System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting data from a monitoring system; means for detecting an anomaly based on the collected data; means for generating an alert if an anomaly is detected; and means for automatically performing an initial response procedure based on the generated alert.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] System operation requires 24 / 7 monitoring, requiring a significant amount of human resources. This has led to issues such as a decline in employee motivation, sudden resignations, and increased difficulty in handover due to the increasing complexity of IT systems. Furthermore, manual operational errors and time-consuming operational work reduce corporate productivity. As such, human-dependent system operation is costly and inefficient. [Means for solving the problem]

[0005] To address these issues, the present invention provides a system that collects data from a monitoring system, detects anomalies based on the collected data, generates an alert when an anomaly is detected, and automatically executes initial response procedures based on the generated alert. The system also includes functions for scheduling routine tasks and automatically executing the scheduled routine tasks, as well as generating appropriate responses based on requests or inquiries and providing the generated responses. The system also includes functions for monitoring contract information, determining when contract renewals are due, and sending notifications when renewals are due, as well as managing budget information, monitoring budget usage, and sending notifications when budgets are exceeded. These methods automate system operations and minimize human intervention, thereby improving operational quality and reducing costs.

[0006] A "monitoring system" is a function or device that monitors the operating status of the entire system in real time and detects abnormal events.

[0007] "Means for collecting data" refers to functions and devices for collecting necessary information from the monitoring system in real time.

[0008] "Means for detecting anomalies" are functions or devices that analyze collected data and identify unusual events or patterns.

[0009] An "alert generating means" is a function or device for issuing a warning based on a detected abnormality.

[0010] The "means for automatically executing the initial response procedure" refers to a function or device for automatically executing a predefined response procedure upon receiving a generated alert.

[0011] A "means for scheduling routine work" is a function or device for systematically allocating operational tasks that are required on a daily basis.

[0012] "Means for automatically executing routine tasks" refers to functions or devices for automatically executing routine operational tasks according to a schedule.

[0013] The "means for generating an appropriate answer" refers to a function or device for extracting optimal information from a knowledge database and generating an answer in response to a user's inquiry.

[0014] The "means for providing the generated answer" refers to a function or device for providing the generated answer to the user.

[0015] "Means for monitoring contract information" refers to functions and devices that periodically monitor contract information for systems and services and grasp the status of the contract.

[0016] The "means for determining when to renew a contract" refers to a function or device for checking the expiration date of a contract and identifying when renewal is required.

[0017] The "means for notifying contract renewal" refers to a function or device for notifying the parties concerned when the contract renewal date has arrived.

[0018] The "means for managing budget information" refers to functions and devices for centrally managing budget information related to system operation.

[0019] "Means for monitoring budget usage" refers to functions or devices for monitoring budget consumption in real time and reporting as necessary.

[0020] "Means for notifying of budget overruns" refers to functions or devices for notifying relevant parties when the budget exceeds a set limit. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0035] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This system is equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, responding to inquiries, contract management, and budget management in order to streamline system operations and minimize manual intervention. This system is composed of a server, terminals, and users.

[0043] Surveillance System Embodiments

[0044] The server collects data in real time from the monitoring system. The collected data is monitored for CPU usage, memory usage, network traffic, etc. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[0045] Routine work scheduling and execution embodiment

[0046] The server schedules routine operational tasks, such as daily backups and regular software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs automatic backups every night and organizes log files.

[0047] Implementation of inquiry response

[0048] When a user has a problem or question about the system, they contact the system through a chatbot interface. The server then consults a knowledge database to provide the most appropriate answer. For example, in response to a question like, "My system is slow. What should I do?", the server will provide specific advice such as, "Check your network speed and close any unnecessary processes."

[0049] Contract and budget management practices

[0050] The server centrally manages system and service contract information and monitors it regularly. When a contract is due for renewal, the server notifies the relevant parties and automatically proposes the necessary procedures. For example, when a software license needs to be renewed, it notifies the parties and generates an estimate at the same time. The server also manages budget information and monitors budget usage in real time. If the budget is about to exceed the set limit, it issues a warning. For example, if 80% of the budget has already been used by the middle of the month, it will notify the team and encourage them to stay within the budget.

[0051] Specific processing explanation

[0052] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. Contract information and budget information are centrally managed by the server, and notifications and warnings are issued in a timely manner.

[0053] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[0054] The processing flow will be explained below.

[0055] Monitoring system data collection and alert processing

[0056] Step 1:

[0057] The server collects real-time data from the monitoring system, specifically capturing key metrics such as CPU usage, memory usage, and network traffic.

[0058] Step 2:

[0059] The server analyzes the collected data and detects anomalies, using configured thresholds and machine learning models to identify patterns that deviate from normal behavior.

[0060] Step 3:

[0061] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[0062] Step 4:

[0063] The server automatically executes initial response procedures based on the generated alert, such as restarting the process where the problem occurred.

[0064] Scheduling and executing routine tasks

[0065] Step 1:

[0066] The server runs routine, pre-defined tasks on a schedule, including routine backups and software updates.

[0067] Step 2:

[0068] The server sends schedule information and the terminal receives instructions. Specifically, the RPA tool is executed.

[0069] Step 3:

[0070] The terminal automatically performs routine tasks according to a schedule, such as backing up data and cleaning up log files every night.

[0071] Processing inquiries

[0072] Step 1:

[0073] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[0074] Step 2:

[0075] The server receives the query and generates the best answer by consulting a knowledge database.

[0076] Step 3:

[0077] The user receives a server-generated response, which provides specific instructions and solutions.

[0078] Contract management processing

[0079] Step 1:

[0080] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[0081] Step 2:

[0082] The server determines when the contract is due for renewal and notifies the parties involved if renewal is required.

[0083] Step 3:

[0084] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[0085] Budgetary Control Processing

[0086] Step 1:

[0087] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[0088] Step 2:

[0089] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[0090] Step 3:

[0091] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, such as reallocating the budget or requesting additional funding.

[0092] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Conventional system operation required a significant amount of human intervention, which not only reduced operational efficiency but also increased costs. Furthermore, because a wide range of tasks, such as anomaly detection and initial response, routine task scheduling and execution, inquiry response, contract management, and budget management, were performed individually, centralized management was difficult. As a result, it was difficult to improve operational quality and reduce costs.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine tasks, means for automatically executing the scheduled routine tasks, means for generating appropriate responses based on requests or inquiries, means for providing the generated responses, means for monitoring contract information, means for determining when the contract renewal period is due, means for notifying when the renewal period is due, means for managing budget information and monitoring the budget usage, and means for notifying when the budget is exceeded. This makes it possible to centrally manage and automate a wide range of tasks required for system operation. This minimizes human intervention, improves operational quality, and reduces costs.

[0098] A "monitoring system" is a mechanism that monitors a system's performance and operating status in real time and collects necessary data.

[0099] "Data collection" is the process of gathering and storing information obtained from a monitoring system.

[0100] "Anomaly detection" is a function that analyzes collected data and identifies abnormal behavior or conditions.

[0101] "Alert generation" is a mechanism that issues a warning when an abnormality is detected.

[0102] "Automatic execution of initial response procedures" is an operation that automatically executes a pre-defined response procedure based on the generated alert.

[0103] "Regular work" refers to work that is performed periodically in system operation.

[0104] "Scheduling" refers to planning when to perform a particular task or work.

[0105] "Automatic execution" refers to the operation of performing a task according to a pre-set procedure or schedule without human intervention.

[0106] "Request or Inquiry" means a request for help or a question from a User.

[0107] "Answer generation" refers to the process of providing appropriate information and solutions in response to requests or inquiries.

[0108] "Contract information" refers to information regarding the terms of use and contract contents of the service or software.

[0109] "Monitoring" is the act of continuously watching specific information and detecting abnormalities or changes.

[0110] "Contract renewal determination" is the process of determining when a contract needs to be renewed.

[0111] "Notification" is a function that notifies relevant parties of information from the system.

[0112] "Budget information" refers to information regarding costs and expenditures for system operation.

[0113] "Monitoring budget usage" refers to checking in real time whether the budget is being used as planned.

[0114] "Budget Overage Notification" is a function that issues a warning when the budget exceeds a set limit.

[0115] This invention provides various functions to streamline system operations and minimize manual intervention. This system consists of a server, terminals, and users, and provides integrated functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management.

[0116] surveillance system

[0117] The server collects data in real time from the monitoring system. This data includes, for example, CPU usage, memory usage, and network traffic. This data is analyzed using the Python library Scikit-learn, and an alert is generated if an abnormal value is detected. The generated alert is notified via a communication tool such as Slack, and initial response procedures are automatically executed using an RPA (Robotic Process Automation) tool. For example, a specific process may be restarted.

[0118] Scheduling and executing routine tasks

[0119] The server schedules routine tasks and configures them to be executed automatically on the terminal. For example, backups are automatically executed and log files are organized at midnight every day. This is done using Cron jobs or RPA tools such as UiPath.

[0120] Inquiry response

[0121] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server then refers to the knowledge database and uses a generative AI model (e.g., GPT-3) to provide an appropriate answer. For example, in response to a query such as "My system is slow, what should I do?", the server generates specific advice such as "Check your network speed and close unnecessary processes."

[0122] Contract and budget management

[0123] The server centrally manages contract information and monitors it regularly. When a contract is due for renewal, it notifies the relevant parties and automatically suggests the necessary procedures. For example, if a software license needs to be renewed, a quote is generated and a notification is sent. It also manages budget information and monitors budget usage in real time. A budget management tool such as QuickBooks is used to issue a warning if the budget is about to be exceeded. For example, if 80% of the budget has been used in the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[0124] Examples and prompts

[0125] Here are some examples of specific prompts:

[0126] "What steps should I take if I detect abnormal CPU usage?"

[0127] "Please show me how to set up a daily backup schedule"

[0128] "Please tell me in detail about the flow of system contract management and budget management."

[0129] This system makes it possible to centrally manage and automate the wide range of tasks required for system operation, minimizing human intervention, improving the quality of operations and reducing costs.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] Data collection

[0133] The server collects various data from the monitoring system in real time.

[0134] Input: Monitoring data such as CPU usage, memory usage, and network traffic from your monitoring system.

[0135] Processing: Periodically retrieve monitoring data and store it in a database.

[0136] Output: Collected monitoring data.

[0137] Specific operation: The server obtains monitoring data through the API and stores the obtained data in a database.

[0138] Step 2:

[0139] Data analysis

[0140] The server analyzes the collected data and detects abnormal values.

[0141] Input: Collected monitoring data.

[0142] Processing: Analyze the data using Python's Scikit-learn library and run anomaly detection algorithms.

[0143] Output: Anomaly information if an anomaly is detected.

[0144] Specific operation: The server periodically analyzes the collected data and generates anomaly information if an abnormal value is detected.

[0145] Step 3:

[0146] Alert Generation

[0147] The server automatically generates an alert if an abnormality is detected.

[0148] Input: Anomaly information.

[0149] Processing: Generate a notification of an abnormality based on the abnormality information.

[0150] Output: An alert notification of an anomaly.

[0151] Specific operation: When an abnormality is detected, the server sends an alert notification using a communication tool such as Slack.

[0152] Step 4:

[0153] Performing initial response procedures

[0154] The server automatically executes initial response procedures based on the generated alert.

[0155] Input: Alert notification.

[0156] Action: Follow pre-defined initial response procedures and take action.

[0157] Output: Results of the initial response procedures.

[0158] What it does: If abnormal memory usage is detected, the server runs a Bash script to restart certain processes.

[0159] Step 5:

[0160] Routine work scheduling

[0161] The server schedules routine work.

[0162] Input: Routine work list and execution schedule.

[0163] Action: Set up a Cron job to schedule tasks to run at specified times.

[0164] Output: Routine work schedule.

[0165] Specific operation: Set up a Cron job to automatically perform a backup every night at midnight.

[0166] Step 6:

[0167] Automatic execution of routine tasks

[0168] The terminal automatically executes scheduled routine tasks.

[0169] Input: Routine work schedule.

[0170] Processing: Perform routine tasks using RPA tools (e.g. UiPath).

[0171] Output: Results of routine work execution.

[0172] Specific operations: Automatically performs routine tasks such as software updates and backups according to a schedule.

[0173] Step 7:

[0174] Receiving inquiries

[0175] Users can ask questions about the system through the chatbot interface.

[0176] Input: User's query.

[0177] Processing: The chatbot uses natural language processing to understand the user's question.

[0178] Output: Analysis result of the query.

[0179] Specific operation: The chatbot receives user inquiries as text and analyzes them using natural language processing.

[0180] Step 8:

[0181] Providing answers

[0182] The server consults a knowledge base and generates an appropriate answer.

[0183] Input: Analysis results of the query.

[0184] Processing: Generate answers to queries using a generative AI model (e.g., GPT-3).

[0185] Output: The best answer.

[0186] How it works: Based on the query, the server uses its knowledge base and generative AI models to quickly provide an answer.

[0187] Step 9:

[0188] Contract information management

[0189] The server centrally manages and monitors system and service contract information.

[0190] Input: Contract information.

[0191] Processing: Periodically update contract information and determine when the contract is due for renewal.

[0192] Output: Notification of when it's time to update.

[0193] Specific operations: Integrates with management tools such as Google Sheets to regularly update contract information and notify relevant parties when renewal dates approach.

[0194] Step 10:

[0195] Managing and monitoring budget information

[0196] The server manages the budget information and monitors the budget usage in real time.

[0197] Input: Budget information.

[0198] Action: Use a budgeting tool (e.g., QuickBooks) to monitor budget usage in real time.

[0199] Output: Budget overrun warning.

[0200] What it does: If there is a risk of exceeding the budget, the server will immediately issue a warning and notify the operations team.

[0201] (Application example 1)

[0202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0203] In modern factories, it is important to monitor the operating status of production lines and equipment in real time and to take prompt and appropriate action when an abnormality occurs. However, constant manual monitoring and response is labor-intensive and inefficient. Furthermore, routine maintenance work, contract management, and budget management are prone to errors when done manually. Furthermore, responding quickly to troubles and questions within the factory requires advanced specialized knowledge and rapid responses. To solve these issues and streamline factory operations, a system that automates these tasks is needed.

[0204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0205] In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine work, means for automatically executing the scheduled routine work, means for generating an appropriate response based on a request or inquiry, means for providing the generated response, means for monitoring contract information, means for determining when the contract renewal is due, means for notifying when the renewal date has arrived, means for managing budget information and monitoring the usage status of the budget, means for notifying when the budget is exceeded, means for monitoring the operating status of factory production lines and machines in real time, means for temporarily suspending a specific process or machine when an anomaly is detected, means for providing an appropriate response using a knowledge database related to the inquiry, and means for scheduling and automatically executing routine maintenance work in the factory. This makes it possible to improve the efficiency and quality of factory operations and minimize human intervention.

[0206] A "monitoring system" is a system that monitors the operating conditions and environment of factories, facilities, etc. in real time and collects data.

[0207] The "data collection means" is a means having a function of collecting various sensor data and operating status information collected from the monitoring system.

[0208] The "abnormality detection means" is a means having a function for analyzing collected data and detecting operations or states that deviate from normal states.

[0209] The "alert generation means" is a means having a function of generating an alert to notify information about an abnormality when an abnormality is detected.

[0210] The "initial response means" is a means having a function of automatically executing an initial response procedure based on the generated alert.

[0211] The "routine work scheduling means" is a means having a function for scheduling maintenance work and other routine work that is carried out periodically.

[0212] The "routine task execution means" is a means having a function of automatically executing scheduled routine tasks.

[0213] The "inquiry response means" is a means having the function of generating and providing an appropriate response based on a request or inquiry.

[0214] A "knowledge database" is a database that stores and references knowledge information such as past troubleshooting and FAQs.

[0215] "Contract monitoring means" refers to means that has the function of monitoring contract information for systems and services and checking their status.

[0216] The "contract renewal determination means" is a means having a function of determining when it is time to renew a contract and issuing an alert or notification when that time has come.

[0217] The "budget management means" is a means having the function of managing budget information and monitoring usage status in real time.

[0218] The "budget monitoring means" is a means that has the function of monitoring the usage of the set budget in real time and issuing warnings or notifications as necessary.

[0219] The "operational status monitoring means" is a means having the function of monitoring the operational status of factory production lines and machines in real time.

[0220] The "process suspension means" is a means having a function of temporarily halting a specific process or machine when an abnormality is detected.

[0221] The "maintenance work scheduling means" is a means having the function of scheduling routine maintenance work within a factory and automatically executing it.

[0222] The present invention provides a system for maximizing operational efficiency and minimizing human intervention in factory production lines, including a monitoring system, alert generation, automated first response procedures, routine task scheduling and execution, inquiry response, contract management, and budget management.

[0223] Surveillance System Embodiments

[0224] The server has a means for collecting data in real time from the monitoring system. For example, it collects data such as operating status, temperature, and vibration from production line monitoring sensors in a factory. The collected data is analyzed by anomaly detection means, and an alert is generated if an abnormality is detected. For example, if abnormal vibration is detected, the alert generation means is activated to prompt a response.

[0225] Routine work scheduling and execution embodiment

[0226] The server has a means for scheduling routine tasks. For example, it schedules periodic maintenance work and part replacement work. Based on this schedule, a means for automatically executing routine tasks is activated, and the tasks are carried out automatically at the set time. Specifically, this includes automatically backing up the system every night and organizing log files.

[0227] Implementation of inquiry response

[0228] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server will refer to the knowledge database and provide the most appropriate answer. For example, in response to a question such as, "There's a lot of vibration on the production line. What should I do?", the server will provide specific advice such as, "Check the operating status of the machine and perform maintenance work if necessary."

[0229] Contract and budget management practices

[0230] The server has a means for monitoring contract information. For example, it sends a notification when a machine maintenance contract is due for renewal. It also has a means for managing budget information and monitoring budget usage in real time. For example, it issues a warning and notifies relevant parties when the monthly budget is exceeded by 80%.

[0231] A concrete example of the entire system

[0232] When an abnormal temperature rise is detected on a production line in a factory, the system generates an alert in real time and shuts down the affected machine as an initial response. At the same time, it notifies specialist staff and urges them to take the necessary action. This enables rapid troubleshooting and minimizes damage.

[0233] Hardware and software used

[0234] Hardware: Various sensors, monitoring devices, and robots with control functions in the factory

[0235] Software: Python, APScheduler (scheduling), watchdog (file system monitoring), chatbot interface

[0236] Specific prompt examples

[0237] How can I monitor the status of my production line in real time and generate alerts and take action when an abnormality is detected?

[0238] Write Python code to automatically schedule and execute routine maintenance tasks in your factory.

[0239] Please explain the initial response method for a system that automatically restarts a machine when an abnormality occurs.

[0240] This will enable more efficient factory operations, improve quality, and minimize human intervention.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] The server collects data from the monitoring system. Specifically, it obtains real-time data such as operating status, temperature, and vibration from various sensors in the factory. The input data are the measured values ​​from the various sensors, and the output data is raw data stored in the server.

[0244] Step 2:

[0245] The server analyzes the collected data and detects abnormalities. A generative AI model is used for data analysis to identify abnormal patterns. The input is the raw data obtained in step 1, and the output is a result indicating whether or not there is an abnormality. Specifically, if the vibration data exceeds a certain threshold, it is determined to be "abnormal."

[0246] Step 3:

[0247] The server generates an alert when an abnormality is detected. The generated alert includes the location and cause of the abnormality, as well as the initial response procedure. The input is the anomaly detection result from step 2, and the output is an alert message. Specifically, when an abnormality occurs, a pop-up notification is generated on the server and an email is sent to the responsible person.

[0248] Step 4:

[0249] The server automatically executes initial response procedures based on the generated alerts. For example, if abnormal vibrations are detected, it issues a command to suspend a specific process or machine. The input is the alert message, and the output is a message confirming that the machine has stopped.

[0250] Step 5:

[0251] The server schedules routine tasks and issues commands to the routine task execution means. For example, it sets up a schedule for automatic system backup every night and executes it. The input is the routine task schedule setting, and the output is a message confirming the execution schedule.

[0252] Step 6:

[0253] The server generates an appropriate answer to a user's inquiry. When a user makes an inquiry through the chatbot interface, the server refers to the knowledge database and provides the most appropriate answer. The input is the user's inquiry, and the output is a response message.

[0254] Step 7:

[0255] The server monitors contract information and determines when the contract is due for renewal. When the renewal date is reached, it notifies the relevant parties. The input is the contract information and renewal conditions, and the output is a notification message. Specifically, an email notification is sent to the person in charge one month before the contract is due for renewal.

[0256] Step 8:

[0257] The server manages budget information and monitors budget usage. It notifies users when the budget is exceeded. The input is budget information and usage data, and the output is a budget exceedance warning message. Specifically, a warning message is sent when the monthly budget exceeds 80%.

[0258] Step 9:

[0259] When users operate within a factory, they receive the necessary information and instructions from the system. For example, if an abnormality occurs on a production line, an alert is displayed on the user's screen (tablet or PC) and initial response procedures are instructed. The input is the alert information, and the output is specific initial response procedures.

[0260] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0261] In order to streamline system operations and minimize manual intervention, the present invention utilizes a system equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, response to inquiries, contract management, and budget management, as well as an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users.

[0262] Surveillance System Embodiments

[0263] The server collects real-time data from the monitoring system. The collected data includes key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[0264] Routine work scheduling and execution embodiment

[0265] The server schedules routine operational tasks, such as daily backups and software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs data backups and organizes log files every night.

[0266] Implementation of inquiry response

[0267] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database and generates the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[0268] Embodiment of Emotion Engine

[0269] The server is equipped with an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server can recognize that emotion and provide a faster and more friendly response. It can also execute different initial response procedures depending on the user's emotional state.

[0270] Contract Management Implementation

[0271] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, when a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[0272] Budget control implementation

[0273] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[0274] Specific processing explanation

[0275] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. An emotion engine recognizes the user's emotions and provides a response based on those emotions. Contract information and budget information are centrally managed by the server, which issues notifications and warnings in a timely manner.

[0276] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[0277] The processing flow will be explained below.

[0278] Surveillance System Embodiments

[0279] Step 1:

[0280] The server collects data in real time from the monitoring system, including CPU usage, memory usage, network traffic, etc.

[0281] Step 2:

[0282] The server analyzes the collected data and detects anomalies, using thresholds and machine learning models that have been set for anomaly detection.

[0283] Step 3:

[0284] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[0285] Step 4:

[0286] The server automatically executes first response procedures based on the generated alerts, for example restarting a specific process if abnormal memory usage is detected.

[0287] Routine work scheduling and execution embodiment

[0288] Step 1:

[0289] The server schedules its regular operations, which include routine tasks such as periodic backups and software updates.

[0290] Step 2:

[0291] The server sends instructions to the terminal to execute the schedule. The RPA tool is used.

[0292] Step 3:

[0293] The device automatically performs routine tasks according to a schedule, such as backing up data every night.

[0294] Implementation of inquiry response

[0295] Step 1:

[0296] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[0297] Step 2:

[0298] The server receives the query and generates the best answer by consulting a knowledge database.

[0299] Step 3:

[0300] The user receives a server-generated response, which provides specific instructions and solutions.

[0301] Embodiment of Emotion Engine

[0302] Step 1:

[0303] The server uses an emotion engine to recognize the user's emotions, including facial expression analysis, voice tone analysis, and analysis of text input.

[0304] Step 2:

[0305] The server provides adaptive responses based on the recognized emotions, for example, providing a prompt and polite response if the user is showing signs of annoyance.

[0306] Step 3:

[0307] The server implements different initial response procedures depending on different emotional states, for example, a fast escalation procedure is implemented for a frustrated user.

[0308] Contract Management Implementation

[0309] Step 1:

[0310] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[0311] Step 2:

[0312] The server determines when the contract is due for renewal and notifies the parties involved when renewal is required.

[0313] Step 3:

[0314] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[0315] Budget control implementation

[0316] Step 1:

[0317] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[0318] Step 2:

[0319] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[0320] Step 3:

[0321] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, including budget reallocation and requesting additional funding.

[0322] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[0323] Example 2

[0324] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0325] Modern system operations require efficient and accurate execution of a wide range of tasks, including monitoring, routine tasks, inquiry response, contract management, and budget management. However, these tasks require significant human intervention, raising concerns about increased operational costs and the likelihood of errors. It is also difficult to properly recognize user emotions and respond accordingly. Therefore, a system that can effectively resolve these issues and automate and streamline system operations is needed.

[0326] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0327] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for recognizing a user's emotion and adjusting a response based on the emotion. This makes it possible to automate a series of operational tasks, from anomaly detection to initial response and response based on the user's emotion, and minimize human intervention.

[0328] A "monitoring system" is a mechanism for monitoring various resources and performance data within a system in real time, and for early detection of abnormalities and problems.

[0329] "Data collection" is the process of obtaining the necessary information from the monitoring system and sending it to a server for analysis.

[0330] "Anomaly detection" is the process of analyzing system behavior based on collected data and identifying abnormal conditions that go beyond the normal range.

[0331] "Alert generation" is the process of creating a warning to notify relevant parties when an anomaly is detected.

[0332] An "initial response procedure" is a process for implementing defined countermeasures to quickly deal with a detected abnormality.

[0333] "Emotion recognition" is the process of analyzing a user's facial expressions, voice tone, and text input to determine their emotional state.

[0334] "Regular work" refers to the daily tasks involved in system operation, such as repetitive tasks like data backups and software updates.

[0335] "Scheduling" is the process of scheduling specific tasks to be performed at specified dates and times or intervals.

[0336] "Inquiry handling" is the process of providing appropriate answers to requests and questions from users.

[0337] A "knowledge database" is an information repository that consolidates past case studies and technical information, enabling efficient response to inquiries.

[0338] "Contract management" is the process of centrally storing contract information related to systems and services and managing their expiration dates and conditions.

[0339] "Budget management" is the process of centrally managing the budgets of each department and project, and monitoring and issuing alerts regarding usage.

[0340] This invention is a system with the following functions to streamline system operations and minimize human intervention. It has the functions of a monitoring system, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management, and also uses an emotion engine that recognizes user emotions. This system consists of a server, terminals, and users.

[0341] Surveillance System Embodiments

[0342] The server collects real-time data from the monitoring system. Specifically, it obtains key performance metrics such as CPU usage, memory usage, and network traffic. This is done using the Simple Network Management Protocol (SNMP) or a dedicated monitoring agent. The collected data is analyzed by monitoring tools such as Nagios and Zabbix, and if an abnormality is detected, an alert is automatically generated. For example, an alert is generated if CPU usage exceeds 90%.

[0343] Routine work scheduling and execution embodiment

[0344] The server schedules routine tasks such as daily backups and software updates using cron jobs or Windows Task Scheduler, and the terminals automatically execute these tasks using RPA tools (Robotic Process Automation, such as UiPath). For example, they perform data backups and clean up log files every night.

[0345] Implementation of inquiry response

[0346] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. For example, if a user makes an inquiry such as "The system is slow," the server receives the inquiry, references the knowledge database, and generates the most appropriate answer. The generated answer is then provided to the user. For example, the advice provided might be "Check your network speed and terminate unnecessary processes."

[0347] Embodiment of Emotion Engine

[0348] The server includes an emotion engine for recognizing the user's emotions. The emotion engine uses machine learning models to analyze the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user inputs something that indicates frustration to the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[0349] Contract Management Implementation

[0350] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and relevant parties are notified when renewal is approaching. Notifications are sent via email, and estimates are automatically generated for software licenses that require renewal.

[0351] Budget control implementation

[0352] The server acquires budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. Data is acquired through collaboration with ERP systems (e.g., SAP), and if the budget is about to exceed the set limit, a warning is sent to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to operate within the budget.

[0353] Specific processing examples

[0354] What to do if CPU usage is high

[0355] If the CPU usage continues to be abnormally high for a certain day, the server will first obtain CPU usage data from the monitoring system and detect the abnormality. An alert will then be automatically generated, and an initial response will be taken to restart the specific CPU-intensive process.

[0356] Examples of routine work

[0357] Every night, the terminal uses the RPA tool to automatically back up data and organize log files, ensuring smooth daily operations.

[0358] Specific examples of inquiries

[0359] If a user asks, "My system is slow, what should I do?" the server can consult its knowledge database and provide specific advice such as, "Check your network speed and close unnecessary processes."

[0360] In this way, automating a wide range of operational tasks and minimizing human intervention will improve the quality of operations and reduce costs.

[0361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0362] Specific explanation of processing steps

[0363] surveillance system

[0364] Step 1: Data collection

[0365] The server obtains various metrics (e.g., CPU usage, memory usage, network traffic) from the monitoring system in real time via SNMP or a monitoring agent.

[0366] Input: Metric data from each device or system

[0367] Output: Collected metric data

[0368] Step 2: Data analysis

[0369] The server analyzes the collected data using Nagios or Zabbix and detects abnormalities by comparing it with the set thresholds.

[0370] Input: Collected metrics data

[0371] Output: Anomaly detection results (e.g., information that CPU usage exceeded 90%)

[0372] Step 3: Alert Generation

[0373] If an anomaly is detected, the server generates an alert and notifies relevant parties via an alert management system such as PagerDuty.

[0374] Input: Anomaly detection results

[0375] Output: Generated alerts and notifications

[0376] Step 4: Initial response

[0377] The server will automatically take first response steps based on the generated alerts, using Ansible or Puppet to run specific scripts, for example restarting a process with high CPU usage.

[0378] Input: Generated alert

[0379] Output: Results of the initial response performed

[0380] Scheduling and executing routine tasks

[0381] Step 1: Scheduling

[0382] The server schedules routine tasks (e.g., data backups and software updates) using cron jobs or the Windows Task Scheduler.

[0383] Input: Routine work schedule requirements

[0384] Output: Set schedule

[0385] Step 2: Perform routine tasks

[0386] The terminal automatically performs routine tasks using an RPA tool (e.g., UiPath) based on a set schedule.

[0387] Input: Set schedule

[0388] Output: Results of routine tasks performed (e.g., backup completed, log file cleanup completed)

[0389] Inquiry response

[0390] Step 1: Receiving an inquiry

[0391] Users can contact the chatbot interface with questions or problems they have about the system. For example, a user might say, "The system is slow."

[0392] Input: User inquiry

[0393] Output: Received queries

[0394] Step 2: Processing the inquiry

[0395] The server refers to a knowledge database based on the content of the received inquiry and generates an appropriate answer.

[0396] Input: Received query

[0397] Output: Generated answer (e.g. "Check your network speed and terminate any unnecessary processes")

[0398] Emotion Engine

[0399] Step 1: Sentiment Analysis

[0400] The server uses machine learning models to determine the user's emotional state by analyzing their facial expressions, voice tone, and text input. For example, it analyzes facial expressions and sentences that indicate irritation.

[0401] Input: User facial expression data, voice tone, text input

[0402] Output: Perceived emotional state

[0403] Step 2: Respond based on emotions

[0404] The server adjusts its response procedures based on the results of the sentiment analysis, for example, responding more quickly and politely if the user expresses irritation.

[0405] Input: Perceived emotional state

[0406] Output: Coordinated response and execution

[0407] contract management

[0408] Step 1: Gather contract information

[0409] The server collects system and service contract information for centralized management, utilizing the SMB protocol and API.

[0410] Input: Contract information

[0411] Output: Centralized contract data

[0412] Step 2: Contract monitoring and notification

[0413] The server monitors contract expiration dates and conditions and notifies interested parties when renewal is approaching. Email notifications are sent.

[0414] Input: Centralized contract data

[0415] Output: Notifications sent (e.g. software license renewal notifications)

[0416] Budget Management

[0417] Step 1: Gather budget information

[0418] The server works in conjunction with the ERP system to obtain budget information for each department and project and manages it centrally.

[0419] Input: Budget Data

[0420] Output: Centralized budget data

[0421] Step 2: Budget monitoring and alerts

[0422] The server monitors budget usage in real time and issues alerts to relevant parties if the set limits are about to be exceeded.

[0423] Input: Centralized budget data

[0424] Output: Warning notice issued (e.g., budget overrun warning)

[0425] (Application example 2)

[0426] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0427] In recent years, as factories have become more automated and efficient, there has been a strong demand for more efficient system operations and minimal human intervention. However, many systems still rely on human labor, with tasks such as anomaly detection, alert generation, initial response, routine tasks, and responding to inquiries consuming time and effort. Furthermore, when operating machines or systems, prompt and appropriate responses are required when users experience stress or dissatisfaction, but this also relies heavily on human labor. Therefore, there is a need to perform these tasks efficiently and automatically, and to respond flexibly to the user's emotional state.

[0428] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for determining the emotional state of a user using an emotion engine and providing a response according to the emotion. This makes it possible to automate anomaly detection and initial response in system operation, and to take appropriate action taking user emotions into consideration when operating or performing maintenance on a robot in a factory.

[0429] Definitions of important terms contained in patent specifications

[0430] A "monitoring system" is a means of collecting data to confirm the normal operation of systems and equipment and to detect abnormalities.

[0431] "Data collection" means recording key metrics such as CPU usage, memory usage, and network traffic from monitoring systems.

[0432] "Anomaly detection" is the analysis of collected data to discover unexpected behavior or performance degradation.

[0433] "Alert generation" means creating a warning or notification to notify relevant parties when an abnormality is detected.

[0434] "Automatic execution of initial response procedures" refers to the automatic execution of pre-defined procedures to correct anomalies based on the generated alert.

[0435] The "emotion engine" is a system that analyzes the user's facial expressions, voice tone, text input, etc. to determine the user's emotional state.

[0436] "Routine work scheduling" means planning in advance the tasks that are required on a daily basis, such as backup work and software updates, and carrying them out according to a timetable.

[0437] "Automatic execution of routine tasks" refers to the automatic execution of scheduled routine tasks using RPA tools, etc.

[0438] "Inquiry response" refers to generating and providing appropriate answers to questions or problems from users by referencing a knowledge database.

[0439] "Contract information monitoring" refers to the centralized management of contract information for systems and services, and the monitoring of contract renewal dates and conditions.

[0440] A "contract renewal notice" is a means of informing the parties involved when the contract expiration date is approaching.

[0441] "Budget management" means centrally managing budget information for each department and project and monitoring usage.

[0442] "Budget Overage Notification" is the act of issuing a warning to relevant parties when budget usage is likely to exceed a set limit.

[0443] A "generative AI model" is an artificial intelligence model that is generated based on data obtained from devices or systems, and that makes appropriate responses and predictions.

[0444] A "prompt" is a question or instruction input to a generative AI model that serves as the basis for the model to generate an answer or response.

[0445] MODE FOR CARRYING OUT THE INVENTION

[0446] This invention provides a system that integrates a monitoring system, anomaly detection, alert generation, automatic execution of initial response procedures, an emotion engine, routine work scheduling and execution, inquiry response, contract information monitoring, and budget management to achieve efficient operation in factories. The system is composed of a server, terminals, and users.

[0447] Surveillance System Embodiments

[0448] The server collects data in real time from various devices and systems within the factory. Specifically, it acquires key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. For example, if CPU usage exceeds 90%, an alert is generated and initial response procedures such as restarting a specific process are automatically executed.

[0449] Routine work scheduling and execution embodiment

[0450] The server schedules the factory's routine tasks, including daily data backups and software updates. After the schedule is determined, the terminals use RPA tools to automatically execute these tasks. For example, they can back up data and organize log files every night.

[0451] Implementation of inquiry response

[0452] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database to generate and provide the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[0453] Embodiment of Emotion Engine

[0454] The server is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[0455] Contract Management Implementation

[0456] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, if a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[0457] Budget control implementation

[0458] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[0459] Examples of using generative AI models and prompts

[0460] The server inputs prompts into the generative AI model based on data obtained from factory equipment and utilizes the responses. For example, if a prompt such as "What is the current CPU usage rate?" is input, the generative AI model will respond with "The current CPU usage rate is 85%." This enables more efficient system operation.

[0461] Examples of specific prompts are:

[0462] "Show me today's routine work schedule"

[0463] Check the latest backup data.

[0464] "What is your current network traffic?"

[0465] This system significantly improves factory operational efficiency and minimizes human intervention.

[0466] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0467] System program processing flow

[0468] Step 1:

[0469] The server collects real-time data from various devices and systems in the factory. As input, it obtains data metrics such as device CPU usage, memory usage, and network traffic. As output, it stores this data in a database. Specifically, the server obtains data from each device via API and stores that data in the database.

[0470] Step 2:

[0471] The server analyzes the collected data and detects anomalies. It uses the data collected in step 1 as input. It uses a data analysis algorithm to detect abnormal values ​​and judges anomalies based on this. As output, it generates an alert object if an anomaly is detected. Specifically, it sets a threshold for the data, and judges it to be an anomaly if this threshold is exceeded.

[0472] Step 3:

[0473] The server generates an alert when an anomaly is detected. As input, it uses the alert object created in step 2. As output, it creates an alert message to notify the user. Specifically, it generates an alert message and sends it to the user through the notification system.

[0474] Step 4:

[0475] The server automatically executes initial response procedures based on the generated alert. As input, it uses the alert message generated in step 3. As output, it executes actions to correct the abnormal condition. Specifically, it uses an RPA tool to execute response procedures such as restarting a specific process.

[0476] Step 5:

[0477] The server schedules routine tasks and manages their execution. Tasks such as routine backups and software updates are listed as input. A schedule of routine tasks is generated as output, and each task is automatically executed. Specifically, tasks are set periodically using a scheduling tool, and then automatically executed using an RPA tool.

[0478] Step 6:

[0479] Users can inquire about problems or questions about the system through the chatbot interface. The text entered by the user is used as input. As output, the system generates an appropriate answer and provides it to the user. Specifically, the server refers to a knowledge database to search for and present the appropriate answer.

[0480] Step 7:

[0481] The server uses an emotion engine to determine the user's emotional state. The inputs include the user's facial expression, voice tone, and text input. The output is an analysis of the user's emotional state and a response appropriate to that state. Specifically, the server uses an emotion analysis algorithm to determine the user's emotions and automatically determine the appropriate response.

[0482] Step 8:

[0483] The server monitors contract information and determines when it is time to renew the contract. It uses a contract information database as input. It outputs by notifying relevant parties when contract renewal is approaching. Specifically, it periodically checks contract information, determines when renewal is due, and sends alerts to relevant parties via a notification system.

[0484] Step 9:

[0485] The server manages budget information and monitors budget usage. It uses budget data from each department and project as input. It outputs a warning to relevant parties when budget overruns are predicted. Specifically, it analyzes budget usage in real time and sends notifications when there is a risk of exceeding the limit.

[0486] Step 10:

[0487] The server inputs a prompt statement into the generative AI model based on data obtained from factory equipment and utilizes the response. The prompt statement and equipment data are used as input. The response from the generative AI model is obtained as output and reflected in operations. Specifically, the server inputs a prompt statement such as "What is the current CPU usage rate?" and obtains a response such as "The current CPU usage rate is 85%."

[0488] Through these steps, the invention streamlines factory operations and minimizes human intervention.

[0489] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0490] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0491] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0492] [Second embodiment]

[0493] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0494] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0495] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0496] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0497] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0498] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0499] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0500] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0501] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0502] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0503] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0504] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0505] This system is equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, responding to inquiries, contract management, and budget management in order to streamline system operations and minimize manual intervention. This system is composed of a server, terminals, and users.

[0506] Surveillance System Embodiments

[0507] The server collects data in real time from the monitoring system. The collected data is monitored for CPU usage, memory usage, network traffic, etc. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[0508] Routine work scheduling and execution embodiment

[0509] The server schedules routine operational tasks, such as daily backups and regular software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs automatic backups every night and organizes log files.

[0510] Implementation of inquiry response

[0511] When a user has a problem or question about the system, they contact the system through a chatbot interface. The server then consults a knowledge database to provide the most appropriate answer. For example, in response to a question like, "My system is slow. What should I do?", the server will provide specific advice such as, "Check your network speed and close any unnecessary processes."

[0512] Contract and budget management practices

[0513] The server centrally manages system and service contract information and monitors it regularly. When a contract is due for renewal, the server notifies the relevant parties and automatically proposes the necessary procedures. For example, when a software license needs to be renewed, it notifies the parties and generates an estimate at the same time. The server also manages budget information and monitors budget usage in real time. If the budget is about to exceed the set limit, it issues a warning. For example, if 80% of the budget has already been used by the middle of the month, it will notify the team and encourage them to stay within the budget.

[0514] Specific processing explanation

[0515] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. Contract information and budget information are centrally managed by the server, and notifications and warnings are issued in a timely manner.

[0516] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[0517] The processing flow will be explained below.

[0518] Monitoring system data collection and alert processing

[0519] Step 1:

[0520] The server collects real-time data from the monitoring system, specifically capturing key metrics such as CPU usage, memory usage, and network traffic.

[0521] Step 2:

[0522] The server analyzes the collected data and detects anomalies, using configured thresholds and machine learning models to identify patterns that deviate from normal behavior.

[0523] Step 3:

[0524] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[0525] Step 4:

[0526] The server automatically executes initial response procedures based on the generated alert, such as restarting the process where the problem occurred.

[0527] Scheduling and executing routine tasks

[0528] Step 1:

[0529] The server runs routine, pre-defined tasks on a schedule, including routine backups and software updates.

[0530] Step 2:

[0531] The server sends schedule information and the terminal receives instructions. Specifically, the RPA tool is executed.

[0532] Step 3:

[0533] The terminal automatically performs routine tasks according to a schedule, such as backing up data and cleaning up log files every night.

[0534] Processing inquiries

[0535] Step 1:

[0536] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[0537] Step 2:

[0538] The server receives the query and generates the best answer by consulting a knowledge database.

[0539] Step 3:

[0540] The user receives a server-generated response, which provides specific instructions and solutions.

[0541] Contract management processing

[0542] Step 1:

[0543] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[0544] Step 2:

[0545] The server determines when the contract is due for renewal and notifies the parties involved if renewal is required.

[0546] Step 3:

[0547] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[0548] Budgetary Control Processing

[0549] Step 1:

[0550] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[0551] Step 2:

[0552] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[0553] Step 3:

[0554] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, such as reallocating the budget or requesting additional funding.

[0555] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[0556] Example 1

[0557] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0558] Conventional system operation required a significant amount of human intervention, which not only reduced operational efficiency but also increased costs. Furthermore, because a wide range of tasks, such as anomaly detection and initial response, routine task scheduling and execution, inquiry response, contract management, and budget management, were performed individually, centralized management was difficult. As a result, it was difficult to improve operational quality and reduce costs.

[0559] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0560] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine tasks, means for automatically executing the scheduled routine tasks, means for generating appropriate responses based on requests or inquiries, means for providing the generated responses, means for monitoring contract information, means for determining when the contract renewal period is due, means for notifying when the renewal period is due, means for managing budget information and monitoring the budget usage, and means for notifying when the budget is exceeded. This makes it possible to centrally manage and automate a wide range of tasks required for system operation. This minimizes human intervention, improves operational quality, and reduces costs.

[0561] A "monitoring system" is a mechanism that monitors a system's performance and operating status in real time and collects necessary data.

[0562] "Data collection" is the process of gathering and storing information obtained from a monitoring system.

[0563] "Anomaly detection" is a function that analyzes collected data and identifies abnormal behavior or conditions.

[0564] "Alert generation" is a mechanism that issues a warning when an abnormality is detected.

[0565] "Automatic execution of initial response procedures" is an operation that automatically executes a pre-defined response procedure based on the generated alert.

[0566] "Regular work" refers to work that is performed periodically in system operation.

[0567] "Scheduling" refers to planning when to perform a particular task or work.

[0568] "Automatic execution" refers to the operation of performing a task according to a pre-set procedure or schedule without human intervention.

[0569] "Request or Inquiry" means a request for help or a question from a User.

[0570] "Answer generation" refers to the process of providing appropriate information and solutions in response to requests or inquiries.

[0571] "Contract information" refers to information regarding the terms of use and contract contents of the service or software.

[0572] "Monitoring" is the act of continuously watching specific information and detecting abnormalities or changes.

[0573] "Contract renewal determination" is the process of determining when a contract needs to be renewed.

[0574] "Notification" is a function that notifies relevant parties of information from the system.

[0575] "Budget information" refers to information regarding costs and expenditures for system operation.

[0576] "Monitoring budget usage" refers to checking in real time whether the budget is being used as planned.

[0577] "Budget Overage Notification" is a function that issues a warning when the budget exceeds a set limit.

[0578] This invention provides various functions to streamline system operations and minimize manual intervention. This system consists of a server, terminals, and users, and provides integrated functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management.

[0579] surveillance system

[0580] The server collects data in real time from the monitoring system. This data includes, for example, CPU usage, memory usage, and network traffic. This data is analyzed using the Python library Scikit-learn, and an alert is generated if an abnormal value is detected. The generated alert is notified via a communication tool such as Slack, and initial response procedures are automatically executed using an RPA (Robotic Process Automation) tool. For example, a specific process may be restarted.

[0581] Scheduling and executing routine tasks

[0582] The server schedules routine tasks and configures them to be executed automatically on the terminal. For example, backups are automatically executed and log files are organized at midnight every day. This is done using Cron jobs or RPA tools such as UiPath.

[0583] Inquiry response

[0584] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server then refers to the knowledge database and uses a generative AI model (e.g., GPT-3) to provide an appropriate answer. For example, in response to a query such as "My system is slow, what should I do?", the server generates specific advice such as "Check your network speed and close unnecessary processes."

[0585] Contract and budget management

[0586] The server centrally manages contract information and monitors it regularly. When a contract is due for renewal, it notifies the relevant parties and automatically suggests the necessary procedures. For example, if a software license needs to be renewed, a quote is generated and a notification is sent. It also manages budget information and monitors budget usage in real time. A budget management tool such as QuickBooks is used to issue a warning if the budget is about to be exceeded. For example, if 80% of the budget has been used in the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[0587] Examples and prompts

[0588] Here are some examples of specific prompts:

[0589] "What steps should I take if I detect abnormal CPU usage?"

[0590] "Please show me how to set up a daily backup schedule"

[0591] "Please tell me in detail about the flow of system contract management and budget management."

[0592] This system makes it possible to centrally manage and automate the wide range of tasks required for system operation, minimizing human intervention, improving the quality of operations and reducing costs.

[0593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0594] Step 1:

[0595] Data collection

[0596] The server collects various data from the monitoring system in real time.

[0597] Input: Monitoring data such as CPU usage, memory usage, and network traffic from your monitoring system.

[0598] Processing: Periodically retrieve monitoring data and store it in a database.

[0599] Output: Collected monitoring data.

[0600] Specific operation: The server obtains monitoring data through the API and stores the obtained data in a database.

[0601] Step 2:

[0602] Data analysis

[0603] The server analyzes the collected data and detects abnormal values.

[0604] Input: Collected monitoring data.

[0605] Processing: Analyze the data using Python's Scikit-learn library and run anomaly detection algorithms.

[0606] Output: Anomaly information if an anomaly is detected.

[0607] Specific operation: The server periodically analyzes the collected data and generates anomaly information if an abnormal value is detected.

[0608] Step 3:

[0609] Alert Generation

[0610] The server automatically generates an alert if an abnormality is detected.

[0611] Input: Anomaly information.

[0612] Processing: Generate a notification of an abnormality based on the abnormality information.

[0613] Output: An alert notification of an anomaly.

[0614] Specific operation: When an abnormality is detected, the server sends an alert notification using a communication tool such as Slack.

[0615] Step 4:

[0616] Performing initial response procedures

[0617] The server automatically executes initial response procedures based on the generated alert.

[0618] Input: Alert notification.

[0619] Action: Follow pre-defined initial response procedures and take action.

[0620] Output: Results of the initial response procedures.

[0621] What it does: If abnormal memory usage is detected, the server runs a Bash script to restart certain processes.

[0622] Step 5:

[0623] Routine work scheduling

[0624] The server schedules routine work.

[0625] Input: Routine work list and execution schedule.

[0626] Action: Set up a Cron job to schedule tasks to run at specified times.

[0627] Output: Routine work schedule.

[0628] Specific operation: Set up a Cron job to automatically perform a backup every night at midnight.

[0629] Step 6:

[0630] Automatic execution of routine tasks

[0631] The terminal automatically executes scheduled routine tasks.

[0632] Input: Routine work schedule.

[0633] Processing: Perform routine tasks using RPA tools (e.g. UiPath).

[0634] Output: Results of routine work execution.

[0635] Specific operations: Automatically performs routine tasks such as software updates and backups according to a schedule.

[0636] Step 7:

[0637] Receiving inquiries

[0638] Users can ask questions about the system through the chatbot interface.

[0639] Input: User's query.

[0640] Processing: The chatbot uses natural language processing to understand the user's question.

[0641] Output: Analysis result of the query.

[0642] Specific operation: The chatbot receives user inquiries as text and analyzes them using natural language processing.

[0643] Step 8:

[0644] Providing answers

[0645] The server consults a knowledge base and generates an appropriate answer.

[0646] Input: Analysis results of the query.

[0647] Processing: Generate answers to queries using a generative AI model (e.g., GPT-3).

[0648] Output: The best answer.

[0649] How it works: Based on the query, the server uses its knowledge base and generative AI models to quickly provide an answer.

[0650] Step 9:

[0651] Contract information management

[0652] The server centrally manages and monitors system and service contract information.

[0653] Input: Contract information.

[0654] Processing: Periodically update contract information and determine when the contract is due for renewal.

[0655] Output: Notification of when it's time to update.

[0656] Specific operations: Integrates with management tools such as Google Sheets to regularly update contract information and notify relevant parties when renewal dates approach.

[0657] Step 10:

[0658] Managing and monitoring budget information

[0659] The server manages the budget information and monitors the budget usage in real time.

[0660] Input: Budget information.

[0661] Action: Use a budgeting tool (e.g., QuickBooks) to monitor budget usage in real time.

[0662] Output: Budget overrun warning.

[0663] What it does: If there is a risk of exceeding the budget, the server will immediately issue a warning and notify the operations team.

[0664] (Application example 1)

[0665] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0666] In modern factories, it is important to monitor the operating status of production lines and equipment in real time and to take prompt and appropriate action when an abnormality occurs. However, constant manual monitoring and response is labor-intensive and inefficient. Furthermore, routine maintenance work, contract management, and budget management are prone to errors when done manually. Furthermore, responding quickly to troubles and questions within the factory requires advanced specialized knowledge and rapid responses. To solve these issues and streamline factory operations, a system that automates these tasks is needed.

[0667] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0668] In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine work, means for automatically executing the scheduled routine work, means for generating an appropriate response based on a request or inquiry, means for providing the generated response, means for monitoring contract information, means for determining when the contract renewal is due, means for notifying when the renewal date has arrived, means for managing budget information and monitoring the usage status of the budget, means for notifying when the budget is exceeded, means for monitoring the operating status of factory production lines and machines in real time, means for temporarily suspending a specific process or machine when an anomaly is detected, means for providing an appropriate response using a knowledge database related to the inquiry, and means for scheduling and automatically executing routine maintenance work in the factory. This makes it possible to improve the efficiency and quality of factory operations and minimize human intervention.

[0669] A "monitoring system" is a system that monitors the operating conditions and environment of factories, facilities, etc. in real time and collects data.

[0670] The "data collection means" is a means having a function of collecting various sensor data and operating status information collected from the monitoring system.

[0671] The "abnormality detection means" is a means having a function for analyzing collected data and detecting operations or states that deviate from normal states.

[0672] The "alert generation means" is a means having a function of generating an alert to notify information about an abnormality when an abnormality is detected.

[0673] The "initial response means" is a means having a function of automatically executing an initial response procedure based on the generated alert.

[0674] The "routine work scheduling means" is a means having a function for scheduling maintenance work and other routine work that is carried out periodically.

[0675] The "routine task execution means" is a means having a function of automatically executing scheduled routine tasks.

[0676] The "inquiry response means" is a means having the function of generating and providing an appropriate response based on a request or inquiry.

[0677] A "knowledge database" is a database that stores and references knowledge information such as past troubleshooting and FAQs.

[0678] "Contract monitoring means" refers to means that has the function of monitoring contract information for systems and services and checking their status.

[0679] The "contract renewal determination means" is a means having a function of determining when it is time to renew a contract and issuing an alert or notification when that time has come.

[0680] The "budget management means" is a means having the function of managing budget information and monitoring usage status in real time.

[0681] The "budget monitoring means" is a means that has the function of monitoring the usage of the set budget in real time and issuing warnings or notifications as necessary.

[0682] The "operational status monitoring means" is a means having the function of monitoring the operational status of factory production lines and machines in real time.

[0683] The "process suspension means" is a means having a function of temporarily halting a specific process or machine when an abnormality is detected.

[0684] The "maintenance work scheduling means" is a means having the function of scheduling routine maintenance work within a factory and automatically executing it.

[0685] The present invention provides a system for maximizing operational efficiency and minimizing human intervention in factory production lines, including a monitoring system, alert generation, automated first response procedures, routine task scheduling and execution, inquiry response, contract management, and budget management.

[0686] Surveillance System Embodiments

[0687] The server has a means for collecting data in real time from the monitoring system. For example, it collects data such as operating status, temperature, and vibration from production line monitoring sensors in a factory. The collected data is analyzed by anomaly detection means, and an alert is generated if an abnormality is detected. For example, if abnormal vibration is detected, the alert generation means is activated to prompt a response.

[0688] Routine work scheduling and execution embodiment

[0689] The server has a means for scheduling routine tasks. For example, it schedules periodic maintenance work and part replacement work. Based on this schedule, a means for automatically executing routine tasks is activated, and the tasks are carried out automatically at the set time. Specifically, this includes automatically backing up the system every night and organizing log files.

[0690] Implementation of inquiry response

[0691] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server will refer to the knowledge database and provide the most appropriate answer. For example, in response to a question such as, "There's a lot of vibration on the production line. What should I do?", the server will provide specific advice such as, "Check the operating status of the machine and perform maintenance work if necessary."

[0692] Contract and budget management practices

[0693] The server has a means for monitoring contract information. For example, it sends a notification when a machine maintenance contract is due for renewal. It also has a means for managing budget information and monitoring budget usage in real time. For example, it issues a warning and notifies relevant parties when the monthly budget is exceeded by 80%.

[0694] A concrete example of the entire system

[0695] When an abnormal temperature rise is detected on a production line in a factory, the system generates an alert in real time and shuts down the affected machine as an initial response. At the same time, it notifies specialist staff and urges them to take the necessary action. This enables rapid troubleshooting and minimizes damage.

[0696] Hardware and software used

[0697] Hardware: Various sensors, monitoring devices, and robots with control functions in the factory

[0698] Software: Python, APScheduler (scheduling), watchdog (file system monitoring), chatbot interface

[0699] Specific prompt examples

[0700] How can I monitor the status of my production line in real time and generate alerts and take action when an abnormality is detected?

[0701] Write Python code to automatically schedule and execute routine maintenance tasks in your factory.

[0702] Please explain the initial response method for a system that automatically restarts a machine when an abnormality occurs.

[0703] This will enable more efficient factory operations, improve quality, and minimize human intervention.

[0704] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0705] Step 1:

[0706] The server collects data from the monitoring system. Specifically, it obtains real-time data such as operating status, temperature, and vibration from various sensors in the factory. The input data are the measured values ​​from the various sensors, and the output data is raw data stored in the server.

[0707] Step 2:

[0708] The server analyzes the collected data and detects abnormalities. A generative AI model is used for data analysis to identify abnormal patterns. The input is the raw data obtained in step 1, and the output is a result indicating whether or not there is an abnormality. Specifically, if the vibration data exceeds a certain threshold, it is determined to be "abnormal."

[0709] Step 3:

[0710] The server generates an alert when an abnormality is detected. The generated alert includes the location and cause of the abnormality, as well as the initial response procedure. The input is the anomaly detection result from step 2, and the output is an alert message. Specifically, when an abnormality occurs, a pop-up notification is generated on the server and an email is sent to the responsible person.

[0711] Step 4:

[0712] The server automatically executes initial response procedures based on the generated alerts. For example, if abnormal vibrations are detected, it issues a command to suspend a specific process or machine. The input is the alert message, and the output is a message confirming that the machine has stopped.

[0713] Step 5:

[0714] The server schedules routine tasks and issues commands to the routine task execution means. For example, it sets up a schedule for automatic system backup every night and executes it. The input is the routine task schedule setting, and the output is a message confirming the execution schedule.

[0715] Step 6:

[0716] The server generates an appropriate answer to a user's inquiry. When a user makes an inquiry through the chatbot interface, the server refers to the knowledge database and provides the most appropriate answer. The input is the user's inquiry, and the output is a response message.

[0717] Step 7:

[0718] The server monitors contract information and determines when the contract is due for renewal. When the renewal date is reached, it notifies the relevant parties. The input is the contract information and renewal conditions, and the output is a notification message. Specifically, an email notification is sent to the person in charge one month before the contract is due for renewal.

[0719] Step 8:

[0720] The server manages budget information and monitors budget usage. It notifies users when the budget is exceeded. The input is budget information and usage data, and the output is a budget exceedance warning message. Specifically, a warning message is sent when the monthly budget exceeds 80%.

[0721] Step 9:

[0722] When users operate within a factory, they receive the necessary information and instructions from the system. For example, if an abnormality occurs on a production line, an alert is displayed on the user's screen (tablet or PC) and initial response procedures are instructed. The input is the alert information, and the output is specific initial response procedures.

[0723] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0724] In order to streamline system operations and minimize manual intervention, the present invention utilizes a system equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, response to inquiries, contract management, and budget management, as well as an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users.

[0725] Surveillance System Embodiments

[0726] The server collects real-time data from the monitoring system. The collected data includes key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[0727] Routine work scheduling and execution embodiment

[0728] The server schedules routine operational tasks, such as daily backups and software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs data backups and organizes log files every night.

[0729] Implementation of inquiry response

[0730] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database and generates the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[0731] Embodiment of Emotion Engine

[0732] The server is equipped with an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server can recognize that emotion and provide a faster and more friendly response. It can also execute different initial response procedures depending on the user's emotional state.

[0733] Contract Management Implementation

[0734] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, when a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[0735] Budget control implementation

[0736] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[0737] Specific processing explanation

[0738] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. An emotion engine recognizes the user's emotions and provides a response based on those emotions. Contract information and budget information are centrally managed by the server, which issues notifications and warnings in a timely manner.

[0739] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[0740] The processing flow will be explained below.

[0741] Surveillance System Embodiments

[0742] Step 1:

[0743] The server collects data in real time from the monitoring system, including CPU usage, memory usage, network traffic, etc.

[0744] Step 2:

[0745] The server analyzes the collected data and detects anomalies, using thresholds and machine learning models that have been set for anomaly detection.

[0746] Step 3:

[0747] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[0748] Step 4:

[0749] The server automatically executes first response procedures based on the generated alerts, for example restarting a specific process if abnormal memory usage is detected.

[0750] Routine work scheduling and execution embodiment

[0751] Step 1:

[0752] The server schedules its regular operations, which include routine tasks such as periodic backups and software updates.

[0753] Step 2:

[0754] The server sends instructions to the terminal to execute the schedule. The RPA tool is used.

[0755] Step 3:

[0756] The device automatically performs routine tasks according to a schedule, such as backing up data every night.

[0757] Implementation of inquiry response

[0758] Step 1:

[0759] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[0760] Step 2:

[0761] The server receives the query and generates the best answer by consulting a knowledge database.

[0762] Step 3:

[0763] The user receives a server-generated response, which provides specific instructions and solutions.

[0764] Embodiment of Emotion Engine

[0765] Step 1:

[0766] The server uses an emotion engine to recognize the user's emotions, including facial expression analysis, voice tone analysis, and analysis of text input.

[0767] Step 2:

[0768] The server provides adaptive responses based on the recognized emotions, for example, providing a prompt and polite response if the user is showing signs of annoyance.

[0769] Step 3:

[0770] The server implements different initial response procedures depending on different emotional states, for example, a fast escalation procedure is implemented for a frustrated user.

[0771] Contract Management Implementation

[0772] Step 1:

[0773] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[0774] Step 2:

[0775] The server determines when the contract is due for renewal and notifies the parties involved when renewal is required.

[0776] Step 3:

[0777] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[0778] Budget control implementation

[0779] Step 1:

[0780] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[0781] Step 2:

[0782] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[0783] Step 3:

[0784] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, including budget reallocation and requesting additional funding.

[0785] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[0786] Example 2

[0787] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0788] Modern system operations require efficient and accurate execution of a wide range of tasks, including monitoring, routine tasks, inquiry response, contract management, and budget management. However, these tasks require significant human intervention, raising concerns about increased operational costs and the likelihood of errors. It is also difficult to properly recognize user emotions and respond accordingly. Therefore, a system that can effectively resolve these issues and automate and streamline system operations is needed.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0790] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for recognizing a user's emotion and adjusting a response based on the emotion. This makes it possible to automate a series of operational tasks, from anomaly detection to initial response and response based on the user's emotion, and minimize human intervention.

[0791] A "monitoring system" is a mechanism for monitoring various resources and performance data within a system in real time, and for early detection of abnormalities and problems.

[0792] "Data collection" is the process of obtaining the necessary information from the monitoring system and sending it to a server for analysis.

[0793] "Anomaly detection" is the process of analyzing system behavior based on collected data and identifying abnormal conditions that go beyond the normal range.

[0794] "Alert generation" is the process of creating a warning to notify relevant parties when an anomaly is detected.

[0795] An "initial response procedure" is a process for implementing defined countermeasures to quickly deal with a detected abnormality.

[0796] "Emotion recognition" is the process of analyzing a user's facial expressions, voice tone, and text input to determine their emotional state.

[0797] "Regular work" refers to the daily tasks involved in system operation, such as repetitive tasks like data backups and software updates.

[0798] "Scheduling" is the process of scheduling specific tasks to be performed at specified dates and times or intervals.

[0799] "Inquiry handling" is the process of providing appropriate answers to requests and questions from users.

[0800] A "knowledge database" is an information repository that consolidates past case studies and technical information, enabling efficient response to inquiries.

[0801] "Contract management" is the process of centrally storing contract information related to systems and services and managing their expiration dates and conditions.

[0802] "Budget management" is the process of centrally managing the budgets of each department and project, and monitoring and issuing alerts regarding usage.

[0803] This invention is a system with the following functions to streamline system operations and minimize human intervention. It has the functions of a monitoring system, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management, and also uses an emotion engine that recognizes user emotions. This system consists of a server, terminals, and users.

[0804] Surveillance System Embodiments

[0805] The server collects real-time data from the monitoring system. Specifically, it obtains key performance metrics such as CPU usage, memory usage, and network traffic. This is done using the Simple Network Management Protocol (SNMP) or a dedicated monitoring agent. The collected data is analyzed by monitoring tools such as Nagios and Zabbix, and if an abnormality is detected, an alert is automatically generated. For example, an alert is generated if CPU usage exceeds 90%.

[0806] Routine work scheduling and execution embodiment

[0807] The server schedules routine tasks such as daily backups and software updates using cron jobs or Windows Task Scheduler, and the terminals automatically execute these tasks using RPA tools (Robotic Process Automation, such as UiPath). For example, they perform data backups and clean up log files every night.

[0808] Implementation of inquiry response

[0809] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. For example, if a user makes an inquiry such as "The system is slow," the server receives the inquiry, references the knowledge database, and generates the most appropriate answer. The generated answer is then provided to the user. For example, the advice provided might be "Check your network speed and terminate unnecessary processes."

[0810] Embodiment of Emotion Engine

[0811] The server includes an emotion engine for recognizing the user's emotions. The emotion engine uses machine learning models to analyze the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user inputs something that indicates frustration to the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[0812] Contract Management Implementation

[0813] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and relevant parties are notified when renewal is approaching. Notifications are sent via email, and estimates are automatically generated for software licenses that require renewal.

[0814] Budget control implementation

[0815] The server acquires budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. Data is acquired through collaboration with ERP systems (e.g., SAP), and if the budget is about to exceed the set limit, a warning is sent to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to operate within the budget.

[0816] Specific processing examples

[0817] What to do if CPU usage is high

[0818] If the CPU usage continues to be abnormally high for a certain day, the server will first obtain CPU usage data from the monitoring system and detect the abnormality. An alert will then be automatically generated, and an initial response will be taken to restart the specific CPU-intensive process.

[0819] Examples of routine work

[0820] Every night, the terminal uses the RPA tool to automatically back up data and organize log files, ensuring smooth daily operations.

[0821] Specific examples of inquiries

[0822] If a user asks, "My system is slow, what should I do?" the server can consult its knowledge database and provide specific advice such as, "Check your network speed and close unnecessary processes."

[0823] In this way, automating a wide range of operational tasks and minimizing human intervention will improve the quality of operations and reduce costs.

[0824] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0825] Specific explanation of processing steps

[0826] surveillance system

[0827] Step 1: Data collection

[0828] The server obtains various metrics (e.g., CPU usage, memory usage, network traffic) from the monitoring system in real time via SNMP or a monitoring agent.

[0829] Input: Metric data from each device or system

[0830] Output: Collected metric data

[0831] Step 2: Data analysis

[0832] The server analyzes the collected data using Nagios or Zabbix and detects abnormalities by comparing it with the set thresholds.

[0833] Input: Collected metrics data

[0834] Output: Anomaly detection results (e.g., information that CPU usage exceeded 90%)

[0835] Step 3: Alert Generation

[0836] If an anomaly is detected, the server generates an alert and notifies relevant parties via an alert management system such as PagerDuty.

[0837] Input: Anomaly detection results

[0838] Output: Generated alerts and notifications

[0839] Step 4: Initial response

[0840] The server will automatically take first response steps based on the generated alerts, using Ansible or Puppet to run specific scripts, for example restarting a process with high CPU usage.

[0841] Input: Generated alert

[0842] Output: Results of the initial response performed

[0843] Scheduling and executing routine tasks

[0844] Step 1: Scheduling

[0845] The server schedules routine tasks (e.g., data backups and software updates) using cron jobs or the Windows Task Scheduler.

[0846] Input: Routine work schedule requirements

[0847] Output: Set schedule

[0848] Step 2: Perform routine tasks

[0849] The terminal automatically performs routine tasks using an RPA tool (e.g., UiPath) based on a set schedule.

[0850] Input: Set schedule

[0851] Output: Results of routine tasks performed (e.g., backup completed, log file cleanup completed)

[0852] Inquiry response

[0853] Step 1: Receiving an inquiry

[0854] Users can contact the chatbot interface with questions or problems they have about the system. For example, a user might say, "The system is slow."

[0855] Input: User inquiry

[0856] Output: Received queries

[0857] Step 2: Processing the inquiry

[0858] The server refers to a knowledge database based on the content of the received inquiry and generates an appropriate answer.

[0859] Input: Received query

[0860] Output: Generated answer (e.g. "Check your network speed and terminate any unnecessary processes")

[0861] Emotion Engine

[0862] Step 1: Sentiment Analysis

[0863] The server uses machine learning models to determine the user's emotional state by analyzing their facial expressions, voice tone, and text input. For example, it analyzes facial expressions and sentences that indicate irritation.

[0864] Input: User facial expression data, voice tone, text input

[0865] Output: Perceived emotional state

[0866] Step 2: Respond based on emotions

[0867] The server adjusts its response procedures based on the results of the sentiment analysis, for example, responding more quickly and politely if the user expresses irritation.

[0868] Input: Perceived emotional state

[0869] Output: Coordinated response and execution

[0870] contract management

[0871] Step 1: Gather contract information

[0872] The server collects system and service contract information for centralized management, utilizing the SMB protocol and API.

[0873] Input: Contract information

[0874] Output: Centralized contract data

[0875] Step 2: Contract monitoring and notification

[0876] The server monitors contract expiration dates and conditions and notifies interested parties when renewal is approaching. Email notifications are sent.

[0877] Input: Centralized contract data

[0878] Output: Notifications sent (e.g. software license renewal notifications)

[0879] Budget Management

[0880] Step 1: Gather budget information

[0881] The server works in conjunction with the ERP system to obtain budget information for each department and project and manages it centrally.

[0882] Input: Budget Data

[0883] Output: Centralized budget data

[0884] Step 2: Budget monitoring and alerts

[0885] The server monitors budget usage in real time and issues alerts to relevant parties if the set limits are about to be exceeded.

[0886] Input: Centralized budget data

[0887] Output: Warning notice issued (e.g., budget overrun warning)

[0888] (Application example 2)

[0889] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0890] In recent years, as factories have become more automated and efficient, there has been a strong demand for more efficient system operations and minimal human intervention. However, many systems still rely on human labor, with tasks such as anomaly detection, alert generation, initial response, routine tasks, and responding to inquiries consuming time and effort. Furthermore, when operating machines or systems, prompt and appropriate responses are required when users experience stress or dissatisfaction, but this also relies heavily on human labor. Therefore, there is a need to perform these tasks efficiently and automatically, and to respond flexibly to the user's emotional state.

[0891] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for determining the emotional state of a user using an emotion engine and providing a response according to the emotion. This makes it possible to automate anomaly detection and initial response in system operation, and to take appropriate action taking user emotions into consideration when operating or performing maintenance on a robot in a factory.

[0892] Definitions of important terms contained in patent specifications

[0893] A "monitoring system" is a means of collecting data to confirm the normal operation of systems and equipment and to detect abnormalities.

[0894] "Data collection" means recording key metrics such as CPU usage, memory usage, and network traffic from monitoring systems.

[0895] "Anomaly detection" is the analysis of collected data to discover unexpected behavior or performance degradation.

[0896] "Alert generation" means creating a warning or notification to notify relevant parties when an abnormality is detected.

[0897] "Automatic execution of initial response procedures" refers to the automatic execution of pre-defined procedures to correct anomalies based on the generated alert.

[0898] The "emotion engine" is a system that analyzes the user's facial expressions, voice tone, text input, etc. to determine the user's emotional state.

[0899] "Routine work scheduling" means planning in advance the tasks that are required on a daily basis, such as backup work and software updates, and carrying them out according to a timetable.

[0900] "Automatic execution of routine tasks" refers to the automatic execution of scheduled routine tasks using RPA tools, etc.

[0901] "Inquiry response" refers to generating and providing appropriate answers to questions or problems from users by referencing a knowledge database.

[0902] "Contract information monitoring" refers to the centralized management of contract information for systems and services, and the monitoring of contract renewal dates and conditions.

[0903] A "contract renewal notice" is a means of informing the parties involved when the contract expiration date is approaching.

[0904] "Budget management" means centrally managing budget information for each department and project and monitoring usage.

[0905] "Budget Overage Notification" is the act of issuing a warning to relevant parties when budget usage is likely to exceed a set limit.

[0906] A "generative AI model" is an artificial intelligence model that is generated based on data obtained from devices or systems, and that makes appropriate responses and predictions.

[0907] A "prompt" is a question or instruction input to a generative AI model that serves as the basis for the model to generate an answer or response.

[0908] MODE FOR CARRYING OUT THE INVENTION

[0909] This invention provides a system that integrates a monitoring system, anomaly detection, alert generation, automatic execution of initial response procedures, an emotion engine, routine work scheduling and execution, inquiry response, contract information monitoring, and budget management to achieve efficient operation in factories. The system is composed of a server, terminals, and users.

[0910] Surveillance System Embodiments

[0911] The server collects data in real time from various devices and systems within the factory. Specifically, it acquires key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. For example, if CPU usage exceeds 90%, an alert is generated and initial response procedures such as restarting a specific process are automatically executed.

[0912] Routine work scheduling and execution embodiment

[0913] The server schedules the factory's routine tasks, including daily data backups and software updates. After the schedule is determined, the terminals use RPA tools to automatically execute these tasks. For example, they can back up data and organize log files every night.

[0914] Implementation of inquiry response

[0915] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database to generate and provide the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[0916] Embodiment of Emotion Engine

[0917] The server is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[0918] Contract Management Implementation

[0919] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, if a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[0920] Budget control implementation

[0921] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[0922] Examples of using generative AI models and prompts

[0923] The server inputs prompts into the generative AI model based on data obtained from factory equipment and utilizes the responses. For example, if a prompt such as "What is the current CPU usage rate?" is input, the generative AI model will respond with "The current CPU usage rate is 85%." This enables more efficient system operation.

[0924] Examples of specific prompts are:

[0925] "Show me today's routine work schedule"

[0926] Check the latest backup data.

[0927] "What is your current network traffic?"

[0928] This system significantly improves factory operational efficiency and minimizes human intervention.

[0929] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0930] System program processing flow

[0931] Step 1:

[0932] The server collects real-time data from various devices and systems in the factory. As input, it obtains data metrics such as device CPU usage, memory usage, and network traffic. As output, it stores this data in a database. Specifically, the server obtains data from each device via API and stores that data in the database.

[0933] Step 2:

[0934] The server analyzes the collected data and detects anomalies. It uses the data collected in step 1 as input. It uses a data analysis algorithm to detect abnormal values ​​and judges anomalies based on this. As output, it generates an alert object if an anomaly is detected. Specifically, it sets a threshold for the data, and judges it to be an anomaly if this threshold is exceeded.

[0935] Step 3:

[0936] The server generates an alert when an anomaly is detected. As input, it uses the alert object created in step 2. As output, it creates an alert message to notify the user. Specifically, it generates an alert message and sends it to the user through the notification system.

[0937] Step 4:

[0938] The server automatically executes initial response procedures based on the generated alert. As input, it uses the alert message generated in step 3. As output, it executes actions to correct the abnormal condition. Specifically, it uses an RPA tool to execute response procedures such as restarting a specific process.

[0939] Step 5:

[0940] The server schedules routine tasks and manages their execution. Tasks such as routine backups and software updates are listed as input. A schedule of routine tasks is generated as output, and each task is automatically executed. Specifically, tasks are set periodically using a scheduling tool, and then automatically executed using an RPA tool.

[0941] Step 6:

[0942] Users can inquire about problems or questions about the system through the chatbot interface. The text entered by the user is used as input. As output, the system generates an appropriate answer and provides it to the user. Specifically, the server refers to a knowledge database to search for and present the appropriate answer.

[0943] Step 7:

[0944] The server uses an emotion engine to determine the user's emotional state. The inputs include the user's facial expression, voice tone, and text input. The output is an analysis of the user's emotional state and a response appropriate to that state. Specifically, the server uses an emotion analysis algorithm to determine the user's emotions and automatically determine the appropriate response.

[0945] Step 8:

[0946] The server monitors contract information and determines when it is time to renew the contract. It uses a contract information database as input. It outputs by notifying relevant parties when contract renewal is approaching. Specifically, it periodically checks contract information, determines when renewal is due, and sends alerts to relevant parties via a notification system.

[0947] Step 9:

[0948] The server manages budget information and monitors budget usage. It uses budget data from each department and project as input. It outputs a warning to relevant parties when budget overruns are predicted. Specifically, it analyzes budget usage in real time and sends notifications when there is a risk of exceeding the limit.

[0949] Step 10:

[0950] The server inputs a prompt statement into the generative AI model based on data obtained from factory equipment and utilizes the response. The prompt statement and equipment data are used as input. The response from the generative AI model is obtained as output and reflected in operations. Specifically, the server inputs a prompt statement such as "What is the current CPU usage rate?" and obtains a response such as "The current CPU usage rate is 85%."

[0951] Through these steps, the invention streamlines factory operations and minimizes human intervention.

[0952] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0953] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0954] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0955] [Third embodiment]

[0956] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0957] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0958] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0959] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0960] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0961] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0962] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0963] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0964] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0965] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0966] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0967] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0968] This system is equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, responding to inquiries, contract management, and budget management in order to streamline system operations and minimize manual intervention. This system is composed of a server, terminals, and users.

[0969] Surveillance System Embodiments

[0970] The server collects data in real time from the monitoring system. The collected data is monitored for CPU usage, memory usage, network traffic, etc. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[0971] Routine work scheduling and execution embodiment

[0972] The server schedules routine operational tasks, such as daily backups and regular software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs automatic backups every night and organizes log files.

[0973] Implementation of inquiry response

[0974] When a user has a problem or question about the system, they contact the system through a chatbot interface. The server then consults a knowledge database to provide the most appropriate answer. For example, in response to a question like, "My system is slow. What should I do?", the server will provide specific advice such as, "Check your network speed and close any unnecessary processes."

[0975] Contract and budget management practices

[0976] The server centrally manages system and service contract information and monitors it regularly. When a contract is due for renewal, the server notifies the relevant parties and automatically proposes the necessary procedures. For example, when a software license needs to be renewed, it notifies the parties and generates an estimate at the same time. The server also manages budget information and monitors budget usage in real time. If the budget is about to exceed the set limit, it issues a warning. For example, if 80% of the budget has already been used by the middle of the month, it will notify the team and encourage them to stay within the budget.

[0977] Specific processing explanation

[0978] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. Contract information and budget information are centrally managed by the server, and notifications and warnings are issued in a timely manner.

[0979] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[0980] The processing flow will be explained below.

[0981] Monitoring system data collection and alert processing

[0982] Step 1:

[0983] The server collects real-time data from the monitoring system, specifically capturing key metrics such as CPU usage, memory usage, and network traffic.

[0984] Step 2:

[0985] The server analyzes the collected data and detects anomalies, using configured thresholds and machine learning models to identify patterns that deviate from normal behavior.

[0986] Step 3:

[0987] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[0988] Step 4:

[0989] The server automatically executes initial response procedures based on the generated alert, such as restarting the process where the problem occurred.

[0990] Scheduling and executing routine tasks

[0991] Step 1:

[0992] The server runs routine, pre-defined tasks on a schedule, including routine backups and software updates.

[0993] Step 2:

[0994] The server sends schedule information and the terminal receives instructions. Specifically, the RPA tool is executed.

[0995] Step 3:

[0996] The terminal automatically performs routine tasks according to a schedule, such as backing up data and cleaning up log files every night.

[0997] Processing inquiries

[0998] Step 1:

[0999] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[1000] Step 2:

[1001] The server receives the query and generates the best answer by consulting a knowledge database.

[1002] Step 3:

[1003] The user receives a server-generated response, which provides specific instructions and solutions.

[1004] Contract management processing

[1005] Step 1:

[1006] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[1007] Step 2:

[1008] The server determines when the contract is due for renewal and notifies the parties involved if renewal is required.

[1009] Step 3:

[1010] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[1011] Budgetary Control Processing

[1012] Step 1:

[1013] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[1014] Step 2:

[1015] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[1016] Step 3:

[1017] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, such as reallocating the budget or requesting additional funding.

[1018] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[1019] Example 1

[1020] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1021] Conventional system operation required a significant amount of human intervention, which not only reduced operational efficiency but also increased costs. Furthermore, because a wide range of tasks, such as anomaly detection and initial response, routine task scheduling and execution, inquiry response, contract management, and budget management, were performed individually, centralized management was difficult. As a result, it was difficult to improve operational quality and reduce costs.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1023] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine tasks, means for automatically executing the scheduled routine tasks, means for generating appropriate responses based on requests or inquiries, means for providing the generated responses, means for monitoring contract information, means for determining when the contract renewal period is due, means for notifying when the renewal period is due, means for managing budget information and monitoring the budget usage, and means for notifying when the budget is exceeded. This makes it possible to centrally manage and automate a wide range of tasks required for system operation. This minimizes human intervention, improves operational quality, and reduces costs.

[1024] A "monitoring system" is a mechanism that monitors a system's performance and operating status in real time and collects necessary data.

[1025] "Data collection" is the process of gathering and storing information obtained from a monitoring system.

[1026] "Anomaly detection" is a function that analyzes collected data and identifies abnormal behavior or conditions.

[1027] "Alert generation" is a mechanism that issues a warning when an abnormality is detected.

[1028] "Automatic execution of initial response procedures" is an operation that automatically executes a pre-defined response procedure based on the generated alert.

[1029] "Regular work" refers to work that is performed periodically in system operation.

[1030] "Scheduling" refers to planning when to perform a particular task or work.

[1031] "Automatic execution" refers to the operation of performing a task according to a pre-set procedure or schedule without human intervention.

[1032] "Request or Inquiry" means a request for help or a question from a User.

[1033] "Answer generation" refers to the process of providing appropriate information and solutions in response to requests or inquiries.

[1034] "Contract information" refers to information regarding the terms of use and contract contents of the service or software.

[1035] "Monitoring" is the act of continuously watching specific information and detecting abnormalities or changes.

[1036] "Contract renewal determination" is the process of determining when a contract needs to be renewed.

[1037] "Notification" is a function that notifies relevant parties of information from the system.

[1038] "Budget information" refers to information regarding costs and expenditures for system operation.

[1039] "Monitoring budget usage" refers to checking in real time whether the budget is being used as planned.

[1040] "Budget Overage Notification" is a function that issues a warning when the budget exceeds a set limit.

[1041] This invention provides various functions to streamline system operations and minimize manual intervention. This system consists of a server, terminals, and users, and provides integrated functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management.

[1042] surveillance system

[1043] The server collects data in real time from the monitoring system. This data includes, for example, CPU usage, memory usage, and network traffic. This data is analyzed using the Python library Scikit-learn, and an alert is generated if an abnormal value is detected. The generated alert is notified via a communication tool such as Slack, and initial response procedures are automatically executed using an RPA (Robotic Process Automation) tool. For example, a specific process may be restarted.

[1044] Scheduling and executing routine tasks

[1045] The server schedules routine tasks and configures them to be executed automatically on the terminal. For example, backups are automatically executed and log files are organized at midnight every day. This is done using Cron jobs or RPA tools such as UiPath.

[1046] Inquiry response

[1047] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server then refers to the knowledge database and uses a generative AI model (e.g., GPT-3) to provide an appropriate answer. For example, in response to a query such as "My system is slow, what should I do?", the server generates specific advice such as "Check your network speed and close unnecessary processes."

[1048] Contract and budget management

[1049] The server centrally manages contract information and monitors it regularly. When a contract is due for renewal, it notifies the relevant parties and automatically suggests the necessary procedures. For example, if a software license needs to be renewed, a quote is generated and a notification is sent. It also manages budget information and monitors budget usage in real time. A budget management tool such as QuickBooks is used to issue a warning if the budget is about to be exceeded. For example, if 80% of the budget has been used in the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[1050] Examples and prompts

[1051] Here are some examples of specific prompts:

[1052] "What steps should I take if I detect abnormal CPU usage?"

[1053] "Please show me how to set up a daily backup schedule"

[1054] "Please tell me in detail about the flow of system contract management and budget management."

[1055] This system makes it possible to centrally manage and automate the wide range of tasks required for system operation, minimizing human intervention, improving the quality of operations and reducing costs.

[1056] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1057] Step 1:

[1058] Data collection

[1059] The server collects various data from the monitoring system in real time.

[1060] Input: Monitoring data such as CPU usage, memory usage, and network traffic from your monitoring system.

[1061] Processing: Periodically retrieve monitoring data and store it in a database.

[1062] Output: Collected monitoring data.

[1063] Specific operation: The server obtains monitoring data through the API and stores the obtained data in a database.

[1064] Step 2:

[1065] Data analysis

[1066] The server analyzes the collected data and detects abnormal values.

[1067] Input: Collected monitoring data.

[1068] Processing: Analyze the data using Python's Scikit-learn library and run anomaly detection algorithms.

[1069] Output: Anomaly information if an anomaly is detected.

[1070] Specific operation: The server periodically analyzes the collected data and generates anomaly information if an abnormal value is detected.

[1071] Step 3:

[1072] Alert Generation

[1073] The server automatically generates an alert if an abnormality is detected.

[1074] Input: Anomaly information.

[1075] Processing: Generate a notification of an abnormality based on the abnormality information.

[1076] Output: An alert notification of an anomaly.

[1077] Specific operation: When an abnormality is detected, the server sends an alert notification using a communication tool such as Slack.

[1078] Step 4:

[1079] Performing initial response procedures

[1080] The server automatically executes initial response procedures based on the generated alert.

[1081] Input: Alert notification.

[1082] Action: Follow pre-defined initial response procedures and take action.

[1083] Output: Results of the initial response procedures.

[1084] What it does: If abnormal memory usage is detected, the server runs a Bash script to restart certain processes.

[1085] Step 5:

[1086] Routine work scheduling

[1087] The server schedules routine work.

[1088] Input: Routine work list and execution schedule.

[1089] Action: Set up a Cron job to schedule tasks to run at specified times.

[1090] Output: Routine work schedule.

[1091] Specific operation: Set up a Cron job to automatically perform a backup every night at midnight.

[1092] Step 6:

[1093] Automatic execution of routine tasks

[1094] The terminal automatically executes scheduled routine tasks.

[1095] Input: Routine work schedule.

[1096] Processing: Perform routine tasks using RPA tools (e.g. UiPath).

[1097] Output: Results of routine work execution.

[1098] Specific operations: Automatically performs routine tasks such as software updates and backups according to a schedule.

[1099] Step 7:

[1100] Receiving inquiries

[1101] Users can ask questions about the system through the chatbot interface.

[1102] Input: User's query.

[1103] Processing: The chatbot uses natural language processing to understand the user's question.

[1104] Output: Analysis result of the query.

[1105] Specific operation: The chatbot receives user inquiries as text and analyzes them using natural language processing.

[1106] Step 8:

[1107] Providing answers

[1108] The server consults a knowledge base and generates an appropriate answer.

[1109] Input: Analysis results of the query.

[1110] Processing: Generate answers to queries using a generative AI model (e.g., GPT-3).

[1111] Output: The best answer.

[1112] How it works: Based on the query, the server uses its knowledge base and generative AI models to quickly provide an answer.

[1113] Step 9:

[1114] Contract information management

[1115] The server centrally manages and monitors system and service contract information.

[1116] Input: Contract information.

[1117] Processing: Periodically update contract information and determine when the contract is due for renewal.

[1118] Output: Notification of when it's time to update.

[1119] Specific operations: Integrates with management tools such as Google Sheets to regularly update contract information and notify relevant parties when renewal dates approach.

[1120] Step 10:

[1121] Managing and monitoring budget information

[1122] The server manages the budget information and monitors the budget usage in real time.

[1123] Input: Budget information.

[1124] Action: Use a budgeting tool (e.g., QuickBooks) to monitor budget usage in real time.

[1125] Output: Budget overrun warning.

[1126] What it does: If there is a risk of exceeding the budget, the server will immediately issue a warning and notify the operations team.

[1127] (Application example 1)

[1128] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1129] In modern factories, it is important to monitor the operating status of production lines and equipment in real time and to take prompt and appropriate action when an abnormality occurs. However, constant manual monitoring and response is labor-intensive and inefficient. Furthermore, routine maintenance work, contract management, and budget management are prone to errors when done manually. Furthermore, responding quickly to troubles and questions within the factory requires advanced specialized knowledge and rapid responses. To solve these issues and streamline factory operations, a system that automates these tasks is needed.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1131] In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine work, means for automatically executing the scheduled routine work, means for generating an appropriate response based on a request or inquiry, means for providing the generated response, means for monitoring contract information, means for determining when the contract renewal is due, means for notifying when the renewal date has arrived, means for managing budget information and monitoring the usage status of the budget, means for notifying when the budget is exceeded, means for monitoring the operating status of factory production lines and machines in real time, means for temporarily suspending a specific process or machine when an anomaly is detected, means for providing an appropriate response using a knowledge database related to the inquiry, and means for scheduling and automatically executing routine maintenance work in the factory. This makes it possible to improve the efficiency and quality of factory operations and minimize human intervention.

[1132] A "monitoring system" is a system that monitors the operating conditions and environment of factories, facilities, etc. in real time and collects data.

[1133] The "data collection means" is a means having a function of collecting various sensor data and operating status information collected from the monitoring system.

[1134] The "abnormality detection means" is a means having a function for analyzing collected data and detecting operations or states that deviate from normal states.

[1135] The "alert generation means" is a means having a function of generating an alert to notify information about an abnormality when an abnormality is detected.

[1136] The "initial response means" is a means having a function of automatically executing an initial response procedure based on the generated alert.

[1137] The "routine work scheduling means" is a means having a function for scheduling maintenance work and other routine work that is carried out periodically.

[1138] The "routine task execution means" is a means having a function of automatically executing scheduled routine tasks.

[1139] The "inquiry response means" is a means having the function of generating and providing an appropriate response based on a request or inquiry.

[1140] A "knowledge database" is a database that stores and references knowledge information such as past troubleshooting and FAQs.

[1141] "Contract monitoring means" refers to means that has the function of monitoring contract information for systems and services and checking their status.

[1142] The "contract renewal determination means" is a means having a function of determining when it is time to renew a contract and issuing an alert or notification when that time has come.

[1143] The "budget management means" is a means having the function of managing budget information and monitoring usage status in real time.

[1144] The "budget monitoring means" is a means that has the function of monitoring the usage of the set budget in real time and issuing warnings or notifications as necessary.

[1145] The "operational status monitoring means" is a means having the function of monitoring the operational status of factory production lines and machines in real time.

[1146] The "process suspension means" is a means having a function of temporarily halting a specific process or machine when an abnormality is detected.

[1147] The "maintenance work scheduling means" is a means having the function of scheduling routine maintenance work within a factory and automatically executing it.

[1148] The present invention provides a system for maximizing operational efficiency and minimizing human intervention in factory production lines, including a monitoring system, alert generation, automated first response procedures, routine task scheduling and execution, inquiry response, contract management, and budget management.

[1149] Surveillance System Embodiments

[1150] The server has a means for collecting data in real time from the monitoring system. For example, it collects data such as operating status, temperature, and vibration from production line monitoring sensors in a factory. The collected data is analyzed by anomaly detection means, and an alert is generated if an abnormality is detected. For example, if abnormal vibration is detected, the alert generation means is activated to prompt a response.

[1151] Routine work scheduling and execution embodiment

[1152] The server has a means for scheduling routine tasks. For example, it schedules periodic maintenance work and part replacement work. Based on this schedule, a means for automatically executing routine tasks is activated, and the tasks are carried out automatically at the set time. Specifically, this includes automatically backing up the system every night and organizing log files.

[1153] Implementation of inquiry response

[1154] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server will refer to the knowledge database and provide the most appropriate answer. For example, in response to a question such as, "There's a lot of vibration on the production line. What should I do?", the server will provide specific advice such as, "Check the operating status of the machine and perform maintenance work if necessary."

[1155] Contract and budget management practices

[1156] The server has a means for monitoring contract information. For example, it sends a notification when a machine maintenance contract is due for renewal. It also has a means for managing budget information and monitoring budget usage in real time. For example, it issues a warning and notifies relevant parties when the monthly budget is exceeded by 80%.

[1157] A concrete example of the entire system

[1158] When an abnormal temperature rise is detected on a production line in a factory, the system generates an alert in real time and shuts down the affected machine as an initial response. At the same time, it notifies specialist staff and urges them to take the necessary action. This enables rapid troubleshooting and minimizes damage.

[1159] Hardware and software used

[1160] Hardware: Various sensors, monitoring devices, and robots with control functions in the factory

[1161] Software: Python, APScheduler (scheduling), watchdog (file system monitoring), chatbot interface

[1162] Specific prompt examples

[1163] How can I monitor the status of my production line in real time and generate alerts and take action when an abnormality is detected?

[1164] Write Python code to automatically schedule and execute routine maintenance tasks in your factory.

[1165] Please explain the initial response method for a system that automatically restarts a machine when an abnormality occurs.

[1166] This will enable more efficient factory operations, improve quality, and minimize human intervention.

[1167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1168] Step 1:

[1169] The server collects data from the monitoring system. Specifically, it obtains real-time data such as operating status, temperature, and vibration from various sensors in the factory. The input data are the measured values ​​from the various sensors, and the output data is raw data stored in the server.

[1170] Step 2:

[1171] The server analyzes the collected data and detects abnormalities. A generative AI model is used for data analysis to identify abnormal patterns. The input is the raw data obtained in step 1, and the output is a result indicating whether or not there is an abnormality. Specifically, if the vibration data exceeds a certain threshold, it is determined to be "abnormal."

[1172] Step 3:

[1173] The server generates an alert when an abnormality is detected. The generated alert includes the location and cause of the abnormality, as well as the initial response procedure. The input is the anomaly detection result from step 2, and the output is an alert message. Specifically, when an abnormality occurs, a pop-up notification is generated on the server and an email is sent to the responsible person.

[1174] Step 4:

[1175] The server automatically executes initial response procedures based on the generated alerts. For example, if abnormal vibrations are detected, it issues a command to suspend a specific process or machine. The input is the alert message, and the output is a message confirming that the machine has stopped.

[1176] Step 5:

[1177] The server schedules routine tasks and issues commands to the routine task execution means. For example, it sets up a schedule for automatic system backup every night and executes it. The input is the routine task schedule setting, and the output is a message confirming the execution schedule.

[1178] Step 6:

[1179] The server generates an appropriate answer to a user's inquiry. When a user makes an inquiry through the chatbot interface, the server refers to the knowledge database and provides the most appropriate answer. The input is the user's inquiry, and the output is a response message.

[1180] Step 7:

[1181] The server monitors contract information and determines when the contract is due for renewal. When the renewal date is reached, it notifies the relevant parties. The input is the contract information and renewal conditions, and the output is a notification message. Specifically, an email notification is sent to the person in charge one month before the contract is due for renewal.

[1182] Step 8:

[1183] The server manages budget information and monitors budget usage. It notifies users when the budget is exceeded. The input is budget information and usage data, and the output is a budget exceedance warning message. Specifically, a warning message is sent when the monthly budget exceeds 80%.

[1184] Step 9:

[1185] When users operate within a factory, they receive the necessary information and instructions from the system. For example, if an abnormality occurs on a production line, an alert is displayed on the user's screen (tablet or PC) and initial response procedures are instructed. The input is the alert information, and the output is specific initial response procedures.

[1186] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1187] In order to streamline system operations and minimize manual intervention, the present invention utilizes a system equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, response to inquiries, contract management, and budget management, as well as an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users.

[1188] Surveillance System Embodiments

[1189] The server collects real-time data from the monitoring system. The collected data includes key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[1190] Routine work scheduling and execution embodiment

[1191] The server schedules routine operational tasks, such as daily backups and software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs data backups and organizes log files every night.

[1192] Implementation of inquiry response

[1193] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database and generates the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[1194] Embodiment of Emotion Engine

[1195] The server is equipped with an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server can recognize that emotion and provide a faster and more friendly response. It can also execute different initial response procedures depending on the user's emotional state.

[1196] Contract Management Implementation

[1197] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, when a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[1198] Budget control implementation

[1199] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[1200] Specific processing explanation

[1201] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. An emotion engine recognizes the user's emotions and provides a response based on those emotions. Contract information and budget information are centrally managed by the server, which issues notifications and warnings in a timely manner.

[1202] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[1203] The processing flow will be explained below.

[1204] Surveillance System Embodiments

[1205] Step 1:

[1206] The server collects data in real time from the monitoring system, including CPU usage, memory usage, network traffic, etc.

[1207] Step 2:

[1208] The server analyzes the collected data and detects anomalies, using thresholds and machine learning models that have been set for anomaly detection.

[1209] Step 3:

[1210] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[1211] Step 4:

[1212] The server automatically executes first response procedures based on the generated alerts, for example restarting a specific process if abnormal memory usage is detected.

[1213] Routine work scheduling and execution embodiment

[1214] Step 1:

[1215] The server schedules its regular operations, which include routine tasks such as periodic backups and software updates.

[1216] Step 2:

[1217] The server sends instructions to the terminal to execute the schedule. The RPA tool is used.

[1218] Step 3:

[1219] The device automatically performs routine tasks according to a schedule, such as backing up data every night.

[1220] Implementation of inquiry response

[1221] Step 1:

[1222] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[1223] Step 2:

[1224] The server receives the query and generates the best answer by consulting a knowledge database.

[1225] Step 3:

[1226] The user receives a server-generated response, which provides specific instructions and solutions.

[1227] Embodiment of Emotion Engine

[1228] Step 1:

[1229] The server uses an emotion engine to recognize the user's emotions, including facial expression analysis, voice tone analysis, and analysis of text input.

[1230] Step 2:

[1231] The server provides adaptive responses based on the recognized emotions, for example, providing a prompt and polite response if the user is showing signs of annoyance.

[1232] Step 3:

[1233] The server implements different initial response procedures depending on different emotional states, for example, a fast escalation procedure is implemented for a frustrated user.

[1234] Contract Management Implementation

[1235] Step 1:

[1236] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[1237] Step 2:

[1238] The server determines when the contract is due for renewal and notifies the parties involved when renewal is required.

[1239] Step 3:

[1240] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[1241] Budget control implementation

[1242] Step 1:

[1243] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[1244] Step 2:

[1245] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[1246] Step 3:

[1247] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, including budget reallocation and requesting additional funding.

[1248] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[1249] Example 2

[1250] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1251] Modern system operations require efficient and accurate execution of a wide range of tasks, including monitoring, routine tasks, inquiry response, contract management, and budget management. However, these tasks require significant human intervention, raising concerns about increased operational costs and the likelihood of errors. It is also difficult to properly recognize user emotions and respond accordingly. Therefore, a system that can effectively resolve these issues and automate and streamline system operations is needed.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1253] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for recognizing a user's emotion and adjusting a response based on the emotion. This makes it possible to automate a series of operational tasks, from anomaly detection to initial response and response based on the user's emotion, and minimize human intervention.

[1254] A "monitoring system" is a mechanism for monitoring various resources and performance data within a system in real time, and for early detection of abnormalities and problems.

[1255] "Data collection" is the process of obtaining the necessary information from the monitoring system and sending it to a server for analysis.

[1256] "Anomaly detection" is the process of analyzing system behavior based on collected data and identifying abnormal conditions that go beyond the normal range.

[1257] "Alert generation" is the process of creating a warning to notify relevant parties when an anomaly is detected.

[1258] An "initial response procedure" is a process for implementing defined countermeasures to quickly deal with a detected abnormality.

[1259] "Emotion recognition" is the process of analyzing a user's facial expressions, voice tone, and text input to determine their emotional state.

[1260] "Regular work" refers to the daily tasks involved in system operation, such as repetitive tasks like data backups and software updates.

[1261] "Scheduling" is the process of scheduling specific tasks to be performed at specified dates and times or intervals.

[1262] "Inquiry handling" is the process of providing appropriate answers to requests and questions from users.

[1263] A "knowledge database" is an information repository that consolidates past case studies and technical information, enabling efficient response to inquiries.

[1264] "Contract management" is the process of centrally storing contract information related to systems and services and managing their expiration dates and conditions.

[1265] "Budget management" is the process of centrally managing the budgets of each department and project, and monitoring and issuing alerts regarding usage.

[1266] This invention is a system with the following functions to streamline system operations and minimize human intervention. It has the functions of a monitoring system, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management, and also uses an emotion engine that recognizes user emotions. This system consists of a server, terminals, and users.

[1267] Surveillance System Embodiments

[1268] The server collects real-time data from the monitoring system. Specifically, it obtains key performance metrics such as CPU usage, memory usage, and network traffic. This is done using the Simple Network Management Protocol (SNMP) or a dedicated monitoring agent. The collected data is analyzed by monitoring tools such as Nagios and Zabbix, and if an abnormality is detected, an alert is automatically generated. For example, an alert is generated if CPU usage exceeds 90%.

[1269] Routine work scheduling and execution embodiment

[1270] The server schedules routine tasks such as daily backups and software updates using cron jobs or Windows Task Scheduler, and the terminals automatically execute these tasks using RPA tools (Robotic Process Automation, such as UiPath). For example, they perform data backups and clean up log files every night.

[1271] Implementation of inquiry response

[1272] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. For example, if a user makes an inquiry such as "The system is slow," the server receives the inquiry, references the knowledge database, and generates the most appropriate answer. The generated answer is then provided to the user. For example, the advice provided might be "Check your network speed and terminate unnecessary processes."

[1273] Embodiment of Emotion Engine

[1274] The server includes an emotion engine for recognizing the user's emotions. The emotion engine uses machine learning models to analyze the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user inputs something that indicates frustration to the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[1275] Contract Management Implementation

[1276] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and relevant parties are notified when renewal is approaching. Notifications are sent via email, and estimates are automatically generated for software licenses that require renewal.

[1277] Budget control implementation

[1278] The server acquires budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. Data is acquired through collaboration with ERP systems (e.g., SAP), and if the budget is about to exceed the set limit, a warning is sent to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to operate within the budget.

[1279] Specific processing examples

[1280] What to do if CPU usage is high

[1281] If the CPU usage continues to be abnormally high for a certain day, the server will first obtain CPU usage data from the monitoring system and detect the abnormality. An alert will then be automatically generated, and an initial response will be taken to restart the specific CPU-intensive process.

[1282] Examples of routine work

[1283] Every night, the terminal uses the RPA tool to automatically back up data and organize log files, ensuring smooth daily operations.

[1284] Specific examples of inquiries

[1285] If a user asks, "My system is slow, what should I do?" the server can consult its knowledge database and provide specific advice such as, "Check your network speed and close unnecessary processes."

[1286] In this way, automating a wide range of operational tasks and minimizing human intervention will improve the quality of operations and reduce costs.

[1287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1288] Specific explanation of processing steps

[1289] surveillance system

[1290] Step 1: Data collection

[1291] The server obtains various metrics (e.g., CPU usage, memory usage, network traffic) from the monitoring system in real time via SNMP or a monitoring agent.

[1292] Input: Metric data from each device or system

[1293] Output: Collected metric data

[1294] Step 2: Data analysis

[1295] The server analyzes the collected data using Nagios or Zabbix and detects abnormalities by comparing it with the set thresholds.

[1296] Input: Collected metrics data

[1297] Output: Anomaly detection results (e.g., information that CPU usage exceeded 90%)

[1298] Step 3: Alert Generation

[1299] If an anomaly is detected, the server generates an alert and notifies relevant parties via an alert management system such as PagerDuty.

[1300] Input: Anomaly detection results

[1301] Output: Generated alerts and notifications

[1302] Step 4: Initial response

[1303] The server will automatically take first response steps based on the generated alerts, using Ansible or Puppet to run specific scripts, for example restarting a process with high CPU usage.

[1304] Input: Generated alert

[1305] Output: Results of the initial response performed

[1306] Scheduling and executing routine tasks

[1307] Step 1: Scheduling

[1308] The server schedules routine tasks (e.g., data backups and software updates) using cron jobs or the Windows Task Scheduler.

[1309] Input: Routine work schedule requirements

[1310] Output: Set schedule

[1311] Step 2: Perform routine tasks

[1312] The terminal automatically performs routine tasks using an RPA tool (e.g., UiPath) based on a set schedule.

[1313] Input: Set schedule

[1314] Output: Results of routine tasks performed (e.g., backup completed, log file cleanup completed)

[1315] Inquiry response

[1316] Step 1: Receiving an inquiry

[1317] Users can contact the chatbot interface with questions or problems they have about the system. For example, a user might say, "The system is slow."

[1318] Input: User inquiry

[1319] Output: Received queries

[1320] Step 2: Processing the inquiry

[1321] The server refers to a knowledge database based on the content of the received inquiry and generates an appropriate answer.

[1322] Input: Received query

[1323] Output: Generated answer (e.g. "Check your network speed and terminate any unnecessary processes")

[1324] Emotion Engine

[1325] Step 1: Sentiment Analysis

[1326] The server uses machine learning models to determine the user's emotional state by analyzing their facial expressions, voice tone, and text input. For example, it analyzes facial expressions and sentences that indicate irritation.

[1327] Input: User facial expression data, voice tone, text input

[1328] Output: Perceived emotional state

[1329] Step 2: Respond based on emotions

[1330] The server adjusts its response procedures based on the results of the sentiment analysis, for example, responding more quickly and politely if the user expresses irritation.

[1331] Input: Perceived emotional state

[1332] Output: Coordinated response and execution

[1333] contract management

[1334] Step 1: Gather contract information

[1335] The server collects system and service contract information for centralized management, utilizing the SMB protocol and API.

[1336] Input: Contract information

[1337] Output: Centralized contract data

[1338] Step 2: Contract monitoring and notification

[1339] The server monitors contract expiration dates and conditions and notifies interested parties when renewal is approaching. Email notifications are sent.

[1340] Input: Centralized contract data

[1341] Output: Notifications sent (e.g. software license renewal notifications)

[1342] Budget Management

[1343] Step 1: Gather budget information

[1344] The server works in conjunction with the ERP system to obtain budget information for each department and project and manages it centrally.

[1345] Input: Budget Data

[1346] Output: Centralized budget data

[1347] Step 2: Budget monitoring and alerts

[1348] The server monitors budget usage in real time and issues alerts to relevant parties if the set limits are about to be exceeded.

[1349] Input: Centralized budget data

[1350] Output: Warning notice issued (e.g., budget overrun warning)

[1351] (Application example 2)

[1352] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1353] In recent years, as factories have become more automated and efficient, there has been a strong demand for more efficient system operations and minimal human intervention. However, many systems still rely on human labor, with tasks such as anomaly detection, alert generation, initial response, routine tasks, and responding to inquiries consuming time and effort. Furthermore, when operating machines or systems, prompt and appropriate responses are required when users experience stress or dissatisfaction, but this also relies heavily on human labor. Therefore, there is a need to perform these tasks efficiently and automatically, and to respond flexibly to the user's emotional state.

[1354] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for determining the emotional state of a user using an emotion engine and providing a response according to the emotion. This makes it possible to automate anomaly detection and initial response in system operation, and to take appropriate action taking user emotions into consideration when operating or performing maintenance on a robot in a factory.

[1355] Definitions of important terms contained in patent specifications

[1356] A "monitoring system" is a means of collecting data to confirm the normal operation of systems and equipment and to detect abnormalities.

[1357] "Data collection" means recording key metrics such as CPU usage, memory usage, and network traffic from monitoring systems.

[1358] "Anomaly detection" is the analysis of collected data to discover unexpected behavior or performance degradation.

[1359] "Alert generation" means creating a warning or notification to notify relevant parties when an abnormality is detected.

[1360] "Automatic execution of initial response procedures" refers to the automatic execution of pre-defined procedures to correct anomalies based on the generated alert.

[1361] The "emotion engine" is a system that analyzes the user's facial expressions, voice tone, text input, etc. to determine the user's emotional state.

[1362] "Routine work scheduling" means planning in advance the tasks that are required on a daily basis, such as backup work and software updates, and carrying them out according to a timetable.

[1363] "Automatic execution of routine tasks" refers to the automatic execution of scheduled routine tasks using RPA tools, etc.

[1364] "Inquiry response" refers to generating and providing appropriate answers to questions or problems from users by referencing a knowledge database.

[1365] "Contract information monitoring" refers to the centralized management of contract information for systems and services, and the monitoring of contract renewal dates and conditions.

[1366] A "contract renewal notice" is a means of informing the parties involved when the contract expiration date is approaching.

[1367] "Budget management" means centrally managing budget information for each department and project and monitoring usage.

[1368] "Budget Overage Notification" is the act of issuing a warning to relevant parties when budget usage is likely to exceed a set limit.

[1369] A "generative AI model" is an artificial intelligence model that is generated based on data obtained from devices or systems, and that makes appropriate responses and predictions.

[1370] A "prompt" is a question or instruction input to a generative AI model that serves as the basis for the model to generate an answer or response.

[1371] MODE FOR CARRYING OUT THE INVENTION

[1372] This invention provides a system that integrates a monitoring system, anomaly detection, alert generation, automatic execution of initial response procedures, an emotion engine, routine work scheduling and execution, inquiry response, contract information monitoring, and budget management to achieve efficient operation in factories. The system is composed of a server, terminals, and users.

[1373] Surveillance System Embodiments

[1374] The server collects data in real time from various devices and systems within the factory. Specifically, it acquires key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. For example, if CPU usage exceeds 90%, an alert is generated and initial response procedures such as restarting a specific process are automatically executed.

[1375] Routine work scheduling and execution embodiment

[1376] The server schedules the factory's routine tasks, including daily data backups and software updates. After the schedule is determined, the terminals use RPA tools to automatically execute these tasks. For example, they can back up data and organize log files every night.

[1377] Implementation of inquiry response

[1378] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database to generate and provide the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[1379] Embodiment of Emotion Engine

[1380] The server is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[1381] Contract Management Implementation

[1382] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, if a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[1383] Budget control implementation

[1384] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[1385] Examples of using generative AI models and prompts

[1386] The server inputs prompts into the generative AI model based on data obtained from factory equipment and utilizes the responses. For example, if a prompt such as "What is the current CPU usage rate?" is input, the generative AI model will respond with "The current CPU usage rate is 85%." This enables more efficient system operation.

[1387] Examples of specific prompts are:

[1388] "Show me today's routine work schedule"

[1389] Check the latest backup data.

[1390] "What is your current network traffic?"

[1391] This system significantly improves factory operational efficiency and minimizes human intervention.

[1392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1393] System program processing flow

[1394] Step 1:

[1395] The server collects real-time data from various devices and systems in the factory. As input, it obtains data metrics such as device CPU usage, memory usage, and network traffic. As output, it stores this data in a database. Specifically, the server obtains data from each device via API and stores that data in the database.

[1396] Step 2:

[1397] The server analyzes the collected data and detects anomalies. It uses the data collected in step 1 as input. It uses a data analysis algorithm to detect abnormal values ​​and judges anomalies based on this. As output, it generates an alert object if an anomaly is detected. Specifically, it sets a threshold for the data, and judges it to be an anomaly if this threshold is exceeded.

[1398] Step 3:

[1399] The server generates an alert when an anomaly is detected. As input, it uses the alert object created in step 2. As output, it creates an alert message to notify the user. Specifically, it generates an alert message and sends it to the user through the notification system.

[1400] Step 4:

[1401] The server automatically executes initial response procedures based on the generated alert. As input, it uses the alert message generated in step 3. As output, it executes actions to correct the abnormal condition. Specifically, it uses an RPA tool to execute response procedures such as restarting a specific process.

[1402] Step 5:

[1403] The server schedules routine tasks and manages their execution. Tasks such as routine backups and software updates are listed as input. A schedule of routine tasks is generated as output, and each task is automatically executed. Specifically, tasks are set periodically using a scheduling tool, and then automatically executed using an RPA tool.

[1404] Step 6:

[1405] Users can inquire about problems or questions about the system through the chatbot interface. The text entered by the user is used as input. As output, the system generates an appropriate answer and provides it to the user. Specifically, the server refers to a knowledge database to search for and present the appropriate answer.

[1406] Step 7:

[1407] The server uses an emotion engine to determine the user's emotional state. The inputs include the user's facial expression, voice tone, and text input. The output is an analysis of the user's emotional state and a response appropriate to that state. Specifically, the server uses an emotion analysis algorithm to determine the user's emotions and automatically determine the appropriate response.

[1408] Step 8:

[1409] The server monitors contract information and determines when it is time to renew the contract. It uses a contract information database as input. It outputs by notifying relevant parties when contract renewal is approaching. Specifically, it periodically checks contract information, determines when renewal is due, and sends alerts to relevant parties via a notification system.

[1410] Step 9:

[1411] The server manages budget information and monitors budget usage. It uses budget data from each department and project as input. It outputs a warning to relevant parties when budget overruns are predicted. Specifically, it analyzes budget usage in real time and sends notifications when there is a risk of exceeding the limit.

[1412] Step 10:

[1413] The server inputs a prompt statement into the generative AI model based on data obtained from factory equipment and utilizes the response. The prompt statement and equipment data are used as input. The response from the generative AI model is obtained as output and reflected in operations. Specifically, the server inputs a prompt statement such as "What is the current CPU usage rate?" and obtains a response such as "The current CPU usage rate is 85%."

[1414] Through these steps, the invention streamlines factory operations and minimizes human intervention.

[1415] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1416] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1417] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1418] [Fourth embodiment]

[1419] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1420] 7, a 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.

[1421] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1422] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1423] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1425] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1426] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1427] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1428] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1429] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1430] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1431] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1432] This system is equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, responding to inquiries, contract management, and budget management in order to streamline system operations and minimize manual intervention. This system is composed of a server, terminals, and users.

[1433] Surveillance System Embodiments

[1434] The server collects data in real time from the monitoring system. The collected data is monitored for CPU usage, memory usage, network traffic, etc. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[1435] Routine work scheduling and execution embodiment

[1436] The server schedules routine operational tasks, such as daily backups and regular software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs automatic backups every night and organizes log files.

[1437] Implementation of inquiry response

[1438] When a user has a problem or question about the system, they contact the system through a chatbot interface. The server then consults a knowledge database to provide the most appropriate answer. For example, in response to a question like, "My system is slow. What should I do?", the server will provide specific advice such as, "Check your network speed and close any unnecessary processes."

[1439] Contract and budget management practices

[1440] The server centrally manages system and service contract information and monitors it regularly. When a contract is due for renewal, the server notifies the relevant parties and automatically proposes the necessary procedures. For example, when a software license needs to be renewed, it notifies the parties and generates an estimate at the same time. The server also manages budget information and monitors budget usage in real time. If the budget is about to exceed the set limit, it issues a warning. For example, if 80% of the budget has already been used by the middle of the month, it will notify the team and encourage them to stay within the budget.

[1441] Specific processing explanation

[1442] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. Contract information and budget information are centrally managed by the server, and notifications and warnings are issued in a timely manner.

[1443] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[1444] The processing flow will be explained below.

[1445] Monitoring system data collection and alert processing

[1446] Step 1:

[1447] The server collects real-time data from the monitoring system, specifically capturing key metrics such as CPU usage, memory usage, and network traffic.

[1448] Step 2:

[1449] The server analyzes the collected data and detects anomalies, using configured thresholds and machine learning models to identify patterns that deviate from normal behavior.

[1450] Step 3:

[1451] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[1452] Step 4:

[1453] The server automatically executes initial response procedures based on the generated alert, such as restarting the process where the problem occurred.

[1454] Scheduling and executing routine tasks

[1455] Step 1:

[1456] The server runs routine, pre-defined tasks on a schedule, including routine backups and software updates.

[1457] Step 2:

[1458] The server sends schedule information and the terminal receives instructions. Specifically, the RPA tool is executed.

[1459] Step 3:

[1460] The terminal automatically performs routine tasks according to a schedule, such as backing up data and cleaning up log files every night.

[1461] Processing inquiries

[1462] Step 1:

[1463] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[1464] Step 2:

[1465] The server receives the query and generates the best answer by consulting a knowledge database.

[1466] Step 3:

[1467] The user receives a server-generated response, which provides specific instructions and solutions.

[1468] Contract management processing

[1469] Step 1:

[1470] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[1471] Step 2:

[1472] The server determines when the contract is due for renewal and notifies the parties involved if renewal is required.

[1473] Step 3:

[1474] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[1475] Budgetary Control Processing

[1476] Step 1:

[1477] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[1478] Step 2:

[1479] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[1480] Step 3:

[1481] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, such as reallocating the budget or requesting additional funding.

[1482] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[1483] Example 1

[1484] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1485] Conventional system operation required a significant amount of human intervention, which not only reduced operational efficiency but also increased costs. Furthermore, because a wide range of tasks, such as anomaly detection and initial response, routine task scheduling and execution, inquiry response, contract management, and budget management, were performed individually, centralized management was difficult. As a result, it was difficult to improve operational quality and reduce costs.

[1486] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1487] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine tasks, means for automatically executing the scheduled routine tasks, means for generating appropriate responses based on requests or inquiries, means for providing the generated responses, means for monitoring contract information, means for determining when the contract renewal period is due, means for notifying when the renewal period is due, means for managing budget information and monitoring the budget usage, and means for notifying when the budget is exceeded. This makes it possible to centrally manage and automate a wide range of tasks required for system operation. This minimizes human intervention, improves operational quality, and reduces costs.

[1488] A "monitoring system" is a mechanism that monitors a system's performance and operating status in real time and collects necessary data.

[1489] "Data collection" is the process of gathering and storing information obtained from a monitoring system.

[1490] "Anomaly detection" is a function that analyzes collected data and identifies abnormal behavior or conditions.

[1491] "Alert generation" is a mechanism that issues a warning when an abnormality is detected.

[1492] "Automatic execution of initial response procedures" is an operation that automatically executes a pre-defined response procedure based on the generated alert.

[1493] "Regular work" refers to work that is performed periodically in system operation.

[1494] "Scheduling" refers to planning when to perform a particular task or work.

[1495] "Automatic execution" refers to the operation of performing a task according to a pre-set procedure or schedule without human intervention.

[1496] "Request or Inquiry" means a request for help or a question from a User.

[1497] "Answer generation" refers to the process of providing appropriate information and solutions in response to requests or inquiries.

[1498] "Contract information" refers to information regarding the terms of use and contract contents of the service or software.

[1499] "Monitoring" is the act of continuously watching specific information and detecting abnormalities or changes.

[1500] "Contract renewal determination" is the process of determining when a contract needs to be renewed.

[1501] "Notification" is a function that notifies relevant parties of information from the system.

[1502] "Budget information" refers to information regarding costs and expenditures for system operation.

[1503] "Monitoring budget usage" refers to checking in real time whether the budget is being used as planned.

[1504] "Budget Overage Notification" is a function that issues a warning when the budget exceeds a set limit.

[1505] This invention provides various functions to streamline system operations and minimize manual intervention. This system consists of a server, terminals, and users, and provides integrated functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management.

[1506] surveillance system

[1507] The server collects data in real time from the monitoring system. This data includes, for example, CPU usage, memory usage, and network traffic. This data is analyzed using the Python library Scikit-learn, and an alert is generated if an abnormal value is detected. The generated alert is notified via a communication tool such as Slack, and initial response procedures are automatically executed using an RPA (Robotic Process Automation) tool. For example, a specific process may be restarted.

[1508] Scheduling and executing routine tasks

[1509] The server schedules routine tasks and configures them to be executed automatically on the terminal. For example, backups are automatically executed and log files are organized at midnight every day. This is done using Cron jobs or RPA tools such as UiPath.

[1510] Inquiry response

[1511] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server then refers to the knowledge database and uses a generative AI model (e.g., GPT-3) to provide an appropriate answer. For example, in response to a query such as "My system is slow, what should I do?", the server generates specific advice such as "Check your network speed and close unnecessary processes."

[1512] Contract and budget management

[1513] The server centrally manages contract information and monitors it regularly. When a contract is due for renewal, it notifies the relevant parties and automatically suggests the necessary procedures. For example, if a software license needs to be renewed, a quote is generated and a notification is sent. It also manages budget information and monitors budget usage in real time. A budget management tool such as QuickBooks is used to issue a warning if the budget is about to be exceeded. For example, if 80% of the budget has been used in the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[1514] Examples and prompts

[1515] Here are some examples of specific prompts:

[1516] "What steps should I take if I detect abnormal CPU usage?"

[1517] "Please show me how to set up a daily backup schedule"

[1518] "Please tell me in detail about the flow of system contract management and budget management."

[1519] This system makes it possible to centrally manage and automate the wide range of tasks required for system operation, minimizing human intervention, improving the quality of operations and reducing costs.

[1520] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1521] Step 1:

[1522] Data collection

[1523] The server collects various data from the monitoring system in real time.

[1524] Input: Monitoring data such as CPU usage, memory usage, and network traffic from your monitoring system.

[1525] Processing: Periodically retrieve monitoring data and store it in a database.

[1526] Output: Collected monitoring data.

[1527] Specific operation: The server obtains monitoring data through the API and stores the obtained data in a database.

[1528] Step 2:

[1529] Data analysis

[1530] The server analyzes the collected data and detects abnormal values.

[1531] Input: Collected monitoring data.

[1532] Processing: Analyze the data using Python's Scikit-learn library and run anomaly detection algorithms.

[1533] Output: Anomaly information if an anomaly is detected.

[1534] Specific operation: The server periodically analyzes the collected data and generates anomaly information if an abnormal value is detected.

[1535] Step 3:

[1536] Alert Generation

[1537] The server automatically generates an alert if an abnormality is detected.

[1538] Input: Anomaly information.

[1539] Processing: Generate a notification of an abnormality based on the abnormality information.

[1540] Output: An alert notification of an anomaly.

[1541] Specific operation: When an abnormality is detected, the server sends an alert notification using a communication tool such as Slack.

[1542] Step 4:

[1543] Performing initial response procedures

[1544] The server automatically executes initial response procedures based on the generated alert.

[1545] Input: Alert notification.

[1546] Action: Follow pre-defined initial response procedures and take action.

[1547] Output: Results of the initial response procedures.

[1548] What it does: If abnormal memory usage is detected, the server runs a Bash script to restart certain processes.

[1549] Step 5:

[1550] Routine work scheduling

[1551] The server schedules routine work.

[1552] Input: Routine work list and execution schedule.

[1553] Action: Set up a Cron job to schedule tasks to run at specified times.

[1554] Output: Routine work schedule.

[1555] Specific operation: Set up a Cron job to automatically perform a backup every night at midnight.

[1556] Step 6:

[1557] Automatic execution of routine tasks

[1558] The terminal automatically executes scheduled routine tasks.

[1559] Input: Routine work schedule.

[1560] Processing: Perform routine tasks using RPA tools (e.g. UiPath).

[1561] Output: Results of routine work execution.

[1562] Specific operations: Automatically performs routine tasks such as software updates and backups according to a schedule.

[1563] Step 7:

[1564] Receiving inquiries

[1565] Users can ask questions about the system through the chatbot interface.

[1566] Input: User's query.

[1567] Processing: The chatbot uses natural language processing to understand the user's question.

[1568] Output: Analysis result of the query.

[1569] Specific operation: The chatbot receives user inquiries as text and analyzes them using natural language processing.

[1570] Step 8:

[1571] Providing answers

[1572] The server consults a knowledge base and generates an appropriate answer.

[1573] Input: Analysis results of the query.

[1574] Processing: Generate answers to queries using a generative AI model (e.g., GPT-3).

[1575] Output: The best answer.

[1576] How it works: Based on the query, the server uses its knowledge base and generative AI models to quickly provide an answer.

[1577] Step 9:

[1578] Contract information management

[1579] The server centrally manages and monitors system and service contract information.

[1580] Input: Contract information.

[1581] Processing: Periodically update contract information and determine when the contract is due for renewal.

[1582] Output: Notification of when it's time to update.

[1583] Specific operations: Integrates with management tools such as Google Sheets to regularly update contract information and notify relevant parties when renewal dates approach.

[1584] Step 10:

[1585] Managing and monitoring budget information

[1586] The server manages the budget information and monitors the budget usage in real time.

[1587] Input: Budget information.

[1588] Action: Use a budgeting tool (e.g., QuickBooks) to monitor budget usage in real time.

[1589] Output: Budget overrun warning.

[1590] What it does: If there is a risk of exceeding the budget, the server will immediately issue a warning and notify the operations team.

[1591] (Application example 1)

[1592] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1593] In modern factories, it is important to monitor the operating status of production lines and equipment in real time and to take prompt and appropriate action when an abnormality occurs. However, constant manual monitoring and response is labor-intensive and inefficient. Furthermore, routine maintenance work, contract management, and budget management are prone to errors when done manually. Furthermore, responding quickly to troubles and questions within the factory requires advanced specialized knowledge and rapid responses. To solve these issues and streamline factory operations, a system that automates these tasks is needed.

[1594] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1595] In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, means for scheduling routine work, means for automatically executing the scheduled routine work, means for generating an appropriate response based on a request or inquiry, means for providing the generated response, means for monitoring contract information, means for determining when the contract renewal is due, means for notifying when the renewal date has arrived, means for managing budget information and monitoring the usage status of the budget, means for notifying when the budget is exceeded, means for monitoring the operating status of factory production lines and machines in real time, means for temporarily suspending a specific process or machine when an anomaly is detected, means for providing an appropriate response using a knowledge database related to the inquiry, and means for scheduling and automatically executing routine maintenance work in the factory. This makes it possible to improve the efficiency and quality of factory operations and minimize human intervention.

[1596] A "monitoring system" is a system that monitors the operating conditions and environment of factories, facilities, etc. in real time and collects data.

[1597] The "data collection means" is a means having a function of collecting various sensor data and operating status information collected from the monitoring system.

[1598] The "abnormality detection means" is a means having a function for analyzing collected data and detecting operations or states that deviate from normal states.

[1599] The "alert generation means" is a means having a function of generating an alert to notify information about an abnormality when an abnormality is detected.

[1600] The "initial response means" is a means having a function of automatically executing an initial response procedure based on the generated alert.

[1601] The "routine work scheduling means" is a means having a function for scheduling maintenance work and other routine work that is carried out periodically.

[1602] The "routine task execution means" is a means having a function of automatically executing scheduled routine tasks.

[1603] The "inquiry response means" is a means having the function of generating and providing an appropriate response based on a request or inquiry.

[1604] A "knowledge database" is a database that stores and references knowledge information such as past troubleshooting and FAQs.

[1605] "Contract monitoring means" refers to means that has the function of monitoring contract information for systems and services and checking their status.

[1606] The "contract renewal determination means" is a means having a function of determining when it is time to renew a contract and issuing an alert or notification when that time has come.

[1607] The "budget management means" is a means having the function of managing budget information and monitoring usage status in real time.

[1608] The "budget monitoring means" is a means that has the function of monitoring the usage of the set budget in real time and issuing warnings or notifications as necessary.

[1609] The "operational status monitoring means" is a means having the function of monitoring the operational status of factory production lines and machines in real time.

[1610] The "process suspension means" is a means having a function of temporarily halting a specific process or machine when an abnormality is detected.

[1611] The "maintenance work scheduling means" is a means having the function of scheduling routine maintenance work within a factory and automatically executing it.

[1612] The present invention provides a system for maximizing operational efficiency and minimizing human intervention in factory production lines, including a monitoring system, alert generation, automated first response procedures, routine task scheduling and execution, inquiry response, contract management, and budget management.

[1613] Surveillance System Embodiments

[1614] The server has a means for collecting data in real time from the monitoring system. For example, it collects data such as operating status, temperature, and vibration from production line monitoring sensors in a factory. The collected data is analyzed by anomaly detection means, and an alert is generated if an abnormality is detected. For example, if abnormal vibration is detected, the alert generation means is activated to prompt a response.

[1615] Routine work scheduling and execution embodiment

[1616] The server has a means for scheduling routine tasks. For example, it schedules periodic maintenance work and part replacement work. Based on this schedule, a means for automatically executing routine tasks is activated, and the tasks are carried out automatically at the set time. Specifically, this includes automatically backing up the system every night and organizing log files.

[1617] Implementation of inquiry response

[1618] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. The server will refer to the knowledge database and provide the most appropriate answer. For example, in response to a question such as, "There's a lot of vibration on the production line. What should I do?", the server will provide specific advice such as, "Check the operating status of the machine and perform maintenance work if necessary."

[1619] Contract and budget management practices

[1620] The server has a means for monitoring contract information. For example, it sends a notification when a machine maintenance contract is due for renewal. It also has a means for managing budget information and monitoring budget usage in real time. For example, it issues a warning and notifies relevant parties when the monthly budget is exceeded by 80%.

[1621] A concrete example of the entire system

[1622] When an abnormal temperature rise is detected on a production line in a factory, the system generates an alert in real time and shuts down the affected machine as an initial response. At the same time, it notifies specialist staff and urges them to take the necessary action. This enables rapid troubleshooting and minimizes damage.

[1623] Hardware and software used

[1624] Hardware: Various sensors, monitoring devices, and robots with control functions in the factory

[1625] Software: Python, APScheduler (scheduling), watchdog (file system monitoring), chatbot interface

[1626] Specific prompt examples

[1627] How can I monitor the status of my production line in real time and generate alerts and take action when an abnormality is detected?

[1628] Write Python code to automatically schedule and execute routine maintenance tasks in your factory.

[1629] Please explain the initial response method for a system that automatically restarts a machine when an abnormality occurs.

[1630] This will enable more efficient factory operations, improve quality, and minimize human intervention.

[1631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1632] Step 1:

[1633] The server collects data from the monitoring system. Specifically, it obtains real-time data such as operating status, temperature, and vibration from various sensors in the factory. The input data are the measured values ​​from the various sensors, and the output data is raw data stored in the server.

[1634] Step 2:

[1635] The server analyzes the collected data and detects abnormalities. A generative AI model is used for data analysis to identify abnormal patterns. The input is the raw data obtained in step 1, and the output is a result indicating whether or not there is an abnormality. Specifically, if the vibration data exceeds a certain threshold, it is determined to be "abnormal."

[1636] Step 3:

[1637] The server generates an alert when an abnormality is detected. The generated alert includes the location and cause of the abnormality, as well as the initial response procedure. The input is the anomaly detection result from step 2, and the output is an alert message. Specifically, when an abnormality occurs, a pop-up notification is generated on the server and an email is sent to the responsible person.

[1638] Step 4:

[1639] The server automatically executes initial response procedures based on the generated alerts. For example, if abnormal vibrations are detected, it issues a command to suspend a specific process or machine. The input is the alert message, and the output is a message confirming that the machine has stopped.

[1640] Step 5:

[1641] The server schedules routine tasks and issues commands to the routine task execution means. For example, it sets up a schedule for automatic system backup every night and executes it. The input is the routine task schedule setting, and the output is a message confirming the execution schedule.

[1642] Step 6:

[1643] The server generates an appropriate answer to a user's inquiry. When a user makes an inquiry through the chatbot interface, the server refers to the knowledge database and provides the most appropriate answer. The input is the user's inquiry, and the output is a response message.

[1644] Step 7:

[1645] The server monitors contract information and determines when the contract is due for renewal. When the renewal date is reached, it notifies the relevant parties. The input is the contract information and renewal conditions, and the output is a notification message. Specifically, an email notification is sent to the person in charge one month before the contract is due for renewal.

[1646] Step 8:

[1647] The server manages budget information and monitors budget usage. It notifies users when the budget is exceeded. The input is budget information and usage data, and the output is a budget exceedance warning message. Specifically, a warning message is sent when the monthly budget exceeds 80%.

[1648] Step 9:

[1649] When users operate within a factory, they receive the necessary information and instructions from the system. For example, if an abnormality occurs on a production line, an alert is displayed on the user's screen (tablet or PC) and initial response procedures are instructed. The input is the alert information, and the output is specific initial response procedures.

[1650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1651] In order to streamline system operations and minimize manual intervention, the present invention utilizes a system equipped with functions for monitoring, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, response to inquiries, contract management, and budget management, as well as an emotion engine that recognizes user emotions. This system is composed of a server, terminals, and users.

[1652] Surveillance System Embodiments

[1653] The server collects real-time data from the monitoring system. The collected data includes key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. When an alert is generated, the server automatically executes initial response procedures. For example, if abnormal memory usage is detected, a specific process is restarted.

[1654] Routine work scheduling and execution embodiment

[1655] The server schedules routine operational tasks, such as daily backups and software updates. After the schedule is determined, the terminal automatically executes these tasks using the RPA tool. For example, it performs data backups and organizes log files every night.

[1656] Implementation of inquiry response

[1657] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database and generates the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[1658] Embodiment of Emotion Engine

[1659] The server is equipped with an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server can recognize that emotion and provide a faster and more friendly response. It can also execute different initial response procedures depending on the user's emotional state.

[1660] Contract Management Implementation

[1661] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, when a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[1662] Budget control implementation

[1663] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[1664] Specific processing explanation

[1665] The server analyzes data received from the monitoring system and generates an alert if an abnormality is detected. This alert automatically executes initial response procedures through the RPA tool. The terminal automatically executes scheduled routine tasks, including backups and software updates. In response to user inquiries, the server references a knowledge database and generates and provides appropriate answers. An emotion engine recognizes the user's emotions and provides a response based on those emotions. Contract information and budget information are centrally managed by the server, which issues notifications and warnings in a timely manner.

[1666] In this way, the present invention automates various tasks required for system operation and minimizes human intervention, thereby improving the quality of operations and reducing costs.

[1667] The processing flow will be explained below.

[1668] Surveillance System Embodiments

[1669] Step 1:

[1670] The server collects data in real time from the monitoring system, including CPU usage, memory usage, network traffic, etc.

[1671] Step 2:

[1672] The server analyzes the collected data and detects anomalies, using thresholds and machine learning models that have been set for anomaly detection.

[1673] Step 3:

[1674] The server generates an alert when an anomaly is detected, which includes the type of anomaly, the scope of impact, and the urgency.

[1675] Step 4:

[1676] The server automatically executes first response procedures based on the generated alerts, for example restarting a specific process if abnormal memory usage is detected.

[1677] Routine work scheduling and execution embodiment

[1678] Step 1:

[1679] The server schedules its regular operations, which include routine tasks such as periodic backups and software updates.

[1680] Step 2:

[1681] The server sends instructions to the terminal to execute the schedule. The RPA tool is used.

[1682] Step 3:

[1683] The device automatically performs routine tasks according to a schedule, such as backing up data every night.

[1684] Implementation of inquiry response

[1685] Step 1:

[1686] If a user has a problem or question about the system, they can contact the system through the chatbot interface.

[1687] Step 2:

[1688] The server receives the query and generates the best answer by consulting a knowledge database.

[1689] Step 3:

[1690] The user receives a server-generated response, which provides specific instructions and solutions.

[1691] Embodiment of Emotion Engine

[1692] Step 1:

[1693] The server uses an emotion engine to recognize the user's emotions, including facial expression analysis, voice tone analysis, and analysis of text input.

[1694] Step 2:

[1695] The server provides adaptive responses based on the recognized emotions, for example, providing a prompt and polite response if the user is showing signs of annoyance.

[1696] Step 3:

[1697] The server implements different initial response procedures depending on different emotional states, for example, a fast escalation procedure is implemented for a frustrated user.

[1698] Contract Management Implementation

[1699] Step 1:

[1700] The server centrally manages system and service contract information, and monitors the expiration date and conditions of each contract.

[1701] Step 2:

[1702] The server determines when the contract is due for renewal and notifies the parties involved when renewal is required.

[1703] Step 3:

[1704] The server automatically proposes the necessary renewal procedures based on the notification, for example, generating a quote when renewing a software license.

[1705] Budget control implementation

[1706] Step 1:

[1707] The server acquires and centrally manages budget information, and budgets are set for each department and project.

[1708] Step 2:

[1709] The server monitors budget usage in real time and issues warnings if the set limits are about to be exceeded.

[1710] Step 3:

[1711] If the server predicts a budget overrun, it notifies the relevant parties and proposes countermeasures, including budget reallocation and requesting additional funding.

[1712] In this way, the system efficiently automates various operational tasks and minimizes human intervention, thereby improving the quality and efficiency of operations.

[1713] Example 2

[1714] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1715] Modern system operations require efficient and accurate execution of a wide range of tasks, including monitoring, routine tasks, inquiry response, contract management, and budget management. However, these tasks require significant human intervention, raising concerns about increased operational costs and the likelihood of errors. It is also difficult to properly recognize user emotions and respond accordingly. Therefore, a system that can effectively resolve these issues and automate and streamline system operations is needed.

[1716] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1717] In this invention, the server includes means for collecting data from the monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for recognizing a user's emotion and adjusting a response based on the emotion. This makes it possible to automate a series of operational tasks, from anomaly detection to initial response and response based on the user's emotion, and minimize human intervention.

[1718] A "monitoring system" is a mechanism for monitoring various resources and performance data within a system in real time, and for early detection of abnormalities and problems.

[1719] "Data collection" is the process of obtaining the necessary information from the monitoring system and sending it to a server for analysis.

[1720] "Anomaly detection" is the process of analyzing system behavior based on collected data and identifying abnormal conditions that go beyond the normal range.

[1721] "Alert generation" is the process of creating a warning to notify relevant parties when an anomaly is detected.

[1722] An "initial response procedure" is a process for implementing defined countermeasures to quickly deal with a detected abnormality.

[1723] "Emotion recognition" is the process of analyzing a user's facial expressions, voice tone, and text input to determine their emotional state.

[1724] "Regular work" refers to the daily tasks involved in system operation, such as repetitive tasks like data backups and software updates.

[1725] "Scheduling" is the process of scheduling specific tasks to be performed at specified dates and times or intervals.

[1726] "Inquiry handling" is the process of providing appropriate answers to requests and questions from users.

[1727] A "knowledge database" is an information repository that consolidates past case studies and technical information, enabling efficient response to inquiries.

[1728] "Contract management" is the process of centrally storing contract information related to systems and services and managing their expiration dates and conditions.

[1729] "Budget management" is the process of centrally managing the budgets of each department and project, and monitoring and issuing alerts regarding usage.

[1730] This invention is a system with the following functions to streamline system operations and minimize human intervention. It has the functions of a monitoring system, alert generation, automation of initial response procedures, scheduling and execution of routine tasks, inquiry response, contract management, and budget management, and also uses an emotion engine that recognizes user emotions. This system consists of a server, terminals, and users.

[1731] Surveillance System Embodiments

[1732] The server collects real-time data from the monitoring system. Specifically, it obtains key performance metrics such as CPU usage, memory usage, and network traffic. This is done using the Simple Network Management Protocol (SNMP) or a dedicated monitoring agent. The collected data is analyzed by monitoring tools such as Nagios and Zabbix, and if an abnormality is detected, an alert is automatically generated. For example, an alert is generated if CPU usage exceeds 90%.

[1733] Routine work scheduling and execution embodiment

[1734] The server schedules routine tasks such as daily backups and software updates using cron jobs or Windows Task Scheduler, and the terminals automatically execute these tasks using RPA tools (Robotic Process Automation, such as UiPath). For example, they perform data backups and clean up log files every night.

[1735] Implementation of inquiry response

[1736] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. For example, if a user makes an inquiry such as "The system is slow," the server receives the inquiry, references the knowledge database, and generates the most appropriate answer. The generated answer is then provided to the user. For example, the advice provided might be "Check your network speed and terminate unnecessary processes."

[1737] Embodiment of Emotion Engine

[1738] The server includes an emotion engine for recognizing the user's emotions. The emotion engine uses machine learning models to analyze the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user inputs something that indicates frustration to the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[1739] Contract Management Implementation

[1740] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and relevant parties are notified when renewal is approaching. Notifications are sent via email, and estimates are automatically generated for software licenses that require renewal.

[1741] Budget control implementation

[1742] The server acquires budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. Data is acquired through collaboration with ERP systems (e.g., SAP), and if the budget is about to exceed the set limit, a warning is sent to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to operate within the budget.

[1743] Specific processing examples

[1744] What to do if CPU usage is high

[1745] If the CPU usage continues to be abnormally high for a certain day, the server will first obtain CPU usage data from the monitoring system and detect the abnormality. An alert will then be automatically generated, and an initial response will be taken to restart the specific CPU-intensive process.

[1746] Examples of routine work

[1747] Every night, the terminal uses the RPA tool to automatically back up data and organize log files, ensuring smooth daily operations.

[1748] Specific examples of inquiries

[1749] If a user asks, "My system is slow, what should I do?" the server can consult its knowledge database and provide specific advice such as, "Check your network speed and close unnecessary processes."

[1750] In this way, automating a wide range of operational tasks and minimizing human intervention will improve the quality of operations and reduce costs.

[1751] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1752] Specific explanation of processing steps

[1753] surveillance system

[1754] Step 1: Data collection

[1755] The server obtains various metrics (e.g., CPU usage, memory usage, network traffic) from the monitoring system in real time via SNMP or a monitoring agent.

[1756] Input: Metric data from each device or system

[1757] Output: Collected metric data

[1758] Step 2: Data analysis

[1759] The server analyzes the collected data using Nagios or Zabbix and detects abnormalities by comparing it with the set thresholds.

[1760] Input: Collected metrics data

[1761] Output: Anomaly detection results (e.g., information that CPU usage exceeded 90%)

[1762] Step 3: Alert Generation

[1763] If an anomaly is detected, the server generates an alert and notifies relevant parties via an alert management system such as PagerDuty.

[1764] Input: Anomaly detection results

[1765] Output: Generated alerts and notifications

[1766] Step 4: Initial response

[1767] The server will automatically take first response steps based on the generated alerts, using Ansible or Puppet to run specific scripts, for example restarting a process with high CPU usage.

[1768] Input: Generated alert

[1769] Output: Results of the initial response performed

[1770] Scheduling and executing routine tasks

[1771] Step 1: Scheduling

[1772] The server schedules routine tasks (e.g., data backups and software updates) using cron jobs or the Windows Task Scheduler.

[1773] Input: Routine work schedule requirements

[1774] Output: Set schedule

[1775] Step 2: Perform routine tasks

[1776] The terminal automatically performs routine tasks using an RPA tool (e.g., UiPath) based on a set schedule.

[1777] Input: Set schedule

[1778] Output: Results of routine tasks performed (e.g., backup completed, log file cleanup completed)

[1779] Inquiry response

[1780] Step 1: Receiving an inquiry

[1781] Users can contact the chatbot interface with questions or problems they have about the system. For example, a user might say, "The system is slow."

[1782] Input: User inquiry

[1783] Output: Received queries

[1784] Step 2: Processing the inquiry

[1785] The server refers to a knowledge database based on the content of the received inquiry and generates an appropriate answer.

[1786] Input: Received query

[1787] Output: Generated answer (e.g. "Check your network speed and terminate any unnecessary processes")

[1788] Emotion Engine

[1789] Step 1: Sentiment Analysis

[1790] The server uses machine learning models to determine the user's emotional state by analyzing their facial expressions, voice tone, and text input. For example, it analyzes facial expressions and sentences that indicate irritation.

[1791] Input: User facial expression data, voice tone, text input

[1792] Output: Perceived emotional state

[1793] Step 2: Respond based on emotions

[1794] The server adjusts its response procedures based on the results of the sentiment analysis, for example, responding more quickly and politely if the user expresses irritation.

[1795] Input: Perceived emotional state

[1796] Output: Coordinated response and execution

[1797] contract management

[1798] Step 1: Gather contract information

[1799] The server collects system and service contract information for centralized management, utilizing the SMB protocol and API.

[1800] Input: Contract information

[1801] Output: Centralized contract data

[1802] Step 2: Contract monitoring and notification

[1803] The server monitors contract expiration dates and conditions and notifies interested parties when renewal is approaching. Email notifications are sent.

[1804] Input: Centralized contract data

[1805] Output: Notifications sent (e.g. software license renewal notifications)

[1806] Budget Management

[1807] Step 1: Gather budget information

[1808] The server works in conjunction with the ERP system to obtain budget information for each department and project and manages it centrally.

[1809] Input: Budget Data

[1810] Output: Centralized budget data

[1811] Step 2: Budget monitoring and alerts

[1812] The server monitors budget usage in real time and issues alerts to relevant parties if the set limits are about to be exceeded.

[1813] Input: Centralized budget data

[1814] Output: Warning notice issued (e.g., budget overrun warning)

[1815] (Application example 2)

[1816] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1817] In recent years, as factories have become more automated and efficient, there has been a strong demand for more efficient system operations and minimal human intervention. However, many systems still rely on human labor, with tasks such as anomaly detection, alert generation, initial response, routine tasks, and responding to inquiries consuming time and effort. Furthermore, when operating machines or systems, prompt and appropriate responses are required when users experience stress or dissatisfaction, but this also relies heavily on human labor. Therefore, there is a need to perform these tasks efficiently and automatically, and to respond flexibly to the user's emotional state.

[1818] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from a monitoring system, means for detecting anomalies based on the collected data, means for generating an alert when an anomaly is detected, means for automatically executing an initial response procedure based on the generated alert, and means for determining the emotional state of a user using an emotion engine and providing a response according to the emotion. This makes it possible to automate anomaly detection and initial response in system operation, and to take appropriate action taking user emotions into consideration when operating or performing maintenance on a robot in a factory.

[1819] Definitions of important terms contained in patent specifications

[1820] A "monitoring system" is a means of collecting data to confirm the normal operation of systems and equipment and to detect abnormalities.

[1821] "Data collection" means recording key metrics such as CPU usage, memory usage, and network traffic from monitoring systems.

[1822] "Anomaly detection" is the analysis of collected data to discover unexpected behavior or performance degradation.

[1823] "Alert generation" means creating a warning or notification to notify relevant parties when an abnormality is detected.

[1824] "Automatic execution of initial response procedures" refers to the automatic execution of pre-defined procedures to correct anomalies based on the generated alert.

[1825] The "emotion engine" is a system that analyzes the user's facial expressions, voice tone, text input, etc. to determine the user's emotional state.

[1826] "Routine work scheduling" means planning in advance the tasks that are required on a daily basis, such as backup work and software updates, and carrying them out according to a timetable.

[1827] "Automatic execution of routine tasks" refers to the automatic execution of scheduled routine tasks using RPA tools, etc.

[1828] "Inquiry response" refers to generating and providing appropriate answers to questions or problems from users by referencing a knowledge database.

[1829] "Contract information monitoring" refers to the centralized management of contract information for systems and services, and the monitoring of contract renewal dates and conditions.

[1830] A "contract renewal notice" is a means of informing the parties involved when the contract expiration date is approaching.

[1831] "Budget management" means centrally managing budget information for each department and project and monitoring usage.

[1832] "Budget Overage Notification" is the act of issuing a warning to relevant parties when budget usage is likely to exceed a set limit.

[1833] A "generative AI model" is an artificial intelligence model that is generated based on data obtained from devices or systems, and that makes appropriate responses and predictions.

[1834] A "prompt" is a question or instruction input to a generative AI model that serves as the basis for the model to generate an answer or response.

[1835] MODE FOR CARRYING OUT THE INVENTION

[1836] This invention provides a system that integrates a monitoring system, anomaly detection, alert generation, automatic execution of initial response procedures, an emotion engine, routine work scheduling and execution, inquiry response, contract information monitoring, and budget management to achieve efficient operation in factories. The system is composed of a server, terminals, and users.

[1837] Surveillance System Embodiments

[1838] The server collects data in real time from various devices and systems within the factory. Specifically, it acquires key metrics such as CPU usage, memory usage, and network traffic. This data is analyzed, and if an abnormality is detected, an alert is automatically generated. For example, if CPU usage exceeds 90%, an alert is generated and initial response procedures such as restarting a specific process are automatically executed.

[1839] Routine work scheduling and execution embodiment

[1840] The server schedules the factory's routine tasks, including daily data backups and software updates. After the schedule is determined, the terminals use RPA tools to automatically execute these tasks. For example, they can back up data and organize log files every night.

[1841] Implementation of inquiry response

[1842] When a user has a problem or question about the system, they can make an inquiry through the chatbot interface within the system. When the server receives the inquiry, it references its knowledge database to generate and provide the most appropriate answer. For example, in response to a question such as "My system is slow, what should I do?" the server will provide specific advice such as "Check your network speed and terminate unnecessary processes."

[1843] Embodiment of Emotion Engine

[1844] The server is equipped with an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, voice tone, and text input to determine the user's emotional state. For example, if the user expresses irritation with the chatbot, the server will recognize that emotion and provide a faster and more friendly response.

[1845] Contract Management Implementation

[1846] The server centrally manages system and service contract information. The expiration date and conditions of each contract are monitored, and when the renewal date approaches, relevant parties are notified. For example, if a software license needs to be renewed, a notification is sent and an estimate is generated at the same time.

[1847] Budget control implementation

[1848] The server retrieves budget information and manages it centrally. Budgets are set for each department and project, and budget usage is monitored in real time. If the budget is about to exceed the set limit, a warning is issued to the relevant parties. For example, if 80% of the budget has already been used by the middle of the month, a notification is sent to the team to encourage them to stay within the budget.

[1849] Examples of using generative AI models and prompts

[1850] The server inputs prompts into the generative AI model based on data obtained from factory equipment and utilizes the responses. For example, if a prompt such as "What is the current CPU usage rate?" is input, the generative AI model will respond with "The current CPU usage rate is 85%." This enables more efficient system operation.

[1851] Examples of specific prompts are:

[1852] "Show me today's routine work schedule"

[1853] Check the latest backup data.

[1854] "What is your current network traffic?"

[1855] This system significantly improves factory operational efficiency and minimizes human intervention.

[1856] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1857] System program processing flow

[1858] Step 1:

[1859] The server collects real-time data from various devices and systems in the factory. As input, it obtains data metrics such as device CPU usage, memory usage, and network traffic. As output, it stores this data in a database. Specifically, the server obtains data from each device via API and stores that data in the database.

[1860] Step 2:

[1861] The server analyzes the collected data and detects anomalies. It uses the data collected in step 1 as input. It uses a data analysis algorithm to detect abnormal values ​​and judges anomalies based on this. As output, it generates an alert object if an anomaly is detected. Specifically, it sets a threshold for the data, and judges it to be an anomaly if this threshold is exceeded.

[1862] Step 3:

[1863] The server generates an alert when an anomaly is detected. As input, it uses the alert object created in step 2. As output, it creates an alert message to notify the user. Specifically, it generates an alert message and sends it to the user through the notification system.

[1864] Step 4:

[1865] The server automatically executes initial response procedures based on the generated alert. As input, it uses the alert message generated in step 3. As output, it executes actions to correct the abnormal condition. Specifically, it uses an RPA tool to execute response procedures such as restarting a specific process.

[1866] Step 5:

[1867] The server schedules routine tasks and manages their execution. Tasks such as routine backups and software updates are listed as input. A schedule of routine tasks is generated as output, and each task is automatically executed. Specifically, tasks are set periodically using a scheduling tool, and then automatically executed using an RPA tool.

[1868] Step 6:

[1869] Users can inquire about problems or questions about the system through the chatbot interface. The text entered by the user is used as input. As output, the system generates an appropriate answer and provides it to the user. Specifically, the server refers to a knowledge database to search for and present the appropriate answer.

[1870] Step 7:

[1871] The server uses an emotion engine to determine the user's emotional state. The inputs include the user's facial expression, voice tone, and text input. The output is an analysis of the user's emotional state and a response appropriate to that state. Specifically, the server uses an emotion analysis algorithm to determine the user's emotions and automatically determine the appropriate response.

[1872] Step 8:

[1873] The server monitors contract information and determines when it is time to renew the contract. It uses a contract information database as input. It outputs by notifying relevant parties when contract renewal is approaching. Specifically, it periodically checks contract information, determines when renewal is due, and sends alerts to relevant parties via a notification system.

[1874] Step 9:

[1875] The server manages budget information and monitors budget usage. It uses budget data from each department and project as input. It outputs a warning to relevant parties when budget overruns are predicted. Specifically, it analyzes budget usage in real time and sends notifications when there is a risk of exceeding the limit.

[1876] Step 10:

[1877] The server inputs a prompt statement into the generative AI model based on data obtained from factory equipment and utilizes the response. The prompt statement and equipment data are used as input. The response from the generative AI model is obtained as output and reflected in operations. Specifically, the server inputs a prompt statement such as "What is the current CPU usage rate?" and obtains a response such as "The current CPU usage rate is 85%."

[1878] Through these steps, the invention streamlines factory operations and minimizes human intervention.

[1879] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1880] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1881] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1882] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1883] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1884] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1885] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1886] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1887] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1888] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1889] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1890] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1891] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1892] 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.

[1893] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1894] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1895] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1896] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1897] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1898] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1899] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1900] The following is further disclosed regarding the above embodiment.

[1901] (Claim 1)

[1902] means for collecting data from the monitoring system;

[1903] means for detecting anomalies based on the collected data;

[1904] a means for generating an alert when an anomaly is detected;

[1905] a means for automatically executing initial response procedures based on the generated alerts;

[1906] A system including:

[1907] (Claim 2)

[1908] a means for scheduling routine work;

[1909] means for automatically executing scheduled routine tasks;

[1910] means for generating an appropriate response based on the request or inquiry;

[1911] a means for providing the generated answer;

[1912] The system of claim 1 further comprising:

[1913] (Claim 3)

[1914] a means for monitoring contract information;

[1915] A means for determining when to renew a contract;

[1916] A means of notifying you when renewal is due;

[1917] a means of managing budget information and monitoring budget usage;

[1918] A means of notification in case of budget overruns;

[1919] The system of claim 1 further comprising:

[1920] "Example 1"

[1921] (Claim 1)

[1922] means for collecting data from the monitoring system;

[1923] means for detecting anomalies based on the collected data;

[1924] a means for generating an alert when an anomaly is detected;

[1925] a means for automatically executing initial response procedures based on the generated alerts;

[1926] a means for scheduling routine work;

[1927] means for automatically executing scheduled routine tasks;

[1928] means for generating an approp...

Claims

1. means for collecting data from the monitoring system; means for detecting anomalies based on the collected data; a means for generating an alert when an anomaly is detected; a means for automatically executing initial response procedures based on the generated alerts; A system including:

2. a means for scheduling routine work; means for automatically executing scheduled routine tasks; means for generating an appropriate response based on the request or inquiry; a means for providing the generated answer; The system of claim 1 further comprising:

3. a means for monitoring contract information; A means for determining when to renew a contract; A means of notifying you when renewal is due; a means of managing budget information and monitoring budget usage; A means of notification in case of budget overruns; The system of claim 1 further comprising:

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