data it receives and, if it determines that the user is feeling anxious or stressed, it flexibly adjusts the tone and content of the feedback.

[0493] Step 10:

[0494] The device connects with a wearable device and periodically sends physiological data to a server.

[0495] Step 11:

[0496] The server monitors physiological data in real time, and if an abnormal value is detected, it generates an abnormality notification and sends it to the terminal.

[0497] Step 12:

[0498] The device immediately reports any abnormalities to the user, who can then take appropriate action as needed.

[0499] (Example 1)

[0500] 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."

[0501] Modern information processing devices have become more complex to operate due to their increased functionality, often making it difficult for new users and the elderly to learn how to use them. Furthermore, the increasing number of problems and stresses caused by user errors, coupled with the growing need for immediate responses in health management, presents challenges that conventional technologies struggle to adequately address.

[0502] 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.

[0503] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing user operation information in real time using a machine learning method and identifying operational errors, and means for detecting the user's emotional state using an emotion recognition device and adjusting the feedback provision method. As a result, users can more easily learn functions intuitively, reduce operational errors, and receive support tailored to their emotional state.

[0504] "Virtual display technology" is a technology that overlays visual guides and information onto the actual display on an information processing device, making it easier for users to visually understand the operating procedures.

[0505] A "machine learning method" is a method that uses operational information accumulated in a device to perform pattern recognition and data analysis, automatically identifying user errors and trends.

[0506] An "emotion recognition device" is a device that detects the user's emotional state through facial expressions, voice data, etc., and adjusts the feedback based on that.

[0507] The "biometric information processing function" is a function that monitors physiological data acquired from portable devices and other sources, and responds appropriately when abnormal values ​​are detected, thereby managing the user's health status.

[0508] "User skill level" is a concept that indicates the user's proficiency and understanding when operating information processing equipment, and appropriate guidance and support should be adjusted accordingly.

[0509] A "voice output device" is a device that transmits instructions and guidance from an information processing device to the user via voice, providing operational support not only through visual information but also through auditory means.

[0510] This invention is an operation support system for an information processing device that integrates virtual display technology and AI technology. Using this system, users can intuitively learn digital technologies while managing their health status.

[0511] The server first utilizes virtual display technology to generate virtual display data corresponding to the application the user is using. This data includes operating procedures and visual guides, allowing users to easily learn new operations. Specifically, it can visually guide users through the installation procedure of a new application.

[0512] The terminal overlays virtual display data received from the server onto the user's screen. This visually provides instructions and helps the user follow the operation flow. The terminal is also equipped with an emotion recognition device that detects the user's facial expressions and voice and sends this information to the server. The server analyzes this information and provides feedback tailored to the user's emotional state. For example, if the user has a confused expression, the feedback is adjusted to a more helpful tone.

[0513] Furthermore, the terminal transmits physiological data acquired from the wearable device to a server, which monitors for abnormal values ​​in real time. If an abnormality is detected, the system immediately alerts the user, enabling a rapid response. For example, the user acquires heart rate data via a smartwatch, and if an abnormality is detected, the system immediately notifies the user.

[0514] Example of a prompt:

[0515] "Design a system that provides a virtual installation guide for the new smartwatch and monitors sudden increases in heart rate to alert the user."

[0516] Thus, the system of the present invention enables seamless information sharing between servers and terminals, provides individualized operational support to users, and also allows for integrated health management.

[0517] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0518] Step 1:

[0519] The server uses virtual display technology to generate virtual display data for the application used by the user. It receives application specifications and user operation history as input, applies a data analysis algorithm based on this data, and generates a visual guide regarding the operation procedure. As output, it sends the generated virtual display data to the terminal.

[0520] Step 2:

[0521] The terminal uses virtual display data received from the server and overlays it onto the user's screen. Specifically, it displays arrows and highlights on the user interface to visually indicate the next step to be performed. The input is virtual display data from the server, and the output is a visual guide presented to the user.

[0522] Step 3:

[0523] The user operates the device while referring to the visual guide provided on the terminal. User input consists of specific operation steps (e.g., button clicks or menu selections), and the terminal checks the conditions for proceeding to the next step based on this input. The output is recorded as an operation log.

[0524] Step 4:

[0525] The terminal monitors user actions and sends that action data to the server. Specifically, it collects detailed operation history, such as user action steps, time, and frequency, as input data. The output is the data sent to the server.

[0526] Step 5:

[0527] The server analyzes the received operation data and uses machine learning algorithms to identify errors and unfamiliar operations. The input data is the operation history from step 4, and data analysis extracts examples of errors and generates necessary corrective feedback. The output is feedback data sent to the terminal.

[0528] Step 6:

[0529] The terminal receives feedback data from the server and provides the user with hints for improving operation. Specifically, it displays markers to draw attention to certain buttons or menus and provides instructions on how to operate them in text or voice. The input is feedback data from the server, and the output is what is presented to the user.

[0530] Step 7:

[0531] The emotion recognition device installed in the terminal acquires the user's facial expressions and voice data and sends it to the server. The input data is emotion-related information acquired from the camera and microphone, and the server adjusts the feedback tone based on this. The output is emotion information sent to the server.

[0532] Step 8:

[0533] The server analyzes the received emotional information and selects a feedback tone appropriate to the user's emotional state. The input is the emotional information from step 7, and the output determines the tone of the feedback message, which is then delivered to the terminal.

[0534] Step 9:

[0535] The terminal acquires physiological data from a wearable device and sends it to a server. The input data includes heart rate and blood pressure information, and the output is data for monitoring on the server.

[0536] Step 10:

[0537] The server monitors physiological data in real time and immediately notifies the user of any deviations from the baseline values. The input data is the physiological information from step 9, and the output is the data that displays the warning message to the user.

[0538] (Application Example 1)

[0539] 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."

[0540] In recent years, with the widespread adoption of information processing devices, the importance of support systems for users to efficiently learn how to operate digital technologies has increased. In particular, in delivery operations, there is a need to cultivate the ability of delivery personnel to deliver quickly and accurately in unfamiliar areas. However, current navigation systems lack intuitive guidance, potentially leading users to choose incorrect routes. Furthermore, there is a lack of technology to monitor the health and mental state of delivery personnel in real time and provide safe and effective support. A system is needed to address these issues and ensure that delivery personnel can reliably perform their duties.

[0541] 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.

[0542] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing operation information in real time using a machine learning algorithm and identifying errors in operation, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological information obtained from a wearable terminal using a health information processing module and notifying when abnormal values ​​are detected, and means for obtaining the delivery person's location using a location measuring device and visually providing virtual guidance. As a result, delivery people can efficiently perform their duties even in new areas through visual guidance, and the associated mental and physical stress can be reduced.

[0543] "Virtual display technology" is a technology that displays information visually overlaid on the user's field of vision, and is a means of providing intuitive operating procedures and guidance.

[0544] "Information processing equipment" is a general term for electronic devices used by users to manipulate and manage digital information.

[0545] A "machine learning algorithm" is a mathematical method used to analyze data, identify patterns, and make predictions and decisions.

[0546] "Operation information" refers to the history of inputs and instructions given by a user to an information processing device.

[0547] An "emotion recognition module" is a general term for hardware or software that detects and evaluates emotions from a user's facial expressions, voice, etc.

[0548] "User" refers to a person who uses this system.

[0549] "Feedback" refers to information and instructions provided by a system to a user, and is offered as a response to the user's actions and behavior.

[0550] A "health information processing module" is a device or program that processes physiological information acquired from wearable devices and other sources to evaluate a person's health status.

[0551] A "wearable device" is a general term for electronic devices that can be worn on the body and used to acquire health information.

[0552] "Physiological information" refers to data that indicates the state of the body, such as heart rate, blood pressure, and body temperature.

[0553] A "location measuring device" is a device used to detect and measure geographical location, and is used for route guidance and tracking.

[0554] A "delivery person" is a general term for a person whose job is to transport specified goods to a specified location.

[0555] "Virtual guidance" refers to providing instructions and directions to users visually using digital information, and is delivered in real time.

[0556] This invention provides an operation support system for an information processing device, specifically a system that combines virtual display technology and AI technology. The server first acquires the user's location information and operation information. In this process, GPS and operation data are used from smart glasses or other wearable devices. Based on this, the server identifies the user's current location and uses virtual display technology to overlay and display the delivery route and information operation steps on the field of view of the smart glasses.

[0557] On the device side, an emotion recognition module is used to acquire the user's facial expressions and voice data, and this information is sent to the server. The server uses an emotion recognition algorithm to analyze the user's emotional state and appropriately adjust the tone and content of the feedback. In addition, a health information processing module obtains physiological information from the wearable device, and if this exceeds a certain threshold, a warning is issued immediately.

[0558] Specifically, delivery drivers who receive guidance through smart glasses can efficiently navigate routes even in unfamiliar areas. This guidance is constantly updated in real time, allowing drivers to reach their destinations without getting lost.

[0559] For example, the system displays information such as, "Turn right at the next intersection and you will reach your delivery destination 200 meters ahead." This eliminates the need for delivery personnel to consult a map while on the move, allowing them to perform their duties while being visually guided.

[0560] Examples of prompt statements generated by the AI ​​model are as follows:

[0561] "Design a system that uses smart glasses to visually display navigation guides for new food delivery drivers venturing into unfamiliar areas, helping them understand the optimal route."

[0562] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0563] Step 1:

[0564] The server acquires the user's location and operation information from wearable devices and smart glasses. GPS data acquired from location measurement devices serves as input. The server analyzes this data to determine the user's current location. The output is the identified geographic location information.

[0565] Step 2:

[0566] The terminal receives delivery route data from the server, which is necessary for real-time virtual display via smart glasses. The inputs used are geographic location information and optimal delivery route information identified by the server. The terminal uses virtual display technology to overlay these routes onto the user's field of view. The output is the delivery route displayed in the user's field of view.

[0567] Step 3:

[0568] The emotion recognition module installed in the device continuously acquires the user's facial expressions and voice data and sends it to the server. Raw data acquired from the camera and microphone is used as input. The server analyzes this data using an emotion recognition algorithm to determine the user's emotional state. The output is analyzed data indicating the user's emotional state.

[0569] Step 4:

[0570] The server adjusts the tone and content of the feedback as needed, based on the analyzed emotional data. Input includes user action data and emotional state data. The server generates feedback content based on this data and sends it to the terminal. The output is the adjusted feedback message.

[0571] Step 5:

[0572] A health information processing module receives physiological data such as heart rate and blood pressure from a wearable device and sends it to a server. The input is physiological data acquired from the wearable device. The server compares the data to reference values ​​and immediately generates a warning if abnormal values ​​are found. The output is a warning message or alert.

[0573] Step 6:

[0574] The user reviews the feedback and warning messages received from the device and takes the necessary actions. The input consists of the feedback and warning messages displayed by the device. The output consists of the specific actions taken by the user.

[0575] 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.

[0576] This invention relates to a system that recognizes a user's emotions, adaptively modifies the operation guide of an information device based on those emotions, and supports health management. The emotion engine is used to understand the user's emotional state by analyzing the user's facial expressions and voice data. Based on this information, the system dynamically adjusts the feedback and guide content to provide more flexible support.

[0577] The server analyzes the emotional data provided by the emotion engine and adaptively adjusts the virtual display content and voice guidance shown on the terminal based on this data. For example, if the user is feeling anxious, the server makes the operation guide clearer and more detailed and communicates it to the terminal. Also, if the terminal determines that the user's stress level is high, it can display relaxation suggestions on the screen.

[0578] The device collects operation data when the user performs an action and sends it to the server. The server analyzes this data using machine learning algorithms and, if it detects an incorrect operation, generates feedback suggesting specific corrective steps. This feedback is displayed on the device in an appropriate manner, taking into account the user's emotional state.

[0579] Furthermore, the device receives health data from wearable devices in real time and periodically sends it to a server. The server monitors this data and immediately notifies the device if any abnormal values ​​deviating from the standard range are detected. The user receives this notification and can promptly take action if health consultation or medical treatment is necessary.

[0580] As a concrete example, consider a scenario where a user's smartwatch detects a high stress level while they are using an application on their device. In this case, the server receives the result from the emotion engine and displays an encouraging message on the device that corresponds to the user's emotional state. Furthermore, if a heart rate exceeding a healthy range is detected, it is possible to immediately suggest relaxation exercises or hydration.

[0581] In this way, the present invention provides a system that comprehensively supports both the acquisition of digital technology and health management while providing flexible feedback that responds to the user's emotions.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The user starts up the terminal and begins the operation guide using a virtual display. At this time, the terminal starts communicating with the server via the internet connection.

[0585] Step 2:

[0586] The device collects the user's facial expressions and voice through an emotion recognition module. This allows for the acquisition of user emotion data in real time.

[0587] Step 3:

[0588] The device sends collected emotional data to the server. The server uses an emotion engine to analyze the data and evaluate the user's emotional state.

[0589] Step 4:

[0590] The server generates feedback that responds to the user's emotional state. Specifically, if the user is feeling anxious, the user guide will be adjusted to be more detailed and gentler in tone.

[0591] Step 5:

[0592] The terminal updates the guidance content displayed virtually based on feedback from the server. For example, it visually highlights the operating procedures on the screen.

[0593] Step 6:

[0594] The user operates the device. The operation data is recorded in real time by the device and sent to the server.

[0595] Step 7:

[0596] The server uses machine learning algorithms to analyze user data and identify incorrect operations or areas requiring improvement. Based on these results, it generates feedback including detailed improvement steps.

[0597] Step 8:

[0598] The device displays feedback sent from the server to the user. The user uses this feedback to correct their actions and continue learning.

[0599] Step 9:

[0600] A wearable device transmits physiological data to a terminal in real time. The terminal periodically sends this data to a server.

[0601] Step 10:

[0602] The server monitors the health data it receives and immediately generates an anomaly notification if any abnormal values ​​are detected.

[0603] Step 11:

[0604] The device reports any abnormalities to the user. If necessary, the device offers the user relaxation suggestions or medical consultation options.

[0605] Step 12:

[0606] Users can check notifications from their devices and take appropriate action for maintaining their health and learning how to use their devices.

[0607] (Example 2)

[0608] 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."

[0609] In modern society, users need to become proficient in operating information devices, and in the process, they often experience errors and health problems. While conventional systems have technologies to address these issues individually, they lack mechanisms to adjust information output and provide flexible feedback by comprehensively considering the user's emotions and physiological state. Therefore, the challenge is to provide a system that adapts to the user's emotions and health condition and supports device operation more effectively and safely.

[0610] 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.

[0611] In this invention, the server includes means for using an emotion engine to analyze the user's emotional state, means for dynamically adjusting information output by utilizing the emotion analysis results based on a generative model, and means for processing operation data in real time using a machine learning algorithm to detect operation errors. This makes it possible to provide customized feedback according to the user's emotions and health condition, and to support the operation of information devices effectively and safely.

[0612] The "emotion engine" is a mechanism that analyzes the user's facial expression data and voice data to identify their emotional state.

[0613] A "generative model" refers to an algorithm or program that uses artificial intelligence technology to generate output under specific conditions.

[0614] A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and makes decisions such as predictions and classifications.

[0615] A "biometric monitoring device" is a device used to measure and record physiological indicators of the body in real time.

[0616] "Information output" refers to visual and auditory instructions and notifications presented to the user.

[0617] "User feedback" refers to the real-time responses and advice that a system provides to its users.

[0618] This invention is a system that supports the operation of information devices based on the user's emotional state and health data. This system includes a server, terminals, and wearable devices.

[0619] The server plays a central role in comprehensively processing the data collected from users. Using an emotion engine, the server analyzes user facial and voice data received from terminals and wearable devices to identify emotional states. This process incorporates a generative AI model that analyzes emotional changes in real time.

[0620] The device collects the user's facial expressions and voice through its camera and microphone. It then sends this data to a server and displays information output based on instructions from the server. Periodically, physiological data from the wearable device is also sent to the server via the device, providing the user with adaptive feedback based on their health status.

[0621] For example, if the camera detects a user's smile while they are using an application on their device, the server will interpret this as joy through its emotion engine and display a message of praise on the device. Conversely, if the heart rate is higher than normal, the system will immediately suggest taking a break to monitor the user's health.

[0622] As an example of a generative AI model, it is possible to input the instruction "Generate appropriate feedback when the user is confused about the operation and their heart rate increases" into the prompt text. Through this system, users can operate information devices in a better way while being mindful of their own emotions and health.

[0623] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0624] Step 1:

[0625] The device collects user facial expression and voice data. This data is acquired through the camera and microphone. Specifically, the device's camera continuously captures the user's facial expressions, and the microphone records their voice. This data is digitized on the spot and sent to the server as input data.

[0626] Step 2:

[0627] The server receives facial expression and audio data transmitted from the terminal and inputs them into the emotion engine. The emotion engine uses a generative AI model to analyze the input data and identify the emotional state. Specifically, a video analysis algorithm determines the movement of facial muscles, and audio analysis technology evaluates tone and speech speed. The output is the user's specific emotional state (joy, anxiety, etc.).

[0628] Step 3:

[0629] The server generates information output based on the emotion analysis results and sends it to the terminal. In this process, a generative AI model is used to select a message appropriate to the situation. For example, if the emotion is identified as "anxiety," the server generates a message such as "Please relax." Specifically, it extracts the appropriate message from the database and sends it to the terminal.

[0630] Step 4:

[0631] The terminal displays the information output received from the server. The user's screen displays text messages and graphical feedback tailored to their emotional state. Specifically, the terminal UI is updated to provide the user with visual or audio guidance. This allows the user to instantly understand the situation.

[0632] Step 5:

[0633] The terminal periodically collects physiological data (e.g., heart rate) from wearable devices and sends it to a server. This information is used to assess health status. Specifically, it uses communication technologies such as Bluetooth to acquire data from the device and transmit that data to the server.

[0634] Step 6:

[0635] The server analyzes physiological data and immediately generates a notification if an abnormal value is detected. Specifically, it performs calculations to compare the value with a baseline, and if a deviation is recognized, it creates an alert and sends it to the terminal. The user receives the alert and can quickly check health-related precautions.

[0636] Through these steps, the system comprehensively supports the user's operation of information devices in accordance with their emotions and health condition.

[0637] (Application Example 2)

[0638] 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."

[0639] In modern work environments, reducing the emotional and physical burden on workers and achieving efficient and safe work practices are crucial challenges. However, conventional machinery and systems often struggle to adjust their operation to accommodate user emotions and health conditions, potentially impacting work quality and safety. Therefore, there is a need to develop systems that can appropriately adapt their operation and guidance based on the user's emotional and health state.

[0640] 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.

[0641] In this invention, the server includes means for visually providing operating procedures for information devices using virtual display technology, means for analyzing operation data in real time using machine learning algorithms and identifying operational errors, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological data obtained from wearable devices using a health information processing module and notifying when abnormal values ​​are detected, and means for adaptively adjusting the operation and guidance of mechanical devices based on the user's emotional state and health state in the work environment. This makes it possible to perform work safely and efficiently while taking into account the user's emotional state and health state.

[0642] "Virtual display technology" is a technology that visually reproduces physical operation methods, allowing users to intuitively understand how to operate them.

[0643] A "machine learning algorithm" is a method that learns patterns based on past data and automatically makes specific predictions or judgments on new data.

[0644] An "emotion recognition module" is a device or software that analyzes data such as a user's facial expressions and voice to estimate their emotional state.

[0645] A "health information processing module" is a system component that monitors and processes physiological data collected from wearable devices to evaluate health status.

[0646] A "wearable device" is an electronic device that can be worn on the body and measures and records data such as health status and activity levels in real time.

[0647] "An adaptive adjustment mechanism" refers to a method for dynamically changing the operation of a machine or device and the content of its operating guide according to the user's situation and condition, thereby providing optimal feedback.

[0648] The term "work environment" refers to the physical or virtual space where specific tasks are performed, and is the space in which users interact with machinery, equipment, and information systems.

[0649] In the system that realizes this invention, various hardware and software components work together to optimize the work environment based on the user's emotions and health condition.

[0650] The server uses an emotion recognition module to analyze the user's emotional state from facial and voice data. For this purpose, it utilizes cloud-based emotion analysis tools such as Microsoft Azure Cognitive Services. Furthermore, for collecting physiological data from wearable devices, smartwatches and other devices are used, leveraging Apple HealthKit and Google Fit APIs.

[0651] The device performs real-time analysis using machine learning algorithms (such as TensorFlow) based on collected emotional state and physiological data. This allows it to detect operational errors and dynamically adjust feedback. Specifically, it provides intuitive operation guides using virtual display technology. In addition, an audio output module provides voice guidance to the user regarding the operation procedure.

[0652] This allows users to receive work support tailored to their emotions and health condition. For example, if they are feeling stressed, the robot's movements will slow down and it will be able to provide more detailed explanations. Furthermore, if there is an abnormality in their health condition, they will receive an instant notification, allowing them to take breaks and manage their health as needed.

[0653] For example, if a facial recognition camera detects signs of fatigue in a worker, it can display a prompt to the user asking, "Should you take a short break?" The generating AI model would utilize prompts such as: "Do you feel tired? If you are feeling tired or stressed, what are some effective ways to relax?"

[0654] These features allow the system to provide detailed feedback based on emotions and health status, regardless of whether the user is an experienced worker or a novice.

[0655] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0656] Step 1:

[0657] The server receives the user's facial and voice data as input to the emotion recognition module. The input data is analyzed as an emotional state using Microsoft Azure Cognitive Services. The output is the user's current emotional state (e.g., stress, exhilaration, relief, etc.). Specifically, the server analyzes facial features and voice patterns and assigns an emotion label.

[0658] Step 2:

[0659] The device acquires physiological data (e.g., heart rate, body temperature) from a wearable device and inputs it into a health information processing module. For data processing, the data is standardized using Apple HealthKit or Google Fit API. The output provides an assessment of the current health status (e.g., normal, abnormal). Specifically, the device continuously collects data and prepares real-time notifications when abnormal values ​​are detected.

[0660] Step 3:

[0661] The device receives acquired emotional and health status data as input to a machine learning algorithm and analyzes the operation data in real time. TensorFlow is used for data processing to detect errors in the current operation based on past training data. The output includes operation steps that need correction and improvement suggestions. Specifically, the device analyzes the operation history and generates optimal feedback.

[0662] Step 4:

[0663] The device provides operation guidance using virtual display technology based on the user's emotions and health status. Feedback data from the previous step is used as input. The output is a visual guide optimized for the user displayed on the screen. Specifically, the guide content is dynamically adjusted to be easily understood by the user.

[0664] Step 5:

[0665] The user receives feedback from the device, performs the necessary actions, and continues working based on the provided guide. As an output, an efficient and safe work environment is maintained. Specifically, the user improves productivity by following the presented instructions and implementing improvement suggestions.

[0666] Step 6:

[0667] The terminal uses a voice output module to provide voice guidance to the user. Feedback content is used as input. As output, an easy-to-understand voice guide is provided. Specifically, the voice tone and pace are adjusted to convey instructions that are appropriate to the user's situation.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] [Fourth Embodiment]

[0672] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0673] 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.

[0674] 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).

[0675] 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.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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".

[0685] This invention is an information device operation support system that combines virtual display technology and AI technology, with the aim of enabling users to intuitively and confidently acquire digital technology and manage their health.

[0686] In this system, the server first generates data for virtual display and distributes it to the terminal. The terminal then uses this data to display a virtual version overlaid on the user's screen. For example, it can visually show the installation procedure for a new application. This allows users to learn how to operate the application while viewing a visual guide.

[0687] The terminal also monitors each step of the user's operation and sends it to the server. The server analyzes this operation data and uses machine learning algorithms to identify incorrect or unfamiliar operations. For identified errors, it generates feedback including specific corrective methods and sends it back to the terminal. The terminal supports the user by displaying hints for improving their operation, thereby boosting their confidence.

[0688] In addition, an emotion recognition module installed in the device acquires the user's facial expressions and voice data, and the server analyzes this information. The server uses emotion recognition algorithms to determine the user's emotional state and adjusts the tone and content of the feedback as needed. This makes it possible to provide support that is always considerate of the user's mental state.

[0689] Furthermore, the health information processing module acquires physiological data from the wearable device and periodically sends it to the server. The server monitors this data in real time, and if it deviates from the standard values ​​(e.g., a sudden increase in blood pressure), it immediately issues a warning to the user through the terminal. The user can then receive this warning and take appropriate action quickly.

[0690] For example, if a user is wearing a smartwatch, the terminal acquires heart rate data from this device. The server monitors the heart rate data in real time, and if an abnormality is detected, it immediately sends a notification to the terminal. The terminal can then display a warning message to the user on its screen and suggest necessary countermeasures.

[0691] In this way, this system, which skillfully integrates virtual display technology and AI technology, makes it easier for users to access digital technology and allows them to manage their health with greater peace of mind.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The user starts up the device and accesses the initial setup screen. At this time, the device establishes an internet connection and begins communicating with the server.

[0695] Step 2:

[0696] The device displays a screen where the user can input their skill level and desired learning content. The user enters their technical level and learning objectives, and this information is sent to the server via the device.

[0697] Step 3:

[0698] Based on the data received by the server, it generates an operation lesson plan tailored to the user. The generated plan is sent to the terminal as virtual display data.

[0699] Step 4:

[0700] The device uses virtual display technology to overlay operating procedures and guides onto the user's screen. For example, the icon of a specific application might be highlighted.

[0701] Step 5:

[0702] The user begins operating the device. The device records the user's operation data in real time and sends it to the server.

[0703] Step 6:

[0704] The server analyzes the received operation data to identify errors and operations that require improvement. Based on the results, specific feedback is generated and sent to the terminal.

[0705] Step 7:

[0706] The device notifies the user of feedback from the server. Based on the notification, the user corrects errors or learns new ways of operating.

[0707] Step 8:

[0708] The device monitors the user's emotional state using an emotion recognition module. The device then sends the acquired data to the server.

[0709] Step 9:

[0710] The server analyzes the emotional data it receives and, if it determines that the user is feeling anxious or stressed, it flexibly adjusts the tone and content of the feedback.

[0711] Step 10:

[0712] The device connects with a wearable device and periodically sends physiological data to a server.

[0713] Step 11:

[0714] The server monitors physiological data in real time, and if an abnormal value is detected, it generates an abnormality notification and sends it to the terminal.

[0715] Step 12:

[0716] The device immediately reports any abnormalities to the user, who can then take appropriate action as needed.

[0717] (Example 1)

[0718] 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".

[0719] Modern information processing devices have become more complex to operate due to their increased functionality, often making it difficult for new users and the elderly to learn how to use them. Furthermore, the increasing number of problems and stresses caused by user errors, coupled with the growing need for immediate responses in health management, presents challenges that conventional technologies struggle to adequately address.

[0720] 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.

[0721] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing user operation information in real time using a machine learning method and identifying operational errors, and means for detecting the user's emotional state using an emotion recognition device and adjusting the feedback provision method. As a result, users can more easily learn functions intuitively, reduce operational errors, and receive support tailored to their emotional state.

[0722] "Virtual display technology" is a technology that overlays visual guides and information onto the actual display on an information processing device, making it easier for users to visually understand the operating procedures.

[0723] A "machine learning method" is a method that uses operational information accumulated in a device to perform pattern recognition and data analysis, automatically identifying user errors and trends.

[0724] An "emotion recognition device" is a device that detects the user's emotional state through facial expressions, voice data, etc., and adjusts the feedback based on that.

[0725] The "biometric information processing function" is a function that monitors physiological data acquired from portable devices and other sources, and responds appropriately when abnormal values ​​are detected, thereby managing the user's health status.

[0726] "User skill level" is a concept that indicates the user's proficiency and understanding when operating information processing equipment, and appropriate guidance and support should be adjusted accordingly.

[0727] A "voice output device" is a device that transmits instructions and guidance from an information processing device to the user via voice, providing operational support not only through visual information but also through auditory means.

[0728] This invention is an operation support system for an information processing device that integrates virtual display technology and AI technology. Using this system, users can intuitively learn digital technologies while managing their health status.

[0729] The server first utilizes virtual display technology to generate virtual display data corresponding to the application the user is using. This data includes operating procedures and visual guides, allowing users to easily learn new operations. Specifically, it can visually guide users through the installation procedure of a new application.

[0730] The terminal overlays virtual display data received from the server onto the user's screen. This visually provides instructions and helps the user follow the operation flow. The terminal is also equipped with an emotion recognition device that detects the user's facial expressions and voice and sends this information to the server. The server analyzes this information and provides feedback tailored to the user's emotional state. For example, if the user has a confused expression, the feedback is adjusted to a more helpful tone.

[0731] Furthermore, the terminal transmits physiological data acquired from the wearable device to a server, which monitors for abnormal values ​​in real time. If an abnormality is detected, the system immediately alerts the user, enabling a rapid response. For example, the user acquires heart rate data via a smartwatch, and if an abnormality is detected, the system immediately notifies the user.

[0732] Example of a prompt:

[0733] "Design a system that provides a virtual installation guide for the new smartwatch and monitors sudden increases in heart rate to alert the user."

[0734] Thus, the system of the present invention enables seamless information sharing between servers and terminals, provides individualized operational support to users, and also allows for integrated health management.

[0735] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0736] Step 1:

[0737] The server uses virtual display technology to generate virtual display data for the application being used by the user. It receives application specifications and user operation history as input, applies a data analysis algorithm based on this data, and generates a visual guide regarding the operation procedure. As output, it sends the generated virtual display data to the terminal.

[0738] Step 2:

[0739] The terminal uses virtual display data received from the server and overlays it onto the user's screen. Specifically, it displays arrows and highlights on the user interface to visually indicate the next step to be performed. The input is virtual display data from the server, and the output is a visual guide presented to the user.

[0740] Step 3:

[0741] The user operates the device while referring to the visual guide provided on the terminal. User input consists of specific operation steps (e.g., button clicks or menu selections), and the terminal checks the conditions for proceeding to the next step based on this input. The output is recorded as an operation log.

[0742] Step 4:

[0743] The terminal monitors user actions and sends that action data to the server. Specifically, it collects detailed operation history, such as user action steps, time, and frequency, as input data. The output is the data sent to the server.

[0744] Step 5:

[0745] The server analyzes the received operation data and uses machine learning algorithms to identify errors and unfamiliar operations. The input data is the operation history from step 4, and data analysis extracts examples of errors and generates necessary corrective feedback. The output is feedback data sent to the terminal.

[0746] Step 6:

[0747] The terminal receives feedback data from the server and provides the user with hints for improving operation. Specifically, it displays markers to draw attention to certain buttons or menus and provides instructions on how to operate them in text or voice. The input is feedback data from the server, and the output is what is presented to the user.

[0748] Step 7:

[0749] The emotion recognition device installed in the terminal acquires the user's facial expressions and voice data and sends it to the server. The input data is emotion-related information acquired from the camera and microphone, and the server adjusts the feedback tone based on this. The output is emotion information sent to the server.

[0750] Step 8:

[0751] The server analyzes the received emotional information and selects a feedback tone appropriate to the user's emotional state. The input is the emotional information from step 7, and the output determines the tone of the feedback message, which is then delivered to the terminal.

[0752] Step 9:

[0753] The terminal acquires physiological data from a wearable device and sends it to a server. The input data includes heart rate and blood pressure information, and the output is data for monitoring on the server.

[0754] Step 10:

[0755] The server monitors physiological data in real time and immediately notifies the user of any deviations from the baseline values. The input data is the physiological information from step 9, and the output is the data that displays the warning message to the user.

[0756] (Application Example 1)

[0757] 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".

[0758] In recent years, with the widespread adoption of information processing devices, the importance of support systems for users to efficiently learn how to operate digital technologies has increased. In particular, in delivery operations, there is a need to cultivate the ability of delivery personnel to deliver quickly and accurately in unfamiliar areas. However, current navigation systems lack intuitive guidance, potentially leading users to choose incorrect routes. Furthermore, there is a lack of technology to monitor the health and mental state of delivery personnel in real time and provide safe and effective support. A system is needed to address these issues and ensure that delivery personnel can reliably perform their duties.

[0759] 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.

[0760] In this invention, the server includes means for visually providing operating procedures for an information processing device using virtual display technology, means for analyzing operation information in real time using a machine learning algorithm and identifying errors in operation, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological information obtained from a wearable terminal using a health information processing module and notifying when abnormal values ​​are detected, and means for obtaining the delivery person's location using a location measuring device and visually providing virtual guidance. As a result, delivery people can efficiently perform their duties even in new areas through visual guidance, and the associated mental and physical stress can be reduced.

[0761] "Virtual display technology" is a technology that displays information visually overlaid on the user's field of vision, and is a means of providing intuitive operating procedures and guidance.

[0762] "Information processing equipment" is a general term for electronic devices used by users to manipulate and manage digital information.

[0763] A "machine learning algorithm" is a mathematical method used to analyze data, identify patterns, and make predictions and decisions.

[0764] "Operation information" refers to the history of inputs and instructions given by a user to an information processing device.

[0765] An "emotion recognition module" is a general term for hardware or software that detects and evaluates emotions from a user's facial expressions, voice, etc.

[0766] "User" refers to a person who uses this system.

[0767] "Feedback" refers to information and instructions provided by a system to a user, and is offered as a response to the user's actions and behavior.

[0768] A "health information processing module" is a device or program that processes physiological information acquired from wearable devices and other sources to evaluate a person's health status.

[0769] A "wearable device" is a general term for electronic devices that can be worn on the body and used to acquire health information.

[0770] "Physiological information" refers to data that indicates the state of the body, such as heart rate, blood pressure, and body temperature.

[0771] A "location measuring device" is a device used to detect and measure geographical location, and is used for route guidance and tracking.

[0772] A "delivery person" is a general term for a person whose job is to transport specified goods to a specified location.

[0773] "Virtual guidance" refers to providing instructions and directions to users visually using digital information, and is delivered in real time.

[0774] This invention provides an operation support system for an information processing device, specifically a system that combines virtual display technology and AI technology. The server first acquires the user's location information and operation information. In this process, GPS and operation data are used from smart glasses or other wearable devices. Based on this, the server identifies the user's current location and uses virtual display technology to overlay and display the delivery route and information operation steps on the field of view of the smart glasses.

[0775] On the device side, an emotion recognition module is used to acquire the user's facial expressions and voice data, and this information is sent to the server. The server uses an emotion recognition algorithm to analyze the user's emotional state and appropriately adjust the tone and content of the feedback. In addition, a health information processing module obtains physiological information from the wearable device, and if this exceeds a certain threshold, a warning is issued immediately.

[0776] Specifically, delivery drivers who receive guidance through smart glasses can efficiently navigate routes even in unfamiliar areas. This guidance is constantly updated in real time, allowing drivers to reach their destinations without getting lost.

[0777] For example, the system displays information such as, "Turn right at the next intersection and you will reach your delivery destination 200 meters ahead." This eliminates the need for delivery personnel to consult a map while on the move, allowing them to perform their duties while being visually guided.

[0778] Examples of prompt statements generated by the AI ​​model are as follows:

[0779] "Design a system that uses smart glasses to visually display navigation guides for new food delivery drivers venturing into unfamiliar areas, helping them understand the optimal route."

[0780] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0781] Step 1:

[0782] The server acquires the user's location and operation information from wearable devices and smart glasses. GPS data acquired from location measurement devices serves as input. The server analyzes this data to determine the user's current location. The output is the identified geographic location information.

[0783] Step 2:

[0784] The terminal receives delivery route data from the server, which is necessary for real-time virtual display via smart glasses. The inputs used are geographic location information and optimal delivery route information identified by the server. The terminal uses virtual display technology to overlay these routes onto the user's field of view. The output is the delivery route displayed in the user's field of view.

[0785] Step 3:

[0786] The emotion recognition module installed in the device continuously acquires the user's facial expressions and voice data and sends it to the server. Raw data acquired from the camera and microphone is used as input. The server analyzes this data using an emotion recognition algorithm to determine the user's emotional state. The output is analyzed data indicating the user's emotional state.

[0787] Step 4:

[0788] The server adjusts the tone and content of the feedback as needed, based on the analyzed emotional data. Input includes user action data and emotional state data. The server generates feedback content based on this data and sends it to the terminal. The output is the adjusted feedback message.

[0789] Step 5:

[0790] A health information processing module receives physiological data such as heart rate and blood pressure from a wearable device and sends it to a server. The input is physiological data acquired from the wearable device. The server compares the data to reference values ​​and immediately generates a warning if abnormal values ​​are found. The output is a warning message or alert.

[0791] Step 6:

[0792] The user reviews the feedback and warning messages received from the device and takes the necessary actions. The input consists of the feedback and warning messages displayed by the device. The output consists of the specific actions taken by the user.

[0793] 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.

[0794] This invention relates to a system that recognizes a user's emotions, adaptively modifies the operation guide of an information device based on those emotions, and supports health management. The emotion engine is used to understand the user's emotional state by analyzing the user's facial expressions and voice data. Based on this information, the system dynamically adjusts the feedback and guide content to provide more flexible support.

[0795] The server analyzes the emotional data provided by the emotion engine and adaptively adjusts the virtual display content and voice guidance shown on the terminal based on this data. For example, if the user is feeling anxious, the server makes the operation guide clearer and more detailed and communicates it to the terminal. Also, if the terminal determines that the user's stress level is high, it can display relaxation suggestions on the screen.

[0796] The device collects operation data when the user performs an action and sends it to the server. The server analyzes this data using machine learning algorithms and, if it detects an incorrect operation, generates feedback suggesting specific corrective steps. This feedback is displayed on the device in an appropriate manner, taking into account the user's emotional state.

[0797] Furthermore, the device receives health data from wearable devices in real time and periodically sends it to a server. The server monitors this data and immediately notifies the device if any abnormal values ​​deviating from the standard range are detected. The user receives this notification and can promptly take action if health consultation or medical treatment is necessary.

[0798] As a concrete example, consider a scenario where a user's smartwatch detects a high stress level while they are using an application on their device. In this case, the server receives the result from the emotion engine and displays an encouraging message on the device that corresponds to the user's emotional state. Furthermore, if a heart rate exceeding a healthy range is detected, it is possible to immediately suggest relaxation exercises or hydration.

[0799] In this way, the present invention provides a system that comprehensively supports both the acquisition of digital technology and health management while providing flexible feedback that responds to the user's emotions.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] The user starts up the terminal and begins the operation guide using a virtual display. At this time, the terminal starts communicating with the server via the internet connection.

[0803] Step 2:

[0804] The device collects the user's facial expressions and voice through an emotion recognition module. This allows for the acquisition of user emotion data in real time.

[0805] Step 3:

[0806] The device sends collected emotional data to the server. The server uses an emotion engine to analyze the data and evaluate the user's emotional state.

[0807] Step 4:

[0808] The server generates feedback that responds to the user's emotional state. Specifically, if the user is feeling anxious, the user guide will be adjusted to be more detailed and gentler in tone.

[0809] Step 5:

[0810] The terminal updates the guidance content displayed virtually based on feedback from the server. For example, it visually highlights the operating procedures on the screen.

[0811] Step 6:

[0812] The user operates the device. The operation data is recorded in real time by the device and sent to the server.

[0813] Step 7:

[0814] The server uses machine learning algorithms to analyze user data and identify incorrect operations or areas requiring improvement. Based on these results, it generates feedback including detailed improvement steps.

[0815] Step 8:

[0816] The device displays feedback sent from the server to the user. The user uses this feedback to correct their actions and continue learning.

[0817] Step 9:

[0818] A wearable device transmits physiological data to a terminal in real time. The terminal periodically sends this data to a server.

[0819] Step 10:

[0820] The server monitors the health data it receives and immediately generates an anomaly notification if any abnormal values ​​are detected.

[0821] Step 11:

[0822] The device reports any abnormalities to the user. If necessary, the device offers the user relaxation suggestions or medical consultation options.

[0823] Step 12:

[0824] Users can check notifications from their devices and take appropriate action for maintaining their health and learning how to use their devices.

[0825] (Example 2)

[0826] 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".

[0827] In modern society, users need to become proficient in operating information devices, and in the process, they often experience errors and health problems. While conventional systems have technologies to address these issues individually, they lack mechanisms to adjust information output and provide flexible feedback by comprehensively considering the user's emotions and physiological state. Therefore, the challenge is to provide a system that adapts to the user's emotions and health condition and supports device operation more effectively and safely.

[0828] 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.

[0829] In this invention, the server includes means for using an emotion engine to analyze the user's emotional state, means for dynamically adjusting information output by utilizing the emotion analysis results based on a generative model, and means for processing operation data in real time using a machine learning algorithm to detect operation errors. This makes it possible to provide customized feedback according to the user's emotions and health condition, and to support the operation of information devices effectively and safely.

[0830] The "emotion engine" is a mechanism that analyzes the user's facial expression data and voice data to identify their emotional state.

[0831] A "generative model" refers to an algorithm or program that uses artificial intelligence technology to generate output under specific conditions.

[0832] A "machine learning algorithm" is a computational method that learns patterns from large amounts of data and makes decisions such as predictions and classifications.

[0833] A "biometric monitoring device" is a device used to measure and record physiological indicators of the body in real time.

[0834] "Information output" refers to visual and auditory instructions and notifications presented to the user.

[0835] "User feedback" refers to the real-time responses and advice that a system provides to its users.

[0836] This invention is a system that supports the operation of information devices based on the user's emotional state and health data. This system includes a server, terminals, and wearable devices.

[0837] The server plays a central role in comprehensively processing the data collected from users. Using an emotion engine, the server analyzes user facial and voice data received from terminals and wearable devices to identify emotional states. This process incorporates a generative AI model that analyzes emotional changes in real time.

[0838] The device collects the user's facial expressions and voice through its camera and microphone. It then sends this data to a server and displays information output based on instructions from the server. Periodically, physiological data from the wearable device is also sent to the server via the device, providing the user with adaptive feedback based on their health status.

[0839] For example, if the camera detects a user's smile while they are using an application on their device, the server will interpret this as joy through its emotion engine and display a message of praise on the device. Conversely, if the heart rate is higher than normal, the system will immediately suggest taking a break to monitor the user's health.

[0840] As an example of a generative AI model, it is possible to input the instruction "Generate appropriate feedback when the user is confused about the operation and their heart rate increases" into the prompt text. Through this system, users can operate information devices in a better way while being mindful of their own emotions and health.

[0841] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0842] Step 1:

[0843] The device collects user facial expression and voice data. This data is acquired through the camera and microphone. Specifically, the device's camera continuously captures the user's facial expressions, and the microphone records their voice. This data is digitized on the spot and sent to the server as input data.

[0844] Step 2:

[0845] The server receives facial expression and audio data transmitted from the terminal and inputs them into the emotion engine. The emotion engine uses a generative AI model to analyze the input data and identify the emotional state. Specifically, a video analysis algorithm determines the movement of facial muscles, and audio analysis technology evaluates tone and speech speed. The output is the user's specific emotional state (joy, anxiety, etc.).

[0846] Step 3:

[0847] The server generates information output based on the emotion analysis results and sends it to the terminal. In this process, a generative AI model is used to select a message appropriate to the situation. For example, if the emotion is identified as "anxiety," the server generates a message such as "Please relax." Specifically, it extracts the appropriate message from the database and sends it to the terminal.

[0848] Step 4:

[0849] The terminal displays the information output received from the server. The user's screen displays text messages and graphical feedback tailored to their emotional state. Specifically, the terminal UI is updated to provide the user with visual or audio guidance. This allows the user to instantly understand the situation.

[0850] Step 5:

[0851] The terminal periodically collects physiological data (e.g., heart rate) from wearable devices and sends it to a server. This information is used to assess health status. Specifically, it uses communication technologies such as Bluetooth to acquire data from the device and transmit that data to the server.

[0852] Step 6:

[0853] The server analyzes physiological data and immediately generates a notification if an abnormal value is detected. Specifically, it performs calculations to compare the value with a baseline, and if a deviation is recognized, it creates an alert and sends it to the terminal. The user receives the alert and can quickly check health-related precautions.

[0854] Through these steps, the system comprehensively supports the user's operation of information devices in accordance with their emotions and health condition.

[0855] (Application Example 2)

[0856] 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".

[0857] In modern work environments, reducing the emotional and physical burden on workers and achieving efficient and safe work practices are crucial challenges. However, conventional machinery and systems often struggle to adjust their operation to accommodate user emotions and health conditions, potentially impacting work quality and safety. Therefore, there is a need to develop systems that can appropriately adapt their operation and guidance based on the user's emotional and health state.

[0858] 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.

[0859] In this invention, the server includes means for visually providing operating procedures for information devices using virtual display technology, means for analyzing operation data in real time using machine learning algorithms and identifying operational errors, means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, means for monitoring physiological data obtained from wearable devices using a health information processing module and notifying when abnormal values ​​are detected, and means for adaptively adjusting the operation and guidance of mechanical devices based on the user's emotional state and health state in the work environment. This makes it possible to perform work safely and efficiently while taking into account the user's emotional state and health state.

[0860] "Virtual display technology" is a technology that visually reproduces physical operation methods, allowing users to intuitively understand how to operate them.

[0861] A "machine learning algorithm" is a method that learns patterns based on past data and automatically makes specific predictions or judgments on new data.

[0862] An "emotion recognition module" is a device or software that analyzes data such as a user's facial expressions and voice to estimate their emotional state.

[0863] A "health information processing module" is a system component that monitors and processes physiological data collected from wearable devices to evaluate health status.

[0864] A "wearable device" is an electronic device that can be worn on the body and measures and records data such as health status and activity levels in real time.

[0865] "An adaptive adjustment mechanism" refers to a method for dynamically changing the operation of a machine or device and the content of its operating guide according to the user's situation and condition, thereby providing optimal feedback.

[0866] The term "work environment" refers to the physical or virtual space where specific tasks are performed, and is the space in which users interact with machinery, equipment, and information systems.

[0867] In the system that realizes this invention, various hardware and software components work together to optimize the work environment based on the user's emotions and health condition.

[0868] The server uses an emotion recognition module to analyze the user's emotional state from facial and voice data. For this purpose, it utilizes cloud-based emotion analysis tools such as Microsoft Azure Cognitive Services. Furthermore, for collecting physiological data from wearable devices, smartwatches and other devices are used, leveraging Apple HealthKit and Google Fit APIs.

[0869] The device performs real-time analysis using machine learning algorithms (such as TensorFlow) based on collected emotional state and physiological data. This allows it to detect operational errors and dynamically adjust feedback. Specifically, it provides intuitive operation guides using virtual display technology. In addition, an audio output module provides voice guidance to the user regarding the operation procedure.

[0870] This allows users to receive work support tailored to their emotions and health condition. For example, if they are feeling stressed, the robot's movements will slow down and it will be able to provide more detailed explanations. Furthermore, if there is an abnormality in their health condition, they will receive an instant notification, allowing them to take breaks and manage their health as needed.

[0871] For example, if a facial recognition camera detects signs of fatigue in a worker, it can display a prompt to the user asking, "Should you take a short break?" The generating AI model would utilize prompts such as: "Do you feel tired? If you are feeling tired or stressed, what are some effective ways to relax?"

[0872] These features allow the system to provide detailed feedback based on emotions and health status, regardless of whether the user is an experienced worker or a novice.

[0873] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0874] Step 1:

[0875] The server receives the user's facial and voice data as input to the emotion recognition module. The input data is analyzed as an emotional state using Microsoft Azure Cognitive Services. The output is the user's current emotional state (e.g., stress, exhilaration, relief, etc.). Specifically, the server analyzes facial features and voice patterns and assigns an emotion label.

[0876] Step 2:

[0877] The device acquires physiological data (e.g., heart rate, body temperature) from a wearable device and inputs it into a health information processing module. For data processing, the data is standardized using Apple HealthKit or Google Fit API. The output provides an assessment of the current health status (e.g., normal, abnormal). Specifically, the device continuously collects data and prepares real-time notifications when abnormal values ​​are detected.

[0878] Step 3:

[0879] The device receives acquired emotional and health status data as input to a machine learning algorithm and analyzes the operation data in real time. TensorFlow is used for data processing to detect errors in the current operation based on past training data. The output includes operation steps that need correction and improvement suggestions. Specifically, the device analyzes the operation history and generates optimal feedback.

[0880] Step 4:

[0881] The device provides operation guidance using virtual display technology based on the user's emotions and health status. Feedback data from the previous step is used as input. The output is a visual guide optimized for the user displayed on the screen. Specifically, the guide content is dynamically adjusted to be easily understood by the user.

[0882] Step 5:

[0883] The user receives feedback from the device, performs the necessary actions, and continues working based on the provided guide. As an output, an efficient and safe work environment is maintained. Specifically, the user improves productivity by following the presented instructions and implementing improvement suggestions.

[0884] Step 6:

[0885] The terminal uses a voice output module to provide voice guidance to the user. Feedback content is used as input. As output, an easy-to-understand voice guide is provided. Specifically, the voice tone and pace are adjusted to convey instructions that are appropriate to the user's situation.

[0886] 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.

[0887] 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.

[0888] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0889] 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.

[0890] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0907] The following is further disclosed regarding the embodiments described above.

[0908] (Claim 1)

[0909] A means of visually providing operating procedures for information devices using virtual display technology,

[0910] A means of analyzing operational data in real time using machine learning algorithms to identify operational errors,

[0911] A means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method,

[0912] A means of monitoring physiological data obtained from a wearable device using a health information processing module and notifying when abnormal values ​​are detected,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, which generates a customized operation guide according to the user's skill level.

[0916] (Claim 3)

[0917] The system according to claim 1, further comprising a means for providing voice guidance on operating procedures using an audio output module.

[0918] "Example 1"

[0919] (Claim 1)

[0920] A means of visually providing operating procedures for an information processing device using virtual display technology,

[0921] A means of analyzing user operation information in real time using machine learning methods to identify operational errors,

[0922] A means for detecting the user's emotional state using an emotion recognition device and adjusting the method of providing feedback,

[0923] A means of monitoring physiological information acquired from a portable device using biometric information processing functions and issuing a warning when abnormal values ​​are detected,

[0924] A means of monitoring user actions and providing specific corrective measures,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, which generates customized operation instructions according to the user's skill level.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising means for providing voice guidance on operating procedures using an audio output device.

[0930] "Application Example 1"

[0931] (Claim 1)

[0932] A means of visually providing operating procedures for an information processing device using virtual display technology,

[0933] A means of analyzing operational information in real time using machine learning algorithms to identify operational errors,

[0934] A means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method,

[0935] A means of monitoring physiological information obtained from a wearable device using a health information processing module and notifying when abnormal values ​​are detected,

[0936] A means of obtaining the delivery person's location using a location measuring device and providing virtual directions visually,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, which optimizes the delivery route based on current location information from a location measuring device and provides visual guidance.

[0940] (Claim 3)

[0941] The system according to claim 1, further comprising means for providing voice guidance on operating procedures using an audio output device.

[0942] "Example 2 of combining an emotion engine"

[0943] (Claim 1)

[0944] A means for collecting user facial expression data and voice data using an emotion engine that analyzes the user's emotional state,

[0945] A means of dynamically adjusting information output by utilizing emotion analysis results based on a generative model,

[0946] A means for processing operation data in real time using a machine learning algorithm to detect operation errors,

[0947] A means of monitoring health information received from a biometric monitoring device and notifying if abnormal values ​​increase,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, which generates customized user feedback according to emotional and physiological states.

[0951] (Claim 3)

[0952] The system according to claim 1, further comprising means for providing operation instructions by voice using voice transmission means.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] A means of visually providing operating procedures for information devices using virtual display technology,

[0956] A means of analyzing operational data in real time using machine learning algorithms to identify operational errors,

[0957] A means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method,

[0958] A means of monitoring physiological data obtained from a wearable device using a health information processing module and notifying when abnormal values ​​are detected,

[0959] A means for adaptively adjusting the operation and guidance of machinery and equipment based on the emotional state and health condition of the user in the work environment,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, which generates a customized operation guide according to the user's skill level.

[0963] (Claim 3)

[0964] The system according to claim 1, further comprising a means for providing voice guidance on operating procedures using an audio output module. [Explanation of Symbols]

[0965] 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. A means of visually providing operating procedures for information devices using virtual display technology, A means of analyzing operational data in real time using machine learning algorithms to identify operational errors, A means for detecting the user's emotional state using an emotion recognition module and adjusting the feedback method, A means of monitoring physiological data obtained from a wearable device using a health information processing module and notifying when abnormal values ​​are detected, A system that includes this.

2. The system according to claim 1, which generates a customized operation guide according to the user's skill level.

3. The system according to claim 1, further comprising means for providing voice guidance on operating procedures using an audio output module.

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