system

Abstract

We provide the system. [Solution] A means of receiving data in different formats and structuring it into a unified format, A means of managing and integrating structured device information in real time, A means of monitoring the device status and automatically generating maintenance notifications, A means of providing integrated device information via a user interface, A means to manage the lifecycle of devices, identify and delete information about unnecessary devices, 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 persona chatbot control method performed by at least one processor, the method including 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] In an office environment, information on IoT devices supplied from various vendors is provided as documents in different formats, making it difficult to manage this information centrally. As a result, there is a risk of an increase in unmanaged devices, so-called "zombie IoT". Such "zombie IoT" may lead to an increase in security risks and management costs, and thus a method for efficiently managing it is required.

Means for Solving the Problems

[0005] This invention provides a system for managing device information in real time by receiving device information in different formats from various vendors and structuring it into a unified format. This system continuously monitors the status of devices and automatically generates notifications when maintenance is required. Furthermore, by managing the device lifecycle and having means to identify and delete device information deemed unnecessary, this system can reduce management costs and mitigate security risks.

[0006] "Data in different formats" refers to information or documents provided by multiple sources and recorded in different structures and formats.

[0007] "Means of structuring data into a unified format" refers to methods and systems that convert data in different formats into a consistent standard or format, making data management and analysis easier.

[0008] "Structured device information" refers to device data that has been converted into a unified format and organized, making it easier to manage and access.

[0009] "Means of real-time management and integration" refers to methods and systems that process information obtained from devices immediately and reflect it in the overall system, thereby managing it as a consistent dataset.

[0010] "Means for automatically generating maintenance notifications" refers to systems or programs that automatically recognize the need for maintenance based on the device status and predictions, and send notifications to relevant parties.

[0011] "Means of providing information via a user interface" refers to methods and systems that visually present information through screens and operating environments that allow users to access device information.

[0012] "Means for managing, identifying, and deleting device lifecycles" refers to methods and systems for managing the period from device deployment to disposal, and for recognizing and removing unnecessary device information during that process. [Brief explanation of the drawing]

[0013] [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 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage 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.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for effectively managing a wide variety of IoT devices in offices and commercial buildings. This system can save a great deal of effort by receiving device information from different vendors, converting it into a unified format, and integrating and managing the information in real time.

[0035] Specifically, when installing devices, the server receives delivery documents in various formats provided by vendors. These documents contain diverse designs and formats, which can make them difficult to analyze. However, the server uses a generative AI model to analyze these different formats of data, extracting device IDs, functions, installation locations, maintenance information, etc., and structuring them into a unified format.

[0036] Structured information is aggregated in a database by the server and managed in real time. This allows users to immediately see where each device is installed and which vendor provided it. The server also constantly monitors the status of the devices and automatically generates notifications to inform users when maintenance is deemed necessary.

[0037] For example, if an IoT device in an air conditioning system is detected as not functioning correctly, the server immediately notifies the user of the situation. This allows the user to take prompt action and prevent equipment problems from occurring.

[0038] Furthermore, this system also performs device lifecycle management, identifying devices that have reached the end of their service life or are no longer needed, and deleting their information from the server. This reduces management effort and mitigates security risks.

[0039] In this way, the present invention provides an effective solution for preventing the proliferation of "zombie IoT" devices and streamlining device management operations.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal scans the delivery documents provided by the vendor after the IoT device has been installed and uploads them to the system.

[0043] Step 2:

[0044] The server receives the uploaded documents and verifies the integrity and completeness of the data.

[0045] Step 3:

[0046] The server uses a generation AI model to analyze incoming documents and extract important information such as device ID, function, installation location, and maintenance information.

[0047] Step 4:

[0048] The server converts the extracted information into a unified format and registers it in its internal database.

[0049] Step 5:

[0050] The server updates the management ledger in real time and integrates information from all IoT devices.

[0051] Step 6:

[0052] Users access device information and obtain necessary information through a dedicated user interface.

[0053] Step 7:

[0054] The server continuously monitors the status of IoT devices and detects malfunctions or situations requiring maintenance.

[0055] Step 8:

[0056] If the server determines that maintenance is required, it will automatically generate a notification and send it to the designated user.

[0057] Step 9:

[0058] Users will take prompt action based on the maintenance notice they receive.

[0059] Step 10:

[0060] The server periodically evaluates device lifecycle data, identifies unnecessary or inactive devices, and deletes or archives that information.

[0061] (Example 1)

[0062] 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."

[0063] Efficiently managing electronic data in various formats from diverse suppliers presents a problem: significant effort is required for data analysis and integration. Furthermore, traditional methods for monitoring equipment status and managing maintenance are time-consuming and cumbersome, making rapid response difficult in critical situations. Additionally, identifying and deleting unnecessary equipment information is a complex process that consumes considerable management resources.

[0064] 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.

[0065] In this invention, the server includes means for receiving electronic data in different formats and converting it into a unified format using a generation AI model; means for aggregating the converted information into a unified format in a data storage device and managing it in real time; and means for monitoring the status of the device and automatically generating maintenance notifications based on prompt messages. This makes it possible to efficiently standardize and manage diverse electronic data, automate device status monitoring and maintenance management, and enable rapid response and proper organization of unnecessary information.

[0066] "Electronic data" refers to data in digital format recorded using information technology, which is typically transmitted and received through computer systems and networks.

[0067] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to learn patterns from large amounts of data and is used to automate and optimize various tasks.

[0068] A "unified format" refers to a common data format used to standardize and unify data of different formats, with the aim of maintaining data consistency and compatibility.

[0069] "Data storage devices" refer to hardware and software solutions for storing and retrieving digital data, and typically include databases and storage solutions.

[0070] A "prompt statement" is a text input used to give instructions to a generative AI model, and is a set of instructions used to control the model's behavior and output.

[0071] "Device status" refers to information indicating how a device is currently operating, whether it is functioning normally or abnormally, and is typically a factor sensed by sensors or monitoring tools.

[0072] A "maintenance notification" refers to a warning or notice that is automatically issued when a particular device or system is determined to require maintenance.

[0073] A "user interface" refers to the screen or device through which a user interacts with a system or application, and is typically a means for users to input or receive information.

[0074] This invention is a system for standardizing and centrally managing a wide variety of electronic data. This system operates through the collaboration of a server, terminals, and users. First, the server receives diverse forms of electronic data from different suppliers. This electronic data includes various data formats such as XML and JSON.

[0075] The server uses a generative AI model to analyze the received data. The generative AI model uses prompts to identify the data and convert the necessary information into a unified format. This format conversion ensures the consistency and compatibility of the electronic data.

[0076] Next, the server aggregates the information, which has been converted to a unified format, into a data storage device. This data storage device is a common database solution capable of managing large amounts of data in real time. The data accessed via terminals is always kept up-to-date and can be efficiently retrieved when needed.

[0077] Furthermore, the server continuously monitors the device's status and automatically generates maintenance notifications based on prompt messages. For example, it analyzes data from the device's sensors and, if it detects an anomaly, issues a warning to enable the user to take prompt action. This helps to proactively mitigate the risk of maintenance and failures.

[0078] Furthermore, this system also manages the lifecycle of devices. It identifies information about devices that have reached the end of their service life and deletes unnecessary data via the server. This function ensures that the information in the database is always organized, preventing the consumption of unnecessary resources.

[0079] As a concrete example, suppose that in the monitoring of an air conditioning system, a prompt message is generated stating, "The temperature sensor on the second floor of Building B has detected an abnormal value." Based on this, the AI ​​model generates a notification, and the necessary maintenance notification is sent to the user. Upon receiving this notification, the user can quickly take on-site action and resolve the problem, thereby maintaining the stability of the system.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server receives electronic data in different formats from each vendor. The input includes data in XML and JSON formats, containing information such as device ID, function, and installation location. This data is treated as initial input and prepared for subsequent processing.

[0083] Step 2:

[0084] The server passes the received data to the generating AI model. The input electronic data is analyzed by the generating AI model using prompt messages. Specifically, the AI ​​model identifies the data format and extracts necessary information such as device ID, installation location, and function. This analysis converts the information into a unified format, which is then output.

[0085] Step 3:

[0086] The server stores the parsed and transformed information in a data storage device. At this stage, the input is data in a unified format. The server aggregates this information into a database in real time, making it easy to view and use. The output is an up-to-date and manageable dataset.

[0087] Step 4:

[0088] The server continuously monitors the information in the data storage device and checks the status of the equipment. This process also includes real-time data from sensors as input. Using prompt statements, it automatically generates and outputs notifications when maintenance is deemed necessary. Specifically, if an anomaly is detected, the server immediately issues a maintenance alert.

[0089] Step 5:

[0090] The user receives a maintenance notification from the server. Specifically, the user goes to the site and inspects or repairs the equipment according to the instructions. The input here is the maintenance notification, and the output is the actions taken to resolve the problem and the records of those actions.

[0091] Step 6:

[0092] The server manages the lifecycle of devices, identifying and deleting information about devices that are no longer needed. Inputs include device usage and end-of-life reports. Specifically, the server detects and removes unnecessary device information from the database, maintaining a clean and efficient data storage system.

[0093] (Application Example 1)

[0094] 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."

[0095] Managing data in different formats from multiple suppliers of automated equipment is extremely difficult, and rapid and accurate responses are required, especially in real-time monitoring of operating status and maintenance management. However, currently, the integration of this data and the rapid detection and notification of anomalies are not adequately performed, leading to decreased operational efficiency, maintenance delays, and ultimately, loss of productivity. The objective of this invention is to solve this problem.

[0096] 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.

[0097] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for processing and integrating the structured automated device information in real time, and means for detecting anomalies related to automated devices in real time and providing notifications to administrators. This makes it possible to efficiently integrate information from different automated devices and to immediately detect and respond to anomalies.

[0098] "Data in different formats" refers to data consisting of documents and information provided by multiple sources, which are composed of various designs and formats.

[0099] A "unified format" is a data format that standardizes data from different formats, converts it into a common format, and structures it in that way.

[0100] "Structured automation equipment information" refers to information about equipment that has been converted into a unified format and is organized in a way that allows for real-time processing and integration.

[0101] "Real-time processing" refers to the immediate calculation and analysis of information and data without delay.

[0102] "Monitoring operational status" refers to a series of processes that continuously check whether automated equipment is functioning correctly.

[0103] A "maintenance notification" is a message or alert sent to the administrator to contact them or warn them when an abnormality is detected in an automated device.

[0104] "User interface" refers to all interfaces that allow users to access a system and manipulate or view data and information.

[0105] The "operational cycle of automated equipment" refers to the entire lifecycle of automated equipment, from its installation to its removal.

[0106] "Real-time anomaly detection" refers to a function that immediately identifies a problem when it occurs in the device and notifies the user that action is required.

[0107] The system that implements this application consists of a program operated by a server. The server receives data in different formats from various sources and converts it into a unified format using a generative AI model. This makes it possible to manage information from different sources in a unified format.

[0108] Furthermore, the server processes the integrated data in real time and constantly monitors the operational status of the automated equipment. If an anomaly is detected, it quickly generates a notification and sends it to the administrator's terminal. This allows administrators to take immediate action, thereby improving operational efficiency and productivity.

[0109] The system's user interface is accessible via user devices such as smartphones and head-mounted displays. These devices allow administrators to view real-time information on automated equipment and perform operations and maintenance as needed.

[0110] As a concrete example, consider a scenario where numerous automated devices are operating within a factory. The server periodically checks the status of each device, and if any abnormality is detected, it sends a notification to the administrator stating, "A device in Zone 5 has started malfunctioning." At this time, the generated AI model is input with a prompt such as, "Generate the latest device status report and identify the zone with the abnormality."

[0111] In this way, this system is an effective means of improving the operational efficiency of automated equipment in factories and enabling faster response to anomalies.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives data in different formats from multiple sources. This data includes information about the ID, status, and location of automated equipment, and is treated as input.

[0115] Step 2:

[0116] The server inputs the received data in different formats into a generating AI model. The AI ​​model analyzes each piece of data based on prompts and converts it into a unified format. This conversion standardizes data from different sources, resulting in an integrated output.

[0117] Step 3:

[0118] The server processes automated equipment information converted into a unified format in real time. As part of the processing, it monitors the operating status of each device and stores its status (normal, abnormal, etc.) in a database. This allows the current operating status to be provided as output.

[0119] Step 4:

[0120] The server monitors device operating status data in the database and, if an anomaly is detected, generates a notification message using a generation AI model. This process uses the nature of the anomaly and the location information of the affected device as input and produces a detailed notification message for administrators as output.

[0121] Step 5:

[0122] A device (for example, the administrator's smartphone or head-mounted display) receives notification messages sent from the server. The administrator can view these in real time and take specific actions to address anomalies. By using the receipt of notifications as input and the administrator's action plan as output, the overall operational efficiency of the system is optimized.

[0123] 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.

[0124] This invention enhances the user experience by incorporating an emotion engine that recognizes user emotions, in addition to a system that comprehensively manages various IoT devices. The introduction of the emotion engine allows the system to dynamically adjust the way information is presented and the content of maintenance notifications according to the user's emotional state.

[0125] Specifically, the server integrates and manages data from IoT devices in real time, and analyzes user emotion data received from the devices through an API provided by the emotion engine. This emotion data is typically extracted from the user's voice, facial expressions, and behavioral patterns.

[0126] The server analyzes the user's emotional state and dynamically changes the interface design and information prioritization based on the results. For example, if the user is stressed, the server simplifies the information displayed and highlights only the most important information. If emotional data indicates that the user is positive towards the information, it can present more detailed data or additional options.

[0127] Furthermore, the emotion engine continuously learns from user feedback, improving its emotion recognition accuracy to suit individual users. This allows the system to adapt to users over time and provide a more personalized experience.

[0128] For example, if the emotion engine detects stress while a user is reviewing energy consumption data within a building, the server displays a simplified, visually easy-to-understand graph and prompts the user to confirm whether further information is needed. This approach allows users to utilize the system without stress.

[0129] As described above, by incorporating an emotion engine, the present invention provides a user-centered, interactive, and user-friendly IoT device management system.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] After the IoT device is installed, the terminal uploads data received from the device, along with user voice and image data, to the system.

[0133] Step 2:

[0134] The server receives the uploaded user audio and image data and prepares it to analyze the user's emotional state using the emotion engine API.

[0135] Step 3:

[0136] The server activates the emotion engine and identifies the user's emotional state, such as positive, negative, or neutral, based on their voice tone and facial expression analysis.

[0137] Step 4:

[0138] The server adjusts the interface layout and displayed information appropriately based on the user's emotional state, providing information in a way that is sensitive to the user's feelings.

[0139] Step 5:

[0140] Users access the status and related information of IoT devices and perform necessary actions through a customized interface. The displayed content changes according to the user's emotions, allowing them to receive information without stress.

[0141] Step 6:

[0142] The server continuously collects user feedback and provides it to the emotion engine as training data, thereby improving the accuracy of emotion recognition for each user.

[0143] Step 7:

[0144] If a user's emotional state is negative, the server will resend important notifications and alerts in a concise format to reduce stress while ensuring necessary action is taken.

[0145] Step 8:

[0146] Based on the data obtained from the emotion engine, the server performs analysis for future system improvements and develops strategies to provide even more effective user interactions.

[0147] (Example 2)

[0148] 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".

[0149] In real-time device management systems that handle diverse data formats, a challenge lies in the difficulty of configuring interfaces that reflect the user's emotional state. While conventional methods can achieve integrated management of device information and generation of maintenance notifications, they are insufficient for interactive interface adjustments that improve the user experience.

[0150] 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.

[0151] In this invention, the server includes means for receiving sensor data in different formats and structuring it into a unified format, means for managing and integrating the structured device information in real time, and means for analyzing the user's emotional state using an emotion analysis engine and adjusting the interface display. This enables the presentation of dynamic and personalized information based on the user's emotional state.

[0152] "Sensor data in different formats" refers to data in multiple formats acquired from various sensors.

[0153] "Structuring data into a unified format" refers to the process of converting data in different formats into a standardized, common format.

[0154] "Structured device information" refers to data that is organized according to a unified format and hierarchically or categorized.

[0155] "Managing and integrating in real time" refers to a process where data is managed and integrated as soon as it is generated, with the goal of making the data immediately available.

[0156] An "emotion analysis engine" refers to algorithms and software used to analyze a user's emotional state from their voice, facial expressions, and behavior.

[0157] "Adjusting the interface display" refers to the process of changing the screen design and displayed content based on the user's emotional state.

[0158] "Dynamic and personalized information presentation based on the user's emotional state" refers to a function that provides information optimized according to the user's current emotions.

[0159] The embodiments for carrying out the present invention will be described in detail below.

[0160] This system manages data acquired from various sensor devices in real time through server, terminal, and user interaction, and provides dynamic information tailored to the user's emotional state.

[0161] Hardware and software usage:

[0162] The server, utilizing either a cloud-based or on-premises network system, is the core component responsible for data collection, integration, and analysis. The server implements a generative AI model for sentiment analysis. This AI model includes algorithms for speech recognition, facial recognition, and behavioral pattern analysis, and acts as the sentiment analysis engine.

[0163] The terminal is a device that acts as an interface with the user, collecting data and performing user interaction. This includes a microphone for voice input, a camera for facial recognition, and a touchscreen for acquiring data on operation patterns.

[0164] Data processing and calculations:

[0165] The server receives sensor data in various formats transmitted from the terminal and structures the data into a unified format. Noise filtering and data cleaning are performed during this process to generate a highly reliable dataset. Next, using the data converted to the unified format, a generative AI model performs sentiment analysis and quantifies the user's emotional state.

[0166] Based on these analysis results, the server adjusts the user interface display to present information tailored to the user's emotions. For example, if the user is stressed, the information is presented concisely and clearly; if they are relaxed, more detailed information and additional options are provided.

[0167] Examples of specific cases and prompt statements:

[0168] As a concrete example, suppose a user is checking their home energy usage and the system detects stress from the user's tone of voice and facial expression. In this case, the server provides a simple graphical interface and highlights only the important data points.

[0169] An example of a prompt message could be, "Analyze the user's emotional response to the displayed information." This prompt helps the generative AI model analyze the user's emotional state and adjust the interface accordingly.

[0170] This system aims to significantly improve the user experience and reduce stress.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device collects the user's voice using a microphone, captures their facial expressions with a camera, and records their operation patterns with sensors. This data is temporarily stored within the device as initial input data. Next, the device compresses this data and sends it to the server. The input consists of voice, image, and operation signal data, and the compressed data is sent to the server as output.

[0174] Step 2:

[0175] The server decompresses the compressed data received from the terminal and performs data cleaning to remove noise. The decompressed data is structured into a unified format and processed to enhance reliability. The input is compressed user data, and the output is formatted data in a unified format. Specific operations include timestamp matching and filtering of invalid data.

[0176] Step 3:

[0177] The server sends the obtained data in a unified format to the emotion analysis engine. Using a generative AI model, the analysis is performed to identify the user's emotional state. The input is formatted user data, and the output is data converted into emotion indicators. This analysis process includes voice tone analysis, facial expression analysis, and evaluation of behavioral patterns.

[0178] Step 4:

[0179] The server adjusts the information presentation interface based on the user's emotional state, using the results of the emotion analysis. This includes dynamically changing the screen design and prioritizing the displayed information. The input is emotion index data, and the output is the adjusted interface design. Specifically, if stress is detected, a simple infographic is displayed to reduce the complexity of the information.

[0180] Step 5:

[0181] The user reviews the information presented by the server and performs actions or provides feedback. This feedback is then sent back to the server via the terminal. The input consists of the user's interaction and feedback, while the output is used as training data for the system to improve the accuracy of subsequent sentiment analysis. A concrete example is when the user clicks a button to request more information.

[0182] (Application Example 2)

[0183] 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 device 14 will be referred to as the "terminal."

[0184] Conventional IoT device management systems have struggled to adjust information presentation methods and environments in response to the user's emotional state. This often leads to users experiencing stress while using the system, resulting in a lack of a comfortable user experience. In particular, there is a strong need for a system that appropriately reflects the emotional states of both the driver and passengers and optimizes the in-vehicle environment within autonomous vehicles.

[0185] 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.

[0186] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for analyzing the user's emotional state, dynamically adjusting the way information is presented, and manipulating the vehicle environment, and means for managing the device lifecycle and identifying and deleting information on unnecessary devices. This enables the optimization of the interface and adjustment of the in-vehicle environment in accordance with the user's emotional state.

[0187] "Data in different formats" refers to diverse data formats obtained from different sources or protocols, and is used to facilitate unification under common operational standards.

[0188] A "unified format" is a standard for converting diverse data formats into a single, standardized format, and is used to facilitate information exchange between different devices.

[0189] "Structured device information" refers to device-related information systematically organized according to a specific data format, enabling rapid access and analysis.

[0190] "User emotional state" refers to the emotional responses that a user exhibits under specific circumstances, and is detected through voice, facial expressions, and behavioral patterns.

[0191] "Dynamically adjusting how information is presented" refers to the process of changing how information is displayed and delivered in real time based on the user's current emotional state.

[0192] "Manipulating the vehicle environment" refers to operations that control physical or virtual elements within the vehicle to make adjustments that improve the riding experience.

[0193] The system for carrying out this invention includes a server for receiving data in different formats and structuring it into a unified format. The server also analyzes the user's emotional state in real time and dynamically adjusts the way information is presented based on the analysis results. Furthermore, it is capable of generating and transmitting commands necessary to operate the environment inside the vehicle.

[0194] This system is implemented using an emotion engine API to detect the user's emotional state based on their voice, facial expressions, and operation patterns. Based on the obtained emotional state, the server operates the navigation system and vehicle environment control system to make environmental adjustments, including route selection, music playback, and temperature control. The hardware consists of edge devices, cameras, and microphones, while the software uses emotion recognition algorithms and navigation control APIs.

[0195] For example, if a user feels stressed while driving during a family trip, this system detects that emotion, selects the most scenic route, and plays relaxing music to provide the user with a comfortable environment. This reduces stress and allows the user to enjoy a safer and more comfortable driving experience.

[0196] An example of an input prompt for a generative AI model is: "Explain how to use the emotion engine to promote a comfortable driving experience if passengers in the car are feeling stressed."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The device collects user emotion data, including voice and facial expression data. This input data is sent to the emotion engine. The sensors used by the device include a microphone and a camera. This allows the device to capture the user's emotional state in real time.

[0200] Step 2:

[0201] The server analyzes the data received through the emotion engine API. This analysis classifies the user's emotional state. The server processes data such as voice tone and facial features to determine emotions such as stress, comfort, and surprise, and outputs the results.

[0202] Step 3:

[0203] The server generates appropriate vehicle environment control commands based on the analyzed emotional state. For example, if the server determines that the user is stressed, it will generate commands to play relaxing music or adjust the temperature, and instruct the navigation system to select a scenic route. This ensures that the output is tailored to the user's emotional state.

[0204] Step 4:

[0205] The server sends the generated vehicle environment control commands to the relevant systems within the vehicle. This causes the navigation system to select an unconventional route, the audio system to play specified music, and the air conditioning system to adjust the temperature. As a result of this process, feedback is sent to the user, dynamically improving the user experience.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is a system for effectively managing a wide variety of IoT devices in offices and commercial buildings. This system can save a great deal of effort by receiving device information from different vendors, converting it into a unified format, and integrating and managing the information in real time.

[0223] Specifically, when installing devices, the server receives delivery documents in various formats provided by vendors. These documents contain diverse designs and formats, which can make them difficult to analyze. However, the server uses a generative AI model to analyze these different formats of data, extracting device IDs, functions, installation locations, maintenance information, etc., and structuring them into a unified format.

[0224] Structured information is aggregated in a database by the server and managed in real time. This allows users to immediately see where each device is installed and which vendor provided it. The server also constantly monitors the status of the devices and automatically generates notifications to inform users when maintenance is deemed necessary.

[0225] For example, if an IoT device in an air conditioning system is detected as not functioning correctly, the server immediately notifies the user of the situation. This allows the user to take prompt action and prevent equipment problems from occurring.

[0226] Furthermore, this system also performs device lifecycle management, identifying devices that have reached the end of their service life or are no longer needed, and deleting their information from the server. This reduces management effort and mitigates security risks.

[0227] In this way, the present invention provides an effective solution for preventing the proliferation of "zombie IoT" devices and streamlining device management operations.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The terminal scans the delivery documents provided by the vendor after the IoT device has been installed and uploads them to the system.

[0231] Step 2:

[0232] The server receives the uploaded documents and verifies the integrity and completeness of the data.

[0233] Step 3:

[0234] The server uses a generation AI model to analyze incoming documents and extract important information such as device ID, function, installation location, and maintenance information.

[0235] Step 4:

[0236] The server converts the extracted information into a unified format and registers it in its internal database.

[0237] Step 5:

[0238] The server updates the management ledger in real time and integrates information from all IoT devices.

[0239] Step 6:

[0240] Users access device information and obtain necessary information through a dedicated user interface.

[0241] Step 7:

[0242] The server continuously monitors the status of IoT devices and detects malfunctions or situations requiring maintenance.

[0243] Step 8:

[0244] If the server determines that maintenance is required, it will automatically generate a notification and send it to the designated user.

[0245] Step 9:

[0246] Users will take prompt action based on the maintenance notice they receive.

[0247] Step 10:

[0248] The server periodically evaluates device lifecycle data, identifies unnecessary or inactive devices, and deletes or archives that information.

[0249] (Example 1)

[0250] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0251] Efficiently managing electronic data in various formats from diverse suppliers presents a problem: significant effort is required for data analysis and integration. Furthermore, traditional methods for monitoring equipment status and managing maintenance are time-consuming and cumbersome, making rapid response difficult in critical situations. Additionally, identifying and deleting unnecessary equipment information is a complex process that consumes considerable management resources.

[0252] 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.

[0253] In this invention, the server includes means for receiving electronic data in different formats and converting it into a unified format using a generation AI model; means for aggregating the converted information into a unified format in a data storage device and managing it in real time; and means for monitoring the status of the device and automatically generating maintenance notifications based on prompt messages. This makes it possible to efficiently standardize and manage diverse electronic data, automate device status monitoring and maintenance management, and enable rapid response and proper organization of unnecessary information.

[0254] "Electronic data" refers to data in digital format recorded using information technology, which is typically transmitted and received through computer systems and networks.

[0255] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to learn patterns from large amounts of data and is used to automate and optimize various tasks.

[0256] A "unified format" refers to a common data format used to standardize and unify data of different formats, with the aim of maintaining data consistency and compatibility.

[0257] "Data storage devices" refer to hardware and software solutions for storing and retrieving digital data, and typically include databases and storage solutions.

[0258] A "prompt statement" is a text input used to give instructions to a generative AI model, and is a set of instructions used to control the model's behavior and output.

[0259] "Device status" refers to information indicating how a device is currently operating, whether it is functioning normally or abnormally, and is typically a factor sensed by sensors or monitoring tools.

[0260] A "maintenance notification" refers to a warning or notice that is automatically issued when a particular device or system is determined to require maintenance.

[0261] A "user interface" refers to the screen or device through which a user interacts with a system or application, and is typically a means for users to input or receive information.

[0262] This invention is a system for standardizing and centrally managing a wide variety of electronic data. This system operates through the collaboration of a server, terminals, and users. First, the server receives diverse forms of electronic data from different suppliers. This electronic data includes various data formats such as XML and JSON.

[0263] The server uses a generative AI model to analyze the received data. The generative AI model uses prompts to identify the data and convert the necessary information into a unified format. This format conversion ensures the consistency and compatibility of the electronic data.

[0264] Next, the server aggregates the information, which has been converted to a unified format, into a data storage device. This data storage device is a common database solution capable of managing large amounts of data in real time. The data accessed via terminals is always kept up-to-date and can be efficiently retrieved when needed.

[0265] Furthermore, the server continuously monitors the device's status and automatically generates maintenance notifications based on prompt messages. For example, it analyzes data from the device's sensors and, if it detects an anomaly, issues a warning to enable the user to take prompt action. This helps to proactively mitigate the risk of maintenance and failures.

[0266] Furthermore, this system also manages the lifecycle of devices. It identifies information about devices that have reached the end of their service life and deletes unnecessary data via the server. This function ensures that the information in the database is always organized, preventing the consumption of unnecessary resources.

[0267] As a concrete example, suppose that in the monitoring of an air conditioning system, a prompt message is generated stating, "The temperature sensor on the second floor of Building B has detected an abnormal value." Based on this, the AI ​​model generates a notification, and the necessary maintenance notification is sent to the user. Upon receiving this notification, the user can quickly take on-site action and resolve the problem, thereby maintaining the stability of the system.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The server receives electronic data in different formats from each vendor. The input includes data in XML and JSON formats, containing information such as device ID, function, and installation location. This data is treated as initial input and prepared for subsequent processing.

[0271] Step 2:

[0272] The server passes the received data to the generating AI model. The input electronic data is analyzed by the generating AI model using prompt messages. Specifically, the AI ​​model identifies the data format and extracts necessary information such as device ID, installation location, and function. This analysis converts the information into a unified format, which is then output.

[0273] Step 3:

[0274] The server stores the parsed and transformed information in a data storage device. At this stage, the input is data in a unified format. The server aggregates this information into a database in real time, making it easy to view and use. The output is an up-to-date and manageable dataset.

[0275] Step 4:

[0276] The server continuously monitors the information in the data storage device and checks the status of the equipment. This process also includes real-time data from sensors as input. Using prompt statements, it automatically generates and outputs notifications when maintenance is deemed necessary. Specifically, if an anomaly is detected, the server immediately issues a maintenance alert.

[0277] Step 5:

[0278] The user receives a maintenance notice from the server. As a specific operation, the user who receives the notice goes to the site and inspects and repairs the device based on the instructions. Here, the input is the maintenance notice, and the output is the actions and records for problem solving.

[0279] Step 6:

[0280] The server manages the life cycle of the device, identifies and deletes the information of the device that has become unnecessary. The input includes the usage status of the device and the end-of-function report. Specifically, the server detects unnecessary device information, organizes and deletes it from the database to maintain a clean and efficient data storage device.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In an automation device provided by multiple different suppliers, it is very difficult to uniformly manage different forms of data. Especially in the monitoring of the operating status and maintenance management in real time, a quick and accurate response is required. However, currently, the integration of these data, the rapid detection of abnormalities, and notifications are not sufficiently carried out, resulting in a decrease in operation efficiency and a delay in maintenance, which in turn leads to a loss of productivity. Solving this problem is the object of the present invention.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for processing and integrating the structured automation device information in real time, and means for detecting anomalies related to the automation device in real time and providing notifications to the administrator. This enables the efficient integration of information from different automation devices and the immediate detection and response to anomalies.

[0286] "Data in different formats" refers to data in which documents and information provided from multiple sources are composed of various designs and formats.

[0287] "Unified format" refers to a format of structured data obtained by standardizing data in different formats and converting it into a common format.

[0288] "Structured automation device information" refers to information about the device that has been converted into a unified format and organized in a form that enables real-time processing and integration.

[0289] "Processing in real time" refers to immediately calculating and analyzing information and data without delay.

[0290] "Monitoring of operating status" refers to a series of processes for continuously checking whether an automation device is operating normally.

[0291] "Maintenance notification" refers to a message or alert for sending a notice or warning to the administrator when an anomaly is detected in an automation device.

[0292] "User interface" refers to the entire interface for a user to access the system and operate and view data and information.

[0293] "Operation cycle of automation device" refers to a series of life cycles from when an automation device is installed until it is removed.

[0294] "Real-time anomaly detection" refers to a function that immediately identifies a problem when it occurs in the device and notifies the user that action is required.

[0295] The system that implements this application consists of a program operated by a server. The server receives data in different formats from various sources and converts it into a unified format using a generative AI model. This makes it possible to manage information from different sources in a unified format.

[0296] Furthermore, the server processes the integrated data in real time and constantly monitors the operational status of the automated equipment. If an anomaly is detected, it quickly generates a notification and sends it to the administrator's terminal. This allows administrators to take immediate action, thereby improving operational efficiency and productivity.

[0297] The system's user interface is accessible via user devices such as smartphones and head-mounted displays. These devices allow administrators to view real-time information on automated equipment and perform operations and maintenance as needed.

[0298] As a concrete example, consider a scenario where numerous automated devices are operating within a factory. The server periodically checks the status of each device, and if any abnormality is detected, it sends a notification to the administrator stating, "A device in Zone 5 has started malfunctioning." At this time, the generated AI model is input with a prompt such as, "Generate the latest device status report and identify the zone with the abnormality."

[0299] In this way, this system is an effective means of improving the operational efficiency of automated equipment in factories and enabling faster response to anomalies.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The server receives data in different formats sent from multiple suppliers. This data includes information about the ID, status, and installation location of automation devices, and these are treated as inputs.

[0303] Step 2:

[0304] The server inputs the received data in different formats into the generative AI model. The AI model analyzes each data based on the prompt text and converts it into a unified format. Through this conversion, data from different suppliers is standardized and an integrated output is obtained.

[0305] Step 3:

[0306] The server processes the automation device information converted into the unified format in real time. As part of the processing, it monitors the operating status of each device and stores the status such as normal or abnormal in the database. Thereby, the current operating status is provided as an output.

[0307] Step 4:

[0308] The server monitors the device operating status data in the database and, when an abnormality is detected, uses the generative AI model to generate a notification message. In this process, the details of the abnormality and the location information of the target device are used as inputs, and a detailed notification message for the administrator is obtained as an output.

[0309] Step 5:

[0310] The terminal (e.g., the administrator's smartphone or head-mounted display) receives the notification message sent from the server. The administrator can view this in real time and take specific actions for abnormality handling. Taking the receipt of the notification as an input and the administrator's action guidelines as an output, the operating efficiency of the entire system is optimized.

[0311] 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.

[0312] This invention enhances the user experience by incorporating an emotion engine that recognizes user emotions, in addition to a system that comprehensively manages various IoT devices. The introduction of the emotion engine allows the system to dynamically adjust the way information is presented and the content of maintenance notifications according to the user's emotional state.

[0313] Specifically, the server integrates and manages data from IoT devices in real time, and analyzes user emotion data received from the devices through an API provided by the emotion engine. This emotion data is typically extracted from the user's voice, facial expressions, and behavioral patterns.

[0314] The server analyzes the user's emotional state and dynamically changes the interface design and information prioritization based on the results. For example, if the user is stressed, the server simplifies the information displayed and highlights only the most important information. If emotional data indicates that the user is positive towards the information, it can present more detailed data or additional options.

[0315] Furthermore, the emotion engine continuously learns from user feedback, improving its emotion recognition accuracy to suit individual users. This allows the system to adapt to users over time and provide a more personalized experience.

[0316] For example, if the emotion engine detects stress while a user is reviewing energy consumption data within a building, the server displays a simplified, visually easy-to-understand graph and prompts the user to confirm whether further information is needed. This approach allows users to utilize the system without stress.

[0317] As described above, by incorporating an emotion engine, the present invention provides a user-centered, interactive, and user-friendly IoT device management system.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] After the IoT device is installed, the terminal uploads data received from the device, along with user voice and image data, to the system.

[0321] Step 2:

[0322] The server receives the uploaded user audio and image data and prepares it to analyze the user's emotional state using the emotion engine API.

[0323] Step 3:

[0324] The server activates the emotion engine and identifies the user's emotional state, such as positive, negative, or neutral, based on their voice tone and facial expression analysis.

[0325] Step 4:

[0326] The server adjusts the interface layout and displayed information appropriately based on the user's emotional state, providing information in a way that is sensitive to the user's feelings.

[0327] Step 5:

[0328] Users access the status and related information of IoT devices and perform necessary actions through a customized interface. The displayed content changes according to the user's emotions, allowing them to receive information without stress.

[0329] Step 6:

[0330] The server continuously collects user feedback and provides it to the emotion engine as training data, thereby improving the accuracy of emotion recognition for each user.

[0331] Step 7:

[0332] If a user's emotional state is negative, the server will resend important notifications and alerts in a concise format to reduce stress while ensuring necessary action is taken.

[0333] Step 8:

[0334] Based on the data obtained from the emotion engine, the server performs analysis for future system improvements and develops strategies to provide even more effective user interactions.

[0335] (Example 2)

[0336] 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".

[0337] In real-time device management systems that handle diverse data formats, a challenge lies in the difficulty of configuring interfaces that reflect the user's emotional state. While conventional methods can achieve integrated management of device information and generation of maintenance notifications, they are insufficient for interactive interface adjustments that improve the user experience.

[0338] 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.

[0339] In this invention, the server includes means for receiving sensor data in different formats and structuring it into a unified format, means for managing and integrating the structured device information in real time, and means for analyzing the user's emotional state using an emotion analysis engine and adjusting the interface display. This enables the presentation of dynamic and personalized information based on the user's emotional state.

[0340] "Sensor data in different formats" refers to data in multiple formats acquired from various sensors.

[0341] "Structuring data into a unified format" refers to the process of converting data in different formats into a standardized, common format.

[0342] "Structured device information" refers to data that is organized according to a unified format and hierarchically or categorized.

[0343] "Managing and integrating in real time" refers to a process where data is managed and integrated as soon as it is generated, with the goal of making the data immediately available.

[0344] An "emotion analysis engine" refers to algorithms and software used to analyze a user's emotional state from their voice, facial expressions, and behavior.

[0345] "Adjusting the interface display" refers to the process of changing the screen design and displayed content based on the user's emotional state.

[0346] "Dynamic and personalized information presentation based on the user's emotional state" refers to a function that provides information optimized according to the user's current emotions.

[0347] The embodiments for carrying out the present invention will be described in detail below.

[0348] This system manages data acquired from various sensor devices in real time through server, terminal, and user interaction, and provides dynamic information tailored to the user's emotional state.

[0349] Hardware and software usage:

[0350] The server, utilizing either a cloud-based or on-premises network system, is the core component responsible for data collection, integration, and analysis. The server implements a generative AI model for sentiment analysis. This AI model includes algorithms for speech recognition, facial recognition, and behavioral pattern analysis, and acts as the sentiment analysis engine.

[0351] The terminal is a device that acts as an interface with the user, collecting data and performing user interaction. This includes a microphone for voice input, a camera for facial recognition, and a touchscreen for acquiring data on operation patterns.

[0352] Data processing and calculations:

[0353] The server receives sensor data in various formats transmitted from the terminal and structures the data into a unified format. Noise filtering and data cleaning are performed during this process to generate a highly reliable dataset. Next, using the data converted to the unified format, a generative AI model performs sentiment analysis and quantifies the user's emotional state.

[0354] Based on these analysis results, the server adjusts the user interface display to present information tailored to the user's emotions. For example, if the user is stressed, the information is presented concisely and clearly; if they are relaxed, more detailed information and additional options are provided.

[0355] Examples of specific cases and prompt statements:

[0356] As a concrete example, suppose a user is checking their home energy usage and the system detects stress from the user's tone of voice and facial expression. In this case, the server provides a simple graphical interface and highlights only the important data points.

[0357] An example of a prompt message could be, "Analyze the user's emotional response to the displayed information." This prompt helps the generative AI model analyze the user's emotional state and adjust the interface accordingly.

[0358] This system aims to significantly improve the user experience and reduce stress.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The device collects the user's voice using a microphone, captures their facial expressions with a camera, and records their operation patterns with sensors. This data is temporarily stored within the device as initial input data. Next, the device compresses this data and sends it to the server. The input consists of voice, image, and operation signal data, and the compressed data is sent to the server as output.

[0362] Step 2:

[0363] The server decompresses the compressed data received from the terminal and performs data cleaning to remove noise. The decompressed data is structured into a unified format and processed to enhance reliability. The input is compressed user data, and the output is formatted data in a unified format. Specific operations include timestamp matching and filtering of invalid data.

[0364] Step 3:

[0365] The server sends the obtained data in a unified format to the emotion analysis engine. Using a generative AI model, the analysis is performed to identify the user's emotional state. The input is formatted user data, and the output is data converted into emotion indicators. This analysis process includes voice tone analysis, facial expression analysis, and evaluation of behavioral patterns.

[0366] Step 4:

[0367] The server adjusts the information presentation interface based on the user's emotional state, using the results of the emotion analysis. This includes dynamically changing the screen design and prioritizing the displayed information. The input is emotion index data, and the output is the adjusted interface design. Specifically, if stress is detected, a simple infographic is displayed to reduce the complexity of the information.

[0368] Step 5:

[0369] The user reviews the information presented by the server and performs actions or provides feedback. This feedback is then sent back to the server via the terminal. The input consists of the user's interaction and feedback, while the output is used as training data for the system to improve the accuracy of subsequent sentiment analysis. A concrete example is when the user clicks a button to request more information.

[0370] (Application Example 2)

[0371] 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."

[0372] Conventional IoT device management systems have struggled to adjust information presentation methods and environments in response to the user's emotional state. This often leads to users experiencing stress while using the system, resulting in a lack of a comfortable user experience. In particular, there is a strong need for a system that appropriately reflects the emotional states of both the driver and passengers and optimizes the in-vehicle environment within autonomous vehicles.

[0373] 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.

[0374] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for analyzing the user's emotional state, dynamically adjusting the way information is presented, and manipulating the vehicle environment, and means for managing the device lifecycle and identifying and deleting information on unnecessary devices. This enables the optimization of the interface and adjustment of the in-vehicle environment in accordance with the user's emotional state.

[0375] "Data in different formats" refers to diverse data formats obtained from different sources or protocols, and is used to facilitate unification under common operational standards.

[0376] A "unified format" is a standard for converting diverse data formats into a single, standardized format, and is used to facilitate information exchange between different devices.

[0377] "Structured device information" refers to device-related information systematically organized according to a specific data format, enabling rapid access and analysis.

[0378] "User emotional state" refers to the emotional responses that a user exhibits under specific circumstances, and is detected through voice, facial expressions, and behavioral patterns.

[0379] "Dynamically adjusting how information is presented" refers to the process of changing how information is displayed and delivered in real time based on the user's current emotional state.

[0380] "Manipulating the vehicle environment" refers to operations that control physical or virtual elements within the vehicle to make adjustments that improve the riding experience.

[0381] The system for carrying out this invention includes a server for receiving data in different formats and structuring it into a unified format. The server also analyzes the user's emotional state in real time and dynamically adjusts the way information is presented based on the analysis results. Furthermore, it is capable of generating and transmitting commands necessary to operate the environment inside the vehicle.

[0382] This system is implemented using an emotion engine API to detect the user's emotional state based on their voice, facial expressions, and operation patterns. Based on the obtained emotional state, the server operates the navigation system and vehicle environment control system to make environmental adjustments, including route selection, music playback, and temperature control. The hardware consists of edge devices, cameras, and microphones, while the software uses emotion recognition algorithms and navigation control APIs.

[0383] For example, if a user feels stressed while driving during a family trip, this system detects that emotion, selects the most scenic route, and plays relaxing music to provide the user with a comfortable environment. This reduces stress and allows the user to enjoy a safer and more comfortable driving experience.

[0384] An example of an input prompt for a generative AI model is: "Explain how to use the emotion engine to promote a comfortable driving experience if passengers in the car are feeling stressed."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The device collects user emotion data, including voice and facial expression data. This input data is sent to the emotion engine. The sensors used by the device include a microphone and a camera. This allows the device to capture the user's emotional state in real time.

[0388] Step 2:

[0389] The server analyzes the data received through the emotion engine API. This analysis classifies the user's emotional state. The server processes data such as voice tone and facial features to determine emotions such as stress, comfort, and surprise, and outputs the results.

[0390] Step 3:

[0391] The server generates appropriate vehicle environment control commands based on the analyzed emotional state. For example, if the server determines that the user is stressed, it will generate commands to play relaxing music or adjust the temperature, and instruct the navigation system to select a scenic route. This ensures that the output is tailored to the user's emotional state.

[0392] Step 4:

[0393] The server sends the generated vehicle environment control commands to the relevant systems within the vehicle. This causes the navigation system to select an unconventional route, the audio system to play specified music, and the air conditioning system to adjust the temperature. As a result of this process, feedback is sent to the user, dynamically improving the user experience.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] This invention is a system for effectively managing a wide variety of IoT devices in offices and commercial buildings. This system can save a great deal of effort by receiving device information from different vendors, converting it into a unified format, and integrating and managing the information in real time.

[0411] Specifically, when installing devices, the server receives delivery documents in various formats provided by vendors. These documents contain diverse designs and formats, which can make them difficult to analyze. However, the server uses a generative AI model to analyze these different formats of data, extracting device IDs, functions, installation locations, maintenance information, etc., and structuring them into a unified format.

[0412] Structured information is aggregated in a database by the server and managed in real time. This allows users to immediately see where each device is installed and which vendor provided it. The server also constantly monitors the status of the devices and automatically generates notifications to inform users when maintenance is deemed necessary.

[0413] For example, if an IoT device in an air conditioning system is detected as not functioning correctly, the server immediately notifies the user of the situation. This allows the user to take prompt action and prevent equipment problems from occurring.

[0414] Furthermore, this system also performs device lifecycle management, identifying devices that have reached the end of their service life or are no longer needed, and deleting their information from the server. This reduces management effort and mitigates security risks.

[0415] In this way, the present invention provides an effective solution for preventing the proliferation of "zombie IoT" devices and streamlining device management operations.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The terminal scans the delivery documents provided by the vendor after the IoT device has been installed and uploads them to the system.

[0419] Step 2:

[0420] The server receives the uploaded documents and verifies the integrity and completeness of the data.

[0421] Step 3:

[0422] The server uses a generation AI model to analyze incoming documents and extract important information such as device ID, function, installation location, and maintenance information.

[0423] Step 4:

[0424] The server converts the extracted information into a unified format and registers it in its internal database.

[0425] Step 5:

[0426] The server updates the management ledger in real time and integrates information from all IoT devices.

[0427] Step 6:

[0428] Users access device information and obtain necessary information through a dedicated user interface.

[0429] Step 7:

[0430] The server continuously monitors the status of IoT devices and detects malfunctions or situations requiring maintenance.

[0431] Step 8:

[0432] If the server determines that maintenance is required, it will automatically generate a notification and send it to the designated user.

[0433] Step 9:

[0434] Users will take prompt action based on the maintenance notice they receive.

[0435] Step 10:

[0436] The server periodically evaluates device lifecycle data, identifies unnecessary or inactive devices, and deletes or archives that information.

[0437] (Example 1)

[0438] 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."

[0439] Efficiently managing electronic data in various formats from diverse suppliers presents a problem: significant effort is required for data analysis and integration. Furthermore, traditional methods for monitoring equipment status and managing maintenance are time-consuming and cumbersome, making rapid response difficult in critical situations. Additionally, identifying and deleting unnecessary equipment information is a complex process that consumes considerable management resources.

[0440] 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.

[0441] In this invention, the server includes means for receiving electronic data in different formats and converting it into a unified format using a generation AI model; means for aggregating the converted information into a unified format in a data storage device and managing it in real time; and means for monitoring the status of the device and automatically generating maintenance notifications based on prompt messages. This makes it possible to efficiently standardize and manage diverse electronic data, automate device status monitoring and maintenance management, and enable rapid response and proper organization of unnecessary information.

[0442] "Electronic data" refers to data in digital format recorded using information technology, which is typically transmitted and received through computer systems and networks.

[0443] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to learn patterns from large amounts of data and is used to automate and optimize various tasks.

[0444] A "unified format" refers to a common data format used to standardize and unify data of different formats, with the aim of maintaining data consistency and compatibility.

[0445] "Data storage devices" refer to hardware and software solutions for storing and retrieving digital data, and typically include databases and storage solutions.

[0446] A "prompt statement" is a text input used to give instructions to a generative AI model, and is a set of instructions used to control the model's behavior and output.

[0447] "Device status" refers to information indicating how a device is currently operating, whether it is functioning normally or abnormally, and is typically a factor sensed by sensors or monitoring tools.

[0448] A "maintenance notification" refers to a warning or notice that is automatically issued when a particular device or system is determined to require maintenance.

[0449] A "user interface" refers to the screen or device through which a user interacts with a system or application, and is typically a means for users to input or receive information.

[0450] This invention is a system for standardizing and centrally managing a wide variety of electronic data. This system operates through the collaboration of a server, terminals, and users. First, the server receives diverse forms of electronic data from different suppliers. This electronic data includes various data formats such as XML and JSON.

[0451] The server uses a generative AI model to analyze the received data. The generative AI model uses prompts to identify the data and convert the necessary information into a unified format. This format conversion ensures the consistency and compatibility of the electronic data.

[0452] Next, the server aggregates the information, which has been converted to a unified format, into a data storage device. This data storage device is a common database solution capable of managing large amounts of data in real time. The data accessed via terminals is always kept up-to-date and can be efficiently retrieved when needed.

[0453] Furthermore, the server continuously monitors the device's status and automatically generates maintenance notifications based on prompt messages. For example, it analyzes data from the device's sensors and, if it detects an anomaly, issues a warning to enable the user to take prompt action. This helps to proactively mitigate the risk of maintenance and failures.

[0454] Furthermore, this system also manages the lifecycle of devices. It identifies information about devices that have reached the end of their service life and deletes unnecessary data via the server. This function ensures that the information in the database is always organized, preventing the consumption of unnecessary resources.

[0455] As a concrete example, suppose that in the monitoring of an air conditioning system, a prompt message is generated stating, "The temperature sensor on the second floor of Building B has detected an abnormal value." Based on this, the AI ​​model generates a notification, and the necessary maintenance notification is sent to the user. Upon receiving this notification, the user can quickly take on-site action and resolve the problem, thereby maintaining the stability of the system.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The server receives electronic data in different formats from each vendor. The input includes data in XML and JSON formats, containing information such as device ID, function, and installation location. This data is treated as initial input and prepared for subsequent processing.

[0459] Step 2:

[0460] The server passes the received data to the generating AI model. The input electronic data is analyzed by the generating AI model using prompt messages. Specifically, the AI ​​model identifies the data format and extracts necessary information such as device ID, installation location, and function. This analysis converts the information into a unified format, which is then output.

[0461] Step 3:

[0462] The server stores the parsed and transformed information in a data storage device. At this stage, the input is data in a unified format. The server aggregates this information into a database in real time, making it easy to view and use. The output is an up-to-date and manageable dataset.

[0463] Step 4:

[0464] The server continuously monitors the information in the data storage device and checks the status of the equipment. This process also includes real-time data from sensors as input. Using prompt statements, it automatically generates and outputs notifications when maintenance is deemed necessary. Specifically, if an anomaly is detected, the server immediately issues a maintenance alert.

[0465] Step 5:

[0466] The user receives a maintenance notification from the server. Specifically, the user goes to the site and inspects or repairs the equipment according to the instructions. The input here is the maintenance notification, and the output is the actions taken to resolve the problem and the records of those actions.

[0467] Step 6:

[0468] The server manages the lifecycle of devices, identifying and deleting information about devices that are no longer needed. Inputs include device usage and end-of-life reports. Specifically, the server detects and removes unnecessary device information from the database, maintaining a clean and efficient data storage system.

[0469] (Application Example 1)

[0470] 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."

[0471] Managing data in different formats from multiple suppliers of automated equipment is extremely difficult, and rapid and accurate responses are required, especially in real-time monitoring of operating status and maintenance management. However, currently, the integration of this data and the rapid detection and notification of anomalies are not adequately performed, leading to decreased operational efficiency, maintenance delays, and ultimately, loss of productivity. The objective of this invention is to solve this problem.

[0472] 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.

[0473] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for processing and integrating the structured automated device information in real time, and means for detecting anomalies related to automated devices in real time and providing notifications to administrators. This makes it possible to efficiently integrate information from different automated devices and to immediately detect and respond to anomalies.

[0474] "Data in different formats" refers to data consisting of documents and information provided by multiple sources, which are composed of various designs and formats.

[0475] A "unified format" is a data format that standardizes data from different formats, converts it into a common format, and structures it in that way.

[0476] "Structured automation equipment information" refers to information about equipment that has been converted into a unified format and is organized in a way that allows for real-time processing and integration.

[0477] "Real-time processing" refers to the immediate calculation and analysis of information and data without delay.

[0478] "Monitoring operational status" refers to a series of processes that continuously check whether automated equipment is functioning correctly.

[0479] A "maintenance notification" is a message or alert sent to the administrator to contact them or warn them when an abnormality is detected in an automated device.

[0480] "User interface" refers to all interfaces that allow users to access a system and manipulate or view data and information.

[0481] The "operational cycle of automated equipment" refers to the entire lifecycle of automated equipment, from its installation to its removal.

[0482] "Real-time anomaly detection" refers to a function that immediately identifies a problem when it occurs in the device and notifies the user that action is required.

[0483] The system that implements this application consists of a program operated by a server. The server receives data in different formats from various sources and converts it into a unified format using a generative AI model. This makes it possible to manage information from different sources in a unified format.

[0484] Furthermore, the server processes the integrated data in real time and constantly monitors the operational status of the automated equipment. If an anomaly is detected, it quickly generates a notification and sends it to the administrator's terminal. This allows administrators to take immediate action, thereby improving operational efficiency and productivity.

[0485] The system's user interface is accessible via user devices such as smartphones and head-mounted displays. These devices allow administrators to view real-time information on automated equipment and perform operations and maintenance as needed.

[0486] As a concrete example, consider a scenario where numerous automated devices are operating within a factory. The server periodically checks the status of each device, and if any abnormality is detected, it sends a notification to the administrator stating, "A device in Zone 5 has started malfunctioning." At this time, the generated AI model is input with a prompt such as, "Generate the latest device status report and identify the zone with the abnormality."

[0487] In this way, this system is an effective means of improving the operational efficiency of automated equipment in factories and enabling faster response to anomalies.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The server receives data in different formats from multiple sources. This data includes information about the ID, status, and location of automated equipment, and is treated as input.

[0491] Step 2:

[0492] The server inputs the received data in different formats into a generating AI model. The AI ​​model analyzes each piece of data based on prompts and converts it into a unified format. This conversion standardizes data from different sources, resulting in an integrated output.

[0493] Step 3:

[0494] The server processes automated equipment information converted into a unified format in real time. As part of the processing, it monitors the operating status of each device and stores its status (normal, abnormal, etc.) in a database. This allows the current operating status to be provided as output.

[0495] Step 4:

[0496] The server monitors device operating status data in the database and, if an anomaly is detected, generates a notification message using a generation AI model. This process uses the nature of the anomaly and the location information of the affected device as input and produces a detailed notification message for administrators as output.

[0497] Step 5:

[0498] A device (for example, the administrator's smartphone or head-mounted display) receives notification messages sent from the server. The administrator can view these in real time and take specific actions to address anomalies. By using the receipt of notifications as input and the administrator's action plan as output, the overall operational efficiency of the system is optimized.

[0499] 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.

[0500] This invention enhances the user experience by incorporating an emotion engine that recognizes user emotions, in addition to a system that comprehensively manages various IoT devices. The introduction of the emotion engine allows the system to dynamically adjust the way information is presented and the content of maintenance notifications according to the user's emotional state.

[0501] Specifically, the server integrates and manages data from IoT devices in real time, and analyzes user emotion data received from the devices through an API provided by the emotion engine. This emotion data is typically extracted from the user's voice, facial expressions, and behavioral patterns.

[0502] The server analyzes the user's emotional state and dynamically changes the interface design and information prioritization based on the results. For example, if the user is stressed, the server simplifies the information displayed and highlights only the most important information. If emotional data indicates that the user is positive towards the information, it can present more detailed data or additional options.

[0503] Furthermore, the emotion engine continuously learns from user feedback, improving its emotion recognition accuracy to suit individual users. This allows the system to adapt to users over time and provide a more personalized experience.

[0504] For example, if the emotion engine detects stress while a user is reviewing energy consumption data within a building, the server displays a simplified, visually easy-to-understand graph and prompts the user to confirm whether further information is needed. This approach allows users to utilize the system without stress.

[0505] As described above, by incorporating an emotion engine, the present invention provides a user-centered, interactive, and user-friendly IoT device management system.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] After the IoT device is installed, the terminal uploads data received from the device, along with user voice and image data, to the system.

[0509] Step 2:

[0510] The server receives the uploaded user audio and image data and prepares it to analyze the user's emotional state using the emotion engine API.

[0511] Step 3:

[0512] The server activates the emotion engine and identifies the user's emotional state, such as positive, negative, or neutral, based on their voice tone and facial expression analysis.

[0513] Step 4:

[0514] The server adjusts the interface layout and displayed information appropriately based on the user's emotional state, providing information in a way that is sensitive to the user's feelings.

[0515] Step 5:

[0516] Users access the status and related information of IoT devices and perform necessary actions through a customized interface. The displayed content changes according to the user's emotions, allowing them to receive information without stress.

[0517] Step 6:

[0518] The server continuously collects user feedback and provides it to the emotion engine as training data, thereby improving the accuracy of emotion recognition for each user.

[0519] Step 7:

[0520] If a user's emotional state is negative, the server will resend important notifications and alerts in a concise format to reduce stress while ensuring necessary action is taken.

[0521] Step 8:

[0522] Based on the data obtained from the emotion engine, the server performs analysis for future system improvements and develops strategies to provide even more effective user interactions.

[0523] (Example 2)

[0524] 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."

[0525] In real-time device management systems that handle diverse data formats, a challenge lies in the difficulty of configuring interfaces that reflect the user's emotional state. While conventional methods can achieve integrated management of device information and generation of maintenance notifications, they are insufficient for interactive interface adjustments that improve the user experience.

[0526] 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.

[0527] In this invention, the server includes means for receiving sensor data in different formats and structuring it into a unified format, means for managing and integrating the structured device information in real time, and means for analyzing the user's emotional state using an emotion analysis engine and adjusting the interface display. This enables the presentation of dynamic and personalized information based on the user's emotional state.

[0528] "Sensor data in different formats" refers to data in multiple formats acquired from various sensors.

[0529] "Structuring data into a unified format" refers to the process of converting data in different formats into a standardized, common format.

[0530] "Structured device information" refers to data that is organized according to a unified format and hierarchically or categorized.

[0531] "Managing and integrating in real time" refers to a process where data is managed and integrated as soon as it is generated, with the goal of making the data immediately available.

[0532] An "emotion analysis engine" refers to algorithms and software used to analyze a user's emotional state from their voice, facial expressions, and behavior.

[0533] "Adjusting the interface display" refers to the process of changing the screen design and displayed content based on the user's emotional state.

[0534] "Dynamic and personalized information presentation based on the user's emotional state" refers to a function that provides information optimized according to the user's current emotions.

[0535] The embodiments for carrying out the present invention will be described in detail below.

[0536] This system manages data acquired from various sensor devices in real time through server, terminal, and user interaction, and provides dynamic information tailored to the user's emotional state.

[0537] Hardware and software usage:

[0538] The server, utilizing either a cloud-based or on-premises network system, is the core component responsible for data collection, integration, and analysis. The server implements a generative AI model for sentiment analysis. This AI model includes algorithms for speech recognition, facial recognition, and behavioral pattern analysis, and acts as the sentiment analysis engine.

[0539] The terminal is a device that acts as an interface with the user, collecting data and performing user interaction. This includes a microphone for voice input, a camera for facial recognition, and a touchscreen for acquiring data on operation patterns.

[0540] Data processing and calculations:

[0541] The server receives sensor data in various formats transmitted from the terminal and structures the data into a unified format. Noise filtering and data cleaning are performed during this process to generate a highly reliable dataset. Next, using the data converted to the unified format, a generative AI model performs sentiment analysis and quantifies the user's emotional state.

[0542] Based on these analysis results, the server adjusts the user interface display to present information tailored to the user's emotions. For example, if the user is stressed, the information is presented concisely and clearly; if they are relaxed, more detailed information and additional options are provided.

[0543] Examples of specific cases and prompt statements:

[0544] As a concrete example, suppose a user is checking their home energy usage and the system detects stress from the user's tone of voice and facial expression. In this case, the server provides a simple graphical interface and highlights only the important data points.

[0545] An example of a prompt message could be, "Analyze the user's emotional response to the displayed information." This prompt helps the generative AI model analyze the user's emotional state and adjust the interface accordingly.

[0546] This system aims to significantly improve the user experience and reduce stress.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The device collects the user's voice using a microphone, captures their facial expressions with a camera, and records their operation patterns with sensors. This data is temporarily stored within the device as initial input data. Next, the device compresses this data and sends it to the server. The input consists of voice, image, and operation signal data, and the compressed data is sent to the server as output.

[0550] Step 2:

[0551] The server decompresses the compressed data received from the terminal and performs data cleaning to remove noise. The decompressed data is structured into a unified format and processed to enhance reliability. The input is compressed user data, and the output is formatted data in a unified format. Specific operations include timestamp matching and filtering of invalid data.

[0552] Step 3:

[0553] The server sends the obtained data in a unified format to the emotion analysis engine. Using a generative AI model, the analysis is performed to identify the user's emotional state. The input is formatted user data, and the output is data converted into emotion indicators. This analysis process includes voice tone analysis, facial expression analysis, and evaluation of behavioral patterns.

[0554] Step 4:

[0555] The server adjusts the information presentation interface based on the user's emotional state, using the results of the emotion analysis. This includes dynamically changing the screen design and prioritizing the displayed information. The input is emotion index data, and the output is the adjusted interface design. Specifically, if stress is detected, a simple infographic is displayed to reduce the complexity of the information.

[0556] Step 5:

[0557] The user reviews the information presented by the server and performs actions or provides feedback. This feedback is then sent back to the server via the terminal. The input consists of the user's interaction and feedback, while the output is used as training data for the system to improve the accuracy of subsequent sentiment analysis. A concrete example is when the user clicks a button to request more information.

[0558] (Application Example 2)

[0559] 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."

[0560] Conventional IoT device management systems have struggled to adjust information presentation methods and environments in response to the user's emotional state. This often leads to users experiencing stress while using the system, resulting in a lack of a comfortable user experience. In particular, there is a strong need for a system that appropriately reflects the emotional states of both the driver and passengers and optimizes the in-vehicle environment within autonomous vehicles.

[0561] 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.

[0562] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for analyzing the user's emotional state, dynamically adjusting the way information is presented, and manipulating the vehicle environment, and means for managing the device lifecycle and identifying and deleting information on unnecessary devices. This enables the optimization of the interface and adjustment of the in-vehicle environment in accordance with the user's emotional state.

[0563] "Data in different formats" refers to diverse data formats obtained from different sources or protocols, and is used to facilitate unification under common operational standards.

[0564] A "unified format" is a standard for converting diverse data formats into a single, standardized format, and is used to facilitate information exchange between different devices.

[0565] "Structured device information" refers to device-related information systematically organized according to a specific data format, enabling rapid access and analysis.

[0566] "User emotional state" refers to the emotional responses that a user exhibits under specific circumstances, and is detected through voice, facial expressions, and behavioral patterns.

[0567] "Dynamically adjusting how information is presented" refers to the process of changing how information is displayed and delivered in real time based on the user's current emotional state.

[0568] "Manipulating the vehicle environment" refers to operations that control physical or virtual elements within the vehicle to make adjustments that improve the riding experience.

[0569] The system for carrying out this invention includes a server for receiving data in different formats and structuring it into a unified format. The server also analyzes the user's emotional state in real time and dynamically adjusts the way information is presented based on the analysis results. Furthermore, it is capable of generating and transmitting commands necessary to operate the environment inside the vehicle.

[0570] This system is implemented using an emotion engine API to detect the user's emotional state based on their voice, facial expressions, and operation patterns. Based on the obtained emotional state, the server operates the navigation system and vehicle environment control system to make environmental adjustments, including route selection, music playback, and temperature control. The hardware consists of edge devices, cameras, and microphones, while the software uses emotion recognition algorithms and navigation control APIs.

[0571] For example, if a user feels stressed while driving during a family trip, this system detects that emotion, selects the most scenic route, and plays relaxing music to provide the user with a comfortable environment. This reduces stress and allows the user to enjoy a safer and more comfortable driving experience.

[0572] An example of an input prompt for a generative AI model is: "Explain how to use the emotion engine to promote a comfortable driving experience if passengers in the car are feeling stressed."

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The device collects user emotion data, including voice and facial expression data. This input data is sent to the emotion engine. The sensors used by the device include a microphone and a camera. This allows the device to capture the user's emotional state in real time.

[0576] Step 2:

[0577] The server analyzes the data received through the emotion engine API. This analysis classifies the user's emotional state. The server processes data such as voice tone and facial features to determine emotions such as stress, comfort, and surprise, and outputs the results.

[0578] Step 3:

[0579] The server generates appropriate vehicle environment control commands based on the analyzed emotional state. For example, if the server determines that the user is stressed, it will generate commands to play relaxing music or adjust the temperature, and instruct the navigation system to select a scenic route. This ensures that the output is tailored to the user's emotional state.

[0580] Step 4:

[0581] The server sends the generated vehicle environment control commands to the relevant systems within the vehicle. This causes the navigation system to select an unconventional route, the audio system to play specified music, and the air conditioning system to adjust the temperature. As a result of this process, feedback is sent to the user, dynamically improving the user experience.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] This invention is a system for effectively managing a wide variety of IoT devices in offices and commercial buildings. This system can save a great deal of effort by receiving device information from different vendors, converting it into a unified format, and integrating and managing the information in real time.

[0600] Specifically, when installing devices, the server receives delivery documents in various formats provided by vendors. These documents contain diverse designs and formats, which can make them difficult to analyze. However, the server uses a generative AI model to analyze these different formats of data, extracting device IDs, functions, installation locations, maintenance information, etc., and structuring them into a unified format.

[0601] Structured information is aggregated in a database by the server and managed in real time. This allows users to immediately see where each device is installed and which vendor provided it. The server also constantly monitors the status of the devices and automatically generates notifications to inform users when maintenance is deemed necessary.

[0602] For example, if an IoT device in an air conditioning system is detected as not functioning correctly, the server immediately notifies the user of the situation. This allows the user to take prompt action and prevent equipment problems from occurring.

[0603] Furthermore, this system also performs device lifecycle management, identifying devices that have reached the end of their service life or are no longer needed, and deleting their information from the server. This reduces management effort and mitigates security risks.

[0604] In this way, the present invention provides an effective solution for preventing the proliferation of "zombie IoT" devices and streamlining device management operations.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The terminal scans the delivery documents provided by the vendor after the IoT device has been installed and uploads them to the system.

[0608] Step 2:

[0609] The server receives the uploaded documents and verifies the integrity and completeness of the data.

[0610] Step 3:

[0611] The server uses a generation AI model to analyze incoming documents and extract important information such as device ID, function, installation location, and maintenance information.

[0612] Step 4:

[0613] The server converts the extracted information into a unified format and registers it in its internal database.

[0614] Step 5:

[0615] The server updates the management ledger in real time and integrates information from all IoT devices.

[0616] Step 6:

[0617] Users access device information and obtain necessary information through a dedicated user interface.

[0618] Step 7:

[0619] The server continuously monitors the status of IoT devices and detects malfunctions or situations requiring maintenance.

[0620] Step 8:

[0621] If the server determines that maintenance is required, it will automatically generate a notification and send it to the designated user.

[0622] Step 9:

[0623] Users will take prompt action based on the maintenance notice they receive.

[0624] Step 10:

[0625] The server periodically evaluates device lifecycle data, identifies unnecessary or inactive devices, and deletes or archives that information.

[0626] (Example 1)

[0627] 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".

[0628] Efficiently managing electronic data in various formats from diverse suppliers presents a problem: significant effort is required for data analysis and integration. Furthermore, traditional methods for monitoring equipment status and managing maintenance are time-consuming and cumbersome, making rapid response difficult in critical situations. Additionally, identifying and deleting unnecessary equipment information is a complex process that consumes considerable management resources.

[0629] 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.

[0630] In this invention, the server includes means for receiving electronic data in different formats and converting it into a unified format using a generation AI model; means for aggregating the converted information into a unified format in a data storage device and managing it in real time; and means for monitoring the status of the device and automatically generating maintenance notifications based on prompt messages. This makes it possible to efficiently standardize and manage diverse electronic data, automate device status monitoring and maintenance management, and enable rapid response and proper organization of unnecessary information.

[0631] "Electronic data" refers to data in digital format recorded using information technology, which is typically transmitted and received through computer systems and networks.

[0632] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to learn patterns from large amounts of data and is used to automate and optimize various tasks.

[0633] A "unified format" refers to a common data format used to standardize and unify data of different formats, with the aim of maintaining data consistency and compatibility.

[0634] "Data storage devices" refer to hardware and software solutions for storing and retrieving digital data, and typically include databases and storage solutions.

[0635] A "prompt statement" is a text input used to give instructions to a generative AI model, and is a set of instructions used to control the model's behavior and output.

[0636] "Device status" refers to information indicating how a device is currently operating, whether it is functioning normally or abnormally, and is typically a factor sensed by sensors or monitoring tools.

[0637] A "maintenance notification" refers to a warning or notice that is automatically issued when a particular device or system is determined to require maintenance.

[0638] A "user interface" refers to the screen or device through which a user interacts with a system or application, and is typically a means for users to input or receive information.

[0639] This invention is a system for standardizing and centrally managing a wide variety of electronic data. This system operates through the collaboration of a server, terminals, and users. First, the server receives diverse forms of electronic data from different suppliers. This electronic data includes various data formats such as XML and JSON.

[0640] The server uses a generative AI model to analyze the received data. The generative AI model uses prompts to identify the data and convert the necessary information into a unified format. This format conversion ensures the consistency and compatibility of the electronic data.

[0641] Next, the server aggregates the information, which has been converted to a unified format, into a data storage device. This data storage device is a common database solution capable of managing large amounts of data in real time. The data accessed via terminals is always kept up-to-date and can be efficiently retrieved when needed.

[0642] Furthermore, the server continuously monitors the device's status and automatically generates maintenance notifications based on prompt messages. For example, it analyzes data from the device's sensors and, if it detects an anomaly, issues a warning to enable the user to take prompt action. This helps to proactively mitigate the risk of maintenance and failures.

[0643] Furthermore, this system also manages the lifecycle of devices. It identifies information about devices that have reached the end of their service life and deletes unnecessary data via the server. This function ensures that the information in the database is always organized, preventing the consumption of unnecessary resources.

[0644] As a concrete example, suppose that in the monitoring of an air conditioning system, a prompt message is generated stating, "The temperature sensor on the second floor of Building B has detected an abnormal value." Based on this, the AI ​​model generates a notification, and the necessary maintenance notification is sent to the user. Upon receiving this notification, the user can quickly take on-site action and resolve the problem, thereby maintaining the stability of the system.

[0645] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0646] Step 1:

[0647] The server receives electronic data in different formats from each vendor. The input includes data in XML and JSON formats, containing information such as device ID, function, and installation location. This data is treated as initial input and prepared for subsequent processing.

[0648] Step 2:

[0649] The server passes the received data to the generating AI model. The input electronic data is analyzed by the generating AI model using prompt messages. Specifically, the AI ​​model identifies the data format and extracts necessary information such as device ID, installation location, and function. This analysis converts the information into a unified format, which is then output.

[0650] Step 3:

[0651] The server stores the parsed and transformed information in a data storage device. At this stage, the input is data in a unified format. The server aggregates this information into a database in real time, making it easy to view and use. The output is an up-to-date and manageable dataset.

[0652] Step 4:

[0653] The server continuously monitors the information in the data storage device and checks the status of the equipment. This process also includes real-time data from sensors as input. Using prompt statements, it automatically generates and outputs notifications when maintenance is deemed necessary. Specifically, if an anomaly is detected, the server immediately issues a maintenance alert.

[0654] Step 5:

[0655] The user receives a maintenance notification from the server. Specifically, the user goes to the site and inspects or repairs the equipment according to the instructions. The input here is the maintenance notification, and the output is the actions taken to resolve the problem and the records of those actions.

[0656] Step 6:

[0657] The server manages the lifecycle of devices, identifying and deleting information about devices that are no longer needed. Inputs include device usage and end-of-life reports. Specifically, the server detects and removes unnecessary device information from the database, maintaining a clean and efficient data storage system.

[0658] (Application Example 1)

[0659] 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".

[0660] Managing data in different formats from multiple suppliers of automated equipment is extremely difficult, and rapid and accurate responses are required, especially in real-time monitoring of operating status and maintenance management. However, currently, the integration of this data and the rapid detection and notification of anomalies are not adequately performed, leading to decreased operational efficiency, maintenance delays, and ultimately, loss of productivity. The objective of this invention is to solve this problem.

[0661] 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.

[0662] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for processing and integrating the structured automated device information in real time, and means for detecting anomalies related to automated devices in real time and providing notifications to administrators. This makes it possible to efficiently integrate information from different automated devices and to immediately detect and respond to anomalies.

[0663] "Data in different formats" refers to data consisting of documents and information provided by multiple sources, which are composed of various designs and formats.

[0664] A "unified format" is a data format that standardizes data from different formats, converts it into a common format, and structures it in that way.

[0665] "Structured automation equipment information" refers to information about equipment that has been converted into a unified format and is organized in a way that allows for real-time processing and integration.

[0666] "Real-time processing" refers to the immediate calculation and analysis of information and data without delay.

[0667] "Monitoring operational status" refers to a series of processes that continuously check whether automated equipment is functioning correctly.

[0668] A "maintenance notification" is a message or alert sent to the administrator to contact them or warn them when an abnormality is detected in an automated device.

[0669] "User interface" refers to all interfaces that allow users to access a system and manipulate or view data and information.

[0670] The "operational cycle of automated equipment" refers to the entire lifecycle of automated equipment, from its installation to its removal.

[0671] "Real-time anomaly detection" refers to a function that immediately identifies a problem when it occurs in the device and notifies the user that action is required.

[0672] The system that implements this application consists of a program operated by a server. The server receives data in different formats from various sources and converts it into a unified format using a generative AI model. This makes it possible to manage information from different sources in a unified format.

[0673] Furthermore, the server processes the integrated data in real time and constantly monitors the operational status of the automated equipment. If an anomaly is detected, it quickly generates a notification and sends it to the administrator's terminal. This allows administrators to take immediate action, thereby improving operational efficiency and productivity.

[0674] The system's user interface is accessible via user devices such as smartphones and head-mounted displays. These devices allow administrators to view real-time information on automated equipment and perform operations and maintenance as needed.

[0675] As a concrete example, consider a scenario where numerous automated devices are operating within a factory. The server periodically checks the status of each device, and if any abnormality is detected, it sends a notification to the administrator stating, "A device in Zone 5 has started malfunctioning." At this time, the generated AI model is input with a prompt such as, "Generate the latest device status report and identify the zone with the abnormality."

[0676] In this way, this system is an effective means of improving the operational efficiency of automated equipment in factories and enabling faster response to anomalies.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The server receives data in different formats from multiple sources. This data includes information about the ID, status, and location of automated equipment, and is treated as input.

[0680] Step 2:

[0681] The server inputs the received data in different formats into a generating AI model. The AI ​​model analyzes each piece of data based on prompts and converts it into a unified format. This conversion standardizes data from different sources, resulting in an integrated output.

[0682] Step 3:

[0683] The server processes automated equipment information converted into a unified format in real time. As part of the processing, it monitors the operating status of each device and stores its status (normal, abnormal, etc.) in a database. This allows the current operating status to be provided as output.

[0684] Step 4:

[0685] The server monitors device operating status data in the database and, if an anomaly is detected, generates a notification message using a generation AI model. This process uses the nature of the anomaly and the location information of the affected device as input and produces a detailed notification message for administrators as output.

[0686] Step 5:

[0687] A device (for example, the administrator's smartphone or head-mounted display) receives notification messages sent from the server. The administrator can view these in real time and take specific actions to address anomalies. By using the receipt of notifications as input and the administrator's action plan as output, the overall operational efficiency of the system is optimized.

[0688] 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.

[0689] This invention enhances the user experience by incorporating an emotion engine that recognizes user emotions, in addition to a system that comprehensively manages various IoT devices. The introduction of the emotion engine allows the system to dynamically adjust the way information is presented and the content of maintenance notifications according to the user's emotional state.

[0690] Specifically, the server integrates and manages data from IoT devices in real time, and analyzes user emotion data received from the devices through an API provided by the emotion engine. This emotion data is typically extracted from the user's voice, facial expressions, and behavioral patterns.

[0691] The server analyzes the user's emotional state and dynamically changes the interface design and information prioritization based on the results. For example, if the user is stressed, the server simplifies the information displayed and highlights only the most important information. If emotional data indicates that the user is positive towards the information, it can present more detailed data or additional options.

[0692] Furthermore, the emotion engine continuously learns from user feedback, improving its emotion recognition accuracy to suit individual users. This allows the system to adapt to users over time and provide a more personalized experience.

[0693] For example, if the emotion engine detects stress while a user is reviewing energy consumption data within a building, the server displays a simplified, visually easy-to-understand graph and prompts the user to confirm whether further information is needed. This approach allows users to utilize the system without stress.

[0694] As described above, by incorporating an emotion engine, the present invention provides a user-centered, interactive, and user-friendly IoT device management system.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] After the IoT device is installed, the terminal uploads data received from the device, along with user voice and image data, to the system.

[0698] Step 2:

[0699] The server receives the uploaded user audio and image data and prepares it to analyze the user's emotional state using the emotion engine API.

[0700] Step 3:

[0701] The server activates the emotion engine and identifies the user's emotional state, such as positive, negative, or neutral, based on their voice tone and facial expression analysis.

[0702] Step 4:

[0703] The server adjusts the interface layout and displayed information appropriately based on the user's emotional state, providing information in a way that is sensitive to the user's feelings.

[0704] Step 5:

[0705] Users access the status and related information of IoT devices and perform necessary actions through a customized interface. The displayed content changes according to the user's emotions, allowing them to receive information without stress.

[0706] Step 6:

[0707] The server continuously collects user feedback and provides it to the emotion engine as training data, thereby improving the accuracy of emotion recognition for each user.

[0708] Step 7:

[0709] If a user's emotional state is negative, the server will resend important notifications and alerts in a concise format to reduce stress while ensuring necessary action is taken.

[0710] Step 8:

[0711] Based on the data obtained from the emotion engine, the server performs analysis for future system improvements and develops strategies to provide even more effective user interactions.

[0712] (Example 2)

[0713] 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".

[0714] In real-time device management systems that handle diverse data formats, a challenge lies in the difficulty of configuring interfaces that reflect the user's emotional state. While conventional methods can achieve integrated management of device information and generation of maintenance notifications, they are insufficient for interactive interface adjustments that improve the user experience.

[0715] 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.

[0716] In this invention, the server includes means for receiving sensor data in different formats and structuring it into a unified format, means for managing and integrating the structured device information in real time, and means for analyzing the user's emotional state using an emotion analysis engine and adjusting the interface display. This enables the presentation of dynamic and personalized information based on the user's emotional state.

[0717] "Sensor data in different formats" refers to data in multiple formats acquired from various sensors.

[0718] "Structuring data into a unified format" refers to the process of converting data in different formats into a standardized, common format.

[0719] "Structured device information" refers to data that is organized according to a unified format and hierarchically or categorized.

[0720] "Managing and integrating in real time" refers to a process where data is managed and integrated as soon as it is generated, with the goal of making the data immediately available.

[0721] An "emotion analysis engine" refers to algorithms and software used to analyze a user's emotional state from their voice, facial expressions, and behavior.

[0722] "Adjusting the interface display" refers to the process of changing the screen design and displayed content based on the user's emotional state.

[0723] "Dynamic and personalized information presentation based on the user's emotional state" refers to a function that provides information optimized according to the user's current emotions.

[0724] The embodiments for carrying out the present invention will be described in detail below.

[0725] This system manages data acquired from various sensor devices in real time through interactions between servers, terminals, and users, and provides dynamic information tailored to the user's emotional state.

[0726] Hardware and software usage:

[0727] The server, utilizing either a cloud-based or on-premises network system, is the core component responsible for data collection, integration, and analysis. The server implements a generative AI model for sentiment analysis. This AI model includes algorithms for speech recognition, facial recognition, and behavioral pattern analysis, and acts as the sentiment analysis engine.

[0728] The terminal is a device that acts as an interface with the user, collecting data and performing user interaction. This includes a microphone for voice input, a camera for facial recognition, and a touchscreen for acquiring data on operation patterns.

[0729] Data processing and calculations:

[0730] The server receives sensor data in various formats transmitted from the terminal and structures the data into a unified format. Noise filtering and data cleaning are performed during this process to generate a highly reliable dataset. Next, using the data converted to the unified format, a generative AI model performs sentiment analysis and quantifies the user's emotional state.

[0731] Based on these analysis results, the server adjusts the user interface display to present information tailored to the user's emotions. For example, if the user is stressed, the information is presented concisely and clearly; if they are relaxed, more detailed information and additional options are provided.

[0732] Examples of specific cases and prompt statements:

[0733] As a concrete example, suppose a user is checking their home energy usage and the system detects stress from the user's tone of voice and facial expression. In this case, the server provides a simple graphical interface and highlights only the important data points.

[0734] An example of a prompt message could be, "Analyze the user's emotional response to the displayed information." This prompt helps the generative AI model analyze the user's emotional state and adjust the interface accordingly.

[0735] This system aims to significantly improve the user experience and reduce stress.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The device collects the user's voice using a microphone, captures their facial expressions with a camera, and records their operation patterns with sensors. This data is temporarily stored within the device as initial input data. Next, the device compresses this data and sends it to the server. The input consists of voice, image, and operation signal data, and the compressed data is sent to the server as output.

[0739] Step 2:

[0740] The server decompresses the compressed data received from the terminal and performs data cleaning to remove noise. The decompressed data is structured into a unified format and processed to enhance reliability. The input is compressed user data, and the output is formatted data in a unified format. Specific operations include timestamp matching and filtering of invalid data.

[0741] Step 3:

[0742] The server sends the obtained data in a unified format to the emotion analysis engine. Using a generative AI model, the analysis is performed to identify the user's emotional state. The input is formatted user data, and the output is data converted into emotion indicators. This analysis process includes voice tone analysis, facial expression analysis, and evaluation of behavioral patterns.

[0743] Step 4:

[0744] The server adjusts the information presentation interface based on the user's emotional state, using the results of the emotion analysis. This includes dynamically changing the screen design and prioritizing the displayed information. The input is emotion index data, and the output is the adjusted interface design. Specifically, if stress is detected, a simple infographic is displayed to reduce the complexity of the information.

[0745] Step 5:

[0746] The user reviews the information presented by the server and performs actions or provides feedback. This feedback is then sent back to the server via the terminal. The input consists of the user's interaction and feedback, while the output is used as training data for the system to improve the accuracy of subsequent sentiment analysis. A concrete example is when the user clicks a button to request more information.

[0747] (Application Example 2)

[0748] 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".

[0749] Conventional IoT device management systems have struggled to adjust information presentation methods and environments in response to the user's emotional state. This often leads to users experiencing stress while using the system, resulting in a lack of a comfortable user experience. In particular, there is a strong need for a system that appropriately reflects the emotional states of both the driver and passengers and optimizes the in-vehicle environment within autonomous vehicles.

[0750] 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.

[0751] In this invention, the server includes means for receiving data in different formats and structuring it into a unified format, means for analyzing the user's emotional state, dynamically adjusting the way information is presented, and manipulating the vehicle environment, and means for managing the device lifecycle and identifying and deleting information on unnecessary devices. This enables the optimization of the interface and adjustment of the in-vehicle environment in accordance with the user's emotional state.

[0752] "Data in different formats" refers to diverse data formats obtained from different sources or protocols, and is used to facilitate unification under common operational standards.

[0753] A "unified format" is a standard for converting diverse data formats into a single, standardized format, and is used to facilitate information exchange between different devices.

[0754] "Structured device information" refers to device-related information systematically organized according to a specific data format, enabling rapid access and analysis.

[0755] "User emotional state" refers to the emotional responses that a user exhibits under specific circumstances, and is detected through voice, facial expressions, and behavioral patterns.

[0756] "Dynamically adjusting how information is presented" refers to the process of changing how information is displayed and delivered in real time based on the user's current emotional state.

[0757] "Manipulating the vehicle environment" refers to operations that control physical or virtual elements within the vehicle to make adjustments that improve the riding experience.

[0758] The system for carrying out this invention includes a server for receiving data in different formats and structuring it into a unified format. The server also analyzes the user's emotional state in real time and dynamically adjusts the way information is presented based on the analysis results. Furthermore, it is capable of generating and transmitting commands necessary to operate the environment inside the vehicle.

[0759] This system is implemented using an emotion engine API to detect the user's emotional state based on their voice, facial expressions, and operation patterns. Based on the obtained emotional state, the server operates the navigation system and vehicle environment control system to make environmental adjustments, including route selection, music playback, and temperature control. The hardware consists of edge devices, cameras, and microphones, while the software uses emotion recognition algorithms and navigation control APIs.

[0760] For example, if a user feels stressed while driving during a family trip, this system detects that emotion, selects the most scenic route, and plays relaxing music to provide the user with a comfortable environment. This reduces stress and allows the user to enjoy a safer and more comfortable driving experience.

[0761] An example of an input prompt for a generative AI model is: "Explain how to use the emotion engine to promote a comfortable driving experience if passengers in the car are feeling stressed."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The device collects user emotion data, including voice and facial expression data. This input data is sent to the emotion engine. The sensors used by the device include a microphone and a camera. This allows the device to capture the user's emotional state in real time.

[0765] Step 2:

[0766] The server analyzes the data received through the emotion engine API. This analysis classifies the user's emotional state. The server processes data such as voice tone and facial features to determine emotions such as stress, comfort, and surprise, and outputs the results.

[0767] Step 3:

[0768] The server generates appropriate vehicle environment control commands based on the analyzed emotional state. For example, if the server determines that the user is stressed, it will generate commands to play relaxing music or adjust the temperature, and instruct the navigation system to select a scenic route. This ensures that the output is tailored to the user's emotional state.

[0769] Step 4:

[0770] The server sends the generated vehicle environment control commands to the relevant systems within the vehicle. This causes the navigation system to select an unconventional route, the audio system to play specified music, and the air conditioning system to adjust the temperature. As a result of this process, feedback is sent to the user, dynamically improving the user experience.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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."

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A means of receiving data in different formats and structuring it into a unified format,

[0795] A means of managing and integrating structured device information in real time,

[0796] A means of monitoring the device status and automatically generating maintenance notifications,

[0797] A means of providing integrated device information via a user interface,

[0798] A means to manage the lifecycle of devices, identify and delete information about unnecessary devices,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the unified format for structured device information is a standard that covers document formats provided by multiple different vendors.

[0802] (Claim 3)

[0803] The system according to claim 1, which includes an algorithm that generates maintenance notifications based on device lifecycle information.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A means of receiving electronic data in different formats and converting it into a unified format using a generation AI model,

[0807] A means of aggregating information converted to a unified format into a data storage device and managing it in real time,

[0808] A means for monitoring the status of the device and automatically generating maintenance notifications based on prompt messages,

[0809] A means of providing integrated information through a user interface,

[0810] A means to manage the lifecycle of a device and identify and delete unnecessary device information,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the unified format is a standard that encompasses data formats provided by multiple different suppliers.

[0814] (Claim 3)

[0815] The system according to claim 1, comprising a method for generating maintenance notifications based on the device's lifecycle information.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A means of receiving data in different formats and structuring it into a unified format,

[0819] A means for processing and integrating structured automated equipment information in real time,

[0820] A means for monitoring the operating status of automated equipment and automatically generating maintenance notifications,

[0821] A means of providing integrated automated equipment information via a user interface,

[0822] A means to manage the operational cycle of automated equipment, identify and delete unnecessary equipment information,

[0823] A means for detecting abnormalities related to automated equipment in real time and providing notifications to administrators,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein a unified format for structured automation equipment information is a standard that covers document formats provided by multiple different suppliers.

[0827] (Claim 3)

[0828] The system according to claim 1, which includes a sequence in which maintenance notifications are generated based on the operational cycle information of the automated equipment.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A means of receiving sensor data in different formats and structuring it into a unified format,

[0832] A means of managing and integrating structured device information in real time,

[0833] A means for monitoring the operating status of equipment and automatically generating maintenance notifications,

[0834] A means of analyzing the user's emotional state using an emotion analysis engine and adjusting the interface display accordingly,

[0835] A means of providing integrated device information via a user interface,

[0836] A means to manage the lifecycle of equipment, identify and delete unnecessary equipment information,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, wherein the unified format for structured device information is a standard that encompasses document formats provided by multiple different manufacturers.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the maintenance notification includes an algorithm that generates the notification based on the equipment lifecycle information and the user's emotional state.

[0842] "Application example 2 when combining with an emotional engine"

[0843] (Claim 1)

[0844] A means of receiving data in different formats and structuring it into a unified format,

[0845] A means of managing and integrating structured device information in real time,

[0846] A means of monitoring the device status and automatically generating maintenance notifications,

[0847] A means of providing integrated device information via a user interface,

[0848] A means to manage the lifecycle of devices, identify and delete information about unnecessary devices,

[0849] A means of analyzing the user's emotional state, dynamically adjusting the way information is presented, and manipulating the vehicle environment,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, wherein the unified format for structured device information is a standard that covers document formats provided by multiple different vendors.

[0853] (Claim 3)

[0854] The system according to claim 1, which includes an algorithm that generates maintenance notifications based on device lifecycle information. [Explanation of Symbols]

[0855] 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 receiving data in different formats and structuring it into a unified format, A means of managing and integrating structured device information in real time, A means of monitoring the device status and automatically generating maintenance notifications, A means of providing integrated device information via a user interface, A means to manage the lifecycle of devices, identify and delete information about unnecessary devices, A system that includes this.

2. The system according to claim 1, wherein the unified format for structured device information is a standard that covers document formats provided by multiple different vendors.

3. The system according to claim 1, which includes an algorithm that generates maintenance notifications based on device lifecycle information.

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