system

Abstract

We provide the system. [Solution] A means of using multiple sensor devices to acquire environmental data, Means for transmitting data acquired from the aforementioned sensor device via wireless communication, A means for receiving transmitted data and storing it in a database, A means for analyzing the data stored in the aforementioned database and executing an artificial intelligence algorithm for detecting anomalies, A means for generating a warning message based on detected anomalies and notifying the user interface, 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, 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 recent years, while preventive measures against aging and natural disasters of buildings and structures have been emphasized, it has been difficult to quickly detect and respond to abnormalities in conventional manual inspections and maintenance work. For this reason, there is a demand for a system that can efficiently monitor the safety of buildings and enable early detection and prediction of abnormalities.

Means for Solving the Problems

[0005] This invention acquires environmental data in real time using multiple sensor devices and transmits that data to a cloud server via wireless communication. The cloud server stores the received data in a database and performs anomaly detection and predictive analysis using artificial intelligence algorithms. It also generates warning messages for detected anomalies and notifies the user through a user interface. In this way, the building's condition is constantly monitored, enabling rapid response.

[0006] "Environmental data" refers to information that numerically represents the physical conditions and changes of buildings and structures, such as temperature, humidity, vibration, sound, and gases, both inside and outside the building.

[0007] A "sensor device" is a device that detects physical environmental data and outputs it as electrical signals or digital data.

[0008] "Wireless communication" is a technology that uses radio waves to transmit data to a remote location, conveying information without physical media such as cables.

[0009] A "cloud server" is a computer system that processes, stores, and manages data on a remote server accessible via the internet.

[0010] A "database" is a system for efficiently storing, searching, and managing digital data.

[0011] An "artificial intelligence algorithm" is a program or computational procedure that allows a computer to perform analysis and judgments that mimic human intellectual activity based on given data.

[0012] Anomaly detection refers to the process of identifying and specifying patterns or data that deviate from standard or normal conditions.

[0013] "Predictive analytics" is an analytical method that uses past data to predict future trends and events.

[0014] A "warning message" is a message containing information to notify the user of abnormal conditions or risks detected by the system and to draw their attention to them.

[0015] A "user interface" is a collection of screens and operating devices that visualize and operate the functions of a system in a way that makes them easy for the user to operate. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

[0021] In the following embodiments, the 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 disk (e.g., hard disk), or magnetic tape, etc.

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention is a system in which multiple sensor devices installed in buildings and structures collect environmental data in real time and transmit that information wirelessly to a cloud server. The sensor devices monitor indicators such as temperature, humidity, vibration, sound, and gas, and transmit this data at appropriate intervals.

[0038] When the server receives data transmitted from the sensor, it first stores it in a database. Based on the received data, the server uses artificial intelligence algorithms to analyze the data and detect anomalies that deviate from normal patterns. The server can also use past data to predict future anomaly occurrences.

[0039] If an anomaly is detected, the server promptly generates a warning message and notifies the user interface. This allows users to understand the building's status in real time and take quick action as needed. For example, if a vibration sensor detects vibrations exceeding a certain threshold, the server will determine this to be an anomaly and immediately inform the user of the situation, enabling early arrangement of maintenance or repairs.

[0040] The user interface visualizes feedback from the cloud server on a dashboard, displaying trends in sensor data and warnings in graph and report formats. This allows users to intuitively understand the building's status and take appropriate action.

[0041] In this way, this system enhances building safety and enables efficient maintenance and cost reduction.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed within the building. The terminal temporarily stores this data and performs initial filtering to remove noise and abnormal values.

[0045] Step 2:

[0046] The terminal transmits processed data to a cloud server using wireless communication technology. Wi-Fi or LTE is used for transmission, and a timestamp and sensor ID are added to the data.

[0047] Step 3:

[0048] The server receives data sent from the terminal. The received data is structured, formatted into the appropriate format, and then stored in the database.

[0049] Step 4:

[0050] The server analyzes the data stored in the database and runs an artificial intelligence algorithm to detect anomalies by comparing it to existing data patterns. In this process, any deviation from the normal pattern is identified as an anomaly.

[0051] Step 5:

[0052] Based on the detection results of anomalies, the server predicts future anomalies from past data. Machine learning algorithms are used for prediction, and risk assessments are performed based on quantitative indicators.

[0053] Step 6:

[0054] If the server detects an anomaly or foreshadows a risk, it generates a warning message and notifies the user interface. This warning message includes details of the detected anomaly and recommended countermeasures.

[0055] Step 7:

[0056] Users receive warning messages on the interface and analyze visualized data. If necessary, users can send instructions for maintenance to the relevant maintenance staff or department.

[0057] Step 8:

[0058] The user interface displays received information on a dashboard and updates it in real time to support users in data analysis and decision-making.

[0059] (Example 1)

[0060] 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."

[0061] Modern buildings and structures require improved safety and operational efficiency. However, conventional technologies struggle to analyze environmental information from sensors in real time, quickly detect anomalies, and predict future anomalies. There is a need to provide a system that solves this problem and enables faster and more accurate anomaly detection and prediction.

[0062] 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.

[0063] In this invention, the server includes means for executing a machine learning algorithm to analyze information stored on an information recording medium and detect deviations from normal patterns, means for generating warning notifications based on detected deviations and notifying the user screen, and means for performing predictive analysis to predict future anomalies based on past information. This enables real-time anomaly detection and prediction of future anomalies.

[0064] "Environmental information" refers to data that shows the conditions of temperature, humidity, vibration, sound, gas, etc., inside and outside buildings and structures.

[0065] A "sensor device" is hardware used to detect specific environmental information and acquire that information as a digital signal.

[0066] Wireless communication is a technology that transmits and receives digital information using radio waves without using cables or wires.

[0067] An "information recording medium" is a data storage system for storing received environmental information, such as a database.

[0068] A "machine learning algorithm" is a programming technique that learns patterns from large amounts of data and autonomously detects anomalies in new data.

[0069] A "warning notification" is a cautionary message generated when an abnormality exceeding a predetermined standard is detected.

[0070] A "user screen" is an interface that visually displays warning notifications and analysis results, allowing users to easily understand the information.

[0071] "Predictive analytics" is a technique that uses past data to predict anomalies that may occur in the future.

[0072] This invention is a system for detecting anomalies in real time by analyzing environmental information collected by multiple sensor devices installed in buildings and structures on a server in the cloud. Specifically, the sensor devices acquire information such as temperature, humidity, vibration, sound, and gas, and transmit this information to the server using wireless communication.

[0073] The server first stores the received information on a data storage medium, such as a cloud database. Common cloud services can be used for this purpose. The server then analyzes this data using machine learning algorithms, and tools such as TENSORFLOW® and PyTorch can be used for analysis. This allows for the rapid detection of deviations from normal patterns, as well as the use of historical data to predict the likelihood of future anomalies.

[0074] When an anomaly is detected, the server immediately generates a warning notification and sends it to the user terminal. The user terminal displays this on the user screen, visualizing the information in real time in the form of graphs and reports. This allows users to intuitively understand the status of buildings and structures and take prompt action as needed.

[0075] For example, if a vibration sensor detects vibrations exceeding a set threshold, the server will determine this to be an anomaly and send a warning to the user, enabling early maintenance arrangements.

[0076] Furthermore, an example of a prompt message when using a generative AI model could be: "Based on temperature and humidity sensor data, please predict the likelihood of anomalies occurring over the next week and create a report." This would enable more advanced and efficient monitoring and management.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The sensor device acquires environmental information within the building at pre-set intervals. Specifically, it samples data such as temperature, humidity, vibration, sound, and gas every 5 minutes. This acquired data becomes the input, and the sensor device transmits the data to a cloud server using a wireless communication module. The output is digital data sent as a wireless signal.

[0080] Step 2:

[0081] The server receives wireless signals transmitted from the sensor device. The input includes sensor data, which the server saves to a cloud database, a data storage medium. Specifically, it checks the data format for consistency, corrects any inconsistencies, and then records the data along with a timestamp. The output is the saved sensor data.

[0082] Step 3:

[0083] The server executes machine learning algorithms based on data stored in a cloud database. The input is stored sensor data, and the data is analyzed using TensorFlow or PyTorch. Specifically, the data is passed through a model trained on the normal range to detect anomalies. The output is the detected anomaly pattern and its detailed information.

[0084] Step 4:

[0085] The server generates warning notifications based on detected anomalies. The input is data on anomaly patterns, and a Python script is used to generate warning messages. Specifically, it formats detailed information such as the type, location, and time of the anomaly into a text message, and includes recommended actions based on the error level. The output is the formatted warning message.

[0086] Step 5:

[0087] The user terminal receives warning messages sent from the server. The input is the warning message, which the user terminal displays on its screen. Specifically, the system visualizes the anomaly information in real time on the dashboard and presents it in graph and report formats so that the user can understand it intuitively. The output is the visual information displayed to the user.

[0088] (Application Example 1)

[0089] 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."

[0090] Ensuring safety and efficient operation within factories and buildings today is time-consuming and costly. Furthermore, immediate detection and response to environmental changes and equipment malfunctions are required, but conventional systems struggle to provide such immediate responses, potentially leading to accidents and problems. To address these challenges, a system is needed that collects environmental information in real time, rapidly detects and notifies of anomalies, and enables appropriate responses.

[0091] 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.

[0092] In this invention, the server includes means for using a plurality of detection devices for acquiring environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for using a wearable display device that displays the warning notification in real time and enables a rapid response. This enables safe and efficient operation by immediately detecting abnormalities in the environment and notifying the user immediately.

[0093] "Environmental information" refers to data indicating physical conditions such as temperature, humidity, and vibration acquired within buildings and factories.

[0094] A "detection device" is a sensor device installed to acquire environmental information, allowing for real-time data collection.

[0095] "Wireless communication" is a communication method used to transmit information from a sensor device to a server, enabling data transmission without the need for cables.

[0096] An "information recording device" is a storage device within a system that stores received environmental information, and it serves as a database.

[0097] An "intelligent algorithm" is a computational process that uses artificial intelligence technology to analyze stored environmental information and detect anomalies.

[0098] A "warning notification" is a message generated based on detected anomalies, and its role is to promptly inform the user of any danger.

[0099] A "terminal device" is a device used by a user to receive information and check analysis results and warning notifications.

[0100] A "wearable display device" refers to a device that displays information in a form that can be worn by the user, and typically includes smart glasses or headsets.

[0101] "Charts and graphs" are a format for visually presenting analyzed data and notification content, and are important for facilitating intuitive understanding among users.

[0102] A "report format" is a document format that systematically summarizes analysis results and alerts and presents them in a way that is easy for users to understand.

[0103] To implement this invention, first, multiple detection devices are installed at points within a building or factory where monitoring is required. These detection devices acquire environmental information such as temperature, humidity, and vibration in real time and transmit the information to a server using wireless communication technology.

[0104] The server stores the received information in an information recording device and analyzes this information using an intelligent algorithm. The intelligent algorithm detects anomalies from the recorded information and generates a warning notification based on them. This notification is immediately sent to the terminal device.

[0105] The terminal device plays a role in visualizing notifications and analysis results so that users can quickly check information. This uses charts and reports, allowing users to intuitively understand the information. Furthermore, by using a wearable display device, users can receive warning notifications in real time, regardless of their location.

[0106] As a concrete example, if a temperature anomaly occurs in the refrigeration section of a factory, a temperature sensor will detect it, and an intelligent algorithm will determine that an anomaly has occurred. A warning notification will immediately appear on a wearable display device, allowing the user to take prompt action. This seamless coordination of processes is expected to improve both safety and efficiency.

[0107] An example of a prompt message for the generating AI model could be: "We have obtained temperature and humidity sensor data from inside the factory. Please suggest how to display an alert if an accurate temperature increase is detected."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server receives environmental information in real time via wireless communication from multiple detection devices. The input is environmental data such as temperature, humidity, and vibration, and the output is the storage of the data in an information recording device.

[0111] Step 2:

[0112] The server analyzes the data stored in the information recording device using intelligent algorithms. In this step, it uses the environmental data received as input to perform data calculations that detect anomalies in the data. The output is detailed information about the detected anomalies.

[0113] Step 3:

[0114] The server generates a warning notification when an anomaly is detected. The input is the result of the anomaly detection, and based on that information, it generates a specific warning message. The output is the warning notification.

[0115] Step 4:

[0116] The server sends the generated warning notification to the terminal device. The input is the warning notification, and the output is the reception status on the terminal device. This immediately notifies the user.

[0117] Step 5:

[0118] The terminal visualizes received warning notifications and analysis results in charts and reports. Input consists of warning notifications and analysis results, which are processed into an intuitively understandable format. Output is the visualized information displayed on the user screen.

[0119] Step 6:

[0120] Users can receive real-time warning notifications through a wearable display device and take necessary actions quickly. The input is a visualized warning notification, and the output is the corresponding action to be taken. This step enables rapid response in the field.

[0121] 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.

[0122] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The sensor devices acquire environmental data such as temperature, humidity, vibration, sound, and gas in real time and transmit this data to a cloud server via wireless communication.

[0123] The server receives data transmitted from sensors and stores it in a database. It then runs an artificial intelligence algorithm to analyze the data and detect anomalies. This algorithm also performs predictive analysis based on past data, providing information to mitigate the risk of future anomalies.

[0124] In addition, this system features an emotion engine that recognizes the user's emotions. Through this emotion engine, the server infers the user's emotions via the user interface and customizes the content and presentation of warning messages to the user. Specifically, if the user shows signs of caution or anxiety, the server provides a more detailed and reassuring message.

[0125] The user interface visualizes information on the dashboard, displaying received warning messages and analysis results in graphs and reports. It also employs a user-friendly design to enable users to take quick action based on the information. For example, if the system detects that a structure's vibration level exceeds safety standards, the emotional engine recommends a reassuring message to the user detailing the cause of the vibration and how to contact the administrator.

[0126] This system ensures the safety of buildings, enables efficient maintenance, and reduces the mental burden on users.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed inside the building. This data is temporarily stored inside the terminal and initial filtering is performed as needed.

[0130] Step 2:

[0131] The device uses wireless communication to transmit collected environmental data to a cloud server. The data includes metadata such as timestamps and sensor IDs.

[0132] Step 3:

[0133] The server receives data sent from the terminal and stores it in a database. The received data is formatted as needed and prepared for analysis by AI algorithms.

[0134] Step 4:

[0135] The server activates an artificial intelligence algorithm to analyze the data stored in the database. The purpose of the analysis is to detect anomalies by comparing the data patterns with those of normal conditions, and special tags are assigned to patterns that are deemed abnormal.

[0136] Step 5:

[0137] The server uses the data from which anomaly detection processing has been completed to predict and analyze potential risks that may occur in the future. This prediction process references historical data, preparing for proactive responses to future anomalies.

[0138] Step 6:

[0139] The server generates warning messages based on anomalies and risk predictions. Furthermore, it utilizes an emotion engine to analyze the user's emotions and customize the warning messages to a format that is more acceptable to the user.

[0140] Step 7:

[0141] The user interface receives warning messages and analysis results from the server. The received data is visualized on the dashboard and displayed as graphs and reports in a format that is easy for the user to understand.

[0142] Step 8:

[0143] Users take necessary actions based on the information provided through the interface. For example, if the anomaly is severe, they can contact the administrator and arrange for immediate action.

[0144] Step 9:

[0145] The emotion engine analyzes user actions and reactions and stores them in a database as long-term data. This record is used to generate future messages and to provide more appropriate responses to the user.

[0146] (Example 2)

[0147] 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".

[0148] Modern buildings and structures require both safety and efficient maintenance. However, monitoring these in real time and accurately detecting and predicting anomalies is not easy. Furthermore, a challenge remains in providing information that takes into account the feelings of users, resulting in insufficient reduction of user anxiety in response to warnings.

[0149] 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.

[0150] In this invention, the server includes means for using multiple detection devices to acquire environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for receiving the transmitted information and storing it in a storage device. This makes it possible to monitor the safety of a building in real time and perform anomaly detection and predictive analysis. Furthermore, by using emotional state estimation means, it is possible to reduce anxiety by providing appropriate information based on the user's emotions.

[0151] "Environmental information" refers to data that indicates the environmental conditions surrounding a building or structure, such as temperature, humidity, vibration, sound, and gas.

[0152] A "detection device" is a device that includes sensors that measure environmental information and output it as digital data.

[0153] "Wireless communication" is a technology that uses radio waves to send and receive data, and it is the method used for information transmission between the sensor device and the server in this system.

[0154] A "storage device" refers to a digital data storage device that can permanently store data and is used to securely store received information.

[0155] A "machine learning algorithm" is a computational method that allows computers to learn from data, recognize patterns, and make decisions.

[0156] A "generative AI model" is an artificial intelligence model that uses large amounts of data to perform pattern recognition and generation.

[0157] "Emotional state inference means" refers to technology for determining a user's emotions based on their input actions.

[0158] An "output device" is hardware used to transmit information to the user, and is a device used to visualize warning messages or analysis results.

[0159] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The system acquires environmental information such as temperature, humidity, vibration, sound, and gas, and transmits it to a server via wireless communication. The server stores the received data in a database and analyzes anomalies in the data using machine learning algorithms. This analysis uses a generative AI model, which learns patterns from the data and can detect anomalies with high accuracy.

[0160] The server predicts the risk of future anomalies based on historical data. This process involves referencing past data and using machine learning to identify patterns that may lead to anomalies. Furthermore, the server analyzes user emotions in real time through emotion state prediction mechanisms and customizes warning messages accordingly. To alleviate user anxiety, it can generate warning messages containing detailed information.

[0161] The user terminal receives these warning messages and analysis results, and visualizes the information through a graphical interface. This allows users to immediately grasp the situation and take necessary actions. This system is particularly important for administrators to quickly check the status of buildings and maintain safety.

[0162] As a concrete example, if vibrations in a building exceed a certain threshold, the server immediately detects this and, while monitoring the user's reaction using emotional state estimation tools, sends a warning message to the user's terminal containing a detailed explanation to provide reassurance. This allows the user to obtain information about the cause of the vibrations and receive support to contact the appropriate administrator.

[0163] (Example of a prompt message)

[0164] "Please generate a warning message to send to the user after an anomaly is detected. The message should be reassuring to the user."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server acquires environmental information from multiple sensor devices installed in the building. Real-time data from each sensor is transmitted as input, and this data includes temperature, humidity, vibration, sound, gas, etc. The server receives the data via wireless communication and temporarily stores it. During this process, the server verifies the integrity of the data and confirms that accurate information has been obtained.

[0168] Step 2:

[0169] The server stores the received environmental information in a database. The input data consists of individual measurements from each sensor device, and the server saves this data in the database in an appropriate format. Storing the data in the database accumulates historical data that can be used for subsequent analysis. The server organizes and stores the data by adding timestamps and sensor device identifiers.

[0170] Step 3:

[0171] The server executes machine learning algorithms based on stored data to detect anomalies. Using database information as input, the algorithm extracts patterns from the data using a generative AI model and detects outliers. Through this process, the server understands the occurrence of anomalies in real time and sets flags according to the degree of anomaly.

[0172] Step 4:

[0173] The server uses detected anomaly information to infer the user's emotional state and generate an appropriate warning message. It considers anomaly data and the user's past response data as input, and uses an emotional state inference method to determine the user's psychological state. Based on this analysis, a generation AI model creates a warning message using prompts and sends it to the user.

[0174] Step 5:

[0175] The user terminal receives and visualizes warning messages and analysis results sent from the server. It receives data from the server as input and displays it graphically on a dashboard on the terminal. Based on the message content and analysis results, the user can view detailed information and take necessary actions. Specifically, the user clicks on each warning message to view additional information and solutions.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] This invention relates to a system that monitors the safety of buildings and structures in real time and provides appropriate warnings to users when abnormalities are detected. The objective is to reduce user stress and facilitate quick and appropriate responses by providing warnings tailored to the user's emotional state. Furthermore, in factories where a large amount of information is aggregated, it is difficult for workers to constantly monitor the situation; therefore, the development of a system that efficiently processes information and provides optimal feedback to workers is also necessary.

[0179] 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.

[0180] In this invention, the server includes means for using multiple sensor devices to acquire environmental information, means for analyzing information stored in an information processing device and executing an intelligent machine algorithm to detect anomalies, and means for analyzing the user's emotions and changing the content and method of warning notifications based on the user's emotional state. This enables workers to grasp the current situation in real time and provide optimal information according to the user's situation. Furthermore, by customizing the warning content, it is possible to obtain feedback that is easier for the user to understand and more beneficial.

[0181] "Environmental information" refers to data that shows the conditions around a building or structure, and is acquired by sensors such as those for temperature, humidity, vibration, sound, and gas.

[0182] A "sensor device" is a mechanical device used to detect environmental information and is capable of acquiring multiple types of data.

[0183] "Wireless communication" is a means of communication for transmitting data without physical contact, and is a technology that uses radio waves to send information to a remote location.

[0184] An "information processing device" is a computing device used to organize, store, and analyze received information as needed, and includes equipment such as a database.

[0185] An "intelligent machine algorithm" is a series of procedures for automating information processing and analysis, and is an algorithm that uses artificial intelligence technology to detect anomalies.

[0186] A "warning notification" is an informational message that alerts users to detected anomalies and recommends specific actions.

[0187] A "user screen" is an interface device for users to receive information and is equipped with the function of displaying information visually.

[0188] "Visualization" is a method of converting data and information into a form that is easy for humans to understand and displaying it in the form of diagrams, graphs, and other visual representations.

[0189] As an embodiment of the present invention, a safety monitoring system for use in a factory will be described. This system acquires environmental information from multiple sensor devices and transmits it to a cloud server via wireless communication. The server stores the received information in an information processing device and analyzes the data using an intelligent machine algorithm. This analysis detects anomalies and generates warning notifications as necessary.

[0190] The system's hardware utilizes various standard sensors (e.g., temperature sensors, vibration sensors) as sensor devices, and wireless communication technology is used for communication. The information processing unit uses a cloud-based database and an AI algorithm platform (e.g., TensorFlow).

[0191] The intelligent machine algorithm analyzes received environmental information to detect and predict abnormal patterns. Furthermore, to analyze the user's emotions, it performs emotion analysis using video data obtained from cameras installed in smart glasses or terminals. For this purpose, image processing libraries such as OpenCV are used. Based on the analysis results, it generates customized warning messages according to the user's emotional state and provides them to the user's screen.

[0192] For example, if the vibration of equipment in the factory exceeds the normal range, the system will analyze the cause and display a warning such as, "The vibration level is higher than normal, but our technicians are addressing the issue." If the user appears surprised upon receiving this message, additional information such as, "The specific cause of the vibration is motor wear. It will be replaced soon," will be provided to reassure them.

[0193] An example of a prompt is: "Create a specific use case for a real-time monitoring system for factory equipment. Also, explain how the alert messages will be customized based on the user's emotions."

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server acquires environmental information from multiple sensor devices installed in the factory. Inputs include data from each sensor (temperature, vibration, humidity, etc.). The server centralizes this data, standardizes the format, and receives it via wireless communication. The output is structured environmental information data that is updated in real time.

[0197] Step 2:

[0198] The server stores the received environmental information data in the database of the information processing device. The input data is the environmental information obtained in step 1. Here, the data is written to the database and saved for future analysis. The output is a clean dataset stored in the information processing device.

[0199] Step 3:

[0200] The server executes intelligent machine algorithms on environmental information stored in a database. The input is a dataset stored in an information processing device, and anomaly detection and future predictive analysis are performed based on this data. Specifically, it compares current data with past data and detects anomalies if a certain threshold is exceeded. The output is a flag indicating whether an anomaly was detected or not, and the related analysis results.

[0201] Step 4:

[0202] If an anomaly is detected, the server generates a warning notification and displays it on the user screen. The input is the anomaly detection flag and analysis result from step 3. Based on this, the server generates a warning message describing the nature of the anomaly and specific countermeasures. The output is a text message displayed on the user screen.

[0203] Step 5:

[0204] The device acquires video data from its built-in camera to analyze the user's emotions. The input is the user's facial data, which is used for emotion analysis. Specifically, it uses an emotion analysis library to identify the emotional state the user is exhibiting. The output is a parameter indicating the user's emotional state.

[0205] Step 6:

[0206] The server customizes the content and method of warning notifications based on the emotional state. The input is the emotional state parameters obtained in step 5, and the server uses this to customize existing warning messages. Specifically, if the user shows surprise or anxiety, it adds and provides reassuring content. The output is a customized warning message adapted to the emotional state.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention is a system in which multiple sensor devices installed in buildings and structures collect environmental data in real time and transmit that information wirelessly to a cloud server. The sensor devices monitor indicators such as temperature, humidity, vibration, sound, and gas, and transmit this data at appropriate intervals.

[0224] When the server receives data transmitted from the sensor, it first stores it in a database. Based on the received data, the server uses artificial intelligence algorithms to analyze the data and detect anomalies that deviate from normal patterns. The server can also use past data to predict future anomaly occurrences.

[0225] If an anomaly is detected, the server promptly generates a warning message and notifies the user interface. This allows users to understand the building's status in real time and take quick action as needed. For example, if a vibration sensor detects vibrations exceeding a certain threshold, the server will determine this to be an anomaly and immediately inform the user of the situation, enabling early arrangement of maintenance or repairs.

[0226] The user interface visualizes feedback from the cloud server on a dashboard, displaying trends in sensor data and warnings in graph and report formats. This allows users to intuitively understand the building's status and take appropriate action.

[0227] In this way, this system enhances building safety and enables efficient maintenance and cost reduction.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed within the building. The terminal temporarily stores this data and performs initial filtering to remove noise and abnormal values.

[0231] Step 2:

[0232] The terminal transmits processed data to a cloud server using wireless communication technology. Wi-Fi or LTE is used for transmission, and a timestamp and sensor ID are added to the data.

[0233] Step 3:

[0234] The server receives data sent from the terminal. The received data is structured, formatted into the appropriate format, and then stored in the database.

[0235] Step 4:

[0236] The server analyzes the data stored in the database and runs an artificial intelligence algorithm to detect anomalies by comparing it to existing data patterns. In this process, any deviation from the normal pattern is identified as an anomaly.

[0237] Step 5:

[0238] Based on the detection results of anomalies, the server predicts future anomalies from past data. Machine learning algorithms are used for prediction, and risk assessments are performed based on quantitative indicators.

[0239] Step 6:

[0240] If the server detects an anomaly or foreshadows a risk, it generates a warning message and notifies the user interface. This warning message includes details of the detected anomaly and recommended countermeasures.

[0241] Step 7:

[0242] Users receive warning messages on the interface and analyze visualized data. If necessary, users can send instructions for maintenance to the relevant maintenance staff or department.

[0243] Step 8:

[0244] The user interface displays received information on a dashboard and updates it in real time to support users in data analysis and decision-making.

[0245] (Example 1)

[0246] 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."

[0247] Modern buildings and structures require improved safety and operational efficiency. However, conventional technologies struggle to analyze environmental information from sensors in real time, quickly detect anomalies, and predict future anomalies. There is a need to provide a system that solves this problem and enables faster and more accurate anomaly detection and prediction.

[0248] 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.

[0249] In this invention, the server includes means for executing a machine learning algorithm to analyze information stored on an information recording medium and detect deviations from normal patterns, means for generating warning notifications based on detected deviations and notifying the user screen, and means for performing predictive analysis to predict future anomalies based on past information. This enables real-time anomaly detection and prediction of future anomalies.

[0250] "Environmental information" refers to data that shows the conditions of temperature, humidity, vibration, sound, gas, etc., inside and outside buildings and structures.

[0251] A "sensor device" is hardware used to detect specific environmental information and acquire that information as a digital signal.

[0252] Wireless communication is a technology that transmits and receives digital information using radio waves without using cables or wires.

[0253] An "information recording medium" is a data storage system for storing received environmental information, such as a database.

[0254] A "machine learning algorithm" is a programming technique that learns patterns from large amounts of data and autonomously detects anomalies in new data.

[0255] A "warning notification" is a cautionary message generated when an abnormality exceeding a predetermined standard is detected.

[0256] A "user screen" is an interface that visually displays warning notifications and analysis results, allowing users to easily understand the information.

[0257] "Predictive analytics" is a technique that uses past data to predict anomalies that may occur in the future.

[0258] This invention is a system for detecting anomalies in real time by analyzing environmental information collected by multiple sensor devices installed in buildings and structures on a server in the cloud. Specifically, the sensor devices acquire information such as temperature, humidity, vibration, sound, and gas, and transmit this information to the server using wireless communication.

[0259] The server first stores the received information on a data storage medium, such as a cloud database. Common cloud services can be used for this purpose. The server then analyzes this data using machine learning algorithms, with tools like TensorFlow and PyTorch available for analysis. This allows for the rapid detection of deviations from normal patterns, as well as the use of historical data to predict the likelihood of future anomalies.

[0260] When an anomaly is detected, the server immediately generates a warning notification and sends it to the user terminal. The user terminal displays this on the user screen, visualizing the information in real time in the form of graphs and reports. This allows users to intuitively understand the status of buildings and structures and take prompt action as needed.

[0261] For example, if a vibration sensor detects vibrations exceeding a set threshold, the server will determine this to be an anomaly and send a warning to the user, enabling early maintenance arrangements.

[0262] Furthermore, an example of a prompt message when using a generative AI model could be: "Based on temperature and humidity sensor data, please predict the likelihood of anomalies occurring over the next week and create a report." This would enable more advanced and efficient monitoring and management.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The sensor device acquires environmental information within the building at pre-set intervals. Specifically, it samples data such as temperature, humidity, vibration, sound, and gas every 5 minutes. This acquired data becomes the input, and the sensor device transmits the data to a cloud server using a wireless communication module. The output is digital data sent as a wireless signal.

[0266] Step 2:

[0267] The server receives wireless signals transmitted from the sensor device. The input includes sensor data, which the server saves to a cloud database, a data storage medium. Specifically, it checks the data format for consistency, corrects any inconsistencies, and then records the data along with a timestamp. The output is the saved sensor data.

[0268] Step 3:

[0269] The server executes machine learning algorithms based on data stored in a cloud database. The input is stored sensor data, and the data is analyzed using TensorFlow or PyTorch. Specifically, the data is passed through a model trained on the normal range to detect anomalies. The output is the detected anomaly pattern and its detailed information.

[0270] Step 4:

[0271] The server generates warning notifications based on detected anomalies. The input is data on anomaly patterns, and a Python script is used to generate warning messages. Specifically, it formats detailed information such as the type, location, and time of the anomaly into a text message, and includes recommended actions based on the error level. The output is the formatted warning message.

[0272] Step 5:

[0273] The user terminal receives warning messages sent from the server. The input is the warning message, which the user terminal displays on its screen. Specifically, the system visualizes the anomaly information in real time on the dashboard and presents it in graph and report formats so that the user can understand it intuitively. The output is the visual information displayed to the user.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Ensuring safety and efficient operation within factories and buildings today is time-consuming and costly. Furthermore, immediate detection and response to environmental changes and equipment malfunctions are required, but conventional systems struggle to provide such immediate responses, potentially leading to accidents and problems. To address these challenges, a system is needed that collects environmental information in real time, rapidly detects and notifies of anomalies, and enables appropriate responses.

[0277] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for using a plurality of detection devices for acquiring environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for using a wearable display device for displaying the warning notification in real time to enable prompt response. Thereby, by immediately detecting an abnormality in the environment and immediately notifying the user, safe and efficient operation becomes possible.

[0279] "Environmental information" refers to data indicating physical states such as temperature, humidity, and vibration acquired in a building or a factory.

[0280] "Detection device" refers to a sensor device installed for acquiring environmental information, whereby data can be collected in real time.

[0281] "Wireless communication" refers to a communication means used when transmitting information from a sensor device to a server, enabling transmission of data without the need for a cable.

[0282] "Information recording device" refers to a storage device within a system for storing the received environmental information, serving the role of a database.

[0283] "Intelligent algorithm" refers to an arithmetic process using artificial intelligence technology used for analyzing the stored environmental information and detecting abnormalities.

[0284] "Warning notification" refers to a message generated based on the detected abnormality, playing the role of promptly notifying the user of danger.

[0285] "Terminal device" refers to a device for a user to receive information and confirm analysis results and warning notifications.

[0286] The "wearable display device" refers to a device that displays information in a form that can be worn by a user, usually corresponding to smart glasses or headsets.

[0287] A "chart" is a form for visually presenting analyzed data or notification content, and is important for facilitating intuitive understanding by the user.

[0288] The "report format" is a document format that systematically summarizes analysis results and alerts and presents them in a form that is easy for the user to understand.

[0289] To implement this invention, first, a plurality of detection devices are installed at points in buildings or factories where monitoring is required. These detection devices acquire environmental information such as temperature, humidity, and vibration in real time and transmit the information to a server using wireless communication technology.

[0290] The server stores the received information in an information recording device and analyzes this information using intelligent algorithms. The intelligent algorithms detect anomalies from the recorded information and generate warning notifications based on them. This notification is immediately transmitted to the terminal device.

[0291] The terminal device is responsible for visualizing notifications and analysis results so that the user can immediately check the information. Charts and report formats are used for this, and the user can intuitively understand the information. Furthermore, by using a wearable display device, the user can receive warning notifications in real time regardless of location.

[0292] As a specific example, when a temperature anomaly occurs in the refrigeration section of a certain factory, the temperature sensor detects it, and the intelligent algorithm determines it as an anomaly. The warning notification is immediately displayed on the wearable display device, and the user can respond quickly. It is expected that the seamless cooperation in this process will improve safety and efficiency.

[0293] An example of a prompt message for the generating AI model could be: "We have obtained temperature and humidity sensor data from inside the factory. Please suggest how to display an alert if an accurate temperature increase is detected."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server receives environmental information in real time via wireless communication from multiple detection devices. The input is environmental data such as temperature, humidity, and vibration, and the output is the storage of the data in an information recording device.

[0297] Step 2:

[0298] The server analyzes the data stored in the information recording device using intelligent algorithms. In this step, it uses the environmental data received as input to perform data calculations that detect anomalies in the data. The output is detailed information about the detected anomalies.

[0299] Step 3:

[0300] The server generates a warning notification when an anomaly is detected. The input is the result of the anomaly detection, and based on that information, it generates a specific warning message. The output is the warning notification.

[0301] Step 4:

[0302] The server sends the generated warning notification to the terminal device. The input is the warning notification, and the output is the reception status on the terminal device. This immediately notifies the user.

[0303] Step 5:

[0304] The terminal visualizes the received warning notifications and analysis results in the form of charts and reports. The inputs are the warning notifications and analysis results, which are processed into a form that can be intuitively understood. The output is the visualized information displayed on the user screen.

[0305] Step 6:

[0306] The user can receive warning notifications in real time through the wearable display device and quickly take necessary actions. The input is the visualized warning notification, and the output is the corresponding action to be taken. This step enables rapid response on-site.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0308] This invention is a system that uses a plurality of sensor devices to monitor the safety of buildings and structures in real time. The sensor devices acquire environmental data such as temperature, humidity, vibration, sound, and gas in real time and transmit the data to a cloud server via wireless communication.

[0309] The server receives the data transmitted from the sensors and stores it in a database. Then, it executes an artificial intelligence algorithm to analyze the data and detect anomalies. This algorithm also performs predictive analysis based on past data and provides information for reducing the risk of future anomalies.

[0310] In addition, this system has an emotion engine for recognizing the user's emotion. The server推测 the user's emotion through the emotion engine via the user interface and customizes the content and method of the warning message for the user. Specifically, when the user shows vigilance or anxiety, the server provides a more detailed and reassuring message.

[0311] The user interface visualizes information on the dashboard, displaying received warning messages and analysis results in graphs and reports. It also employs a user-friendly design to enable users to take quick action based on the information. For example, if the system detects that a structure's vibration level exceeds safety standards, the emotional engine recommends a reassuring message to the user detailing the cause of the vibration and how to contact the administrator.

[0312] This system ensures the safety of buildings, enables efficient maintenance, and reduces the mental burden on users.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed inside the building. This data is temporarily stored inside the terminal and initial filtering is performed as needed.

[0316] Step 2:

[0317] The device uses wireless communication to transmit collected environmental data to a cloud server. The data includes metadata such as timestamps and sensor IDs.

[0318] Step 3:

[0319] The server receives data sent from the terminal and stores it in a database. The received data is formatted as needed and prepared for analysis by AI algorithms.

[0320] Step 4:

[0321] The server activates an artificial intelligence algorithm to analyze the data stored in the database. The purpose of the analysis is to detect anomalies by comparing the data patterns with those of normal conditions, and special tags are assigned to patterns that are deemed abnormal.

[0322] Step 5:

[0323] The server uses the data from which anomaly detection processing has been completed to predict and analyze potential risks that may occur in the future. This prediction process references historical data, preparing for proactive responses to future anomalies.

[0324] Step 6:

[0325] The server generates warning messages based on anomalies and risk predictions. Furthermore, it utilizes an emotion engine to analyze the user's emotions and customize the warning messages to a format that is more acceptable to the user.

[0326] Step 7:

[0327] The user interface receives warning messages and analysis results from the server. The received data is visualized on the dashboard and displayed as graphs and reports in a format that is easy for the user to understand.

[0328] Step 8:

[0329] Users take necessary actions based on the information provided through the interface. For example, if the anomaly is severe, they can contact the administrator and arrange for immediate action.

[0330] Step 9:

[0331] The emotion engine analyzes user actions and reactions and stores them in a database as long-term data. This record is used to generate future messages and to provide more appropriate responses to the user.

[0332] (Example 2)

[0333] 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".

[0334] Modern buildings and structures require both safety and efficient maintenance. However, monitoring these in real time and accurately detecting and predicting anomalies is not easy. Furthermore, a challenge remains in providing information that takes into account the feelings of users, resulting in insufficient reduction of user anxiety in response to warnings.

[0335] 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.

[0336] In this invention, the server includes means for using multiple detection devices to acquire environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for receiving the transmitted information and storing it in a storage device. This makes it possible to monitor the safety of a building in real time and perform anomaly detection and predictive analysis. Furthermore, by using emotional state estimation means, it is possible to reduce anxiety by providing appropriate information based on the user's emotions.

[0337] "Environmental information" refers to data that indicates the environmental conditions surrounding a building or structure, such as temperature, humidity, vibration, sound, and gas.

[0338] A "detection device" is a device that includes sensors that measure environmental information and output it as digital data.

[0339] "Wireless communication" is a technology that uses radio waves to send and receive data, and it is the method used for information transmission between the sensor device and the server in this system.

[0340] A "storage device" refers to a digital data storage device that can permanently store data and is used to securely store received information.

[0341] A "machine learning algorithm" is a computational method that allows computers to learn from data, recognize patterns, and make decisions.

[0342] A "generative AI model" is an artificial intelligence model that uses large amounts of data to perform pattern recognition and generation.

[0343] "Emotional state inference means" refers to technology for determining a user's emotions based on their input actions.

[0344] An "output device" is hardware used to transmit information to the user, and is a device used to visualize warning messages or analysis results.

[0345] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The system acquires environmental information such as temperature, humidity, vibration, sound, and gas, and transmits it to a server via wireless communication. The server stores the received data in a database and analyzes anomalies in the data using machine learning algorithms. This analysis uses a generative AI model, which learns patterns from the data and can detect anomalies with high accuracy.

[0346] The server predicts the risk of future anomalies based on historical data. This process involves referencing past data and using machine learning to identify patterns that may lead to anomalies. Furthermore, the server analyzes user emotions in real time through emotion state prediction mechanisms and customizes warning messages accordingly. To alleviate user anxiety, it can generate warning messages containing detailed information.

[0347] The user terminal receives these warning messages and analysis results, and visualizes the information through a graphical interface. This allows users to immediately grasp the situation and take necessary actions. This system is particularly important for administrators to quickly check the status of buildings and maintain safety.

[0348] As a concrete example, if vibrations in a building exceed a certain threshold, the server immediately detects this and, while monitoring the user's reaction using emotional state estimation tools, sends a warning message to the user's terminal containing a detailed explanation to provide reassurance. This allows the user to obtain information about the cause of the vibrations and receive support to contact the appropriate administrator.

[0349] (Example of a prompt message)

[0350] "Please generate a warning message to send to the user after an anomaly is detected. The message should be reassuring to the user."

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The server acquires environmental information from multiple sensor devices installed in the building. Real-time data from each sensor is transmitted as input, and this data includes temperature, humidity, vibration, sound, gas, etc. The server receives the data via wireless communication and temporarily stores it. During this process, the server verifies the integrity of the data and confirms that accurate information has been obtained.

[0354] Step 2:

[0355] The server stores the received environmental information in a database. The input data consists of individual measurements from each sensor device, and the server saves this data in the database in an appropriate format. Storing the data in the database accumulates historical data that can be used for subsequent analysis. The server organizes and stores the data by adding timestamps and sensor device identifiers.

[0356] Step 3:

[0357] The server executes machine learning algorithms based on stored data to detect anomalies. Using database information as input, the algorithm extracts patterns from the data using a generative AI model and detects outliers. Through this process, the server understands the occurrence of anomalies in real time and sets flags according to the degree of anomaly.

[0358] Step 4:

[0359] The server uses detected anomaly information to infer the user's emotional state and generate an appropriate warning message. It considers anomaly data and the user's past response data as input, and uses an emotional state inference method to determine the user's psychological state. Based on this analysis, a generation AI model creates a warning message using prompts and sends it to the user.

[0360] Step 5:

[0361] The user terminal receives and visualizes warning messages and analysis results sent from the server. It receives data from the server as input and displays it graphically on a dashboard on the terminal. Based on the message content and analysis results, the user can view detailed information and take necessary actions. Specifically, the user clicks on each warning message to view additional information and solutions.

[0362] (Application Example 2)

[0363] 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."

[0364] This invention relates to a system that monitors the safety of buildings and structures in real time and provides appropriate warnings to users when abnormalities are detected. The objective is to reduce user stress and facilitate quick and appropriate responses by providing warnings tailored to the user's emotional state. Furthermore, in factories where a large amount of information is aggregated, it is difficult for workers to constantly monitor the situation; therefore, the development of a system that efficiently processes information and provides optimal feedback to workers is also necessary.

[0365] 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.

[0366] In this invention, the server includes means for using multiple sensor devices to acquire environmental information, means for analyzing information stored in an information processing device and executing an intelligent machine algorithm to detect anomalies, and means for analyzing the user's emotions and changing the content and method of warning notifications based on the user's emotional state. This enables workers to grasp the current situation in real time and provide optimal information according to the user's situation. Furthermore, by customizing the warning content, it is possible to obtain feedback that is easier for the user to understand and more beneficial.

[0367] "Environmental information" refers to data that shows the conditions around a building or structure, and is acquired by sensors such as those for temperature, humidity, vibration, sound, and gas.

[0368] A "sensor device" is a mechanical device used to detect environmental information and is capable of acquiring multiple types of data.

[0369] "Wireless communication" is a means of communication for transmitting data without physical contact, and is a technology that uses radio waves to send information to a remote location.

[0370] An "information processing device" is a computing device used to organize, store, and analyze received information as needed, and includes equipment such as a database.

[0371] An "intelligent machine algorithm" is a series of procedures for automating information processing and analysis, and is an algorithm that uses artificial intelligence technology to detect anomalies.

[0372] A "warning notification" is an informational message that alerts users to detected anomalies and recommends specific actions.

[0373] A "user screen" is an interface device for users to receive information and is equipped with the function of displaying information visually.

[0374] "Visualization" is a method of converting data and information into a form that is easy for humans to understand and displaying it in the form of diagrams, graphs, and other visual representations.

[0375] As an embodiment of the present invention, a safety monitoring system for use in a factory will be described. This system acquires environmental information from multiple sensor devices and transmits it to a cloud server via wireless communication. The server stores the received information in an information processing device and analyzes the data using an intelligent machine algorithm. This analysis detects anomalies and generates warning notifications as necessary.

[0376] The system's hardware utilizes various standard sensors (e.g., temperature sensors, vibration sensors) as sensor devices, and wireless communication technology is used for communication. The information processing unit uses a cloud-based database and an AI algorithm platform (e.g., TensorFlow).

[0377] The intelligent machine algorithm analyzes received environmental information to detect and predict abnormal patterns. Furthermore, to analyze the user's emotions, it performs emotion analysis using video data obtained from cameras installed in smart glasses or terminals. For this purpose, image processing libraries such as OpenCV are used. Based on the analysis results, it generates customized warning messages according to the user's emotional state and provides them to the user's screen.

[0378] For example, if the vibration of equipment in the factory exceeds the normal range, the system will analyze the cause and display a warning such as, "The vibration level is higher than normal, but our technicians are addressing the issue." If the user appears surprised upon receiving this message, additional information such as, "The specific cause of the vibration is motor wear. It will be replaced soon," will be provided to reassure them.

[0379] An example of a prompt is: "Create a specific use case for a real-time monitoring system for factory equipment. Also, explain how the alert messages will be customized based on the user's emotions."

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server acquires environmental information from multiple sensor devices installed in the factory. Inputs include data from each sensor (temperature, vibration, humidity, etc.). The server centralizes this data, standardizes the format, and receives it via wireless communication. The output is structured environmental information data that is updated in real time.

[0383] Step 2:

[0384] The server stores the received environmental information data in the database of the information processing device. The input data is the environmental information obtained in step 1. Here, the data is written to the database and saved for future analysis. The output is a clean dataset stored in the information processing device.

[0385] Step 3:

[0386] The server executes intelligent machine algorithms on environmental information stored in a database. The input is a dataset stored in an information processing device, and anomaly detection and future predictive analysis are performed based on this data. Specifically, it compares current data with past data and detects anomalies if a certain threshold is exceeded. The output is a flag indicating whether an anomaly was detected or not, and the related analysis results.

[0387] Step 4:

[0388] If an anomaly is detected, the server generates a warning notification and displays it on the user screen. The input is the anomaly detection flag and analysis result from step 3. Based on this, the server generates a warning message describing the nature of the anomaly and specific countermeasures. The output is a text message displayed on the user screen.

[0389] Step 5:

[0390] The device acquires video data from its built-in camera to analyze the user's emotions. The input is the user's facial data, which is used for emotion analysis. Specifically, it uses an emotion analysis library to identify the emotional state the user is exhibiting. The output is a parameter indicating the user's emotional state.

[0391] Step 6:

[0392] The server customizes the content and method of warning notifications based on the emotional state. The input is the emotional state parameters obtained in step 5, and the server uses this to customize existing warning messages. Specifically, if the user shows surprise or anxiety, it adds and provides reassuring content. The output is a customized warning message adapted to the emotional state.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a system in which multiple sensor devices installed in buildings and structures collect environmental data in real time and transmit that information wirelessly to a cloud server. The sensor devices monitor indicators such as temperature, humidity, vibration, sound, and gas, and transmit this data at appropriate intervals.

[0410] When the server receives data transmitted from the sensor, it first stores it in a database. Based on the received data, the server uses artificial intelligence algorithms to analyze the data and detect anomalies that deviate from normal patterns. The server can also use past data to predict future anomaly occurrences.

[0411] If an anomaly is detected, the server promptly generates a warning message and notifies the user interface. This allows users to understand the building's status in real time and take quick action as needed. For example, if a vibration sensor detects vibrations exceeding a certain threshold, the server will determine this to be an anomaly and immediately inform the user of the situation, enabling early arrangement of maintenance or repairs.

[0412] The user interface visualizes feedback from the cloud server on a dashboard, displaying trends in sensor data and warnings in graph and report formats. This allows users to intuitively understand the building's status and take appropriate action.

[0413] In this way, this system enhances building safety and enables efficient maintenance and cost reduction.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed within the building. The terminal temporarily stores this data and performs initial filtering to remove noise and abnormal values.

[0417] Step 2:

[0418] The terminal transmits processed data to a cloud server using wireless communication technology. Wi-Fi or LTE is used for transmission, and a timestamp and sensor ID are added to the data.

[0419] Step 3:

[0420] The server receives data sent from the terminal. The received data is structured, formatted into the appropriate format, and then stored in the database.

[0421] Step 4:

[0422] The server analyzes the data stored in the database and runs an artificial intelligence algorithm to detect anomalies by comparing it to existing data patterns. In this process, any deviation from the normal pattern is identified as an anomaly.

[0423] Step 5:

[0424] Based on the detection results of anomalies, the server predicts future anomalies from past data. Machine learning algorithms are used for prediction, and risk assessments are performed based on quantitative indicators.

[0425] Step 6:

[0426] If the server detects an anomaly or foreshadows a risk, it generates a warning message and notifies the user interface. This warning message includes details of the detected anomaly and recommended countermeasures.

[0427] Step 7:

[0428] Users receive warning messages on the interface and analyze visualized data. If necessary, users can send instructions for maintenance to the relevant maintenance staff or department.

[0429] Step 8:

[0430] The user interface displays received information on a dashboard and updates it in real time to support users in data analysis and decision-making.

[0431] (Example 1)

[0432] 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."

[0433] Modern buildings and structures require improved safety and operational efficiency. However, conventional technologies struggle to analyze environmental information from sensors in real time, quickly detect anomalies, and predict future anomalies. There is a need to provide a system that solves this problem and enables faster and more accurate anomaly detection and prediction.

[0434] 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.

[0435] In this invention, the server includes means for executing a machine learning algorithm to analyze information stored on an information recording medium and detect deviations from normal patterns, means for generating warning notifications based on detected deviations and notifying the user screen, and means for performing predictive analysis to predict future anomalies based on past information. This enables real-time anomaly detection and prediction of future anomalies.

[0436] "Environmental information" refers to data that shows the conditions of temperature, humidity, vibration, sound, gas, etc., inside and outside buildings and structures.

[0437] A "sensor device" is hardware used to detect specific environmental information and acquire that information as a digital signal.

[0438] Wireless communication is a technology that transmits and receives digital information using radio waves without using cables or wires.

[0439] An "information recording medium" is a data storage system for storing received environmental information, such as a database.

[0440] A "machine learning algorithm" is a programming technique that learns patterns from large amounts of data and autonomously detects anomalies in new data.

[0441] A "warning notification" is a cautionary message generated when an abnormality exceeding a predetermined standard is detected.

[0442] A "user screen" is an interface that visually displays warning notifications and analysis results, allowing users to easily understand the information.

[0443] "Predictive analytics" is a technique that uses past data to predict anomalies that may occur in the future.

[0444] This invention is a system for detecting anomalies in real time by analyzing environmental information collected by multiple sensor devices installed in buildings and structures on a server in the cloud. Specifically, the sensor devices acquire information such as temperature, humidity, vibration, sound, and gas, and transmit this information to the server using wireless communication.

[0445] The server first stores the received information on a data storage medium, such as a cloud database. Common cloud services can be used for this purpose. The server then analyzes this data using machine learning algorithms, with tools like TensorFlow and PyTorch available for analysis. This allows for the rapid detection of deviations from normal patterns, as well as the use of historical data to predict the likelihood of future anomalies.

[0446] When an anomaly is detected, the server immediately generates a warning notification and sends it to the user terminal. The user terminal displays this on the user screen, visualizing the information in real time in the form of graphs and reports. This allows users to intuitively understand the status of buildings and structures and take prompt action as needed.

[0447] For example, if a vibration sensor detects vibrations exceeding a set threshold, the server will determine this to be an anomaly and send a warning to the user, enabling early maintenance arrangements.

[0448] Furthermore, an example of a prompt message when using a generative AI model could be: "Based on temperature and humidity sensor data, please predict the likelihood of anomalies occurring over the next week and create a report." This would enable more advanced and efficient monitoring and management.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The sensor device acquires environmental information within the building at pre-set intervals. Specifically, it samples data such as temperature, humidity, vibration, sound, and gas every 5 minutes. This acquired data becomes the input, and the sensor device transmits the data to a cloud server using a wireless communication module. The output is digital data sent as a wireless signal.

[0452] Step 2:

[0453] The server receives wireless signals transmitted from the sensor device. The input includes sensor data, which the server saves to a cloud database, a data storage medium. Specifically, it checks the data format for consistency, corrects any inconsistencies, and then records the data along with a timestamp. The output is the saved sensor data.

[0454] Step 3:

[0455] The server executes machine learning algorithms based on data stored in a cloud database. The input is stored sensor data, and the data is analyzed using TensorFlow or PyTorch. Specifically, the data is passed through a model trained on the normal range to detect anomalies. The output is the detected anomaly pattern and its detailed information.

[0456] Step 4:

[0457] The server generates warning notifications based on detected anomalies. The input is data on anomaly patterns, and a Python script is used to generate warning messages. Specifically, it formats detailed information such as the type, location, and time of the anomaly into a text message, and includes recommended actions based on the error level. The output is the formatted warning message.

[0458] Step 5:

[0459] The user terminal receives warning messages sent from the server. The input is the warning message, which the user terminal displays on its screen. Specifically, the system visualizes the anomaly information in real time on the dashboard and presents it in graph and report formats so that the user can understand it intuitively. The output is the visual information displayed to the user.

[0460] (Application Example 1)

[0461] 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."

[0462] Ensuring safety and efficient operation within factories and buildings today is time-consuming and costly. Furthermore, immediate detection and response to environmental changes and equipment malfunctions are required, but conventional systems struggle to provide such immediate responses, potentially leading to accidents and problems. To address these challenges, a system is needed that collects environmental information in real time, rapidly detects and notifies of anomalies, and enables appropriate responses.

[0463] 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.

[0464] In this invention, the server includes means for using a plurality of detection devices for acquiring environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for using a wearable display device that displays the warning notification in real time and enables a rapid response. This enables safe and efficient operation by immediately detecting abnormalities in the environment and notifying the user immediately.

[0465] "Environmental information" refers to data indicating physical conditions such as temperature, humidity, and vibration acquired within buildings and factories.

[0466] A "detection device" is a sensor device installed to acquire environmental information, allowing for real-time data collection.

[0467] "Wireless communication" is a communication method used to transmit information from a sensor device to a server, enabling data transmission without the need for cables.

[0468] An "information recording device" is a storage device within a system that stores received environmental information, and it serves as a database.

[0469] An "intelligent algorithm" is a computational process that uses artificial intelligence technology to analyze stored environmental information and detect anomalies.

[0470] A "warning notification" is a message generated based on detected anomalies, and its role is to promptly inform the user of any danger.

[0471] A "terminal device" is a device used by a user to receive information and check analysis results and warning notifications.

[0472] A "wearable display device" refers to a device that displays information in a form that can be worn by the user, and typically includes smart glasses or headsets.

[0473] "Charts and graphs" are a format for visually presenting analyzed data and notification content, and are important for facilitating intuitive understanding among users.

[0474] A "report format" is a document format that systematically summarizes analysis results and alerts and presents them in a way that is easy for users to understand.

[0475] To implement this invention, first, multiple detection devices are installed at points within a building or factory where monitoring is required. These detection devices acquire environmental information such as temperature, humidity, and vibration in real time and transmit the information to a server using wireless communication technology.

[0476] The server stores the received information in an information recording device and analyzes this information using an intelligent algorithm. The intelligent algorithm detects anomalies from the recorded information and generates a warning notification based on them. This notification is immediately sent to the terminal device.

[0477] The terminal device plays a role in visualizing notifications and analysis results so that users can quickly check information. This uses charts and reports, allowing users to intuitively understand the information. Furthermore, by using a wearable display device, users can receive warning notifications in real time, regardless of their location.

[0478] As a concrete example, if a temperature anomaly occurs in the refrigeration section of a factory, a temperature sensor will detect it, and an intelligent algorithm will determine that an anomaly has occurred. A warning notification will immediately appear on a wearable display device, allowing the user to take prompt action. This seamless coordination of processes is expected to improve both safety and efficiency.

[0479] An example of a prompt message for the generating AI model could be: "We have obtained temperature and humidity sensor data from inside the factory. Please suggest how to display an alert if an accurate temperature increase is detected."

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The server receives environmental information in real time via wireless communication from multiple detection devices. The input is environmental data such as temperature, humidity, and vibration, and the output is the storage of the data in an information recording device.

[0483] Step 2:

[0484] The server analyzes the data stored in the information recording device using intelligent algorithms. In this step, it uses the environmental data received as input to perform data calculations that detect anomalies in the data. The output is detailed information about the detected anomalies.

[0485] Step 3:

[0486] The server generates a warning notification when an anomaly is detected. The input is the result of the anomaly detection, and based on that information, it generates a specific warning message. The output is the warning notification.

[0487] Step 4:

[0488] The server sends the generated warning notification to the terminal device. The input is the warning notification, and the output is the reception status on the terminal device. This immediately notifies the user.

[0489] Step 5:

[0490] The terminal visualizes received warning notifications and analysis results in charts and reports. Input consists of warning notifications and analysis results, which are processed into an intuitively understandable format. Output is the visualized information displayed on the user screen.

[0491] Step 6:

[0492] Users can receive real-time warning notifications through a wearable display device and take necessary actions quickly. The input is a visualized warning notification, and the output is the corresponding action to be taken. This step enables rapid response in the field.

[0493] 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.

[0494] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The sensor devices acquire environmental data such as temperature, humidity, vibration, sound, and gas in real time and transmit this data to a cloud server via wireless communication.

[0495] The server receives data transmitted from sensors and stores it in a database. It then runs an artificial intelligence algorithm to analyze the data and detect anomalies. This algorithm also performs predictive analysis based on past data, providing information to mitigate the risk of future anomalies.

[0496] In addition, this system features an emotion engine that recognizes the user's emotions. Through this emotion engine, the server infers the user's emotions via the user interface and customizes the content and presentation of warning messages to the user. Specifically, if the user shows signs of caution or anxiety, the server provides a more detailed and reassuring message.

[0497] The user interface visualizes information on the dashboard, displaying received warning messages and analysis results in graphs and reports. It also employs a user-friendly design to enable users to take quick action based on the information. For example, if the system detects that a structure's vibration level exceeds safety standards, the emotional engine recommends a reassuring message to the user detailing the cause of the vibration and how to contact the administrator.

[0498] This system ensures the safety of buildings, enables efficient maintenance, and reduces the mental burden on users.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed inside the building. This data is temporarily stored inside the terminal and initial filtering is performed as needed.

[0502] Step 2:

[0503] The device uses wireless communication to transmit collected environmental data to a cloud server. The data includes metadata such as timestamps and sensor IDs.

[0504] Step 3:

[0505] The server receives data sent from the terminal and stores it in a database. The received data is formatted as needed and prepared for analysis by AI algorithms.

[0506] Step 4:

[0507] The server activates an artificial intelligence algorithm to analyze the data stored in the database. The purpose of the analysis is to detect anomalies by comparing the data patterns with those of normal conditions, and special tags are assigned to patterns that are deemed abnormal.

[0508] Step 5:

[0509] The server uses the data from which anomaly detection processing has been completed to predict and analyze potential risks that may occur in the future. This prediction process references historical data, preparing for proactive responses to future anomalies.

[0510] Step 6:

[0511] The server generates warning messages based on anomalies and risk predictions. Furthermore, it utilizes an emotion engine to analyze the user's emotions and customize the warning messages to a format that is more acceptable to the user.

[0512] Step 7:

[0513] The user interface receives warning messages and analysis results from the server. The received data is visualized on the dashboard and displayed as graphs and reports in a format that is easy for the user to understand.

[0514] Step 8:

[0515] Users take necessary actions based on the information provided through the interface. For example, if the anomaly is severe, they can contact the administrator and arrange for immediate action.

[0516] Step 9:

[0517] The emotion engine analyzes user actions and reactions and stores them in a database as long-term data. This record is used to generate future messages and to provide more appropriate responses to the user.

[0518] (Example 2)

[0519] 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."

[0520] Modern buildings and structures require both safety and efficient maintenance. However, monitoring these in real time and accurately detecting and predicting anomalies is not easy. Furthermore, a challenge remains in providing information that takes into account the feelings of users, resulting in insufficient reduction of user anxiety in response to warnings.

[0521] 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.

[0522] In this invention, the server includes means for using multiple detection devices to acquire environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for receiving the transmitted information and storing it in a storage device. This makes it possible to monitor the safety of a building in real time and perform anomaly detection and predictive analysis. Furthermore, by using emotional state estimation means, it is possible to reduce anxiety by providing appropriate information based on the user's emotions.

[0523] "Environmental information" refers to data that indicates the environmental conditions surrounding a building or structure, such as temperature, humidity, vibration, sound, and gas.

[0524] A "detection device" is a device that includes sensors that measure environmental information and output it as digital data.

[0525] "Wireless communication" is a technology that uses radio waves to send and receive data, and it is the method used for information transmission between the sensor device and the server in this system.

[0526] A "storage device" refers to a digital data storage device that can permanently store data and is used to securely store received information.

[0527] A "machine learning algorithm" is a computational method that allows computers to learn from data, recognize patterns, and make decisions.

[0528] A "generative AI model" is an artificial intelligence model that uses large amounts of data to perform pattern recognition and generation.

[0529] "Emotional state inference means" refers to technology for determining a user's emotions based on their input actions.

[0530] An "output device" is hardware used to transmit information to the user, and is a device used to visualize warning messages or analysis results.

[0531] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The system acquires environmental information such as temperature, humidity, vibration, sound, and gas, and transmits it to a server via wireless communication. The server stores the received data in a database and analyzes anomalies in the data using machine learning algorithms. This analysis uses a generative AI model, which learns patterns from the data and can detect anomalies with high accuracy.

[0532] The server predicts the risk of future anomalies based on historical data. This process involves referencing past data and using machine learning to identify patterns that may lead to anomalies. Furthermore, the server analyzes user emotions in real time through emotion state prediction mechanisms and customizes warning messages accordingly. To alleviate user anxiety, it can generate warning messages containing detailed information.

[0533] The user terminal receives these warning messages and analysis results, and visualizes the information through a graphical interface. This allows users to immediately grasp the situation and take necessary actions. This system is particularly important for administrators to quickly check the status of buildings and maintain safety.

[0534] As a concrete example, if vibrations in a building exceed a certain threshold, the server immediately detects this and, while monitoring the user's reaction using emotional state estimation tools, sends a warning message to the user's terminal containing a detailed explanation to provide reassurance. This allows the user to obtain information about the cause of the vibrations and receive support to contact the appropriate administrator.

[0535] (Example of a prompt message)

[0536] "Please generate a warning message to send to the user after an anomaly is detected. The message should be reassuring to the user."

[0537] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0538] Step 1:

[0539] The server acquires environmental information from multiple sensor devices installed in the building. Real-time data from each sensor is transmitted as input, and this data includes temperature, humidity, vibration, sound, gas, etc. The server receives the data via wireless communication and temporarily stores it. During this process, the server verifies the integrity of the data and confirms that accurate information has been obtained.

[0540] Step 2:

[0541] The server stores the received environmental information in a database. The input data consists of individual measurements from each sensor device, and the server saves this data in the database in an appropriate format. Storing the data in the database accumulates historical data that can be used for subsequent analysis. The server organizes and stores the data by adding timestamps and sensor device identifiers.

[0542] Step 3:

[0543] The server executes machine learning algorithms based on stored data to detect anomalies. Using database information as input, the algorithm extracts patterns from the data using a generative AI model and detects outliers. Through this process, the server understands the occurrence of anomalies in real time and sets flags according to the degree of anomaly.

[0544] Step 4:

[0545] The server uses detected anomaly information to infer the user's emotional state and generate an appropriate warning message. It considers anomaly data and the user's past response data as input, and uses an emotional state inference method to determine the user's psychological state. Based on this analysis, a generation AI model creates a warning message using prompts and sends it to the user.

[0546] Step 5:

[0547] The user terminal receives and visualizes warning messages and analysis results sent from the server. It receives data from the server as input and displays it graphically on a dashboard on the terminal. Based on the message content and analysis results, the user can view detailed information and take necessary actions. Specifically, the user clicks on each warning message to view additional information and solutions.

[0548] (Application Example 2)

[0549] 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."

[0550] This invention relates to a system that monitors the safety of buildings and structures in real time and provides appropriate warnings to users when abnormalities are detected. The objective is to reduce user stress and facilitate quick and appropriate responses by providing warnings tailored to the user's emotional state. Furthermore, in factories where a large amount of information is aggregated, it is difficult for workers to constantly monitor the situation; therefore, the development of a system that efficiently processes information and provides optimal feedback to workers is also necessary.

[0551] 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.

[0552] In this invention, the server includes means for using multiple sensor devices to acquire environmental information, means for analyzing information stored in an information processing device and executing an intelligent machine algorithm to detect anomalies, and means for analyzing the user's emotions and changing the content and method of warning notifications based on the user's emotional state. This enables workers to grasp the current situation in real time and provide optimal information according to the user's situation. Furthermore, by customizing the warning content, it is possible to obtain feedback that is easier for the user to understand and more beneficial.

[0553] "Environmental information" refers to data that shows the conditions around a building or structure, and is acquired by sensors such as those for temperature, humidity, vibration, sound, and gas.

[0554] A "sensor device" is a mechanical device used to detect environmental information and is capable of acquiring multiple types of data.

[0555] "Wireless communication" is a means of communication for transmitting data without physical contact, and is a technology that uses radio waves to send information to a remote location.

[0556] An "information processing device" is a computing device used to organize, store, and analyze received information as needed, and includes equipment such as a database.

[0557] An "intelligent machine algorithm" is a series of procedures for automating information processing and analysis, and is an algorithm that uses artificial intelligence technology to detect anomalies.

[0558] A "warning notification" is an informational message that alerts users to detected anomalies and recommends specific actions.

[0559] A "user screen" is an interface device for users to receive information and is equipped with the function of displaying information visually.

[0560] "Visualization" is a method of converting data and information into a form that is easy for humans to understand and displaying it in the form of diagrams, graphs, and other visual representations.

[0561] As an embodiment of the present invention, a safety monitoring system for use in a factory will be described. This system acquires environmental information from multiple sensor devices and transmits it to a cloud server via wireless communication. The server stores the received information in an information processing device and analyzes the data using an intelligent machine algorithm. This analysis detects anomalies and generates warning notifications as necessary.

[0562] The system's hardware utilizes various standard sensors (e.g., temperature sensors, vibration sensors) as sensor devices, and wireless communication technology is used for communication. The information processing unit uses a cloud-based database and an AI algorithm platform (e.g., TensorFlow).

[0563] The intelligent machine algorithm analyzes received environmental information to detect and predict abnormal patterns. Furthermore, to analyze the user's emotions, it performs emotion analysis using video data obtained from cameras installed in smart glasses or terminals. For this purpose, image processing libraries such as OpenCV are used. Based on the analysis results, it generates customized warning messages according to the user's emotional state and provides them to the user's screen.

[0564] For example, if the vibration of equipment in the factory exceeds the normal range, the system will analyze the cause and display a warning such as, "The vibration level is higher than normal, but our technicians are addressing the issue." If the user appears surprised upon receiving this message, additional information such as, "The specific cause of the vibration is motor wear. It will be replaced soon," will be provided to reassure them.

[0565] An example of a prompt is: "Create a specific use case for a real-time monitoring system for factory equipment. Also, explain how the alert messages will be customized based on the user's emotions."

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The server acquires environmental information from multiple sensor devices installed in the factory. Inputs include data from each sensor (temperature, vibration, humidity, etc.). The server centralizes this data, standardizes the format, and receives it via wireless communication. The output is structured environmental information data that is updated in real time.

[0569] Step 2:

[0570] The server stores the received environmental information data in the database of the information processing device. The input data is the environmental information obtained in step 1. Here, the data is written to the database and saved for future analysis. The output is a clean dataset stored in the information processing device.

[0571] Step 3:

[0572] The server executes intelligent machine algorithms on environmental information stored in a database. The input is a dataset stored in an information processing device, and anomaly detection and future predictive analysis are performed based on this data. Specifically, it compares current data with past data and detects anomalies if a certain threshold is exceeded. The output is a flag indicating whether an anomaly was detected or not, and the related analysis results.

[0573] Step 4:

[0574] If an anomaly is detected, the server generates a warning notification and displays it on the user screen. The input is the anomaly detection flag and analysis result from step 3. Based on this, the server generates a warning message describing the nature of the anomaly and specific countermeasures. The output is a text message displayed on the user screen.

[0575] Step 5:

[0576] The device acquires video data from its built-in camera to analyze the user's emotions. The input is the user's facial data, which is used for emotion analysis. Specifically, it uses an emotion analysis library to identify the emotional state the user is exhibiting. The output is a parameter indicating the user's emotional state.

[0577] Step 6:

[0578] The server customizes the content and method of warning notifications based on the emotional state. The input is the emotional state parameters obtained in step 5, and the server uses this to customize existing warning messages. Specifically, if the user shows surprise or anxiety, it adds and provides reassuring content. The output is a customized warning message adapted to the emotional state.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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".

[0596] This invention is a system in which multiple sensor devices installed in buildings and structures collect environmental data in real time and transmit that information wirelessly to a cloud server. The sensor devices monitor indicators such as temperature, humidity, vibration, sound, and gas, and transmit this data at appropriate intervals.

[0597] When the server receives data transmitted from the sensor, it first stores it in a database. Based on the received data, the server uses artificial intelligence algorithms to analyze the data and detect anomalies that deviate from normal patterns. The server can also use past data to predict future anomaly occurrences.

[0598] If an anomaly is detected, the server promptly generates a warning message and notifies the user interface. This allows users to understand the building's status in real time and take quick action as needed. For example, if a vibration sensor detects vibrations exceeding a certain threshold, the server will determine this to be an anomaly and immediately inform the user of the situation, enabling early arrangement of maintenance or repairs.

[0599] The user interface visualizes feedback from the cloud server on a dashboard, displaying trends in sensor data and warnings in graph and report formats. This allows users to intuitively understand the building's status and take appropriate action.

[0600] In this way, this system enhances building safety and enables efficient maintenance and cost reduction.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed within the building. The terminal temporarily stores this data and performs initial filtering to remove noise and abnormal values.

[0604] Step 2:

[0605] The terminal transmits processed data to a cloud server using wireless communication technology. Wi-Fi or LTE is used for transmission, and a timestamp and sensor ID are added to the data.

[0606] Step 3:

[0607] The server receives data sent from the terminal. The received data is structured, formatted into the appropriate format, and then stored in the database.

[0608] Step 4:

[0609] The server analyzes the data stored in the database and runs an artificial intelligence algorithm to detect anomalies by comparing it to existing data patterns. In this process, any deviation from the normal pattern is identified as an anomaly.

[0610] Step 5:

[0611] Based on the detection results of anomalies, the server predicts future anomalies from past data. Machine learning algorithms are used for prediction, and risk assessments are performed based on quantitative indicators.

[0612] Step 6:

[0613] If the server detects an anomaly or foreshadows a risk, it generates a warning message and notifies the user interface. This warning message includes details of the detected anomaly and recommended countermeasures.

[0614] Step 7:

[0615] Users receive warning messages on the interface and analyze visualized data. If necessary, users can send instructions for maintenance to the relevant maintenance staff or department.

[0616] Step 8:

[0617] The user interface displays received information on a dashboard and updates it in real time to support users in data analysis and decision-making.

[0618] (Example 1)

[0619] 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".

[0620] Modern buildings and structures require improved safety and operational efficiency. However, conventional technologies struggle to analyze environmental information from sensors in real time, quickly detect anomalies, and predict future anomalies. There is a need to provide a system that solves this problem and enables faster and more accurate anomaly detection and prediction.

[0621] 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.

[0622] In this invention, the server includes means for executing a machine learning algorithm to analyze information stored on an information recording medium and detect deviations from normal patterns, means for generating warning notifications based on detected deviations and notifying the user screen, and means for performing predictive analysis to predict future anomalies based on past information. This enables real-time anomaly detection and prediction of future anomalies.

[0623] "Environmental information" refers to data that shows the conditions of temperature, humidity, vibration, sound, gas, etc., inside and outside buildings and structures.

[0624] A "sensor device" is hardware used to detect specific environmental information and acquire that information as a digital signal.

[0625] Wireless communication is a technology that transmits and receives digital information using radio waves without using cables or wires.

[0626] An "information recording medium" is a data storage system for storing received environmental information, such as a database.

[0627] A "machine learning algorithm" is a programming technique that learns patterns from large amounts of data and autonomously detects anomalies in new data.

[0628] A "warning notification" is a cautionary message generated when an abnormality exceeding a predetermined standard is detected.

[0629] A "user screen" is an interface that visually displays warning notifications and analysis results, allowing users to easily understand the information.

[0630] "Predictive analytics" is a technique that uses past data to predict anomalies that may occur in the future.

[0631] This invention is a system for detecting anomalies in real time by analyzing environmental information collected by multiple sensor devices installed in buildings and structures on a server in the cloud. Specifically, the sensor devices acquire information such as temperature, humidity, vibration, sound, and gas, and transmit this information to the server using wireless communication.

[0632] The server first stores the received information on a data storage medium, such as a cloud database. Common cloud services can be used for this purpose. The server then analyzes this data using machine learning algorithms, with tools like TensorFlow and PyTorch available for analysis. This allows for the rapid detection of deviations from normal patterns, as well as the use of historical data to predict the likelihood of future anomalies.

[0633] When an anomaly is detected, the server immediately generates a warning notification and sends it to the user terminal. The user terminal displays this on the user screen, visualizing the information in real time in the form of graphs and reports. This allows users to intuitively understand the status of buildings and structures and take prompt action as needed.

[0634] For example, if a vibration sensor detects vibrations exceeding a set threshold, the server will determine this to be an anomaly and send a warning to the user, enabling early maintenance arrangements.

[0635] Furthermore, an example of a prompt message when using a generative AI model could be: "Based on temperature and humidity sensor data, please predict the likelihood of anomalies occurring over the next week and create a report." This would enable more advanced and efficient monitoring and management.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The sensor device acquires environmental information within the building at pre-set intervals. Specifically, it samples data such as temperature, humidity, vibration, sound, and gas every 5 minutes. This acquired data becomes the input, and the sensor device transmits the data to a cloud server using a wireless communication module. The output is digital data sent as a wireless signal.

[0639] Step 2:

[0640] The server receives wireless signals transmitted from the sensor device. The input includes sensor data, which the server saves to a cloud database, a data storage medium. Specifically, it checks the data format for consistency, corrects any inconsistencies, and then records the data along with a timestamp. The output is the saved sensor data.

[0641] Step 3:

[0642] The server executes machine learning algorithms based on data stored in a cloud database. The input is stored sensor data, and the data is analyzed using TensorFlow or PyTorch. Specifically, the data is passed through a model trained on the normal range to detect anomalies. The output is the detected anomaly pattern and its detailed information.

[0643] Step 4:

[0644] The server generates warning notifications based on detected anomalies. The input is data on anomaly patterns, and a Python script is used to generate warning messages. Specifically, it formats detailed information such as the type, location, and time of the anomaly into a text message, and includes recommended actions based on the error level. The output is the formatted warning message.

[0645] Step 5:

[0646] The user terminal receives warning messages sent from the server. The input is the warning message, which the user terminal displays on its screen. Specifically, the system visualizes the anomaly information in real time on the dashboard and presents it in graph and report formats so that the user can understand it intuitively. The output is the visual information displayed to the user.

[0647] (Application Example 1)

[0648] 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".

[0649] Ensuring safety and efficient operation within factories and buildings today is time-consuming and costly. Furthermore, immediate detection and response to environmental changes and equipment malfunctions are required, but conventional systems struggle to provide such immediate responses, potentially leading to accidents and problems. To address these challenges, a system is needed that collects environmental information in real time, rapidly detects and notifies of anomalies, and enables appropriate responses.

[0650] 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.

[0651] In this invention, the server includes means for using a plurality of detection devices for acquiring environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for using a wearable display device that displays the warning notification in real time and enables a rapid response. This enables safe and efficient operation by immediately detecting abnormalities in the environment and notifying the user immediately.

[0652] "Environmental information" refers to data indicating physical conditions such as temperature, humidity, and vibration acquired within buildings and factories.

[0653] A "detection device" is a sensor device installed to acquire environmental information, allowing for real-time data collection.

[0654] "Wireless communication" is a communication method used to transmit information from a sensor device to a server, enabling data transmission without the need for cables.

[0655] An "information recording device" is a storage device within a system that stores received environmental information, and it serves as a database.

[0656] An "intelligent algorithm" is a computational process that uses artificial intelligence technology to analyze stored environmental information and detect anomalies.

[0657] A "warning notification" is a message generated based on detected anomalies, and its role is to promptly inform the user of any danger.

[0658] A "terminal device" is a device used by a user to receive information and check analysis results and warning notifications.

[0659] A "wearable display device" refers to a device that displays information in a form that can be worn by the user, and typically includes smart glasses or headsets.

[0660] "Charts and graphs" are a format for visually presenting analyzed data and notification content, and are important for facilitating intuitive understanding among users.

[0661] A "report format" is a document format that systematically summarizes analysis results and alerts and presents them in a way that is easy for users to understand.

[0662] To implement this invention, first, multiple detection devices are installed at points within a building or factory where monitoring is required. These detection devices acquire environmental information such as temperature, humidity, and vibration in real time and transmit the information to a server using wireless communication technology.

[0663] The server stores the received information in an information recording device and analyzes this information using an intelligent algorithm. The intelligent algorithm detects anomalies from the recorded information and generates a warning notification based on them. This notification is immediately sent to the terminal device.

[0664] The terminal device plays a role in visualizing notifications and analysis results so that users can quickly check information. This uses charts and reports, allowing users to intuitively understand the information. Furthermore, by using a wearable display device, users can receive warning notifications in real time, regardless of their location.

[0665] As a concrete example, if a temperature anomaly occurs in the refrigeration section of a factory, a temperature sensor will detect it, and an intelligent algorithm will determine that an anomaly has occurred. A warning notification will immediately appear on a wearable display device, allowing the user to take prompt action. This seamless coordination of processes is expected to improve both safety and efficiency.

[0666] An example of a prompt message for the generating AI model could be: "We have obtained temperature and humidity sensor data from inside the factory. Please suggest how to display an alert if an accurate temperature increase is detected."

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] The server receives environmental information in real time via wireless communication from multiple detection devices. The input is environmental data such as temperature, humidity, and vibration, and the output is the storage of the data in an information recording device.

[0670] Step 2:

[0671] The server analyzes the data stored in the information recording device using intelligent algorithms. In this step, it uses the environmental data received as input to perform data calculations that detect anomalies in the data. The output is detailed information about the detected anomalies.

[0672] Step 3:

[0673] The server generates a warning notification when an anomaly is detected. The input is the result of the anomaly detection, and based on that information, it generates a specific warning message. The output is the warning notification.

[0674] Step 4:

[0675] The server sends the generated warning notification to the terminal device. The input is the warning notification, and the output is the reception status on the terminal device. This immediately notifies the user.

[0676] Step 5:

[0677] The terminal visualizes received warning notifications and analysis results in charts and reports. Input consists of warning notifications and analysis results, which are processed into an intuitively understandable format. Output is the visualized information displayed on the user screen.

[0678] Step 6:

[0679] Users can receive real-time warning notifications through a wearable display device and take necessary actions quickly. The input is a visualized warning notification, and the output is the corresponding action to be taken. This step enables rapid response in the field.

[0680] 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.

[0681] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The sensor devices acquire environmental data such as temperature, humidity, vibration, sound, and gas in real time and transmit this data to a cloud server via wireless communication.

[0682] The server receives data transmitted from sensors and stores it in a database. It then runs an artificial intelligence algorithm to analyze the data and detect anomalies. This algorithm also performs predictive analysis based on past data, providing information to mitigate the risk of future anomalies.

[0683] In addition, this system features an emotion engine that recognizes the user's emotions. Through this emotion engine, the server infers the user's emotions via the user interface and customizes the content and presentation of warning messages to the user. Specifically, if the user shows signs of caution or anxiety, the server provides a more detailed and reassuring message.

[0684] The user interface visualizes information on the dashboard, displaying received warning messages and analysis results in graphs and reports. It also employs a user-friendly design to enable users to take quick action based on the information. For example, if the system detects that a structure's vibration level exceeds safety standards, the emotional engine recommends a reassuring message to the user detailing the cause of the vibration and how to contact the administrator.

[0685] This system ensures the safety of buildings, enables efficient maintenance, and reduces the mental burden on users.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The terminal periodically acquires environmental data such as temperature, humidity, vibration, sound, and gas from various sensors installed inside the building. This data is temporarily stored inside the terminal and initial filtering is performed as needed.

[0689] Step 2:

[0690] The device uses wireless communication to transmit collected environmental data to a cloud server. The data includes metadata such as timestamps and sensor IDs.

[0691] Step 3:

[0692] The server receives data sent from the terminal and stores it in a database. The received data is formatted as needed and prepared for analysis by AI algorithms.

[0693] Step 4:

[0694] The server activates an artificial intelligence algorithm to analyze the data stored in the database. The purpose of the analysis is to detect anomalies by comparing the data patterns with those of normal conditions, and special tags are assigned to patterns that are deemed abnormal.

[0695] Step 5:

[0696] The server uses the data from which anomaly detection processing has been completed to predict and analyze potential risks that may occur in the future. This prediction process references historical data, preparing for proactive responses to future anomalies.

[0697] Step 6:

[0698] The server generates warning messages based on anomalies and risk predictions. Furthermore, it utilizes an emotion engine to analyze the user's emotions and customize the warning messages to a format that is more acceptable to the user.

[0699] Step 7:

[0700] The user interface receives warning messages and analysis results from the server. The received data is visualized on the dashboard and displayed as graphs and reports in a format that is easy for the user to understand.

[0701] Step 8:

[0702] Users take necessary actions based on the information provided through the interface. For example, if the anomaly is severe, they can contact the administrator and arrange for immediate action.

[0703] Step 9:

[0704] The emotion engine analyzes user actions and reactions and stores them in a database as long-term data. This record is used to generate future messages and to provide more appropriate responses to the user.

[0705] (Example 2)

[0706] 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".

[0707] Modern buildings and structures require both safety and efficient maintenance. However, monitoring these in real time and accurately detecting and predicting anomalies is not easy. Furthermore, a challenge remains in providing information that takes into account the feelings of users, resulting in insufficient reduction of user anxiety in response to warnings.

[0708] 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.

[0709] In this invention, the server includes means for using multiple detection devices to acquire environmental information, means for transmitting the information acquired from the detection devices via wireless communication, and means for receiving the transmitted information and storing it in a storage device. This makes it possible to monitor the safety of a building in real time and perform anomaly detection and predictive analysis. Furthermore, by using emotional state estimation means, it is possible to reduce anxiety by providing appropriate information based on the user's emotions.

[0710] "Environmental information" refers to data that indicates the environmental conditions surrounding a building or structure, such as temperature, humidity, vibration, sound, and gas.

[0711] A "detection device" is a device that includes sensors that measure environmental information and output it as digital data.

[0712] "Wireless communication" is a technology that uses radio waves to send and receive data, and it is the method used for information transmission between the sensor device and the server in this system.

[0713] A "storage device" refers to a digital data storage device that can permanently store data and is used to securely store received information.

[0714] A "machine learning algorithm" is a computational method that allows computers to learn from data, recognize patterns, and make decisions.

[0715] A "generative AI model" is an artificial intelligence model that uses large amounts of data to perform pattern recognition and generation.

[0716] "Emotional state inference means" refers to technology for determining a user's emotions based on their input actions.

[0717] An "output device" is hardware used to transmit information to the user, and is a device used to visualize warning messages or analysis results.

[0718] This invention is a system that uses multiple sensor devices to monitor the safety of buildings and structures in real time. The system acquires environmental information such as temperature, humidity, vibration, sound, and gas, and transmits it to a server via wireless communication. The server stores the received data in a database and analyzes anomalies in the data using machine learning algorithms. This analysis uses a generative AI model, which learns patterns from the data and can detect anomalies with high accuracy.

[0719] The server predicts the risk of future anomalies based on historical data. This process involves referencing past data and using machine learning to identify patterns that may lead to anomalies. Furthermore, the server analyzes user emotions in real time through emotion state prediction mechanisms and customizes warning messages accordingly. To alleviate user anxiety, it can generate warning messages containing detailed information.

[0720] The user terminal receives these warning messages and analysis results, and visualizes the information through a graphical interface. This allows users to immediately grasp the situation and take necessary actions. This system is particularly important for administrators to quickly check the status of buildings and maintain safety.

[0721] As a concrete example, if vibrations in a building exceed a certain threshold, the server immediately detects this and, while monitoring the user's reaction using emotional state estimation tools, sends a warning message to the user's terminal containing a detailed explanation to provide reassurance. This allows the user to obtain information about the cause of the vibrations and receive support to contact the appropriate administrator.

[0722] (Example of a prompt message)

[0723] "Please generate a warning message to send to the user after an anomaly is detected. The message should be reassuring to the user."

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] The server acquires environmental information from multiple sensor devices installed in the building. Real-time data from each sensor is transmitted as input, and this data includes temperature, humidity, vibration, sound, gas, etc. The server receives the data via wireless communication and temporarily stores it. During this process, the server verifies the integrity of the data and confirms that accurate information has been obtained.

[0727] Step 2:

[0728] The server stores the received environmental information in a database. The input data consists of individual measurements from each sensor device, and the server saves this data in the database in an appropriate format. Storing the data in the database accumulates historical data that can be used for subsequent analysis. The server organizes and stores the data by adding timestamps and sensor device identifiers.

[0729] Step 3:

[0730] The server executes machine learning algorithms based on stored data to detect anomalies. Using database information as input, the algorithm extracts patterns from the data using a generative AI model and detects outliers. Through this process, the server understands the occurrence of anomalies in real time and sets flags according to the degree of anomaly.

[0731] Step 4:

[0732] The server uses detected anomaly information to infer the user's emotional state and generate an appropriate warning message. It considers anomaly data and the user's past response data as input, and uses an emotional state inference method to determine the user's psychological state. Based on this analysis, a generation AI model creates a warning message using prompts and sends it to the user.

[0733] Step 5:

[0734] The user terminal receives and visualizes warning messages and analysis results sent from the server. It receives data from the server as input and displays it graphically on a dashboard on the terminal. Based on the message content and analysis results, the user can view detailed information and take necessary actions. Specifically, the user clicks on each warning message to view additional information and solutions.

[0735] (Application Example 2)

[0736] 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".

[0737] This invention relates to a system that monitors the safety of buildings and structures in real time and provides appropriate warnings to users when abnormalities are detected. The objective is to reduce user stress and facilitate quick and appropriate responses by providing warnings tailored to the user's emotional state. Furthermore, in factories where a large amount of information is aggregated, it is difficult for workers to constantly monitor the situation; therefore, the development of a system that efficiently processes information and provides optimal feedback to workers is also necessary.

[0738] 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.

[0739] In this invention, the server includes means for using multiple sensor devices to acquire environmental information, means for analyzing information stored in an information processing device and executing an intelligent machine algorithm to detect anomalies, and means for analyzing the user's emotions and changing the content and method of warning notifications based on the user's emotional state. This enables workers to grasp the current situation in real time and provide optimal information according to the user's situation. Furthermore, by customizing the warning content, it is possible to obtain feedback that is easier for the user to understand and more beneficial.

[0740] "Environmental information" refers to data that shows the conditions around a building or structure, and is acquired by sensors such as those for temperature, humidity, vibration, sound, and gas.

[0741] A "sensor device" is a mechanical device used to detect environmental information and is capable of acquiring multiple types of data.

[0742] "Wireless communication" is a means of communication for transmitting data without physical contact, and is a technology that uses radio waves to send information to a remote location.

[0743] An "information processing device" is a computing device used to organize, store, and analyze received information as needed, and includes equipment such as a database.

[0744] An "intelligent machine algorithm" is a series of procedures for automating information processing and analysis, and is an algorithm that uses artificial intelligence technology to detect anomalies.

[0745] A "warning notification" is an informational message that alerts users to detected anomalies and recommends specific actions.

[0746] A "user screen" is an interface device for users to receive information and is equipped with the function of displaying information visually.

[0747] "Visualization" is a method of converting data and information into a form that is easy for humans to understand and displaying it in the form of diagrams, graphs, and other visual representations.

[0748] As an embodiment of the present invention, a safety monitoring system for use in a factory will be described. This system acquires environmental information from multiple sensor devices and transmits it to a cloud server via wireless communication. The server stores the received information in an information processing device and analyzes the data using an intelligent machine algorithm. This analysis detects anomalies and generates warning notifications as necessary.

[0749] The system's hardware utilizes various standard sensors (e.g., temperature sensors, vibration sensors) as sensor devices, and wireless communication technology is used for communication. The information processing unit uses a cloud-based database and an AI algorithm platform (e.g., TensorFlow).

[0750] The intelligent machine algorithm analyzes received environmental information to detect and predict abnormal patterns. Furthermore, to analyze the user's emotions, it performs emotion analysis using video data obtained from cameras installed in smart glasses or terminals. For this purpose, image processing libraries such as OpenCV are used. Based on the analysis results, it generates customized warning messages according to the user's emotional state and provides them to the user's screen.

[0751] For example, if the vibration of equipment in the factory exceeds the normal range, the system will analyze the cause and display a warning such as, "The vibration level is higher than normal, but our technicians are addressing the issue." If the user appears surprised upon receiving this message, additional information such as, "The specific cause of the vibration is motor wear. It will be replaced soon," will be provided to reassure them.

[0752] An example of a prompt is: "Create a specific use case for a real-time monitoring system for factory equipment. Also, explain how the alert messages will be customized based on the user's emotions."

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The server acquires environmental information from multiple sensor devices installed in the factory. Inputs include data from each sensor (temperature, vibration, humidity, etc.). The server centralizes this data, standardizes the format, and receives it via wireless communication. The output is structured environmental information data that is updated in real time.

[0756] Step 2:

[0757] The server stores the received environmental information data in the database of the information processing device. The input data is the environmental information obtained in step 1. Here, the data is written to the database and saved for future analysis. The output is a clean dataset stored in the information processing device.

[0758] Step 3:

[0759] The server executes intelligent machine algorithms on environmental information stored in a database. The input is a dataset stored in an information processing device, and anomaly detection and future predictive analysis are performed based on this data. Specifically, it compares current data with past data and detects anomalies if a certain threshold is exceeded. The output is a flag indicating whether an anomaly was detected or not, and the related analysis results.

[0760] Step 4:

[0761] If an anomaly is detected, the server generates a warning notification and displays it on the user screen. The input is the anomaly detection flag and analysis result from step 3. Based on this, the server generates a warning message describing the nature of the anomaly and specific countermeasures. The output is a text message displayed on the user screen.

[0762] Step 5:

[0763] The device acquires video data from its built-in camera to analyze the user's emotions. The input is the user's facial data, which is used for emotion analysis. Specifically, it uses an emotion analysis library to identify the emotional state the user is exhibiting. The output is a parameter indicating the user's emotional state.

[0764] Step 6:

[0765] The server customizes the content and method of warning notifications based on the emotional state. The input is the emotional state parameters obtained in step 5, and the server uses this to customize existing warning messages. Specifically, if the user shows surprise or anxiety, it adds and provides reassuring content. The output is a customized warning message adapted to the emotional state.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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."

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A means of using multiple sensor devices to acquire environmental data,

[0790] Means for transmitting data acquired from the aforementioned sensor device via wireless communication,

[0791] A means for receiving transmitted data and storing it in a database,

[0792] A means for analyzing the data stored in the aforementioned database and executing an artificial intelligence algorithm for detecting anomalies,

[0793] A means for generating a warning message based on detected anomalies and notifying the user interface,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein the artificial intelligence algorithm performs predictive analysis to predict future anomalies based on past data.

[0797] (Claim 3)

[0798] The system according to claim 1, which visualizes warning messages and analysis results received by the user interface, and allows the user to receive them in the form of graphs or reports.

[0799] "Example 1"

[0800] (Claim 1)

[0801] A means of using multiple sensor devices to acquire environmental information,

[0802] A means for transmitting information acquired from the aforementioned sensor device using wireless communication,

[0803] A means for receiving transmitted information and storing it on a data recording medium,

[0804] A means for analyzing the information recorded on the data recording medium and executing a machine learning algorithm to detect deviations from a normal pattern,

[0805] A means for generating a warning notification based on the detected deviation and displaying it on the user screen,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, wherein the machine learning algorithm performs predictive analysis to predict future anomalies based on past information.

[0809] (Claim 3)

[0810] The system according to claim 1, which visualizes warning notifications and analysis results received by the user screen, and allows the user to receive them in the form of graphs or reports.

[0811] "Application Example 1"

[0812] (Claim 1)

[0813] A means of using multiple detection devices to acquire environmental information,

[0814] A means for transmitting information acquired from the detection device via wireless communication,

[0815] Means for receiving transmitted information and storing it in an information recording device,

[0816] Means for analyzing information stored in the information recording device and executing an intelligent algorithm for detecting anomalies,

[0817] A means for generating a warning notification based on the detected anomaly and notifying the terminal device,

[0818] A means of using a wearable display device that displays the aforementioned warning notification in real time and enables a quick response,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the intelligent algorithm performs predictive analysis to predict future anomalies based on past information.

[0822] (Claim 3)

[0823] The system according to claim 1, which visualizes warning notifications and analysis results received by the terminal device, and enables the user to receive them in the form of charts or reports.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] A means of using multiple detection devices to acquire environmental information,

[0827] A means for transmitting information acquired from the aforementioned detection device via wireless communication,

[0828] A means for receiving transmitted information and storing it in a storage device,

[0829] A means for analyzing the information stored in the aforementioned storage device and executing a machine learning algorithm for detecting anomalies,

[0830] A means of conducting analysis to predict the risk of future anomalies based on past information,

[0831] A means for inferring the user's emotional state, generating a warning message corresponding to that emotion, and notifying an output device;

[0832] The output device visualizes the warning messages and analysis results it receives, and provides a means for the user to receive them in the form of charts, graphs, or reports.

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the machine learning algorithm is operated by a generative AI model.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the emotional state estimation means determines an emotion based on the user's input actions.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] A means of using multiple sensor devices to acquire environmental information,

[0841] Means for transmitting information acquired from the sensor device via wireless communication,

[0842] A means for receiving transmitted information and storing it in an information processing device,

[0843] Means for analyzing information stored in the aforementioned information processing device and executing an intelligent machine algorithm for detecting anomalies,

[0844] A means for generating a warning notification based on the detected anomaly and notifying the user on the screen,

[0845] The aforementioned intelligent machine algorithm analyzes the user's emotions and provides means for changing the content and method of warning notifications based on the user's emotional state.

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the intelligent machine algorithm performs predictive analysis to predict future anomalies based on past information.

[0849] (Claim 3)

[0850] The system according to claim 1, wherein the user screen visualizes the warnings and analysis results received, and enables the user to receive them in the form of diagrams or reports. [Explanation of Symbols]

[0851] 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 using multiple sensor devices to acquire environmental data, Means for transmitting data acquired from the aforementioned sensor device via wireless communication, A means for receiving transmitted data and storing it in a database, A means for analyzing the data stored in the aforementioned database and executing an artificial intelligence algorithm for detecting anomalies, A means for generating a warning message based on detected anomalies and notifying the user interface, A system that includes this.

2. The system according to claim 1, wherein the artificial intelligence algorithm performs predictive analysis to predict future anomalies based on past data.

3. The system according to claim 1, which visualizes warning messages and analysis results received by the user interface, and allows the user to receive them in the form of graphs or reports.

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