system

Abstract

We provide the system. [Solution] A means of collecting documents in non-standardized formats provided by different suppliers, A means of analyzing documents collected using generative AI and structuring the information, A means of integrating structured information into a database for centralized management, A means for transmitting operational information received from connected distributed devices to a server, A means for analyzing operational information and detecting anomalies that exceed a set threshold, A means of notifying the administrator of detected anomalies, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, a variety of distributed devices have been introduced in various facilities, but their management is complicated by documents provided in different formats for each supplier. As a result, it is difficult for administrators to grasp the installation locations and operating status of each device, and there is a risk of an increase in "devices in an unmanaged state" (hereinafter, "zombie devices"). This state causes operational problems such as reducing the operation efficiency of the facility and missing the timing of necessary maintenance. The present invention aims to prevent such a situation and improve the management efficiency of distributed devices.

Means for Solving the Problems

[0005] This invention provides a means for collecting documents in non-standardized formats from different suppliers, and for analyzing and structuring these documents using a generation AI. It also includes a system for integrating this structured information into a database for centralized management, receiving operational information from connected distributed devices, and detecting and notifying of anomalies. Furthermore, it includes a means for identifying the location of devices from the structured information to facilitate understanding their installation locations, and for presenting optimal countermeasures to administrators by referring to past response records, thereby improving the management efficiency of distributed devices.

[0006] A "supplier" is a company or organization that provides distributed equipment or related documentation.

[0007] "Non-standardized documents" are reports or delivery notes provided by different suppliers that do not have a consistent format or description.

[0008] "Generative AI" is an artificial intelligence technology that uses natural language processing and machine learning techniques to analyze text data and extract useful information.

[0009] "Structuring" refers to the process of analyzing unstructured data and organizing it into a manageable format based on certain rules.

[0010] "Distributed devices" are IoT devices and sensors installed within a building or facility, each operating independently.

[0011] A "database" is an electronic information storage system that efficiently stores structured information and allows for easy searching and retrieval.

[0012] "Anomaly detection" is a process that analyzes data received from distributed devices to automatically detect behavior or conditions that exceed the normal range.

[0013] "Notification" refers to the action of informing an administrator via email or application when an anomaly occurs.

[0014] "Location information" refers to the information of the specific location where each distributed device is actually installed.

[0015] "Corresponding record" refers to a record that summarizes the countermeasure history and processing procedures for abnormalities that occurred in the past.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

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 a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), 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 disks (e.g., hard disks), or magnetic tapes, 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 for streamlining the management of distributed devices in buildings and various facilities. This system consists of three main components: a server, terminals, and users.

[0038] Server-side implementation

[0039] The server first collects documents in non-standardized formats provided by each supplier. The server temporarily stores the documents via email or API, and then uses generative AI to analyze them. The generative AI uses natural language processing techniques to extract information from the documents and structure key information such as device type, installation location, and operating conditions. Furthermore, this structured data is integrated into a database to maintain a constantly up-to-date management ledger.

[0040] Terminal-side implementation

[0041] The terminal communicates directly with distributed devices installed within the facility and receives operational data in real time. The received data is periodically sent to the server, during which initial data analysis is also performed. Furthermore, if the terminal detects an anomaly under specific conditions, it notifies the administrator via the server. This notification is achieved by comparing the received data with a pre-configured threshold.

[0042] User roles

[0043] Users can access the system using a specific interface and view information stored in the database. This makes it easy to understand the location and operating status of each distributed device. Users can also view detailed information and take the most appropriate action quickly when an alert is received. Furthermore, efficient problem solving is possible by referring to suggested countermeasures based on past response records.

[0044] Specific example

[0045] For example, when new air conditioning equipment is installed, the delivery documents arrive at the server. The AI ​​analyzes the documents and registers information such as the installation location and specifications in the database. Subsequently, the terminal collects operational data in real time and issues an alert if an anomaly is detected. Upon receiving the alert, the user can quickly check the device status via the interface and take the necessary actions.

[0046] In this way, by coordinating servers, terminals, and users, distributed devices can be managed effectively, preventing the proliferation of "zombie devices" and achieving efficient operational management.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects documents in non-standardized formats provided by various suppliers. Documents are periodically retrieved using email or a dedicated API and stored in temporary storage.

[0050] Step 2:

[0051] The server analyzes the collected documents using a generative AI. The generative AI utilizes natural language processing technology to extract key information such as device name, installation location, and functional specifications from the documents and creates structured data.

[0052] Step 3:

[0053] The server integrates structured information into a database. It maintains an up-to-date device management ledger by matching existing records with new data and updating or adding new entries as needed.

[0054] Step 4:

[0055] The terminal receives operational data in real time from distributed devices within the facility. It analyzes the data packets from the device and performs an initial check to determine if it is within the range of normal operation.

[0056] Step 5:

[0057] The terminal sends the received data to the server. The data is encrypted before transmission, and further analysis and storage are performed on the server side. If the network is unstable, the data is temporarily stored and sent when the connection is restored.

[0058] Step 6:

[0059] The terminal performs anomaly detection based on operational data. If an anomaly exceeding a pre-set threshold is detected, an alert is created, sent to the server, and the administrator is notified.

[0060] Step 7:

[0061] The user reviews the received alert and investigates the details of the problem through the system interface. They refer to the device's location information and past response history to make decisions on how to take appropriate action.

[0062] Step 8:

[0063] The user uses past response records to implement the most appropriate measures. Based on the proposed solutions suggested by the system, they issue necessary instructions to on-site workers to quickly resolve the problem.

[0064] (Example 1)

[0065] 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."

[0066] Existing methods for efficiently analyzing and integrating non-standardized information from different providers, and for locating distributed devices and monitoring their operational status, present challenges in terms of accuracy and speed. Therefore, a system is needed that can quickly and accurately structure information and immediately detect and notify of abnormal situations.

[0067] 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.

[0068] In this invention, the server includes means for receiving and storing non-standardized information from different providers, means for analyzing the received information using a generation AI and extracting attribute information, and means for integrating and maintaining the extracted attribute information in an aggregated information area. This enables efficient management and operation of distributed devices and allows for rapid detection and response to anomalies.

[0069] "Information in a non-standard format" refers to information provided by different providers that does not conform to a single format or style.

[0070] A "provider" refers to an individual or organization that provides information or services.

[0071] "Generative AI" refers to a technology that uses artificial intelligence to analyze data and generate useful information.

[0072] "Attribute information" refers to information that indicates specific characteristics or properties extracted from data or documents.

[0073] A "centralized information area" refers to a part of a database or information system where integrated data is centrally stored and managed.

[0074] "Operational information" refers to data that shows how a device or system is operating.

[0075] The "management area" refers to the part of the system where data is aggregated and analyzed, and which operators can access as needed.

[0076] An "abnormality" refers to a state in which a system or device deviates from its normal operation.

[0077] "Operator" refers to an individual or organization responsible for monitoring and managing the system.

[0078] This invention is a system that enables the efficient management of distributed devices, and is realized through the respective roles of servers, terminals, and users.

[0079] Server Role

[0080] The server receives documents in non-standardized formats from different providers via email or API and stores them temporarily. Next, it analyzes the received documents using a generative AI model. This analysis utilizes natural language processing techniques to extract attribute information such as device type, installation location, and operating conditions. The extracted information is structured and integrated into a consolidated information domain. The server uses this information to manage a database and maintain up-to-date device information.

[0081] Terminal role

[0082] The terminal acquires data in real time from distributed devices located within the facility. The terminal has the function of temporarily storing and analyzing the acquired operational information. This operational information is periodically transmitted from the terminal to the server, and if an anomaly is detected, an alert is immediately issued to the operator. For example, if the temperature sensor exceeds a set threshold, the terminal generates an alert.

[0083] User roles

[0084] Users can access the database on the server through the interface to check the location and operating status of each distributed device. They can also receive notifications of anomalies, quickly understand the details, and take appropriate action. By comparing this information with past response records, efficient problem solving can be achieved.

[0085] Examples of specific cases and prompt statements

[0086] For example, when new air conditioning equipment is installed, delivery information arrives on the server. A generating AI analyzes the document and registers the information in the database. A terminal collects real-time operational data from the air conditioning equipment and issues an alert if there is an anomaly. The user receives the alert, checks the status of the air conditioning equipment through the interface, and takes appropriate action.

[0087] An example of a prompt message is, "Monitor the operating data of the new air conditioning equipment and issue an alert to notify the user if any abnormalities occur."

[0088] In this way, servers, terminals, and users cooperate to manage distributed devices more effectively.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The server receives documents in non-uniform formats from different providers via email or API and temporarily stores them in a database. This process takes the received documents as input and outputs files to a storage folder. Here, the files retain their original format but are properly labeled for later analysis.

[0092] Step 2:

[0093] The server analyzes received documents using a generative AI model. Using the documents as input, it extracts attribute information such as device type, installation location, and operating conditions by utilizing natural language processing technology. The output is this attribute information in a well-organized structured data format. This structured data is crucial for subsequent management.

[0094] Step 3:

[0095] The server verifies the extracted attribute information and integrates and stores it in the database. In this step, it receives the obtained structured data as input and updates the information by matching it with the existing database. The output is a completed, integrated, and up-to-date database. This ensures that the management information is always kept current.

[0096] Step 4:

[0097] The terminal acquires and temporarily stores operational information from distributed devices located within the facility. It receives this operational information as input and prepares to send it to the server. The operational information is then sent to the server as output. At this time, a portion of the data is analyzed for emergency use.

[0098] Step 5:

[0099] The terminal analyzes data acquired in real time to determine if an anomaly has occurred. It takes operational information as input, compares it to a set threshold, and generates an alert if an anomaly is detected. As output, if an anomaly is detected, a notification to the administrator is prepared.

[0100] Step 6:

[0101] Users access the database through a management interface provided by the server to check the location and operating status of devices. They receive views provided by the server as input and manage the devices based on the displayed information as output. Users can then use this information to immediately resolve problems.

[0102] Step 7:

[0103] When users receive an anomaly alert, they can review the details and take prompt and optimal action. The system receives the alert information as input and, referencing past response records stored on the server, executes the most suitable solution as output. This ensures the early resolution of problems.

[0104] (Application Example 1)

[0105] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0106] Efficient management of multiple groups of machinery within a factory is crucial, but rapid response to machine malfunctions and the ability to quickly and accurately identify effective countermeasures based on past response records remain challenges. Furthermore, integrating and managing large amounts of data provided in non-standardized formats is a significant issue.

[0107] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0108] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI technology and structuring the information, and means for integrating the structured information into a storage medium for integrated management. This enables rapid detection of anomalies and the presentation of optimal response plans.

[0109] "Different sources" refers to various external organizations or systems that provide information or data.

[0110] "Information in non-standard formats" refers to data and documents provided in various non-standardized formats.

[0111] "Generative AI technology" refers to technologies that utilize artificial intelligence generative models for data analysis and natural language processing.

[0112] "Structuring information" refers to analyzing disorganized information, organizing it systematically, and converting it into a manageable format.

[0113] "Storage medium for integrated management" refers to a database or storage system for collecting various types of information and centrally storing and managing them.

[0114] A "connected distributed group of devices" refers to a group of independent machines or devices connected through a network.

[0115] "Operational information" refers to operational status and performance data collected in real time from a group of distributed devices.

[0116] An "information processing device" refers to a computer system or server that analyzes received data and performs necessary processing.

[0117] "Detecting an anomaly" refers to the automatic recognition of abnormalities or abnormalities in equipment or systems based on pre-set criteria.

[0118] "Utilizing generative AI to propose optimal solutions based on past response records" means using a generative AI model to analyze past data and cases and propose the most effective countermeasures for the current situation.

[0119] This invention is a system for achieving efficient management of machinery within a factory. A server collects non-standardized information from different sources, analyzes and structures this information using generative AI technology, and stores the structured information in a storage medium for integrated management. The server also analyzes operational information received from connected distributed devices and detects anomalies that exceed set thresholds. This process utilizes Python and TENSORFLOW® to efficiently perform data analysis and execute AI models.

[0120] The terminal communicates with each operating machine within the factory and receives operational information in real time. This information is sent to the server when a specific anomaly is detected. This allows for immediate response when an anomaly occurs. Furthermore, by utilizing AI generation to suggest the optimal response plan based on past response records, efficient problem solving can be achieved.

[0121] Users can access this system via smartphones or smart glasses to check data in real time and immediately see countermeasures if an anomaly is detected. For example, they can use a prompt message such as, "Please tell me the best course of action if the operating temperature of robot A exceeds 85°C," to take appropriate action.

[0122] This system enables efficient management of machinery within the factory and allows for rapid response in the event of malfunctions, thereby improving productivity.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server collects non-standardized information from various external sources. This input includes data in various formats and is received via email or API. The server stores this information in temporary storage.

[0126] Step 2:

[0127] The server analyzes and structures the collected information using generative AI technology. This step utilizes natural language processing techniques to extract key information such as device type, installation location, and operating conditions. The output is structured data in a unified format.

[0128] Step 3:

[0129] The server integrates structured information into a database for centralized management. Using the structured data obtained as input, it adds newly registered devices and their information to the database. This process maintains an up-to-date management ledger.

[0130] Step 4:

[0131] The terminal communicates with the machinery in the factory in real time and receives operational information. The input is real-time data transmitted from each machine, and the output is initial analyzed data sent to the server.

[0132] Step 5:

[0133] The server receives and analyzes operational information sent from the terminal. In this step, it compares the data to a set threshold and performs data calculations to detect anomalies. The output is the result of whether or not an anomaly was detected.

[0134] Step 6:

[0135] If an anomaly is detected, the server will send a notification to the user. It will generate a notification message using the Slack API, email, or other means, and send it to the user's device.

[0136] Step 7:

[0137] The user uses a generated AI based on the received notification to determine the optimal course of action. They then decide on a specific course of action based on suggested solutions derived from past response records. They use prompts such as, "Please tell me the optimal course of action if the operating temperature of robot A exceeds 85°C," to receive suggestions from the AI.

[0138] 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.

[0139] This invention combines a system for efficiently managing distributed devices within buildings and facilities with an emotion engine that recognizes user emotions. The system consists of four main components: a server, a terminal, a user, and the emotion engine.

[0140] Server-side implementation

[0141] The server electronically collects documents in non-standardized formats from various suppliers and analyzes them using generative AI. The information obtained from the analyzed documents is structured and integrated into a database. Furthermore, it receives operational information from each distributed device transmitted from terminals, analyzes this data, and detects anomalies. Through these processes, it is possible to maintain up-to-date and detailed device management information at all times.

[0142] Terminal-side implementation

[0143] The terminal communicates directly with each distributed device within the facility and receives operational data in real time. This data is sent to a server, and in the event of an anomaly, an alert is immediately sent to the administrator via the server. The terminal also identifies the location of the devices and uses this information to support efficient management.

[0144] Implementation of an emotion engine

[0145] The emotion engine recognizes the user's emotional state in real time and reflects it in the interface. This allows for the selection of notification methods tailored to the user's emotions when an anomaly occurs. Furthermore, optimal countermeasures are customized to the user's emotional state and presented in a way that minimizes stress.

[0146] User roles

[0147] Users view information and perform normal administrative tasks through the provided interface. If an anomaly is detected, they receive customized notifications via the emotion engine and can quickly initiate a response. Users can also quickly and effectively resolve problems by referring to suggested solutions based on past response records.

[0148] Specific example

[0149] For example, if a cooling system malfunctions within a facility, the server will detect the anomaly. The emotion engine analyzes the user's current emotional state, and if it recognizes a high stress level, it sends a notification along with a calming message and recommended actions. The user receives the notification and can immediately take appropriate action.

[0150] This system enables highly accurate and efficient management of distributed devices while reducing the psychological burden on users.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The server collects documents in non-standardized formats from various suppliers via email and APIs. This ensures that all necessary documents are stored in temporary storage.

[0154] Step 2:

[0155] The server uses generative AI to analyze the collected documents. The generative AI extracts information from the documents using natural language processing technology and structures it into a standardized format.

[0156] Step 3:

[0157] The server integrates structured data into a centralized database. By comparing it with existing data and automatically performing necessary additions and updates, the device management ledger is kept up-to-date.

[0158] Step 4:

[0159] The terminal receives data in real time from distributed devices within the facility. It performs an initial analysis of the received data to check the operating status of the devices.

[0160] Step 5:

[0161] The terminal sends the received operational data to the server. The data is encrypted and sent to the server while maintaining security.

[0162] Step 6:

[0163] The server analyzes the received operational data and detects anomalies that exceed the set threshold. If an anomaly occurs, it records the details and provides the necessary information to the administrator.

[0164] Step 7:

[0165] The emotion engine analyzes the user's emotional state in real time, thereby evaluating the user's stress level and current emotions.

[0166] Step 8:

[0167] The server adjusts the content and method of abnormal notifications based on the evaluation results of the emotion engine. For example, if a user is showing high stress levels, the notification will be sent using milder language.

[0168] Step 9:

[0169] The user receives a notification and checks the details of the anomaly on the interface. The user can then refer to past response history via the interface as needed and take appropriate action immediately.

[0170] Step 10:

[0171] Based on past response records and proposed solutions, users can make the most appropriate choice for the situation on-site and quickly begin taking action to resolve the problem.

[0172] (Example 2)

[0173] 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 will be referred to as the "terminal."

[0174] In facilities and buildings with an increasing number of distributed devices, the lack of efficient and unified management methods for each device is a challenge. In conventional systems, information provided by each supplier is inconsistent in format, making rapid anomaly detection and response difficult. Furthermore, the lack of flexibility in the method and content of anomaly notifications to administrators is a major problem, failing to alleviate user stress.

[0175] 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.

[0176] In this invention, the server includes means for collecting non-uniform information provided from different sources, means for analyzing the collected information using knowledge processing techniques and organizing the data, and means for integrating the organized data into a recording medium for centralized management. This enables efficient information management, rapid anomaly detection, and flexible notification to administrators.

[0177] "Supplier" refers to an external source or organization that provides information or data.

[0178] "Non-standardized format" refers to a state where information is provided in different formats or formats.

[0179] "Knowledge processing technology" refers to artificial intelligence and natural language processing technologies used to analyze information and organize it into meaningful data.

[0180] "Organizing data" refers to systematically arranging information according to certain standards.

[0181] A "recording medium" refers to a physical or electronic device or system used to store and manage data.

[0182] The term "central system" refers to the main computer system responsible for data processing and management functions for the entire system.

[0183] "Emotional state" refers to the psychological state or mood that the user is experiencing, and analyzing this state enables appropriate responses.

[0184] "Customizing" refers to adjusting and optimizing the content and methods of notifications and services according to individual needs and circumstances.

[0185] This invention provides a system for efficiently managing distributed devices, including information collection and analysis, anomaly detection, user notification, and suggestion of countermeasures. The system mainly consists of four main components: a server, terminals, users, and an emotion engine.

[0186] The server first collects non-standardized information from diverse sources. This includes downloads from email and online storage services, and is automated using programming languages ​​such as Python. Next, the server analyzes the collected information using generative AI models and organizes it into meaningful data. This analysis utilizes natural language processing techniques to extract and structure important information, which can then be centrally managed in a database.

[0187] The terminal communicates directly with each distributed device to acquire operational information in real time. Using IoT-enabled sensors and communication modules, the terminal transmits data such as operating status and operational conditions to the server in real time. This ensures that administrators are immediately notified via the server if an anomaly occurs. This system is designed to efficiently collect operational information without user intervention.

[0188] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications accordingly. If the user is stressed, the emotion engine generates a notification with a calming message to reduce the user's psychological burden. The emotional information obtained is collected through biometric data from smartphones and wearable devices and analyzed in real time.

[0189] As a concrete example, consider a case where a cooling system fails within a facility. The server automatically detects the anomaly based on abnormal data sent from the terminal. The emotion engine checks the user's current emotional state and prepares appropriate response suggestions according to their stress level. The user can then respond quickly by referring to the suggestions prepared by the emotion engine. An example of a prompt message in this case might be, "When a cooling system fails within a facility, how will the generative AI model used to detect the anomaly and notify the user?"

[0190] In this way, the present invention enables the rapid and accurate provision of information to administrators and users, and contributes to the efficient management of distributed devices.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The server collects non-standardized information from various sources. Inputs include documents and data downloaded from sources such as email and cloud storage. Specifically, automated data collection is performed via an API using a Python script. The output is stored on the server as raw data.

[0194] Step 2:

[0195] The server analyzes the collected information using a generative AI model. The input is the raw data collected in step 1. Data processing includes information extraction using natural language processing techniques, and structured data is obtained as the output after analysis. This data is organized in a key-value pair format. Specifically, the meaning of the information is analyzed and converted into a format that can be stored in a database.

[0196] Step 3:

[0197] The terminal communicates with each distributed device and receives operational information in real time. The input is device status data acquired from IoT sensors. This data includes information such as the temperature and operating status of the devices. Specifically, the terminal directly acquires data from the devices and sends it to the server. The output is operational information data sent to the server.

[0198] Step 4:

[0199] The server analyzes the operating information transmitted from the terminal to detect anomalies. The input is the operating information data received in step 3. The data calculation involves anomaly detection based on a set threshold. Specifically, if the operating information exceeds the threshold, it is judged to be an anomaly, and the anomaly detection result is obtained as output.

[0200] Step 5:

[0201] The emotion engine analyzes the user's emotional state and selects the appropriate notification method. Inputs include biometric information and emotional data obtained from the user. Specifically, it collects data in real time from smartphones and wearable devices and analyzes the emotional state. Output is a customized notification message.

[0202] Step 6:

[0203] The user receives a customized notification through the emotion engine and initiates a response. The input is the notification message generated in step 5. The user uses this notification as a reference to quickly take appropriate problem-solving actions. Specifically, they perform actions such as checking or correcting the device based on the notification content. The output is the result of the user's response.

[0204] (Application Example 2)

[0205] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0206] The problem that this invention aims to solve is to efficiently manage the operating status of multiple devices within a facility or factory while reducing the psychological stress on administrators when abnormal situations occur. Conventional systems have the problem that abnormality notifications are issued uniformly without considering the emotional state of administrators, resulting in unnecessary stress during emergency responses.

[0207] 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.

[0208] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI and structuring the knowledge, and means for integrating the structured knowledge into a data storage for centralized management. This enables real-time monitoring of the operating status of multiple devices and allows for optimal anomaly notifications and countermeasure suggestions tailored to the administrator's psychological state.

[0209] A "source" is the origin of diverse information, and the object from which a system obtains data.

[0210] "Non-standardized format" refers to information that is not standardized and has different structures and formats.

[0211] "Generative AI" is a type of artificial intelligence that generates information through natural language processing and data analysis.

[0212] "Knowledge" refers to the content of information that has been collected and analyzed, structured, and stored in a useful way within a system.

[0213] "Data storage" refers to a data storage location for storing structured knowledge and information for centralized management.

[0214] A "multiple device" is a collection of several machines or tools that operate in conjunction with each other within a system.

[0215] "Operating status" refers to real-time information indicating the operating status and presence or absence of abnormalities in multiple devices.

[0216] An "abnormal situation" refers to a state in which a system or device deviates from its normal operation, requiring immediate action.

[0217] An "administrator" is the person responsible for the operation and maintenance of the entire system, and who responds to abnormal situations.

[0218] "Psychological state" refers to the administrator's emotions and mental state, and is a factor that influences their stress when receiving abnormal notifications.

[0219] An "alert method" refers to the method and format used to notify administrators of abnormal situations, and it is optimized according to the emotional state of the user.

[0220] In order to implement the system of the present invention, the server, terminal, and user elements must work in coordination.

[0221] The server first collects non-standardized information from various sources. To do this, the server uses web scraping techniques and APIs to collect data. The collected information is analyzed using generative AI and structured into organized knowledge. The generative AI model utilizes natural language processing libraries such as "Transformers" and data analysis tools such as "Pandas" and "NumPy". This structured knowledge is integrated into data storage for centralized management, using database systems such as "MySQL®" and "PostgreSQL".

[0222] The terminal receives real-time operating status information from multiple devices within the facility and transmits it to a server. IoT protocols such as MQTT and HTTP are used for communication, and the terminal connects to the devices via hardware such as Raspberry Pi or Arduino. This allows the terminal to identify the location information of the devices and assist in efficient management.

[0223] Users receive notifications of abnormal situations through a device or smart glasses equipped with emotion recognition capabilities. Real-time facial expression analysis using libraries such as "OpenCV" is performed to analyze the administrator's psychological state. The abnormal notification is displayed in a customized format, taking into account the user's mental burden. This allows for the selection of the most appropriate alert method and the suggestion of necessary countermeasures.

[0224] As a concrete example, if a piece of equipment on a production line malfunctions in a factory, a fatigued user would receive a gentle notification such as, "Take a short break. The equipment needs to be reset," while a refreshed user would receive a warning such as, "Emergency action is required." An example of a prompt message would be, "What should be displayed if the operator is relaxed?"

[0225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0226] Step 1:

[0227] The server collects non-standardized information from different sources. This information collection involves using API calls and web scraping to gather text data and logs. The input is non-standardized information, and the output is the collected raw data.

[0228] Step 2:

[0229] The server uses a generative AI model to analyze the collected, non-standardized information and convert it into structured knowledge. During this process, the information is analyzed using the natural language processing library "Transformers" and formatted for easier input into the database. The input is the collected raw data, and the output is structured knowledge.

[0230] Step 3:

[0231] The server integrates structured knowledge into data storage. Using database systems such as MySQL or PostgreSQL, the information is stored in a way that allows for efficient searching and retrieval. The input is structured knowledge, and the output is entries in the integrated database.

[0232] Step 4:

[0233] The terminal receives real-time operational status data from multiple devices within the facility and transmits this data to a server. The IoT protocol "MQTT" is used here to collect device operational data in real time. The input is operational data from the devices, and the output is the operational status information transmitted to the server.

[0234] Step 5:

[0235] The server analyzes the operational status received from the terminal and detects any abnormalities that exceed a set threshold. The input is operational status information, and the output is the result of detecting the abnormal situation.

[0236] Step 6:

[0237] The user's device uses emotion recognition functionality to analyze the user's psychological state and provide optimal anomaly notifications. Here, the real-time facial expression analysis library "OpenCV" is used, and the user's facial expression data is output as the emotional state. The input is the user's real-time facial expression data, and the output is the analyzed emotional state.

[0238] Step 7:

[0239] The server customizes anomaly notifications based on the user's emotional state and suggests appropriate alert methods and countermeasures. If the user's emotions indicate stress, it generates a calm notification message and sends it to the user. The input is the analyzed emotional state and anomaly information, and the output is a customized notification message.

[0240] Step 8:

[0241] The user takes appropriate action based on the notification received. This may involve accepting suggestions from the system, resetting the device, or coordinating with other operators. The input is the notification message, and the output is the corrective action to be taken.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] [Second Embodiment]

[0246] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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).

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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".

[0258] This invention is a system for streamlining the management of distributed devices in buildings and various facilities. This system consists of three main components: a server, terminals, and users.

[0259] Server-side implementation

[0260] The server first collects documents in non-standardized formats provided by each supplier. The server temporarily stores the documents via email or API, and then uses generative AI to analyze them. The generative AI uses natural language processing techniques to extract information from the documents and structure key information such as device type, installation location, and operating conditions. Furthermore, this structured data is integrated into a database to maintain a constantly up-to-date management ledger.

[0261] Terminal-side implementation

[0262] The terminal communicates directly with distributed devices installed within the facility and receives operational data in real time. The received data is periodically sent to the server, during which initial data analysis is also performed. Furthermore, if the terminal detects an anomaly under specific conditions, it notifies the administrator via the server. This notification is achieved by comparing the received data with a pre-configured threshold.

[0263] User roles

[0264] Users can access the system using a specific interface and view information stored in the database. This makes it easy to understand the location and operating status of each distributed device. Users can also view detailed information and take the most appropriate action quickly when an alert is received. Furthermore, efficient problem solving is possible by referring to suggested countermeasures based on past response records.

[0265] Specific example

[0266] For example, when new air conditioning equipment is installed, the delivery documents arrive at the server. The AI ​​analyzes the documents and registers information such as the installation location and specifications in the database. Subsequently, the terminal collects operational data in real time and issues an alert if an anomaly is detected. Upon receiving the alert, the user can quickly check the device status via the interface and take the necessary actions.

[0267] In this way, by coordinating servers, terminals, and users, distributed devices can be managed effectively, preventing the proliferation of "zombie devices" and achieving efficient operational management.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] The server collects documents in non-standardized formats provided by various suppliers. Documents are periodically retrieved using email or a dedicated API and stored in temporary storage.

[0271] Step 2:

[0272] The server analyzes the collected documents using a generative AI. The generative AI utilizes natural language processing technology to extract key information such as device name, installation location, and functional specifications from the documents and creates structured data.

[0273] Step 3:

[0274] The server integrates structured information into a database. It maintains an up-to-date device management ledger by matching existing records with new data and updating or adding new entries as needed.

[0275] Step 4:

[0276] The terminal receives operational data in real time from distributed devices within the facility. It analyzes the data packets from the device and performs an initial check to determine if it is within the range of normal operation.

[0277] Step 5:

[0278] The terminal sends the received data to the server. The data is encrypted before transmission, and further analysis and storage are performed on the server side. If the network is unstable, the data is temporarily stored and sent when the connection is restored.

[0279] Step 6:

[0280] The terminal performs anomaly detection based on operation data. When an anomaly exceeding a preset threshold is detected, alert information is created, sent to the server, and notified to the administrator.

[0281] Step 7:

[0282] The user checks the received alert and investigates the details of the problem through the system interface. By referring to the location information of the device and the past response history, a judgment is made to take appropriate measures.

[0283] Step 8:

[0284] The user executes the optimal measure using the past response record. Based on the countermeasure plan presented by the system, necessary instructions are given to the on-site workers to quickly solve the problem.

[0285] (Example 1)

[0286] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In efficiently analyzing and integrating non-uniformly formatted information provided by different providers and monitoring the location and operation status of distributed devices, existing methods have problems with accuracy and speed. Therefore, there is a need for a system that can quickly and accurately structure information and immediately detect and notify abnormal situations. <H

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0289] In this invention, the server includes means for receiving and storing non-standardized information from different providers, means for analyzing the received information using a generation AI and extracting attribute information, and means for integrating and maintaining the extracted attribute information in an aggregated information area. This enables efficient management and operation of distributed devices and allows for rapid detection and response to anomalies.

[0290] "Information in a non-standard format" refers to information provided by different providers that does not conform to a single format or style.

[0291] A "provider" refers to an individual or organization that provides information or services.

[0292] "Generative AI" refers to a technology that uses artificial intelligence to analyze data and generate useful information.

[0293] "Attribute information" refers to information that indicates specific characteristics or properties extracted from data or documents.

[0294] A "centralized information area" refers to a part of a database or information system where integrated data is centrally stored and managed.

[0295] "Operational information" refers to data that shows how a device or system is operating.

[0296] The "management area" refers to the part of the system where data is aggregated and analyzed, and which operators can access as needed.

[0297] An "abnormality" refers to a state in which a system or device deviates from its normal operation.

[0298] "Operator" refers to an individual or organization responsible for monitoring and managing the system.

[0299] This invention is a system that enables the efficient management of distributed devices, and is realized through the respective roles of servers, terminals, and users.

[0300] Server Role

[0301] The server receives documents in non-standardized formats from different providers via email or API and stores them temporarily. Next, it analyzes the received documents using a generative AI model. This analysis utilizes natural language processing techniques to extract attribute information such as device type, installation location, and operating conditions. The extracted information is structured and integrated into a consolidated information domain. The server uses this information to manage a database and maintain up-to-date device information.

[0302] Terminal role

[0303] The terminal acquires data in real time from distributed devices located within the facility. The terminal has the function of temporarily storing and analyzing the acquired operational information. This operational information is periodically transmitted from the terminal to the server, and if an anomaly is detected, an alert is immediately issued to the operator. For example, if the temperature sensor exceeds a set threshold, the terminal generates an alert.

[0304] User roles

[0305] Users can access the database on the server through the interface to check the location and operating status of each distributed device. They can also receive notifications of anomalies, quickly understand the details, and take appropriate action. By comparing this information with past response records, efficient problem solving can be achieved.

[0306] Examples of specific cases and prompt statements

[0307] For example, when a new air conditioning equipment is installed, the delivery information arrives at the server. The generative AI analyzes the document and registers the information in the database. The terminal collects the operation data of the air conditioning equipment in real time and issues an alert if there is an abnormality. The user receives the alert, checks the status of the air conditioning equipment through the interface, and takes appropriate actions.

[0308] As an example of a prompt sentence, "Monitor the operation data of the new air conditioning equipment and issue an alert to notify the user if an abnormality occurs." can be cited.

[0309] In this way, by the server, terminal, and user cooperating with each other, the management of distributed devices can be carried out more effectively.

[0310] The flow of the specific process in Example 1 will be described using FIG. 11.

[0311] Step 1:

[0312] The server receives non-uniform format documents from different providers via email or API and temporarily stores them in the database. In this process, the received document is taken as input, and a file is output to the folder for storage. Here, the format of the file remains as it is and is firmly labeled in preparation for later analysis.

[0313] Step 2:

[0314] The server analyzes the received document using the generative AI model. By using natural language processing technology with the document as input, attribute information such as the type of device, installation location, and operating conditions is extracted. As output, this attribute information is obtained as well-organized structured data. This structured data is important for subsequent management.

[0315] Step 3:

[0316] The server verifies the extracted attribute information and integrates and stores it in the database. In this step, it receives the obtained structured data as input and updates the information by matching it with the existing database. The output is a completed, integrated, and up-to-date database. This ensures that the management information is always kept current.

[0317] Step 4:

[0318] The terminal acquires and temporarily stores operational information from distributed devices located within the facility. It receives this operational information as input and prepares to send it to the server. The operational information is then sent to the server as output. At this time, a portion of the data is analyzed for emergency use.

[0319] Step 5:

[0320] The terminal analyzes data acquired in real time to determine if an anomaly has occurred. It takes operational information as input, compares it to a set threshold, and generates an alert if an anomaly is detected. As output, if an anomaly is detected, a notification to the administrator is prepared.

[0321] Step 6:

[0322] Users access the database through a management interface provided by the server to check the location and operating status of devices. They receive views provided by the server as input and manage the devices based on the displayed information as output. Users can then use this information to immediately resolve problems.

[0323] Step 7:

[0324] When users receive an anomaly alert, they can review the details and take prompt and optimal action. The system receives the alert information as input and, referencing past response records stored on the server, executes the most suitable solution as output. This ensures the early resolution of problems.

[0325] (Application Example 1)

[0326] 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."

[0327] Efficient management of multiple groups of machinery within a factory is crucial, but rapid response to machine malfunctions and the ability to quickly and accurately identify effective countermeasures based on past response records remain challenges. Furthermore, integrating and managing large amounts of data provided in non-standardized formats is a significant issue.

[0328] 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.

[0329] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI technology and structuring the information, and means for integrating the structured information into a storage medium for integrated management. This enables rapid detection of anomalies and the presentation of optimal response plans.

[0330] "Different sources" refers to various external organizations or systems that provide information or data.

[0331] "Information in non-standard formats" refers to data and documents provided in various non-standardized formats.

[0332] "Generative AI technology" refers to technologies that utilize artificial intelligence generative models for data analysis and natural language processing.

[0333] "Structuring information" refers to analyzing disorganized information, organizing it systematically, and converting it into a manageable format.

[0334] "Storage medium for integrated management" refers to a database or storage system for collecting various types of information and centrally storing and managing them.

[0335] A "connected distributed group of devices" refers to a group of independent machines or devices connected through a network.

[0336] "Operational information" refers to operational status and performance data collected in real time from a group of distributed devices.

[0337] An "information processing device" refers to a computer system or server that analyzes received data and performs necessary processing.

[0338] "Detecting an anomaly" refers to the automatic recognition of abnormalities or abnormalities in equipment or systems based on pre-set criteria.

[0339] "Utilizing generative AI to propose optimal solutions based on past response records" means using a generative AI model to analyze past data and cases and propose the most effective countermeasures for the current situation.

[0340] This invention is a system for achieving efficient management of operating machinery within a factory. The server collects non-standardized information from different sources, analyzes and structures this information using generative AI technology, and stores the structured information in a storage medium for integrated management. The server also analyzes operational information received from connected distributed devices and detects anomalies that exceed set thresholds. This process utilizes Python and TensorFlow to efficiently perform data analysis and execute AI models.

[0341] The terminal communicates with each operating machine within the factory and receives operational information in real time. This information is sent to the server when a specific anomaly is detected. This allows for immediate response when an anomaly occurs. Furthermore, by utilizing AI generation to suggest the optimal response plan based on past response records, efficient problem solving can be achieved.

[0342] Users can access this system via smartphones or smart glasses to check data in real time and immediately see countermeasures if an anomaly is detected. For example, they can use a prompt message such as, "Please tell me the best course of action if the operating temperature of robot A exceeds 85°C," to take appropriate action.

[0343] This system enables efficient management of machinery within the factory and allows for rapid response in the event of malfunctions, thereby improving productivity.

[0344] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0345] Step 1:

[0346] The server collects non-standardized information from various external sources. This input includes data in various formats and is received via email or API. The server stores this information in temporary storage.

[0347] Step 2:

[0348] The server analyzes and structures the collected information using generative AI technology. This step utilizes natural language processing techniques to extract key information such as device type, installation location, and operating conditions. The output is structured data in a unified format.

[0349] Step 3:

[0350] The server integrates structured information into a database for centralized management. Using the structured data obtained as input, it adds newly registered devices and their information to the database. This process maintains an up-to-date management ledger.

[0351] Step 4:

[0352] The terminal communicates with the machinery in the factory in real time and receives operational information. The input is real-time data transmitted from each machine, and the output is initial analyzed data sent to the server.

[0353] Step 5:

[0354] The server receives and analyzes operational information sent from the terminal. In this step, it compares the data to a set threshold and performs data calculations to detect anomalies. The output is the result of whether or not an anomaly was detected.

[0355] Step 6:

[0356] If an anomaly is detected, the server will send a notification to the user. It will generate a notification message using the Slack API, email, or other means, and send it to the user's device.

[0357] Step 7:

[0358] The user uses a generated AI based on the received notification to determine the optimal course of action. They then decide on a specific course of action based on suggested solutions derived from past response records. They use prompts such as, "Please tell me the optimal course of action if the operating temperature of robot A exceeds 85°C," to receive suggestions from the AI.

[0359] 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.

[0360] This invention combines a system for efficiently managing distributed devices within buildings and facilities with an emotion engine that recognizes user emotions. The system consists of four main components: a server, a terminal, a user, and the emotion engine.

[0361] Server-side implementation

[0362] The server electronically collects documents in non-standardized formats from various suppliers and analyzes them using generative AI. The information obtained from the analyzed documents is structured and integrated into a database. Furthermore, it receives operational information from each distributed device transmitted from terminals, analyzes this data, and detects anomalies. Through these processes, it is possible to maintain up-to-date and detailed device management information at all times.

[0363] Terminal-side implementation

[0364] The terminal communicates directly with each distributed device within the facility and receives operational data in real time. This data is sent to a server, and in the event of an anomaly, an alert is immediately sent to the administrator via the server. The terminal also identifies the location of the devices and uses this information to support efficient management.

[0365] Implementation of an emotion engine

[0366] The emotion engine recognizes the user's emotional state in real time and reflects it in the interface. This allows for the selection of notification methods tailored to the user's emotions when an anomaly occurs. Furthermore, optimal countermeasures are customized to the user's emotional state and presented in a way that minimizes stress.

[0367] User roles

[0368] Users view information and perform normal administrative tasks through the provided interface. If an anomaly is detected, they receive customized notifications via the emotion engine and can quickly initiate a response. Users can also quickly and effectively resolve problems by referring to suggested solutions based on past response records.

[0369] Specific example

[0370] For example, if a cooling system malfunctions within a facility, the server will detect the anomaly. The emotion engine analyzes the user's current emotional state, and if it recognizes a high stress level, it sends a notification along with a calming message and recommended actions. The user receives the notification and can immediately take appropriate action.

[0371] This system enables highly accurate and efficient management of distributed devices while reducing the psychological burden on users.

[0372] The following describes the processing flow.

[0373] Step 1:

[0374] The server collects documents in non-standardized formats from various suppliers via email and APIs. This ensures that all necessary documents are stored in temporary storage.

[0375] Step 2:

[0376] The server uses generative AI to analyze the collected documents. The generative AI extracts information from the documents using natural language processing technology and structures it into a standardized format.

[0377] Step 3:

[0378] The server integrates structured data into a centralized database. By comparing it with existing data and automatically performing necessary additions and updates, the device management ledger is kept up-to-date.

[0379] Step 4:

[0380] The terminal receives data in real time from distributed devices within the facility. It performs an initial analysis of the received data to check the operating status of the devices.

[0381] Step 5:

[0382] The terminal sends the received operational data to the server. The data is encrypted and sent to the server while maintaining security.

[0383] Step 6:

[0384] The server analyzes the received operational data and detects anomalies that exceed the set threshold. If an anomaly occurs, it records the details and provides the necessary information to the administrator.

[0385] Step 7:

[0386] The emotion engine analyzes the user's emotional state in real time, thereby evaluating the user's stress level and current emotions.

[0387] Step 8:

[0388] The server adjusts the content and method of abnormal notifications based on the evaluation results of the emotion engine. For example, if a user is showing high stress levels, the notification will be sent using milder language.

[0389] Step 9:

[0390] The user receives a notification and checks the details of the anomaly on the interface. The user can then refer to past response history via the interface as needed and take appropriate action immediately.

[0391] Step 10:

[0392] Based on past response records and proposed solutions, users can make the most appropriate choice for the situation on-site and quickly begin taking action to resolve the problem.

[0393] (Example 2)

[0394] 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".

[0395] In facilities and buildings with an increasing number of distributed devices, the lack of efficient and unified management methods for each device is a challenge. In conventional systems, information provided by each supplier is inconsistent in format, making rapid anomaly detection and response difficult. Furthermore, the lack of flexibility in the method and content of anomaly notifications to administrators is a major problem, failing to alleviate user stress.

[0396] 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.

[0397] In this invention, the server includes means for collecting non-uniform information provided from different sources, means for analyzing the collected information using knowledge processing techniques and organizing the data, and means for integrating the organized data into a recording medium for centralized management. This enables efficient information management, rapid anomaly detection, and flexible notification to administrators.

[0398] "Supplier" refers to an external source or organization that provides information or data.

[0399] "Non-standardized format" refers to a state where information is provided in different formats or formats.

[0400] "Knowledge processing technology" refers to artificial intelligence and natural language processing technologies used to analyze information and organize it into meaningful data.

[0401] "Organizing data" refers to systematically arranging information according to certain standards.

[0402] A "recording medium" refers to a physical or electronic device or system used to store and manage data.

[0403] The term "central system" refers to the main computer system responsible for data processing and management functions for the entire system.

[0404] "Emotional state" refers to the psychological state or mood that the user is experiencing, and analyzing this state enables appropriate responses.

[0405] "Customizing" refers to adjusting and optimizing the content and methods of notifications and services according to individual needs and circumstances.

[0406] This invention provides a system for efficiently managing distributed devices, including information collection and analysis, anomaly detection, user notification, and suggestion of countermeasures. The system mainly consists of four main components: a server, terminals, users, and an emotion engine.

[0407] The server first collects non-standardized information from diverse sources. This includes downloads from email and online storage services, and is automated using programming languages ​​such as Python. Next, the server analyzes the collected information using generative AI models and organizes it into meaningful data. This analysis utilizes natural language processing techniques to extract and structure important information, which can then be centrally managed in a database.

[0408] The terminal communicates directly with each distributed device to acquire operational information in real time. Using IoT-enabled sensors and communication modules, the terminal transmits data such as operating status and operational conditions to the server in real time. This ensures that administrators are immediately notified via the server if an anomaly occurs. This system is designed to efficiently collect operational information without user intervention.

[0409] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications accordingly. If the user is stressed, the emotion engine generates a notification with a calming message to reduce the user's psychological burden. The emotional information obtained is collected through biometric data from smartphones and wearable devices and analyzed in real time.

[0410] As a concrete example, consider a case where a cooling system fails within a facility. The server automatically detects the anomaly based on abnormal data sent from the terminal. The emotion engine checks the user's current emotional state and prepares appropriate response suggestions according to their stress level. The user can then respond quickly by referring to the suggestions prepared by the emotion engine. An example of a prompt message in this case might be, "When a cooling system fails within a facility, how will the generative AI model used to detect the anomaly and notify the user?"

[0411] In this way, the present invention enables the rapid and accurate provision of information to administrators and users, and contributes to the efficient management of distributed devices.

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The server collects non-standardized information from various sources. Inputs include documents and data downloaded from sources such as email and cloud storage. Specifically, automated data collection is performed via an API using a Python script. The output is stored on the server as raw data.

[0415] Step 2:

[0416] The server analyzes the collected information using a generative AI model. The input is the raw data collected in step 1. Data processing includes information extraction using natural language processing techniques, and structured data is obtained as the output after analysis. This data is organized in a key-value pair format. Specifically, the meaning of the information is analyzed and converted into a format that can be stored in a database.

[0417] Step 3:

[0418] The terminal communicates with each distributed device and receives operational information in real time. The input is device status data acquired from IoT sensors. This data includes information such as the temperature and operating status of the devices. Specifically, the terminal directly acquires data from the devices and sends it to the server. The output is operational information data sent to the server.

[0419] Step 4:

[0420] The server analyzes the operating information transmitted from the terminal to detect anomalies. The input is the operating information data received in step 3. The data calculation involves anomaly detection based on a set threshold. Specifically, if the operating information exceeds the threshold, it is judged to be an anomaly, and the anomaly detection result is obtained as output.

[0421] Step 5:

[0422] The emotion engine analyzes the user's emotional state and selects the appropriate notification method. Inputs include biometric information and emotional data obtained from the user. Specifically, it collects data in real time from smartphones and wearable devices and analyzes the emotional state. Output is a customized notification message.

[0423] Step 6:

[0424] The user receives a customized notification through the emotion engine and initiates a response. The input is the notification message generated in step 5. The user uses this notification as a reference to quickly take appropriate problem-solving actions. Specifically, they perform actions such as checking or correcting the device based on the notification content. The output is the result of the user's response.

[0425] (Application Example 2)

[0426] 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."

[0427] The problem that this invention aims to solve is to efficiently manage the operating status of multiple devices within a facility or factory while reducing the psychological stress on administrators when abnormal situations occur. Conventional systems have the problem that abnormality notifications are issued uniformly without considering the emotional state of administrators, resulting in unnecessary stress during emergency responses.

[0428] 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.

[0429] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI and structuring the knowledge, and means for integrating the structured knowledge into a data storage for centralized management. This enables real-time monitoring of the operating status of multiple devices and allows for optimal anomaly notifications and countermeasure suggestions tailored to the administrator's psychological state.

[0430] A "source" is the origin of diverse information, and the object from which a system obtains data.

[0431] "Non-standardized format" refers to information that is not standardized and has different structures and formats.

[0432] "Generative AI" is a type of artificial intelligence that generates information through natural language processing and data analysis.

[0433] "Knowledge" refers to the content of information that has been collected and analyzed, structured, and stored in a useful way within a system.

[0434] "Data storage" refers to a data storage location for storing structured knowledge and information for centralized management.

[0435] A "multiple device" is a collection of several machines or tools that operate in conjunction with each other within a system.

[0436] "Operating status" refers to real-time information indicating the operating status and presence or absence of abnormalities in multiple devices.

[0437] An "abnormal situation" refers to a state in which a system or device deviates from its normal operation, requiring immediate action.

[0438] An "administrator" is the person responsible for the operation and maintenance of the entire system, and who responds to abnormal situations.

[0439] "Psychological state" refers to the administrator's emotions and mental state, and is a factor that influences their stress when receiving abnormal notifications.

[0440] An "alert method" refers to the method and format used to notify administrators of abnormal situations, and it is optimized according to the emotional state of the user.

[0441] In order to implement the system of the present invention, the server, terminal, and user elements must work in coordination.

[0442] The server first collects non-standardized information from various sources. To do this, the server uses web scraping techniques and APIs to collect data. The collected information is analyzed using generative AI and structured into organized knowledge. The generative AI model utilizes natural language processing libraries such as "Transformers" and data analysis tools such as "Pandas" and "NumPy". This structured knowledge is integrated into data storage for centralized management, using database systems such as "MySQL" and "PostgreSQL".

[0443] The terminal receives real-time operating status information from multiple devices within the facility and transmits it to a server. IoT protocols such as MQTT and HTTP are used for communication, and the terminal connects to the devices via hardware such as Raspberry Pi or Arduino. This allows the terminal to identify the location information of the devices and assist in efficient management.

[0444] Users receive notifications of abnormal situations through a device or smart glasses equipped with emotion recognition capabilities. Real-time facial expression analysis using libraries such as "OpenCV" is performed to analyze the administrator's psychological state. The abnormal notification is displayed in a customized format, taking into account the user's mental burden. This allows for the selection of the most appropriate alert method and the suggestion of necessary countermeasures.

[0445] As a concrete example, if a piece of equipment on a production line malfunctions in a factory, a fatigued user would receive a gentle notification such as, "Take a short break. The equipment needs to be reset," while a refreshed user would receive a warning such as, "Emergency action is required." An example of a prompt message would be, "What should be displayed if the operator is relaxed?"

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The server collects non-standardized information from different sources. This information collection involves using API calls and web scraping to gather text data and logs. The input is non-standardized information, and the output is the collected raw data.

[0449] Step 2:

[0450] The server uses a generative AI model to analyze the collected, non-standardized information and convert it into structured knowledge. During this process, the information is analyzed using the natural language processing library "Transformers" and formatted for easier input into the database. The input is the collected raw data, and the output is structured knowledge.

[0451] Step 3:

[0452] The server integrates structured knowledge into data storage. Using database systems such as MySQL or PostgreSQL, the information is stored in a way that allows for efficient searching and retrieval. The input is structured knowledge, and the output is entries in the integrated database.

[0453] Step 4:

[0454] The terminal receives real-time operational status data from multiple devices within the facility and transmits this data to a server. The IoT protocol "MQTT" is used here to collect device operational data in real time. The input is operational data from the devices, and the output is the operational status information transmitted to the server.

[0455] Step 5:

[0456] The server analyzes the operational status received from the terminal and detects any abnormalities that exceed a set threshold. The input is operational status information, and the output is the result of detecting the abnormal situation.

[0457] Step 6:

[0458] The user's device uses emotion recognition functionality to analyze the user's psychological state and provide optimal anomaly notifications. Here, the real-time facial expression analysis library "OpenCV" is used, and the user's facial expression data is output as the emotional state. The input is the user's real-time facial expression data, and the output is the analyzed emotional state.

[0459] Step 7:

[0460] The server customizes anomaly notifications based on the user's emotional state and suggests appropriate alert methods and countermeasures. If the user's emotions indicate stress, it generates a calm notification message and sends it to the user. The input is the analyzed emotional state and anomaly information, and the output is a customized notification message.

[0461] Step 8:

[0462] The user takes appropriate action based on the notification received. This may involve accepting suggestions from the system, resetting the device, or coordinating with other operators. The input is the notification message, and the output is the corrective action to be taken.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] [Third Embodiment]

[0467] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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".

[0479] This invention is a system for streamlining the management of distributed devices in buildings and various facilities. This system consists of three main components: a server, terminals, and users.

[0480] Server-side implementation

[0481] The server first collects documents in non-standardized formats provided by each supplier. The server temporarily stores the documents via email or API, and then uses generative AI to analyze them. The generative AI uses natural language processing techniques to extract information from the documents and structure key information such as device type, installation location, and operating conditions. Furthermore, this structured data is integrated into a database to maintain a constantly up-to-date management ledger.

[0482] Terminal-side implementation

[0483] The terminal communicates directly with distributed devices installed within the facility and receives operational data in real time. The received data is periodically sent to the server, during which initial data analysis is also performed. Furthermore, if the terminal detects an anomaly under specific conditions, it notifies the administrator via the server. This notification is achieved by comparing the received data with a pre-configured threshold.

[0484] User roles

[0485] Users can access the system using a specific interface and view information stored in the database. This makes it easy to understand the location and operating status of each distributed device. Users can also view detailed information and take the most appropriate action quickly when an alert is received. Furthermore, efficient problem solving is possible by referring to suggested countermeasures based on past response records.

[0486] Specific example

[0487] For example, when new air conditioning equipment is installed, the delivery documents arrive at the server. The AI ​​analyzes the documents and registers information such as the installation location and specifications in the database. Subsequently, the terminal collects operational data in real time and issues an alert if an anomaly is detected. Upon receiving the alert, the user can quickly check the device status via the interface and take the necessary actions.

[0488] In this way, by coordinating servers, terminals, and users, distributed devices can be managed effectively, preventing the proliferation of "zombie devices" and achieving efficient operational management.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The server collects documents in non-standardized formats provided by various suppliers. Documents are periodically retrieved using email or a dedicated API and stored in temporary storage.

[0492] Step 2:

[0493] The server analyzes the collected documents using a generative AI. The generative AI utilizes natural language processing technology to extract key information such as device name, installation location, and functional specifications from the documents and creates structured data.

[0494] Step 3:

[0495] The server integrates structured information into a database. It maintains an up-to-date device management ledger by matching existing records with new data and updating or adding new entries as needed.

[0496] Step 4:

[0497] The terminal receives operational data in real time from distributed devices within the facility. It analyzes the data packets from the device and performs an initial check to determine if it is within the range of normal operation.

[0498] Step 5:

[0499] The terminal sends the received data to the server. The data is encrypted before transmission, and further analysis and storage are performed on the server side. If the network is unstable, the data is temporarily stored and sent when the connection is restored.

[0500] Step 6:

[0501] The terminal performs anomaly detection based on operational data. If an anomaly exceeding a pre-set threshold is detected, an alert is created, sent to the server, and the administrator is notified.

[0502] Step 7:

[0503] The user reviews the received alert and investigates the details of the problem through the system interface. They refer to the device's location information and past response history to make decisions on how to take appropriate action.

[0504] Step 8:

[0505] The user uses past response records to implement the most appropriate measures. Based on the proposed solutions suggested by the system, they issue necessary instructions to on-site workers to quickly resolve the problem.

[0506] (Example 1)

[0507] 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."

[0508] Existing methods for efficiently analyzing and integrating non-standardized information from different providers, and for locating distributed devices and monitoring their operational status, present challenges in terms of accuracy and speed. Therefore, a system is needed that can quickly and accurately structure information and immediately detect and notify of abnormal situations.

[0509] 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.

[0510] In this invention, the server includes means for receiving and storing non-standardized information from different providers, means for analyzing the received information using a generation AI and extracting attribute information, and means for integrating and maintaining the extracted attribute information in an aggregated information area. This enables efficient management and operation of distributed devices and allows for rapid detection and response to anomalies.

[0511] "Information in a non-standard format" refers to information provided by different providers that does not conform to a single format or style.

[0512] A "provider" refers to an individual or organization that provides information or services.

[0513] "Generative AI" refers to a technology that uses artificial intelligence to analyze data and generate useful information.

[0514] "Attribute information" refers to information that indicates specific characteristics or properties extracted from data or documents.

[0515] A "centralized information area" refers to a part of a database or information system where integrated data is centrally stored and managed.

[0516] "Operational information" refers to data that shows how a device or system is operating.

[0517] The "management area" refers to the part of the system where data is aggregated and analyzed, and which operators can access as needed.

[0518] An "abnormality" refers to a state in which a system or device deviates from its normal operation.

[0519] "Operator" refers to an individual or organization responsible for monitoring and managing the system.

[0520] This invention is a system that enables the efficient management of distributed devices, and is realized through the respective roles of servers, terminals, and users.

[0521] Server Role

[0522] The server receives documents in non-standardized formats from different providers via email or API and stores them temporarily. Next, it analyzes the received documents using a generative AI model. This analysis utilizes natural language processing techniques to extract attribute information such as device type, installation location, and operating conditions. The extracted information is structured and integrated into a consolidated information domain. The server uses this information to manage a database and maintain up-to-date device information.

[0523] Terminal role

[0524] The terminal acquires data in real time from distributed devices located within the facility. The terminal has the function of temporarily storing and analyzing the acquired operational information. This operational information is periodically transmitted from the terminal to the server, and if an anomaly is detected, an alert is immediately issued to the operator. For example, if the temperature sensor exceeds a set threshold, the terminal generates an alert.

[0525] User roles

[0526] Users can access the database on the server through the interface to check the location and operating status of each distributed device. They can also receive notifications of anomalies, quickly understand the details, and take appropriate action. By comparing this information with past response records, efficient problem solving can be achieved.

[0527] Examples of specific cases and prompt statements

[0528] For example, when new air conditioning equipment is installed, delivery information arrives on the server. A generating AI analyzes the document and registers the information in the database. A terminal collects real-time operational data from the air conditioning equipment and issues an alert if there is an anomaly. The user receives the alert, checks the status of the air conditioning equipment through the interface, and takes appropriate action.

[0529] An example of a prompt message is, "Monitor the operating data of the new air conditioning equipment and issue an alert to notify the user if any abnormalities occur."

[0530] In this way, servers, terminals, and users cooperate to manage distributed devices more effectively.

[0531] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0532] Step 1:

[0533] The server receives documents in non-uniform formats from different providers via email or API and temporarily stores them in a database. This process takes the received documents as input and outputs files to a storage folder. Here, the files retain their original format but are properly labeled for later analysis.

[0534] Step 2:

[0535] The server analyzes received documents using a generative AI model. Using the documents as input, it extracts attribute information such as device type, installation location, and operating conditions by utilizing natural language processing technology. The output is this attribute information in a well-organized structured data format. This structured data is crucial for subsequent management.

[0536] Step 3:

[0537] The server verifies the extracted attribute information and integrates and stores it in the database. In this step, it receives the obtained structured data as input and updates the information by matching it with the existing database. The output is a completed, integrated, and up-to-date database. This ensures that the management information is always kept current.

[0538] Step 4:

[0539] The terminal acquires and temporarily stores operational information from distributed devices located within the facility. It receives this operational information as input and prepares to send it to the server. The operational information is then sent to the server as output. At this time, a portion of the data is analyzed for emergency use.

[0540] Step 5:

[0541] The terminal analyzes data acquired in real time to determine if an anomaly has occurred. It takes operational information as input, compares it to a set threshold, and generates an alert if an anomaly is detected. As output, if an anomaly is detected, a notification to the administrator is prepared.

[0542] Step 6:

[0543] Users access the database through a management interface provided by the server to check the location and operating status of devices. They receive views provided by the server as input and manage the devices based on the displayed information as output. Users can then use this information to immediately resolve problems.

[0544] Step 7:

[0545] When users receive an anomaly alert, they can review the details and take prompt and optimal action. The system receives the alert information as input and, referencing past response records stored on the server, executes the most suitable solution as output. This ensures the early resolution of problems.

[0546] (Application Example 1)

[0547] 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."

[0548] Efficient management of multiple groups of machinery within a factory is crucial, but rapid response to machine malfunctions and the ability to quickly and accurately identify effective countermeasures based on past response records remain challenges. Furthermore, integrating and managing large amounts of data provided in non-standardized formats is a significant issue.

[0549] 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.

[0550] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI technology and structuring the information, and means for integrating the structured information into a storage medium for integrated management. This enables rapid detection of anomalies and the presentation of optimal response plans.

[0551] "Different sources" refers to various external organizations or systems that provide information or data.

[0552] "Information in non-standard formats" refers to data and documents provided in various non-standardized formats.

[0553] "Generative AI technology" refers to technologies that utilize artificial intelligence generative models for data analysis and natural language processing.

[0554] "Structuring information" refers to analyzing disorganized information, organizing it systematically, and converting it into a manageable format.

[0555] "Storage medium for integrated management" refers to a database or storage system for collecting various types of information and centrally storing and managing them.

[0556] A "connected distributed group of devices" refers to a group of independent machines or devices connected through a network.

[0557] "Operational information" refers to operational status and performance data collected in real time from a group of distributed devices.

[0558] An "information processing device" refers to a computer system or server that analyzes received data and performs necessary processing.

[0559] "Detecting an anomaly" refers to the automatic recognition of abnormalities or abnormalities in equipment or systems based on pre-set criteria.

[0560] "Utilizing generative AI to propose optimal solutions based on past response records" means using a generative AI model to analyze past data and cases and propose the most effective countermeasures for the current situation.

[0561] This invention is a system for achieving efficient management of operating machinery within a factory. The server collects non-standardized information from different sources, analyzes and structures this information using generative AI technology, and stores the structured information in a storage medium for integrated management. The server also analyzes operational information received from connected distributed devices and detects anomalies that exceed set thresholds. This process utilizes Python and TensorFlow to efficiently perform data analysis and execute AI models.

[0562] The terminal communicates with each operating machine within the factory and receives operational information in real time. This information is sent to the server when a specific anomaly is detected. This allows for immediate response when an anomaly occurs. Furthermore, by utilizing AI generation to suggest the optimal response plan based on past response records, efficient problem solving can be achieved.

[0563] Users can access this system via smartphones or smart glasses to check data in real time and immediately see countermeasures if an anomaly is detected. For example, they can use a prompt message such as, "Please tell me the best course of action if the operating temperature of robot A exceeds 85°C," to take appropriate action.

[0564] This system enables efficient management of machinery within the factory and allows for rapid response in the event of malfunctions, thereby improving productivity.

[0565] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0566] Step 1:

[0567] The server collects non-standardized information from various external sources. This input includes data in various formats and is received via email or API. The server stores this information in temporary storage.

[0568] Step 2:

[0569] The server analyzes and structures the collected information using generative AI technology. This step utilizes natural language processing techniques to extract key information such as device type, installation location, and operating conditions. The output is structured data in a unified format.

[0570] Step 3:

[0571] The server integrates structured information into a database for centralized management. Using the structured data obtained as input, it adds newly registered devices and their information to the database. This process maintains an up-to-date management ledger.

[0572] Step 4:

[0573] The terminal communicates with the machinery in the factory in real time and receives operational information. The input is real-time data transmitted from each machine, and the output is initial analyzed data sent to the server.

[0574] Step 5:

[0575] The server receives and analyzes operational information sent from the terminal. In this step, it compares the data to a set threshold and performs data calculations to detect anomalies. The output is the result of whether or not an anomaly was detected.

[0576] Step 6:

[0577] If an anomaly is detected, the server will send a notification to the user. It will generate a notification message using the Slack API, email, or other means, and send it to the user's device.

[0578] Step 7:

[0579] The user uses a generated AI based on the received notification to determine the optimal course of action. They then decide on a specific course of action based on suggested solutions derived from past response records. They use prompts such as, "Please tell me the optimal course of action if the operating temperature of robot A exceeds 85°C," to receive suggestions from the AI.

[0580] 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.

[0581] This invention combines a system for efficiently managing distributed devices within buildings and facilities with an emotion engine that recognizes user emotions. The system consists of four main components: a server, a terminal, a user, and the emotion engine.

[0582] Server-side implementation

[0583] The server electronically collects documents in non-standardized formats from various suppliers and analyzes them using generative AI. The information obtained from the analyzed documents is structured and integrated into a database. Furthermore, it receives operational information from each distributed device transmitted from terminals, analyzes this data, and detects anomalies. Through these processes, it is possible to maintain up-to-date and detailed device management information at all times.

[0584] Terminal-side implementation

[0585] The terminal communicates directly with each distributed device within the facility and receives operational data in real time. This data is sent to a server, and in the event of an anomaly, an alert is immediately sent to the administrator via the server. The terminal also identifies the location of the devices and uses this information to support efficient management.

[0586] Implementation of an emotion engine

[0587] The emotion engine recognizes the user's emotional state in real time and reflects it in the interface. This allows for the selection of notification methods tailored to the user's emotions when an anomaly occurs. Furthermore, optimal countermeasures are customized to the user's emotional state and presented in a way that minimizes stress.

[0588] User roles

[0589] Users view information and perform normal administrative tasks through the provided interface. If an anomaly is detected, they receive customized notifications via the emotion engine and can quickly initiate a response. Users can also quickly and effectively resolve problems by referring to suggested solutions based on past response records.

[0590] Specific example

[0591] For example, if a cooling system malfunctions within a facility, the server will detect the anomaly. The emotion engine analyzes the user's current emotional state, and if it recognizes a high stress level, it sends a notification along with a calming message and recommended actions. The user receives the notification and can immediately take appropriate action.

[0592] This system enables highly accurate and efficient management of distributed devices while reducing the psychological burden on users.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The server collects documents in non-standardized formats from various suppliers via email and APIs. This ensures that all necessary documents are stored in temporary storage.

[0596] Step 2:

[0597] The server uses generative AI to analyze the collected documents. The generative AI extracts information from the documents using natural language processing technology and structures it into a standardized format.

[0598] Step 3:

[0599] The server integrates structured data into a centralized database. By comparing it with existing data and automatically performing necessary additions and updates, the device management ledger is kept up-to-date.

[0600] Step 4:

[0601] The terminal receives data in real time from distributed devices within the facility. It performs an initial analysis of the received data to check the operating status of the devices.

[0602] Step 5:

[0603] The terminal sends the received operational data to the server. The data is encrypted and sent to the server while maintaining security.

[0604] Step 6:

[0605] The server analyzes the received operational data and detects anomalies that exceed the set threshold. If an anomaly occurs, it records the details and provides the necessary information to the administrator.

[0606] Step 7:

[0607] The emotion engine analyzes the user's emotional state in real time, thereby evaluating the user's stress level and current emotions.

[0608] Step 8:

[0609] The server adjusts the content and method of abnormal notifications based on the evaluation results of the emotion engine. For example, if a user is showing high stress levels, the notification will be sent using milder language.

[0610] Step 9:

[0611] The user receives a notification and checks the details of the anomaly on the interface. The user can then refer to past response history via the interface as needed and take appropriate action immediately.

[0612] Step 10:

[0613] Based on past response records and proposed solutions, users can make the most appropriate choice for the situation on-site and quickly begin taking action to resolve the problem.

[0614] (Example 2)

[0615] 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."

[0616] In facilities and buildings with an increasing number of distributed devices, the lack of efficient and unified management methods for each device is a challenge. In conventional systems, information provided by each supplier is inconsistent in format, making rapid anomaly detection and response difficult. Furthermore, the lack of flexibility in the method and content of anomaly notifications to administrators is a major problem, failing to alleviate user stress.

[0617] 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.

[0618] In this invention, the server includes means for collecting non-uniform information provided from different sources, means for analyzing the collected information using knowledge processing techniques and organizing the data, and means for integrating the organized data into a recording medium for centralized management. This enables efficient information management, rapid anomaly detection, and flexible notification to administrators.

[0619] "Supplier" refers to an external source or organization that provides information or data.

[0620] "Non-standardized format" refers to a state where information is provided in different formats or formats.

[0621] "Knowledge processing technology" refers to artificial intelligence and natural language processing technologies used to analyze information and organize it into meaningful data.

[0622] "Organizing data" refers to systematically arranging information according to certain standards.

[0623] A "recording medium" refers to a physical or electronic device or system used to store and manage data.

[0624] The term "central system" refers to the main computer system responsible for data processing and management functions for the entire system.

[0625] "Emotional state" refers to the psychological state or mood that the user is experiencing, and analyzing this state enables appropriate responses.

[0626] "Customizing" refers to adjusting and optimizing the content and methods of notifications and services according to individual needs and circumstances.

[0627] This invention provides a system for efficiently managing distributed devices, including information collection and analysis, anomaly detection, user notification, and suggestion of countermeasures. The system mainly consists of four main components: a server, terminals, users, and an emotion engine.

[0628] The server first collects non-standardized information from diverse sources. This includes downloads from email and online storage services, and is automated using programming languages ​​such as Python. Next, the server analyzes the collected information using generative AI models and organizes it into meaningful data. This analysis utilizes natural language processing techniques to extract and structure important information, which can then be centrally managed in a database.

[0629] The terminal communicates directly with each distributed device to acquire operational information in real time. Using IoT-enabled sensors and communication modules, the terminal transmits data such as operating status and operational conditions to the server in real time. This ensures that administrators are immediately notified via the server if an anomaly occurs. This system is designed to efficiently collect operational information without user intervention.

[0630] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications accordingly. If the user is stressed, the emotion engine generates a notification with a calming message to reduce the user's psychological burden. The emotional information obtained is collected through biometric data from smartphones and wearable devices and analyzed in real time.

[0631] As a concrete example, consider a case where a cooling system fails within a facility. The server automatically detects the anomaly based on abnormal data sent from the terminal. The emotion engine checks the user's current emotional state and prepares appropriate response suggestions according to their stress level. The user can then respond quickly by referring to the suggestions prepared by the emotion engine. An example of a prompt message in this case might be, "When a cooling system fails within a facility, how will the generative AI model used to detect the anomaly and notify the user?"

[0632] In this way, the present invention enables the rapid and accurate provision of information to administrators and users, and contributes to the efficient management of distributed devices.

[0633] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0634] Step 1:

[0635] The server collects non-standardized information from various sources. Inputs include documents and data downloaded from sources such as email and cloud storage. Specifically, automated data collection is performed via an API using a Python script. The output is stored on the server as raw data.

[0636] Step 2:

[0637] The server analyzes the collected information using a generative AI model. The input is the raw data collected in step 1. Data processing includes information extraction using natural language processing techniques, and structured data is obtained as the output after analysis. This data is organized in a key-value pair format. Specifically, the meaning of the information is analyzed and converted into a format that can be stored in a database.

[0638] Step 3:

[0639] The terminal communicates with each distributed device and receives operational information in real time. The input is device status data acquired from IoT sensors. This data includes information such as the temperature and operating status of the devices. Specifically, the terminal directly acquires data from the devices and sends it to the server. The output is operational information data sent to the server.

[0640] Step 4:

[0641] The server analyzes the operating information transmitted from the terminal to detect anomalies. The input is the operating information data received in step 3. The data calculation involves anomaly detection based on a set threshold. Specifically, if the operating information exceeds the threshold, it is judged to be an anomaly, and the anomaly detection result is obtained as output.

[0642] Step 5:

[0643] The emotion engine analyzes the user's emotional state and selects the appropriate notification method. Inputs include biometric information and emotional data obtained from the user. Specifically, it collects data in real time from smartphones and wearable devices and analyzes the emotional state. Output is a customized notification message.

[0644] Step 6:

[0645] The user receives a customized notification through the emotion engine and initiates a response. The input is the notification message generated in step 5. The user uses this notification as a reference to quickly take appropriate problem-solving actions. Specifically, they perform actions such as checking or correcting the device based on the notification content. The output is the result of the user's response.

[0646] (Application Example 2)

[0647] 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."

[0648] The problem that this invention aims to solve is to efficiently manage the operating status of multiple devices within a facility or factory while reducing the psychological stress on administrators when abnormal situations occur. Conventional systems have the problem that abnormality notifications are issued uniformly without considering the emotional state of administrators, resulting in unnecessary stress during emergency responses.

[0649] 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.

[0650] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI and structuring the knowledge, and means for integrating the structured knowledge into a data storage for centralized management. This enables real-time monitoring of the operating status of multiple devices and allows for optimal anomaly notifications and countermeasure suggestions tailored to the administrator's psychological state.

[0651] A "source" is the origin of diverse information, and the object from which a system obtains data.

[0652] "Non-standardized format" refers to information that is not standardized and has different structures and formats.

[0653] "Generative AI" is a type of artificial intelligence that generates information through natural language processing and data analysis.

[0654] "Knowledge" refers to the content of information that has been collected and analyzed, structured, and stored in a useful way within a system.

[0655] "Data storage" refers to a data storage location for storing structured knowledge and information for centralized management.

[0656] A "multiple device" is a collection of several machines or tools that operate in conjunction with each other within a system.

[0657] "Operating status" refers to real-time information indicating the operating status and presence or absence of abnormalities in multiple devices.

[0658] An "abnormal situation" refers to a state in which a system or device deviates from its normal operation, requiring immediate action.

[0659] An "administrator" is the person responsible for the operation and maintenance of the entire system, and who responds to abnormal situations.

[0660] "Psychological state" refers to the administrator's emotions and mental state, and is a factor that influences their stress when receiving abnormal notifications.

[0661] An "alert method" refers to the method and format used to notify administrators of abnormal situations, and it is optimized according to the emotional state of the user.

[0662] In order to implement the system of the present invention, the server, terminal, and user elements must work in coordination.

[0663] The server first collects non-standardized information from various sources. To do this, the server uses web scraping techniques and APIs to collect data. The collected information is analyzed using generative AI and structured into organized knowledge. The generative AI model utilizes natural language processing libraries such as "Transformers" and data analysis tools such as "Pandas" and "NumPy". This structured knowledge is integrated into data storage for centralized management, using database systems such as "MySQL" and "PostgreSQL".

[0664] The terminal receives real-time operating status information from multiple devices within the facility and transmits it to a server. IoT protocols such as MQTT and HTTP are used for communication, and the terminal connects to the devices via hardware such as Raspberry Pi or Arduino. This allows the terminal to identify the location information of the devices and assist in efficient management.

[0665] Users receive notifications of abnormal situations through a device or smart glasses equipped with emotion recognition capabilities. Real-time facial expression analysis using libraries such as "OpenCV" is performed to analyze the administrator's psychological state. The abnormal notification is displayed in a customized format, taking into account the user's mental burden. This allows for the selection of the most appropriate alert method and the suggestion of necessary countermeasures.

[0666] As a concrete example, if a piece of equipment on a production line malfunctions in a factory, a fatigued user would receive a gentle notification such as, "Take a short break. The equipment needs to be reset," while a refreshed user would receive a warning such as, "Emergency action is required." An example of a prompt message would be, "What should be displayed if the operator is relaxed?"

[0667] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0668] Step 1:

[0669] The server collects non-standardized information from different sources. This information collection involves using API calls and web scraping to gather text data and logs. The input is non-standardized information, and the output is the collected raw data.

[0670] Step 2:

[0671] The server uses a generative AI model to analyze the collected, non-standardized information and convert it into structured knowledge. During this process, the information is analyzed using the natural language processing library "Transformers" and formatted for easier input into the database. The input is the collected raw data, and the output is structured knowledge.

[0672] Step 3:

[0673] The server integrates structured knowledge into data storage. Using database systems such as MySQL or PostgreSQL, the information is stored in a way that allows for efficient searching and retrieval. The input is structured knowledge, and the output is entries in the integrated database.

[0674] Step 4:

[0675] The terminal receives real-time operational status data from multiple devices within the facility and transmits this data to a server. The IoT protocol "MQTT" is used here to collect device operational data in real time. The input is operational data from the devices, and the output is the operational status information transmitted to the server.

[0676] Step 5:

[0677] The server analyzes the operational status received from the terminal and detects any abnormalities that exceed a set threshold. The input is operational status information, and the output is the result of detecting the abnormal situation.

[0678] Step 6:

[0679] The user's device uses emotion recognition functionality to analyze the user's psychological state and provide optimal anomaly notifications. Here, the real-time facial expression analysis library "OpenCV" is used, and the user's facial expression data is output as the emotional state. The input is the user's real-time facial expression data, and the output is the analyzed emotional state.

[0680] Step 7:

[0681] The server customizes anomaly notifications based on the user's emotional state and suggests appropriate alert methods and countermeasures. If the user's emotions indicate stress, it generates a calm notification message and sends it to the user. The input is the analyzed emotional state and anomaly information, and the output is a customized notification message.

[0682] Step 8:

[0683] The user takes appropriate action based on the notification received. This may involve accepting suggestions from the system, resetting the device, or coordinating with other operators. The input is the notification message, and the output is the corrective action to be taken.

[0684] 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.

[0685] 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.

[0686] 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.

[0687] [Fourth Embodiment]

[0688] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0689] 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.

[0690] 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).

[0691] 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.

[0692] 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.

[0693] 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).

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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".

[0701] This invention is a system for streamlining the management of distributed devices in buildings and various facilities. This system consists of three main components: a server, terminals, and users.

[0702] Server-side implementation

[0703] The server first collects documents in non-standardized formats provided by each supplier. The server temporarily stores the documents via email or API, and then uses generative AI to analyze them. The generative AI uses natural language processing techniques to extract information from the documents and structure key information such as device type, installation location, and operating conditions. Furthermore, this structured data is integrated into a database to maintain a constantly up-to-date management ledger.

[0704] Terminal-side implementation

[0705] The terminal communicates directly with distributed devices installed within the facility and receives operational data in real time. The received data is periodically sent to the server, during which initial data analysis is also performed. Furthermore, if the terminal detects an anomaly under specific conditions, it notifies the administrator via the server. This notification is achieved by comparing the received data with a pre-configured threshold.

[0706] User roles

[0707] Users can access the system using a specific interface and view information stored in the database. This makes it easy to understand the location and operating status of each distributed device. Users can also view detailed information and take the most appropriate action quickly when an alert is received. Furthermore, efficient problem solving is possible by referring to suggested countermeasures based on past response records.

[0708] Specific example

[0709] For example, when new air conditioning equipment is installed, the delivery documents arrive at the server. The AI ​​analyzes the documents and registers information such as the installation location and specifications in the database. Subsequently, the terminal collects operational data in real time and issues an alert if an anomaly is detected. Upon receiving the alert, the user can quickly check the device status via the interface and take the necessary actions.

[0710] In this way, by coordinating servers, terminals, and users, distributed devices can be managed effectively, preventing the proliferation of "zombie devices" and achieving efficient operational management.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The server collects documents in non-standardized formats provided by various suppliers. Documents are periodically retrieved using email or a dedicated API and stored in temporary storage.

[0714] Step 2:

[0715] The server analyzes the collected documents using a generative AI. The generative AI utilizes natural language processing technology to extract key information such as device name, installation location, and functional specifications from the documents and creates structured data.

[0716] Step 3:

[0717] The server integrates structured information into a database. It maintains an up-to-date device management ledger by matching existing records with new data and updating or adding new entries as needed.

[0718] Step 4:

[0719] The terminal receives operational data in real time from distributed devices within the facility. It analyzes the data packets from the device and performs an initial check to determine if it is within the range of normal operation.

[0720] Step 5:

[0721] The terminal sends the received data to the server. The data is encrypted before transmission, and further analysis and storage are performed on the server side. If the network is unstable, the data is temporarily stored and sent when the connection is restored.

[0722] Step 6:

[0723] The terminal performs anomaly detection based on operational data. If an anomaly exceeding a pre-set threshold is detected, an alert is created, sent to the server, and the administrator is notified.

[0724] Step 7:

[0725] The user reviews the received alert and investigates the details of the problem through the system interface. They refer to the device's location information and past response history to make decisions on how to take appropriate action.

[0726] Step 8:

[0727] The user uses past response records to implement the most appropriate measures. Based on the proposed solutions suggested by the system, they issue necessary instructions to on-site workers to quickly resolve the problem.

[0728] (Example 1)

[0729] 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".

[0730] Existing methods for efficiently analyzing and integrating non-standardized information from different providers, and for locating distributed devices and monitoring their operational status, present challenges in terms of accuracy and speed. Therefore, a system is needed that can quickly and accurately structure information and immediately detect and notify of abnormal situations.

[0731] 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.

[0732] In this invention, the server includes means for receiving and storing non-standardized information from different providers, means for analyzing the received information using a generation AI and extracting attribute information, and means for integrating and maintaining the extracted attribute information in an aggregated information area. This enables efficient management and operation of distributed devices and allows for rapid detection and response to anomalies.

[0733] "Information in a non-standard format" refers to information provided by different providers that does not conform to a single format or style.

[0734] A "provider" refers to an individual or organization that provides information or services.

[0735] "Generative AI" refers to a technology that uses artificial intelligence to analyze data and generate useful information.

[0736] "Attribute information" refers to information that indicates specific characteristics or properties extracted from data or documents.

[0737] A "centralized information area" refers to a part of a database or information system where integrated data is centrally stored and managed.

[0738] "Operational information" refers to data that shows how a device or system is operating.

[0739] The "management area" refers to the part of the system where data is aggregated and analyzed, and which operators can access as needed.

[0740] An "abnormality" refers to a state in which a system or device deviates from its normal operation.

[0741] "Operator" refers to an individual or organization responsible for monitoring and managing the system.

[0742] This invention is a system that enables the efficient management of distributed devices, and is realized through the respective roles of servers, terminals, and users.

[0743] Server Role

[0744] The server receives documents in non-standardized formats from different providers via email or API and stores them temporarily. Next, it analyzes the received documents using a generative AI model. This analysis utilizes natural language processing techniques to extract attribute information such as device type, installation location, and operating conditions. The extracted information is structured and integrated into a consolidated information domain. The server uses this information to manage a database and maintain up-to-date device information.

[0745] Terminal role

[0746] The terminal acquires data in real time from distributed devices located within the facility. The terminal has the function of temporarily storing and analyzing the acquired operational information. This operational information is periodically transmitted from the terminal to the server, and if an anomaly is detected, an alert is immediately issued to the operator. For example, if the temperature sensor exceeds a set threshold, the terminal generates an alert.

[0747] User roles

[0748] Users can access the database on the server through the interface to check the location and operating status of each distributed device. They can also receive notifications of anomalies, quickly understand the details, and take appropriate action. By comparing this information with past response records, efficient problem solving can be achieved.

[0749] Examples of specific cases and prompt statements

[0750] For example, when new air conditioning equipment is installed, delivery information arrives on the server. A generating AI analyzes the document and registers the information in the database. A terminal collects real-time operational data from the air conditioning equipment and issues an alert if there is an anomaly. The user receives the alert, checks the status of the air conditioning equipment through the interface, and takes appropriate action.

[0751] An example of a prompt message is, "Monitor the operating data of the new air conditioning equipment and issue an alert to notify the user if any abnormalities occur."

[0752] In this way, servers, terminals, and users cooperate to manage distributed devices more effectively.

[0753] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0754] Step 1:

[0755] The server receives documents in non-uniform formats from different providers via email or API and temporarily stores them in a database. This process takes the received documents as input and outputs files to a storage folder. Here, the files retain their original format but are properly labeled for later analysis.

[0756] Step 2:

[0757] The server analyzes received documents using a generative AI model. Using the documents as input, it extracts attribute information such as device type, installation location, and operating conditions by utilizing natural language processing technology. The output is this attribute information in a well-organized structured data format. This structured data is crucial for subsequent management.

[0758] Step 3:

[0759] The server verifies the extracted attribute information and integrates and stores it in the database. In this step, it receives the obtained structured data as input and updates the information by matching it with the existing database. The output is a completed, integrated, and up-to-date database. This ensures that the management information is always kept current.

[0760] Step 4:

[0761] The terminal acquires and temporarily stores operational information from distributed devices located within the facility. It receives this operational information as input and prepares to send it to the server. The operational information is then sent to the server as output. At this time, a portion of the data is analyzed for emergency use.

[0762] Step 5:

[0763] The terminal analyzes data acquired in real time to determine if an anomaly has occurred. It takes operational information as input, compares it to a set threshold, and generates an alert if an anomaly is detected. As output, if an anomaly is detected, a notification to the administrator is prepared.

[0764] Step 6:

[0765] Users access the database through a management interface provided by the server to check the location and operating status of devices. They receive views provided by the server as input and manage the devices based on the displayed information as output. Users can then use this information to immediately resolve problems.

[0766] Step 7:

[0767] When users receive an anomaly alert, they can review the details and take prompt and optimal action. The system receives the alert information as input and, referencing past response records stored on the server, executes the most suitable solution as output. This ensures the early resolution of problems.

[0768] (Application Example 1)

[0769] 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".

[0770] Efficient management of multiple groups of machinery within a factory is crucial, but rapid response to machine malfunctions and the ability to quickly and accurately identify effective countermeasures based on past response records remain challenges. Furthermore, integrating and managing large amounts of data provided in non-standardized formats is a significant issue.

[0771] 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.

[0772] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI technology and structuring the information, and means for integrating the structured information into a storage medium for integrated management. This enables rapid detection of anomalies and the presentation of optimal response plans.

[0773] "Different sources" refers to various external organizations or systems that provide information or data.

[0774] "Information in non-standard formats" refers to data and documents provided in various non-standardized formats.

[0775] "Generative AI technology" refers to technologies that utilize artificial intelligence generative models for data analysis and natural language processing.

[0776] "Structuring information" refers to analyzing disorganized information, organizing it systematically, and converting it into a manageable format.

[0777] "Storage medium for integrated management" refers to a database or storage system for collecting various types of information and centrally storing and managing them.

[0778] A "connected distributed group of devices" refers to a group of independent machines or devices connected through a network.

[0779] "Operational information" refers to operational status and performance data collected in real time from a group of distributed devices.

[0780] An "information processing device" refers to a computer system or server that analyzes received data and performs necessary processing.

[0781] "Detecting an anomaly" refers to the automatic recognition of abnormalities or abnormalities in equipment or systems based on pre-set criteria.

[0782] "Utilizing generative AI to propose optimal solutions based on past response records" means using a generative AI model to analyze past data and cases and propose the most effective countermeasures for the current situation.

[0783] This invention is a system for achieving efficient management of operating machinery within a factory. The server collects non-standardized information from different sources, analyzes and structures this information using generative AI technology, and stores the structured information in a storage medium for integrated management. The server also analyzes operational information received from connected distributed devices and detects anomalies that exceed set thresholds. This process utilizes Python and TensorFlow to efficiently perform data analysis and execute AI models.

[0784] The terminal communicates with each operating machine within the factory and receives operational information in real time. This information is sent to the server when a specific anomaly is detected. This allows for immediate response when an anomaly occurs. Furthermore, by utilizing AI generation to suggest the optimal response plan based on past response records, efficient problem solving can be achieved.

[0785] Users can access this system via smartphones or smart glasses to check data in real time and immediately see countermeasures if an anomaly is detected. For example, they can use a prompt message such as, "Please tell me the best course of action if the operating temperature of robot A exceeds 85°C," to take appropriate action.

[0786] This system enables efficient management of machinery within the factory and allows for rapid response in the event of malfunctions, thereby improving productivity.

[0787] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0788] Step 1:

[0789] The server collects non-standardized information from various external sources. This input includes data in various formats and is received via email or API. The server stores this information in temporary storage.

[0790] Step 2:

[0791] The server analyzes and structures the collected information using generative AI technology. This step utilizes natural language processing techniques to extract key information such as device type, installation location, and operating conditions. The output is structured data in a unified format.

[0792] Step 3:

[0793] The server integrates structured information into a database for centralized management. Using the structured data obtained as input, it adds newly registered devices and their information to the database. This process maintains an up-to-date management ledger.

[0794] Step 4:

[0795] The terminal communicates with the machinery in the factory in real time and receives operational information. The input is real-time data transmitted from each machine, and the output is initial analyzed data sent to the server.

[0796] Step 5:

[0797] The server receives and analyzes operational information sent from the terminal. In this step, it compares the data to a set threshold and performs data calculations to detect anomalies. The output is the result of whether or not an anomaly was detected.

[0798] Step 6:

[0799] If an anomaly is detected, the server will send a notification to the user. It will generate a notification message using the Slack API, email, or other means, and send it to the user's device.

[0800] Step 7:

[0801] The user uses a generated AI based on the received notification to determine the optimal course of action. They then decide on a specific course of action based on suggested solutions derived from past response records. They use prompts such as, "Please tell me the optimal course of action if the operating temperature of robot A exceeds 85°C," to receive suggestions from the AI.

[0802] 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.

[0803] This invention combines a system for efficiently managing distributed devices within buildings and facilities with an emotion engine that recognizes user emotions. The system consists of four main components: a server, a terminal, a user, and the emotion engine.

[0804] Server-side implementation

[0805] The server electronically collects documents in non-standardized formats from various suppliers and analyzes them using generative AI. The information obtained from the analyzed documents is structured and integrated into a database. Furthermore, it receives operational information from each distributed device transmitted from terminals, analyzes this data, and detects anomalies. Through these processes, it is possible to maintain up-to-date and detailed device management information at all times.

[0806] Terminal-side implementation

[0807] The terminal communicates directly with each distributed device within the facility and receives operational data in real time. This data is sent to a server, and in the event of an anomaly, an alert is immediately sent to the administrator via the server. The terminal also identifies the location of the devices and uses this information to support efficient management.

[0808] Implementation of an emotion engine

[0809] The emotion engine recognizes the user's emotional state in real time and reflects it in the interface. This allows for the selection of notification methods tailored to the user's emotions when an anomaly occurs. Furthermore, optimal countermeasures are customized to the user's emotional state and presented in a way that minimizes stress.

[0810] User roles

[0811] Users view information and perform normal administrative tasks through the provided interface. If an anomaly is detected, they receive customized notifications via the emotion engine and can quickly initiate a response. Users can also quickly and effectively resolve problems by referring to suggested solutions based on past response records.

[0812] Specific example

[0813] For example, if a cooling system malfunctions within a facility, the server will detect the anomaly. The emotion engine analyzes the user's current emotional state, and if it recognizes a high stress level, it sends a notification along with a calming message and recommended actions. The user receives the notification and can immediately take appropriate action.

[0814] This system enables highly accurate and efficient management of distributed devices while reducing the psychological burden on users.

[0815] The following describes the processing flow.

[0816] Step 1:

[0817] The server collects documents in non-standardized formats from various suppliers via email and APIs. This ensures that all necessary documents are stored in temporary storage.

[0818] Step 2:

[0819] The server uses generative AI to analyze the collected documents. The generative AI extracts information from the documents using natural language processing technology and structures it into a standardized format.

[0820] Step 3:

[0821] The server integrates structured data into a centralized database. By comparing it with existing data and automatically performing necessary additions and updates, the device management ledger is kept up-to-date.

[0822] Step 4:

[0823] The terminal receives data in real time from distributed devices within the facility. It performs an initial analysis of the received data to check the operating status of the devices.

[0824] Step 5:

[0825] The terminal sends the received operational data to the server. The data is encrypted and sent to the server while maintaining security.

[0826] Step 6:

[0827] The server analyzes the received operational data and detects anomalies that exceed the set threshold. If an anomaly occurs, it records the details and provides the necessary information to the administrator.

[0828] Step 7:

[0829] The emotion engine analyzes the user's emotional state in real time, thereby evaluating the user's stress level and current emotions.

[0830] Step 8:

[0831] The server adjusts the content and method of abnormal notifications based on the evaluation results of the emotion engine. For example, if a user is showing high stress levels, the notification will be sent using milder language.

[0832] Step 9:

[0833] The user receives a notification and checks the details of the anomaly on the interface. The user can then refer to past response history via the interface as needed and take appropriate action immediately.

[0834] Step 10:

[0835] Based on past response records and proposed solutions, users can make the most appropriate choice for the situation on-site and quickly begin taking action to resolve the problem.

[0836] (Example 2)

[0837] 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".

[0838] In facilities and buildings with an increasing number of distributed devices, the lack of efficient and unified management methods for each device is a challenge. In conventional systems, information provided by each supplier is inconsistent in format, making rapid anomaly detection and response difficult. Furthermore, the lack of flexibility in the method and content of anomaly notifications to administrators is a major problem, failing to alleviate user stress.

[0839] 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.

[0840] In this invention, the server includes means for collecting non-uniform information provided from different sources, means for analyzing the collected information using knowledge processing techniques and organizing the data, and means for integrating the organized data into a recording medium for centralized management. This enables efficient information management, rapid anomaly detection, and flexible notification to administrators.

[0841] "Supplier" refers to an external source or organization that provides information or data.

[0842] "Non-standardized format" refers to a state where information is provided in different formats or formats.

[0843] "Knowledge processing technology" refers to artificial intelligence and natural language processing technologies used to analyze information and organize it into meaningful data.

[0844] "Organizing data" refers to systematically arranging information according to certain standards.

[0845] A "recording medium" refers to a physical or electronic device or system used to store and manage data.

[0846] The term "central system" refers to the main computer system responsible for data processing and management functions for the entire system.

[0847] "Emotional state" refers to the psychological state or mood that the user is experiencing, and analyzing this state enables appropriate responses.

[0848] "Customizing" refers to adjusting and optimizing the content and methods of notifications and services according to individual needs and circumstances.

[0849] This invention provides a system for efficiently managing distributed devices, including information collection and analysis, anomaly detection, user notification, and suggestion of countermeasures. The system mainly consists of four main components: a server, terminals, users, and an emotion engine.

[0850] The server first collects non-standardized information from diverse sources. This includes downloads from email and online storage services, and is automated using programming languages ​​such as Python. Next, the server analyzes the collected information using generative AI models and organizes it into meaningful data. This analysis utilizes natural language processing techniques to extract and structure important information, which can then be centrally managed in a database.

[0851] The terminal communicates directly with each distributed device to acquire operational information in real time. Using IoT-enabled sensors and communication modules, the terminal transmits data such as operating status and operational conditions to the server in real time. This ensures that administrators are immediately notified via the server if an anomaly occurs. This system is designed to efficiently collect operational information without user intervention.

[0852] The emotion engine analyzes the user's emotional state and adjusts the content and method of notifications accordingly. If the user is stressed, the emotion engine generates a notification with a calming message to reduce the user's psychological burden. The emotional information obtained is collected through biometric data from smartphones and wearable devices and analyzed in real time.

[0853] As a concrete example, consider a case where a cooling system fails within a facility. The server automatically detects the anomaly based on abnormal data sent from the terminal. The emotion engine checks the user's current emotional state and prepares appropriate response suggestions according to their stress level. The user can then respond quickly by referring to the suggestions prepared by the emotion engine. An example of a prompt message in this case might be, "When a cooling system fails within a facility, how will the generative AI model used to detect the anomaly and notify the user?"

[0854] In this way, the present invention enables the rapid and accurate provision of information to administrators and users, and contributes to the efficient management of distributed devices.

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] The server collects non-standardized information from various sources. Inputs include documents and data downloaded from sources such as email and cloud storage. Specifically, automated data collection is performed via an API using a Python script. The output is stored on the server as raw data.

[0858] Step 2:

[0859] The server analyzes the collected information using a generative AI model. The input is the raw data collected in step 1. Data processing includes information extraction using natural language processing techniques, and structured data is obtained as the output after analysis. This data is organized in a key-value pair format. Specifically, the meaning of the information is analyzed and converted into a format that can be stored in a database.

[0860] Step 3:

[0861] The terminal communicates with each distributed device and receives operational information in real time. The input is device status data acquired from IoT sensors. This data includes information such as the temperature and operating status of the devices. Specifically, the terminal directly acquires data from the devices and sends it to the server. The output is operational information data sent to the server.

[0862] Step 4:

[0863] The server analyzes the operating information transmitted from the terminal to detect anomalies. The input is the operating information data received in step 3. The data calculation involves anomaly detection based on a set threshold. Specifically, if the operating information exceeds the threshold, it is judged to be an anomaly, and the anomaly detection result is obtained as output.

[0864] Step 5:

[0865] The emotion engine analyzes the user's emotional state and selects the appropriate notification method. Inputs include biometric information and emotional data obtained from the user. Specifically, it collects data in real time from smartphones and wearable devices and analyzes the emotional state. Output is a customized notification message.

[0866] Step 6:

[0867] The user receives a customized notification through the emotion engine and initiates a response. The input is the notification message generated in step 5. The user uses this notification as a reference to quickly take appropriate problem-solving actions. Specifically, they perform actions such as checking or correcting the device based on the notification content. The output is the result of the user's response.

[0868] (Application Example 2)

[0869] 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".

[0870] The problem that this invention aims to solve is to efficiently manage the operating status of multiple devices within a facility or factory while reducing the psychological stress on administrators when abnormal situations occur. Conventional systems have the problem that abnormality notifications are issued uniformly without considering the emotional state of administrators, resulting in unnecessary stress during emergency responses.

[0871] 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.

[0872] In this invention, the server includes means for collecting non-standardized information provided from different sources, means for analyzing the collected information using generative AI and structuring the knowledge, and means for integrating the structured knowledge into a data storage for centralized management. This enables real-time monitoring of the operating status of multiple devices and allows for optimal anomaly notifications and countermeasure suggestions tailored to the administrator's psychological state.

[0873] A "source" is the origin of diverse information, and the object from which a system obtains data.

[0874] "Non-standardized format" refers to information that is not standardized and has different structures and formats.

[0875] "Generative AI" is a type of artificial intelligence that generates information through natural language processing and data analysis.

[0876] "Knowledge" refers to the content of information that has been collected and analyzed, structured, and stored in a useful way within a system.

[0877] "Data storage" refers to a data storage location for storing structured knowledge and information for centralized management.

[0878] A "multiple device" is a collection of several machines or tools that operate in conjunction with each other within a system.

[0879] "Operating status" refers to real-time information indicating the operating status and presence or absence of abnormalities in multiple devices.

[0880] An "abnormal situation" refers to a state in which a system or device deviates from its normal operation, requiring immediate action.

[0881] An "administrator" is the person responsible for the operation and maintenance of the entire system, and who responds to abnormal situations.

[0882] "Psychological state" refers to the administrator's emotions and mental state, and is a factor that influences their stress when receiving abnormal notifications.

[0883] An "alert method" refers to the method and format used to notify administrators of abnormal situations, and it is optimized according to the emotional state of the user.

[0884] In order to implement the system of the present invention, the server, terminal, and user elements must work in coordination.

[0885] The server first collects non-standardized information from various sources. To do this, the server uses web scraping techniques and APIs to collect data. The collected information is analyzed using generative AI and structured into organized knowledge. The generative AI model utilizes natural language processing libraries such as "Transformers" and data analysis tools such as "Pandas" and "NumPy". This structured knowledge is integrated into data storage for centralized management, using database systems such as "MySQL" and "PostgreSQL".

[0886] The terminal receives real-time operating status information from multiple devices within the facility and transmits it to a server. IoT protocols such as MQTT and HTTP are used for communication, and the terminal connects to the devices via hardware such as Raspberry Pi or Arduino. This allows the terminal to identify the location information of the devices and assist in efficient management.

[0887] Users receive notifications of abnormal situations through a device or smart glasses equipped with emotion recognition capabilities. Real-time facial expression analysis using libraries such as "OpenCV" is performed to analyze the administrator's psychological state. The abnormal notification is displayed in a customized format, taking into account the user's mental burden. This allows for the selection of the most appropriate alert method and the suggestion of necessary countermeasures.

[0888] As a concrete example, if a piece of equipment on a production line malfunctions in a factory, a fatigued user would receive a gentle notification such as, "Take a short break. The equipment needs to be reset," while a refreshed user would receive a warning such as, "Emergency action is required." An example of a prompt message would be, "What should be displayed if the operator is relaxed?"

[0889] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0890] Step 1:

[0891] The server collects non-standardized information from different sources. This information collection involves using API calls and web scraping to gather text data and logs. The input is non-standardized information, and the output is the collected raw data.

[0892] Step 2:

[0893] The server uses a generative AI model to analyze the collected, non-standardized information and convert it into structured knowledge. During this process, the information is analyzed using the natural language processing library "Transformers" and formatted for easier input into the database. The input is the collected raw data, and the output is structured knowledge.

[0894] Step 3:

[0895] The server integrates structured knowledge into data storage. Using database systems such as MySQL or PostgreSQL, the information is stored in a way that allows for efficient searching and retrieval. The input is structured knowledge, and the output is entries in the integrated database.

[0896] Step 4:

[0897] The terminal receives real-time operational status data from multiple devices within the facility and transmits this data to a server. The IoT protocol "MQTT" is used here to collect device operational data in real time. The input is operational data from the devices, and the output is the operational status information transmitted to the server.

[0898] Step 5:

[0899] The server analyzes the operational status received from the terminal and detects any abnormalities that exceed a set threshold. The input is operational status information, and the output is the result of detecting the abnormal situation.

[0900] Step 6:

[0901] The user's device uses emotion recognition functionality to analyze the user's psychological state and provide optimal anomaly notifications. Here, the real-time facial expression analysis library "OpenCV" is used, and the user's facial expression data is output as the emotional state. The input is the user's real-time facial expression data, and the output is the analyzed emotional state.

[0902] Step 7:

[0903] The server customizes anomaly notifications based on the user's emotional state and suggests appropriate alert methods and countermeasures. If the user's emotions indicate stress, it generates a calm notification message and sends it to the user. The input is the analyzed emotional state and anomaly information, and the output is a customized notification message.

[0904] Step 8:

[0905] The user takes appropriate action based on the notification received. This may involve accepting suggestions from the system, resetting the device, or coordinating with other operators. The input is the notification message, and the output is the corrective action to be taken.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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."

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] The following is further disclosed regarding the embodiments described above.

[0928] (Claim 1)

[0929] A means of collecting documents in non-standardized formats provided by different suppliers,

[0930] A means of analyzing documents collected using generative AI and structuring the information,

[0931] A means of integrating structured information into a database for centralized management,

[0932] A means for transmitting operational information received from connected distributed devices to a server,

[0933] A means for analyzing operational information and detecting anomalies that exceed a set threshold,

[0934] A means of notifying the administrator of detected anomalies,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, which identifies the location information of a distributed device from structured information.

[0938] (Claim 3)

[0939] The system according to claim 1, which, when an anomaly is detected, presents the administrator with the most appropriate countermeasure based on past response records.

[0940] "Example 1"

[0941] (Claim 1)

[0942] A means for receiving and storing non-standardized information from different providers,

[0943] A method for analyzing received information using a generation AI and extracting attribute information,

[0944] A means for integrating and storing extracted attribute information in an aggregated information area,

[0945] A means for transferring operation information acquired from multiple connected devices to a management area,

[0946] A means of analyzing operational information and identifying abnormalities that exceed the criteria,

[0947] A means of notifying the operator of the detected anomaly,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, which recognizes the locations of multiple devices from aggregated attribute information.

[0951] (Claim 3)

[0952] The system according to claim 1, which, when an anomaly is detected, instructs the operator on the most appropriate countermeasure based on past resolution information.

[0953] "Application Example 1"

[0954] (Claim 1)

[0955] A means of collecting non-standardized information provided from different sources,

[0956] A means of analyzing and structuring information collected using generative AI technology,

[0957] Means for integrating structured information into a storage medium for integrated management,

[0958] A means for transmitting operational information received from a group of connected distributed devices to an information processing device,

[0959] A means for analyzing operational information and detecting anomalies that exceed a set threshold,

[0960] A means of notifying the administrator of detected anomalies,

[0961] A means of using AI generation during anomalies to propose the optimal response plan based on past response records,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] The system according to claim 1, which identifies the location information of a group of distributed devices from structured information.

[0965] (Claim 3)

[0966] The system according to claim 1, which, when an anomaly is detected, uses a generating AI to present effective action plans to the administrator based on past response records.

[0967] "Example 2 of combining an emotion engine"

[0968] (Claim 1)

[0969] A means of collecting non-standardized information provided from different sources,

[0970] A means of analyzing information collected using knowledge processing technology and organizing the data,

[0971] A means of integrating organized data into a recording medium for centralized management,

[0972] Means for transmitting operational information received from connected distributed devices to a central device,

[0973] A means for analyzing driving information and detecting abnormalities that exceed set standards,

[0974] A means of notifying the administrator of detected anomalies in an adaptive format,

[0975] A means of analyzing the user's emotional state and adjusting the notification method,

[0976] A system that includes this.

[0977] (Claim 2)

[0978] The system according to claim 1, which identifies the location information of distributed devices from organized data.

[0979] (Claim 3)

[0980] The system according to claim 1, which, upon detecting an anomaly, presents the administrator with the most appropriate countermeasure based on past response records and customizes it according to the emotional state.

[0981] "Application example 2 when combining with an emotional engine"

[0982] (Claim 1)

[0983] A means of collecting non-standardized information provided from different sources,

[0984] A means of analyzing information collected using generative AI and structuring knowledge,

[0985] A means of integrating structured knowledge into data storage for centralized management,

[0986] A means for transmitting the operating status received from multiple connected devices to a server,

[0987] A means for analyzing the operating state and detecting abnormal situations that exceed a set threshold,

[0988] A means of notifying the administrator of detected abnormal situations and providing customized notifications tailored to the administrator's psychological state,

[0989] When an anomaly occurs, a means to analyze the administrator's emotional state using emotion recognition and select an appropriate alert method,

[0990] A system that includes this.

[0991] (Claim 2)

[0992] The system according to claim 1, which identifies the arrangement information of multiple devices from structured knowledge.

[0993] (Claim 3)

[0994] The system according to claim 1, which, upon detecting an abnormal situation, presents the administrator with the most appropriate action based on emotions, derived from past response records. [Explanation of symbols]

[0995] 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 collecting documents in non-standardized formats provided by different suppliers, A means of analyzing documents collected using generative AI and structuring the information, A means of integrating structured information into a database for centralized management, A means for transmitting operational information received from connected distributed devices to a server, A means for analyzing operational information and detecting anomalies that exceed a set threshold, A means of notifying the administrator of detected anomalies, A system that includes this.

2. The system according to claim 1, which identifies the location information of a distributed device from structured information.

3. The system according to claim 1, which, when an anomaly is detected, presents the administrator with the most appropriate countermeasure based on past response records.

Images