system

Abstract

Provide a system. 【Solution means】 Means for monitoring an alarm signal received from a base station, Means for collecting related information based on the alarm signal, Means for obtaining information from a work registration system, power supply information, and other communication devices, Means for analyzing the collected information to identify the cause, Means for notifying the user of information that requires escalation, Means for automatically executing fault handling, Means for recording the handling result and generating a report, Means for detecting network anomalies in real time and immediately issuing an alert, Means for comparing past data with the current situation using a machine learning algorithm and quickly identifying the cause, A system including means for displaying an abnormal situation on the user's mobile device and presenting recommended countermeasures.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the rapid expansion of communication networks, the number of base stations has increased exponentially, resulting in an increased load on network monitoring. Due to this increased load, it has become difficult to operate efficiently with conventional manual monitoring and response. In particular, with the spread of 5G, real-time response is required, and the limitations of human resources have been exposed, and delays in fault response have contributed to threatening the stability of the network. Therefore, there is a need for a system that can quickly and accurately identify the cause from the detection of an alarm to the fault response.

Means for Solving the Problems

[0005] This invention includes means for monitoring alarm signals received from base stations, thereby immediately detecting issued alarms and proactively collecting all relevant information. It also collects further information through means for acquiring information from work registration systems, power supply information, and other communication devices. Next, it uses machine learning algorithms to analyze the collected information and identify the cause with high accuracy. Based on this analysis, it determines whether escalation is necessary and notifies the user of any required escalation. Furthermore, it includes means for performing automated fault response to ensure rapid problem resolution. Finally, it records the response results and generates reports to aid in operations. This series of functions enables efficient and effective monitoring and management of communication networks.

[0006] A "base station" is a facility in a wireless communication network that facilitates communication between terminals and the core network, and is particularly responsible for transmitting and receiving radio waves.

[0007] An "alarm signal" is a signal transmitted to a monitoring system when specific conditions or anomalies are detected, and it functions as information that triggers action to prompt a response.

[0008] "Monitoring means" refers to functions or devices used to continuously observe specific objects or events and understand the situation, and are used for the purpose of detecting abnormalities early.

[0009] "Related information" refers to data and knowledge related to a specific event or problem, and is additional information necessary for problem solving and analysis.

[0010] A "work registration system" is a software or database system used to record network and equipment maintenance and inspections, and to manage schedules and completed tasks.

[0011] "Power supply information" refers to data on the status of power supply and power outage information, and is energy-related information provided by power companies.

[0012] "Communication equipment" refers to devices and systems used for sending and receiving information, and in particular, those used for data communication over a network.

[0013] "Analysis tools" refer to processes and algorithms used to process collected data and derive meaning based on a specific purpose.

[0014] "Escalation" is the process of transferring a problem or anomaly to a higher level of management or a specialized department in order to address it with a higher priority.

[0015] "Users" refer to those who use a system or service, particularly those who perform administrative tasks or are staff responsible for operations.

[0016] "Troubleshooting" refers to a series of actions and procedures taken to repair or restore a system or equipment to normal operation in response to problems or malfunctions that occur.

[0017] A "report" is a document that compiles detailed information about a specific task or result, and is a form of record that is useful for subsequent analysis and improvement. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Implementing the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention is an advanced server-based monitoring system for efficiently managing the operation of a communication network. The server automatically detects alarm signals from base stations and collects and analyzes relevant information. Its embodiments are described in detail below.

[0040] First, the server receives alarm signals transmitted in real time from each base station. These signals indicate that an anomaly has occurred in the network or equipment. The server analyzes the information in these alarm signals to determine their nature and priority. At this stage, the nature of the specific problem is estimated.

[0041] Next, the server proactively collects relevant information. This includes the current work status from the work registration system, the latest supply status and power outage information from the power company as power supply information, and real-time data obtained from other communication devices. This comprehensive information collection makes it possible to evaluate the underlying causes of the problem from multiple perspectives.

[0042] Subsequently, the server performs advanced analysis based on the collected information. Using machine learning algorithms, it compares past failure data with the current situation to quickly and accurately pinpoint the cause. This analysis identifies high-priority issues requiring immediate attention and cases that need escalation.

[0043] The server will send an escalation message to the user (administrator) as needed. This notification will include a summary of the problem and recommended actions to support quick decision-making.

[0044] Furthermore, the server can automatically take actions to respond to failures. For example, in the event of a power outage, it can activate emergency power supplies and establish alternative communication paths. This enables immediate response to problems and minimizes network downtime.

[0045] Finally, the server records all processing steps and generates a detailed report. This report is intended to help improve future operations and incident response. Users can use this report to further optimize and make decisions about their network, thereby improving network reliability and performance.

[0046] As a concrete example, consider a scenario where an unexpected power outage occurs at a base station. The server immediately detects the power outage alarm from this base station and identifies the cause as a power outage by checking the regional power supply information. Subsequently, it automatically issues a command to activate the emergency power supply and, after the response is complete, provides the user with a report containing detailed processing records. Once this process is complete, network stability is maintained.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server receives alarm signals from the base station. Each time an alarm signal is generated, the server immediately analyzes the signal to determine its priority and nature.

[0050] Step 2:

[0051] The server accesses the work registration system to check the current work status. This confirms that the issue is not caused by maintenance work being carried out at the base station.

[0052] Step 3:

[0053] The server accesses power company APIs and websites to collect power supply information. It retrieves the latest information for the affected area to determine if a power outage is the cause.

[0054] Step 4:

[0055] The server retrieves relevant data from other communication devices. This includes information from the network core and neighboring base stations, which helps determine whether the anomaly is localized or global.

[0056] Step 5:

[0057] The server uses machine learning algorithms to analyze all the information it collects. It compares historical failure data with real-time data to identify the cause and scope of the failure.

[0058] Step 6:

[0059] The server will send a notification to the user if escalation is necessary. This notification will include a summary of the problem, its cause, and recommended actions.

[0060] Step 7:

[0061] The server performs automated fault response. Specifically, this includes activating emergency power in the event of a power outage and switching communication paths.

[0062] Step 8:

[0063] The server records the entire process and generates a detailed report. This report is presented to the user and used to improve future operations.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In the operation and management of communication networks, it is necessary to detect abnormal situations quickly in real time, collect and analyze information effectively, and automate appropriate responses. Furthermore, it is crucial to efficiently record the results of these responses and utilize them for future operational improvements. A system that centrally and automatically manages these processes is needed.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for receiving alarm signals from a communication device, means for collecting information related to the alarm signals using a database, and means for acquiring data from various registration systems, supply status information, and other information devices. This enables the detection and response to anomalies in the communication network, as well as the recording and reporting of the results.

[0069] An "alarm signal" is a notification signal from a communication network or device indicating an anomaly, and is transmitted under specific conditions.

[0070] A "communication device" is a device that includes hardware or software for transmitting information and has the function of sending and receiving data over a network.

[0071] A "database" is a system that organizes and stores information, allowing for efficient searching and retrieval as needed.

[0072] A "various registration systems" is a mechanism that holds different management data and allows for monitoring of its status and progress.

[0073] "Supply status information" refers to data provided by external suppliers that shows the current state of resources and energy.

[0074] An "information device" is a device or system that processes, manages, or transmits information.

[0075] "Machine learning methods" are a collection of algorithms used to empirically create models based on data and perform predictions and classifications.

[0076] "Recovery processing" refers to a series of operations performed to restore a system that has experienced an abnormality or failure to a normal state.

[0077] A "report document" is a document that summarizes and describes the results of a specific activity or process, and communicates them to the relevant parties.

[0078] This invention is a monitoring system that rapidly detects anomalies in communication networks, automates effective information collection and analysis, and implements appropriate countermeasures. The server plays a central role in the network, receiving alarm signals from communication devices and aggregating and analyzing data from multiple information sources based on these signals.

[0079] Specifically, the server connects to the communication device via a wide-area network using a communication interface to receive alarm signals. TCP / IP is the primary communication protocol used. After receiving the alarm signal, the server analyzes the signal using Python and related data analysis libraries.

[0080] Based on the analysis, the server collects relevant information using a database and also retrieves supply information and work status from external systems via APIs. Automated scripts are used to streamline information gathering.

[0081] Machine learning techniques are used to analyze the information, and algorithms such as TENSORFLOW® are executed to compare historical data with current data. This analysis helps identify the cause of the problem and assists in determining its importance, such as urgency.

[0082] Subsequently, the server uses a notification system to report important information to the user. Sending emails via SMTP enables rapid escalation to administrators.

[0083] Furthermore, the server automatically performs recovery processes and controls the power management system as needed. This significantly reduces downtime, for example, by automatically operating the emergency power supply in the event of a power outage.

[0084] Finally, to record all processes, the server uses log management tools such as the ELK stack to meticulously record the processing steps and automatically generates a report. This report provides users with information to help monitor and improve network operations.

[0085] A concrete example is the case of an unexpected power outage at a base station. The server detects the power outage alarm signal, checks the power supply information for the area, activates the emergency power supply to stabilize the situation, and then automatically sends a detailed report to the administrator to maintain operations.

[0086] An example of a prompt is: "Propose a method for receiving alarm signals from a base station and identifying the type and priority of an anomaly using pattern recognition."

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The server receives alarm signals from the communication device. The input is real-time alarm signal data; upon receipt, the signal content is added to the analysis queue. The output is the alarm data requiring analysis. Specifically, the server waits for data packets from the base station via a continuously running socket.

[0090] Step 2:

[0091] The server analyzes the received alarm signals. In this step, it uses an analysis library such as Python to identify the signal type and priority based on the input alarm signal. The output is the characteristic data of the analyzed signal. In specific operation, the server compares the signal data with past logs to recognize anomaly patterns.

[0092] Step 3:

[0093] The server collects relevant information. This involves retrieving information from a database containing information about the underlying alarm signal and communicating with external systems via API. The input is initial analysis data related to the alarm, and the output is an aggregated set of information. In practice, API requests are issued via automated scripts to obtain information from the work registration system and supply status.

[0094] Step 4:

[0095] The server analyzes information using machine learning techniques. The input consists of collected relevant information and historical datasets. Based on this, it runs models such as TensorFlow to identify the cause of failures. The output is the result of the cause analysis. Specifically, the server performs scoring that suggests inferences based on a database of past failures.

[0096] Step 5:

[0097] The server sends escalation messages to the user as needed. The input to this process is analysis results indicating high-priority issues, and the output is a notification message to the user. Specifically, the server uses SMTP to send an email to the administrator, quickly informing them of the actions that need to be taken.

[0098] Step 6:

[0099] The server automatically performs the recovery process. Based on pre-configured recovery rules, it receives problem data requiring action as input and takes countermeasures such as activating the emergency power supply. The output indicates a recovery complete state. Specific actions include the server sending instructions to the power management system via an API.

[0100] Step 7:

[0101] The server records the process and generates a report. At this stage, it receives log information of the entire process as input and creates a detailed report as output. Specifically, it uses the ELK stack to organize the logs and sends the report to the user in PDF format.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] Modern communication networks are susceptible to anomalies due to a wide variety of factors, requiring monitoring and rapid response. However, conventional systems often suffer from delays in anomaly detection and reporting, leading to delayed responses. Furthermore, identifying the cause of anomalies can be time-consuming, potentially compromising network stability. To address these challenges, a system is needed that can detect anomalies in real time, immediately notify users, and enable appropriate countermeasures.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information based on the alarm signals, and means for acquiring information from a work registration system, power supply information, and other communication devices. This enables the detection of anomalies in real time, rapid identification of their causes, immediate notification to users, and the suggestion of appropriate countermeasures.

[0107] A "base station" is a device in a communication network that transmits and receives wireless signals and maintains connections with devices.

[0108] An "alarm signal" is a signal that indicates an abnormality has occurred in the network or equipment, and it serves as an important trigger in monitoring systems.

[0109] A "work registration system" is a system for managing work schedules and statuses, and for recording related information.

[0110] "Power supply information" refers to data provided by power companies that shows the current power supply status and information on power outages.

[0111] A "communication device" is a device or apparatus that is connected to a network and transmits and receives data.

[0112] "Escalation" refers to the process of reporting information to higher management and requesting additional action when a problem is not resolved.

[0113] A "machine learning algorithm" is a mathematical model that learns patterns and rules from data and automatically makes predictions and decisions.

[0114] A "generative AI model" is a pre-trained model that uses artificial intelligence to generate text and images.

[0115] A "prompt" is a text of instructions or questions used to elicit a specific response or output from an AI.

[0116] In embodiments of this invention, the server functions as the primary hardware for monitoring the communication network. The server includes a network connectivity device for receiving alarm signals transmitted from a base station. It also has interfaces for acquiring necessary information from external systems, including communication devices, a work registration system, and power supply information.

[0117] The program runs on a server and is implemented using Python. MySQL® is used as the database to appropriately store and manage received alarm signals and related information. This allows the server to analyze data and identify anomalies in real time.

[0118] Machine learning algorithms are used for data analysis. Machine learning frameworks such as TensorFlow are applied to compare historical failure data with real-time data. This allows the server to quickly identify the cause of failures and, if an anomaly is detected, immediately send a notification to the user's mobile device.

[0119] The user's device has an application developed with Flutter® installed, which displays alarm information in real time and suggests recommended countermeasures. By utilizing a generative AI model, appropriate countermeasures for identified anomalies can be provided to the device as prompt messages.

[0120] As a concrete example, if the server detects suspicious traffic on the network, it generates a prompt message saying, "Have you detected abnormal network traffic? Please tell me the cause and recommended course of action," and notifies the user's terminal. This process allows the user to immediately understand the situation and take appropriate action.

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server receives alarm signals transmitted from the base station. The input is the alarm signal emitted from the base station, and the output is the received signal information. The server temporarily stores the received alarm signal in memory.

[0124] Step 2:

[0125] The server collects relevant information based on the received alarm signal. The input is the alarm signal, and the output is a set of relevant information. The server obtains relevant data from the work registration system, the latest power supply information, and other communication devices, integrates them, and stores them in a database.

[0126] Step 3:

[0127] The server uses machine learning algorithms to analyze historical and real-time data to identify the root cause of failures. The input consists of collected data and historical data, while the output is the identified cause of the anomaly. The server utilizes TensorFlow for data analysis, enabling rapid identification of the cause of the anomaly.

[0128] Step 4:

[0129] Based on the identified anomaly, the server generates a prompt message using a generative AI model. The input is information about the cause of the anomaly, and the output is a prompt message to notify the user. The server generates an appropriate prompt message according to the anomaly and sends it to the user's terminal.

[0130] Step 5:

[0131] The terminal displays prompt messages received from the server on its screen, notifying the user of the anomaly and the necessary countermeasures. The input is prompt messages from the server, and the output is a visual display of information for the user. The terminal uses a Flutter application to inform the user of the situation in real time.

[0132] Step 6:

[0133] The user selects and executes the appropriate response based on the prompt messages provided by the terminal. The input is information from the terminal, and the output is the action taken based on the user's decision. The user takes the necessary actions to maintain network stability, referring to the provided countermeasures.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] The system in this invention achieves efficient and adaptive operation of a communication network by combining multiple functions centered around a server. This system includes communication means for receiving alarm signals from base stations, functions for collecting related information, and machine learning analysis means, as well as an emotion engine for recognizing user emotions.

[0136] The server first receives alarm signals transmitted from the base station and collects various data based on these signals. It checks the work status from the work registration system, collects power supply information and information from other communication devices, and integrates it. This information is analyzed by machine learning algorithms to identify the cause of the failure.

[0137] The emotion engine analyzes a user's emotional state based on their words, actions, and responses when they interact with the system. This analysis is then used by the server to escalate or notify the user. For example, if a user is experiencing stress, the system may provide a more detailed and easier-to-understand explanation or optimize escalation alerts according to their urgency.

[0138] As a specific use case, let's consider a network failure. First, the server detects a power outage alarm from the base station and obtains power outage information from the power company. If the analysis determines that a power outage is the cause, it automatically activates the emergency power supply. While handling the failure, the emotion engine analyzes the user's responses and provides optimal information according to their stress level. Throughout this process, notifications are provided in a way that takes into consideration the user's ability to avoid unnecessary stress.

[0139] Furthermore, at the end of all processes, the server generates a detailed report that includes the results of the emotion engine's analysis. This allows for system improvements not only from a technical perspective but also from an ergonomic perspective during post-event reviews and the development of future countermeasures. This invention improves not only the technical aspects but also the user experience and is expected to make a significant contribution to next-generation network operations.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The server receives an alarm signal from the base station. This signal indicates that an anomaly has occurred within the network, and the server immediately analyzes the alarm to determine its nature.

[0143] Step 2:

[0144] The server accesses the work registration system to retrieve information on relevant work and maintenance. This allows it to verify whether any ongoing work is causing an alarm.

[0145] Step 3:

[0146] The server connects to the power company's API to check the power supply status and makes inquiries. It retrieves power outage information and evaluates its correlation with alarms.

[0147] Step 4:

[0148] The server collects real-time data from other communication devices to understand the environment around the base station and the network status. This allows for the assessment of the extent and impact of any anomalies.

[0149] Step 5:

[0150] The server uses machine learning models to analyze collected data and identify the root cause of failures. This analysis process references both historical and real-time data.

[0151] Step 6:

[0152] The emotion engine analyzes the user's emotional state. Based on the user's words and actions when interacting with the system, it evaluates stress levels and emotional intensity.

[0153] Step 7:

[0154] Based on the analysis results, the server notifies the user of escalation as needed. The notification content is customized according to the evaluation results of the emotion engine. The tone and level of detail of the notification are adjusted according to the user's emotional state.

[0155] Step 8:

[0156] The server automatically handles failures. For example, it immediately takes specific measures to resolve the problem, such as activating emergency power or setting up alternative routes.

[0157] Step 9:

[0158] The server records all processing steps and results and generates a detailed report. This report also includes the results of the user's sentiment analysis by the sentiment engine and is provided to the user to help with future improvements.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] In modern communication networks, rapid and effective responses to failures are essential. However, in conventional systems, identifying the cause of a failure and gathering information for resolving it are often done manually, which is time-consuming and labor-intensive. As a result, users tend to experience stress, and the efficiency of network operations decreases.

[0162] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0163] In this invention, the server includes means for monitoring alarm signals received from a base station, means for acquiring relevant information based on the alarm signals, and means for analyzing the user's emotional state and optimizing notification content. This enables rapid and appropriate information provision and response in the event of a failure, reducing user stress and improving the efficiency of network operations.

[0164] A "warning signal" is a signal transmitted from a base station to indicate an anomaly or malfunction in the communication network and to prompt a quick response.

[0165] "Related information" refers to data necessary to identify the cause of the malfunction, and is obtained from the work registration system, supply system, and other communication devices.

[0166] "Processing means" refers to systems and algorithms used to analyze collected information and identify the cause of a problem.

[0167] "User emotional state" refers to information that indicates the user's psychological response when interacting with the system, and is analyzed by the emotion engine.

[0168] "Optimizing notification content" refers to the act of providing information in the most easily understandable and appropriate format, depending on the user's emotional state.

[0169] A "report" is a document generated after all incident response processes are completed, and it includes processing logs and analysis results from the sentiment engine.

[0170] The embodiments for carrying out the present invention are shown below.

[0171] The server plays a central role in this system, receiving alarm signals, collecting and integrating data, and performing analysis. The underlying hardware can be a standard server device, connecting to base stations and other information sources via a communication network. The software utilizes machine learning libraries such as TensorFlow for data analysis. Furthermore, natural language processing (NLP) techniques are used to enable an emotion engine that analyzes the user's emotional state.

[0172] The server first receives an alarm signal from the base station and collects relevant information based on it. Specifically, it obtains information from the supply system's API and the work registration system's database, and then centralizes and analyzes this information. In the analysis process, the collected information is fed into a TensorFlow model to derive the cause of the failure. In user sentiment analysis, NLP is executed based on the input text data to calculate a sentiment score.

[0173] The device receives notifications sent from the server and presents them to the user. The notifications are optimized based on the user's sentiment score and are presented in an easy-to-understand format. For example, if the user is feeling stressed, the server will send a notification with a more detailed and empathetic explanation.

[0174] For example, if a network failure occurs and is caused by a power outage, the server will quickly grasp the power outage information and activate the emergency power supply. The server will then provide users with detailed information about the power outage and, if necessary, escalate the issue to the appropriate personnel. This process incorporates considerations based on sentiment analysis to ensure users do not experience unnecessary stress.

[0175] Examples of prompts when using a generative AI model are as follows:

[0176] "Conduct sentiment analysis on user responses after a power outage alarm is detected, and propose an appropriate notification method."

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server receives an alarm signal transmitted from the base station. The input is the alarm signal received over the network. The server uses this signal as a trigger to activate the next step, the data collection module. This action enables immediate fault response.

[0180] Step 2:

[0181] The server collects relevant information based on the received alarm signals. Specifically, it obtains status information by issuing SQL queries to the work registration system and collects power supply information by accessing the supply system's API. The input is the alarm signal, and the output is a set of collected relevant information. In this process, the server centralizes the information and passes it on to the next analysis stage.

[0182] Step 3:

[0183] The server uses the collected information to perform root cause analysis. It feeds the input data into a TensorFlow machine learning model to infer the root cause of the failure. Historical data is also referenced during this process. The input consists of collected relevant information and historical data, while the output is the identified cause of the failure. Once the analysis is complete, the necessary information for proceeding to the next step is available.

[0184] Step 4:

[0185] The server uses an emotion engine to analyze the user's emotional state. It analyzes the user's text input using natural language processing techniques and calculates an emotion score. The input is the user's text data, and the output is the emotion score. Based on these results, the notification content is optimized.

[0186] Step 5:

[0187] The server sends the most appropriate notification to the device based on the analysis results and sentiment analysis results. The notification is tailored to be friendly based on the user's emotional state. The input is the results of identifying the cause of the problem and the sentiment score, and the output is the customized notification. In this specific stage, the information is delivered to the user as email or app notification.

[0188] Step 6:

[0189] The server generates a detailed report after all processing is complete. It aggregates processing logs and sentiment analysis results to create a report in PDF format. The input is log data from all processes involved in the incident response, and the output is the final report. This report is sent to the relevant departments and used as feedback for future countermeasures.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0192] In modern data center operations, while rapid identification and response to technical problems are crucial, sufficient consideration is often given to the stress and emotional state of administrators. Even when there are no technical issues, administrator motivation and mental state can affect efficiency, resulting in a decline in operational efficiency.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information, and means for obtaining information from a work registration system and power supply information. This enables not only technical fault response but also the provision of optimal information and notifications tailored to the emotional state of the administrator.

[0195] A "base station" is a piece of equipment that forms part of a communication network and transmits warning signals.

[0196] An "alarm signal" is a signal transmitted from a base station when a problem occurs in a communication network.

[0197] "Related information" refers to additional data collected based on alarm signals and is used to identify the cause of the malfunction.

[0198] A "work registration system" is a system for recording and managing the progress and status of work.

[0199] "Power supply information" refers to information about the status of electricity provided by power companies.

[0200] A "communication device" is a device that transmits and receives data.

[0201] "Analysis methods" refer to the methods and techniques used to handle collected information and identify the cause of a problem.

[0202] "Escalation" is the process of promptly and appropriately requesting a higher level of action depending on the severity of the problem.

[0203] "Emotional state" refers to the mental state of users and administrators, and includes factors such as stress and satisfaction.

[0204] "Optimization" is the process of pursuing the best possible state or result under given conditions.

[0205] The system in this invention is designed to efficiently and flexibly respond to alarm signals from base stations in data center management. This system simultaneously provides technical support and administrator assistance by considering the user's emotional state and providing optimal information.

[0206] The server first receives an alarm signal from a base station on the network via a communication method. Based on this signal, the server collects relevant information from the work registration system, power supply information, and other communication devices. The collected information is analyzed using machine learning algorithms to identify the cause of the failure. Frameworks such as TensorFlow are examples of analysis algorithms used.

[0207] Furthermore, the server acquires the administrator's voice and facial expression data from smart devices and analyzes their state using an emotion engine. Google Cloud Vision API and other tools are used for analyzing emotional states. Based on the analysis results, the server provides appropriate notifications to the administrator, ensuring that the information does not overload the system.

[0208] For example, if the load on equipment in a data center suddenly increases, users wearing smart glasses can receive voice instructions in real time. If the user is detected as being under stress, the server simplifies the presentation of detailed information to help administrators respond quickly.

[0209] An example of a prompt statement using a generative AI model is as follows:

[0210] "Design a system that detects the emotional state of data center administrators and provides efficient workflow suggestions. Explain, with specific examples, what types of notifications and suggestions would be effective in reducing administrator fatigue and stress."

[0211] Thus, the present invention enables the operation of a data center that is optimized not only from a technical standpoint but also from an ergonomic perspective.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server receives an alarm signal from the base station via the network. The input is the alarm signal from the base station, and the output is the received alarm signal data. Based on this data, the server prepares to proceed to the next processing step.

[0215] Step 2:

[0216] Based on the alarm signals received by the server, it collects relevant information. Specifically, it collects necessary data from the work registration system, power supply information, and other communication devices. The input is the acquisition of alarm signals and related system data, and the output is the integrated information of this data. The server centrally manages this data, forming the basis for the subsequent analysis process.

[0217] Step 3:

[0218] The server analyzes the information it collects using machine learning algorithms. The input is integrated informational data, and the output is the result of identifying the cause of the failure. The server uses analysis tools such as TensorFlow to analyze the information in real time and identify potential problems.

[0219] Step 4:

[0220] The server acquires voice and facial expression data from the administrator's smart device and analyzes it using an emotion engine. The input is voice and video data from the smart device, and the output is the analysis result of the administrator's emotional state. The server uses the Google Cloud Vision API and other tools to evaluate the administrator's emotional state and determine levels of stress, dissatisfaction, etc.

[0221] Step 5:

[0222] The server provides administrators with optimal notifications and information based on machine learning and sentiment analysis results. Inputs are analysis results and sentiment analysis results, while output is the content of the notifications sent to administrators. The server adjusts the urgency and detail of the notifications according to the administrator's status.

[0223] Step 6:

[0224] The user receives a notification on their smart device. The input is the notification content from the server, and the output is the administrator's acceptance of the information and their subsequent actions. The user can then take appropriate action based on this notification.

[0225] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0226] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0227] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0231] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0232] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0233] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0235] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0236] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0237] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0238] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0239] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0240] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0241] This invention is an advanced server-based monitoring system for efficiently managing the operation of a communication network. The server automatically detects alarm signals from base stations and collects and analyzes relevant information. Its embodiments are described in detail below.

[0242] First, the server receives alarm signals transmitted in real time from each base station. These signals indicate that an anomaly has occurred in the network or equipment. The server analyzes the information in these alarm signals to determine their nature and priority. At this stage, the nature of the specific problem is estimated.

[0243] Next, the server proactively collects relevant information. This includes the current work status from the work registration system, the latest supply status and power outage information from the power company as power supply information, and real-time data obtained from other communication devices. This comprehensive information collection makes it possible to evaluate the underlying causes of the problem from multiple perspectives.

[0244] Subsequently, the server performs advanced analysis based on the collected information. Using machine learning algorithms, it compares past failure data with the current situation to quickly and accurately pinpoint the cause. This analysis identifies high-priority issues requiring immediate attention and cases that need escalation.

[0245] The server will send an escalation message to the user (administrator) as needed. This notification will include a summary of the problem and recommended actions to support quick decision-making.

[0246] Furthermore, the server can automatically take actions to respond to failures. For example, in the event of a power outage, it can activate emergency power supplies and establish alternative communication paths. This enables immediate response to problems and minimizes network downtime.

[0247] Finally, the server records all processing steps and generates a detailed report. This report is intended to help improve future operations and incident response. Users can use this report to further optimize and make decisions about their network, thereby improving network reliability and performance.

[0248] As a concrete example, consider a scenario where an unexpected power outage occurs at a base station. The server immediately detects the power outage alarm from this base station and identifies the cause as a power outage by checking the regional power supply information. Subsequently, it automatically issues a command to activate the emergency power supply and, after the response is complete, provides the user with a report containing detailed processing records. Once this process is complete, network stability is maintained.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The server receives alarm signals from the base station. Each time an alarm signal is generated, the server immediately analyzes the signal to determine its priority and nature.

[0252] Step 2:

[0253] The server accesses the work registration system to check the current work status. This confirms that the issue is not caused by maintenance work being carried out at the base station.

[0254] Step 3:

[0255] The server accesses power company APIs and websites to collect power supply information. It retrieves the latest information for the affected area to determine if a power outage is the cause.

[0256] Step 4:

[0257] The server retrieves relevant data from other communication devices. This includes information from the network core and neighboring base stations, which helps determine whether the anomaly is localized or global.

[0258] Step 5:

[0259] The server uses machine learning algorithms to analyze all the information it collects. It compares historical failure data with real-time data to identify the cause and scope of the failure.

[0260] Step 6:

[0261] The server will send a notification to the user if escalation is necessary. This notification will include a summary of the problem, its cause, and recommended actions.

[0262] Step 7:

[0263] The server performs automated fault response. Specifically, this includes activating emergency power in the event of a power outage and switching communication paths.

[0264] Step 8:

[0265] The server records the entire process and generates a detailed report. This report is presented to the user and used to improve future operations.

[0266] (Example 1)

[0267] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] In the operation and management of communication networks, it is necessary to quickly detect abnormal situations in real time, effectively collect and analyze information, and automate appropriate responses. Furthermore, it is crucial to efficiently record the results of these responses and utilize them for future operational improvements. A system that centrally and automatically manages these processes is needed.

[0269] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0270] In this invention, the server includes means for receiving alarm signals from a communication device, means for collecting information related to the alarm signals using a database, and means for acquiring data from various registration systems, supply status information, and other information devices. This enables the detection and response to anomalies in the communication network, as well as the recording and reporting of the results.

[0271] An "alarm signal" is a notification signal from a communication network or device indicating an anomaly, and is transmitted under specific conditions.

[0272] A "communication device" is a device that includes hardware or software for transmitting information and has the function of sending and receiving data over a network.

[0273] A "database" is a system that organizes and stores information, allowing for efficient searching and retrieval as needed.

[0274] A "various registration systems" is a mechanism that holds different management data and allows for monitoring of its status and progress.

[0275] "Supply status information" refers to data provided by external suppliers that shows the current state of resources and energy.

[0276] An "information device" is a device or system that processes, manages, or transmits information.

[0277] "Machine learning methods" are a collection of algorithms used to empirically create models based on data and perform predictions and classifications.

[0278] "Recovery processing" refers to a series of operations performed to restore a system that has experienced an abnormality or failure to a normal state.

[0279] A "report document" is a document that summarizes and describes the results of a specific activity or process, and communicates them to the relevant parties.

[0280] This invention is a monitoring system that rapidly detects anomalies in communication networks, automates effective information collection and analysis, and implements appropriate countermeasures. The server plays a central role in the network, receiving alarm signals from communication devices and aggregating and analyzing data from multiple information sources based on these signals.

[0281] Specifically, the server is connected to the communication device via a wide area network using a communication interface and receives alarm signals. In this case, TCP / IP is basically applied as the communication protocol. After receiving the alarm signal, the server analyzes the signal using Python and related data analysis libraries.

[0282] Based on the analysis, the server collects relevant information using a database and further obtains supply information and working status from external systems through an API. An automated script for efficiency is used for information collection.

[0283] Machine learning techniques are used for information analysis, and algorithms such as TensorFlow are executed to compare past data with current data. This analysis helps identify the cause of the failure and supports the determination of importance such as urgency.

[0284] After that, the server uses a notification system to report in order to notify the user of important information. Quick escalation to the administrator is achieved by sending an email via SMTP.

[0285] Furthermore, the server automatically executes recovery processing and controls the power management system as needed. This significantly reduces downtime, for example, by automatically operating the emergency power supply in the event of a power outage.

[0286] Finally, to record all processes, the server uses a log management tool such as the ELK stack to record the processing process in detail and automatically generate a report document. This report document provides materials for the user to utilize in network operation monitoring and improvement.

[0287] As a specific example, there is a case of an unexpected power outage at a base station. After the server detects the power outage alarm signal, checks the power supply information in that area, activates the emergency power supply to stabilize the situation, and then automatically sends a detailed report to the administrator to maintain operation.

[0288] An example of a prompt is: "Propose a method for receiving alarm signals from a base station and identifying the type and priority of an anomaly using pattern recognition."

[0289] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0290] Step 1:

[0291] The server receives alarm signals from the communication device. The input is real-time alarm signal data; upon receipt, the signal content is added to the analysis queue. The output is the alarm data requiring analysis. Specifically, the server waits for data packets from the base station via a continuously running socket.

[0292] Step 2:

[0293] The server analyzes the received alarm signals. In this step, it uses an analysis library such as Python to identify the signal type and priority based on the input alarm signal. The output is the characteristic data of the analyzed signal. In specific operation, the server compares the signal data with past logs to recognize anomaly patterns.

[0294] Step 3:

[0295] The server collects relevant information. This involves retrieving information from a database containing information about the underlying alarm signal and communicating with external systems via API. The input is initial analysis data related to the alarm, and the output is an aggregated set of information. In practice, API requests are issued via automated scripts to obtain information from the work registration system and supply status.

[0296] Step 4:

[0297] The server analyzes information using machine learning techniques. The input consists of collected relevant information and historical datasets. Based on this, it runs models such as TensorFlow to identify the cause of failures. The output is the result of the cause analysis. Specifically, the server performs scoring that suggests inferences based on a database of past failures.

[0298] Step 5:

[0299] The server sends escalation messages to the user as needed. The input to this process is analysis results indicating high-priority issues, and the output is a notification message to the user. Specifically, the server uses SMTP to send an email to the administrator, quickly informing them of the actions that need to be taken.

[0300] Step 6:

[0301] The server automatically performs the recovery process. Based on pre-configured recovery rules, it receives problem data requiring action as input and takes countermeasures such as activating the emergency power supply. The output indicates a recovery complete state. Specific actions include the server sending instructions to the power management system via an API.

[0302] Step 7:

[0303] The server records the process and generates a report. At this stage, it receives log information of the entire process as input and creates a detailed report as output. Specifically, it uses the ELK stack to organize the logs and sends the report to the user in PDF format.

[0304] (Application Example 1)

[0305] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0306] Modern communication networks are prone to abnormalities due to a variety of factors, thus necessitating their monitoring and prompt response. However, in conventional systems, the detection and reporting of abnormalities are often delayed, frequently leading to delays in response. Additionally, it takes time to identify the cause of an abnormality, which may consequently compromise the stability of the network. As a solution to such problems, a system that can detect abnormalities in real time, immediately notify users, and take appropriate countermeasures is required.

[0307] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0308] In this invention, the server includes means for monitoring an alarm signal received from a base station, means for collecting related information based on the alarm signal, and means for acquiring information from a work registration system, power supply information, and other communication devices. Thereby, abnormalities can be detected in real time, the cause can be quickly identified, users can be immediately notified, and appropriate countermeasures can be proposed.

[0309] A "base station" is a device that transmits and receives radio signals in a communication network and plays a role in maintaining connections with devices.

[0310] An "alarm signal" is a signal indicating that an abnormality has occurred in a network or facility and serves as an important trigger in a monitoring system.

[0311] A "work registration system" is a system for managing work schedules and statuses and recording related information.

[0312] "Power supply information" is data indicating the current power supply situation and power outage information provided by a power company.

[0313] A "communication device" is a device or apparatus connected to a network for transmitting and receiving data.

[0314] "Escalation" refers to the process of reporting information to higher management and requesting additional action when a problem is not resolved.

[0315] A "machine learning algorithm" is a mathematical model that learns patterns and rules from data and automatically makes predictions and decisions.

[0316] A "generative AI model" is a pre-trained model that uses artificial intelligence to generate text and images.

[0317] A "prompt" is a text of instructions or questions used to elicit a specific response or output from an AI.

[0318] In embodiments of this invention, the server functions as the primary hardware for monitoring the communication network. The server includes a network connectivity device for receiving alarm signals transmitted from a base station. It also has interfaces for acquiring necessary information from external systems, including communication devices, a work registration system, and power supply information.

[0319] The program runs on a server and is implemented using Python. MySQL is used as the database to properly store and manage received alarm signals and related information. This allows the server to analyze data and identify anomalies in real time.

[0320] Machine learning algorithms are used for data analysis. Machine learning frameworks such as TensorFlow are applied to compare historical failure data with real-time data. This allows the server to quickly identify the cause of failures and, if an anomaly is detected, immediately send a notification to the user's mobile device.

[0321] The user's device has an application developed with Flutter installed, which displays alarm information in real time and suggests recommended countermeasures. By utilizing a generative AI model, appropriate countermeasures for identified anomalies can be provided to the device as prompt messages.

[0322] As a concrete example, if the server detects suspicious traffic on the network, it generates a prompt message saying, "Have you detected abnormal network traffic? Please tell me the cause and recommended course of action," and notifies the user's terminal. This process allows the user to immediately understand the situation and take appropriate action.

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server receives alarm signals transmitted from the base station. The input is the alarm signal emitted from the base station, and the output is the received signal information. The server temporarily stores the received alarm signal in memory.

[0326] Step 2:

[0327] The server collects relevant information based on the received alarm signal. The input is the alarm signal, and the output is a set of relevant information. The server obtains relevant data from the work registration system, the latest power supply information, and other communication devices, integrates them, and stores them in a database.

[0328] Step 3:

[0329] The server uses machine learning algorithms to analyze historical and real-time data to identify the root cause of failures. The input consists of collected data and historical data, while the output is the identified cause of the anomaly. The server utilizes TensorFlow for data analysis, enabling rapid identification of the cause of the anomaly.

[0330] Step 4:

[0331] Based on the identified anomaly, the server generates a prompt message using a generative AI model. The input is information about the cause of the anomaly, and the output is a prompt message to notify the user. The server generates an appropriate prompt message according to the anomaly and sends it to the user's terminal.

[0332] Step 5:

[0333] The terminal displays prompt messages received from the server on its screen, notifying the user of the anomaly and the necessary countermeasures. The input is prompt messages from the server, and the output is a visual display of information for the user. The terminal uses a Flutter application to inform the user of the situation in real time.

[0334] Step 6:

[0335] The user selects and executes the appropriate response based on the prompt messages provided by the terminal. The input is information from the terminal, and the output is the action taken based on the user's decision. The user takes the necessary actions to maintain network stability, referring to the provided countermeasures.

[0336] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0337] The system in this invention achieves efficient and adaptive operation of a communication network by combining multiple functions centered around a server. This system includes communication means for receiving alarm signals from base stations, functions for collecting related information, and machine learning analysis means, as well as an emotion engine for recognizing user emotions.

[0338] The server first receives alarm signals transmitted from the base station and collects various data based on these signals. It checks the work status from the work registration system, collects power supply information and information from other communication devices, and integrates it. This information is analyzed by machine learning algorithms to identify the cause of the failure.

[0339] The emotion engine analyzes a user's emotional state based on their words, actions, and responses when they interact with the system. This analysis is then used by the server to escalate or notify the user. For example, if a user is experiencing stress, the system may provide a more detailed and easier-to-understand explanation or optimize escalation alerts according to their urgency.

[0340] As a specific use case, let's consider a network failure. First, the server detects a power outage alarm from the base station and obtains power outage information from the power company. If the analysis determines that a power outage is the cause, it automatically activates the emergency power supply. While handling the failure, the emotion engine analyzes the user's responses and provides optimal information according to their stress level. Throughout this process, notifications are provided in a way that takes into consideration the user's ability to avoid unnecessary stress.

[0341] Furthermore, at the end of all processes, the server generates a detailed report that includes the results of the emotion engine's analysis. This allows for system improvements not only from a technical perspective but also from an ergonomic perspective during post-event reviews and the development of future countermeasures. This invention improves not only the technical aspects but also the user experience and is expected to make a significant contribution to next-generation network operations.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The server receives an alarm signal from the base station. This signal indicates that an anomaly has occurred within the network, and the server immediately analyzes the alarm to determine its nature.

[0345] Step 2:

[0346] The server accesses the work registration system to retrieve information on relevant work and maintenance. This allows it to verify whether any ongoing work is causing an alarm.

[0347] Step 3:

[0348] The server connects to the power company's API to check the power supply status and makes inquiries. It retrieves power outage information and evaluates its correlation with alarms.

[0349] Step 4:

[0350] The server collects real-time data from other communication devices to understand the environment around the base station and the network status. This allows for the assessment of the extent and impact of any anomalies.

[0351] Step 5:

[0352] The server uses machine learning models to analyze collected data and identify the root cause of failures. This analysis process references both historical and real-time data.

[0353] Step 6:

[0354] The emotion engine analyzes the user's emotional state. Based on the user's words and actions when interacting with the system, it evaluates stress levels and emotional intensity.

[0355] Step 7:

[0356] Based on the analysis results, the server notifies the user of escalation as needed. The notification content is customized according to the evaluation results of the emotion engine. The tone and level of detail of the notification are adjusted according to the user's emotional state.

[0357] Step 8:

[0358] The server automatically handles failures. For example, it immediately takes specific measures to resolve the problem, such as activating emergency power or setting up alternative routes.

[0359] Step 9:

[0360] The server records all processing steps and results and generates a detailed report. This report also includes the results of the user's sentiment analysis by the sentiment engine and is provided to the user to help with future improvements.

[0361] (Example 2)

[0362] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0363] In modern communication networks, rapid and effective responses to failures are essential. However, in conventional systems, identifying the cause of a failure and gathering information for resolving it are often done manually, which is time-consuming and labor-intensive. As a result, users tend to experience stress, and the efficiency of network operations decreases.

[0364] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0365] In this invention, the server includes means for monitoring alarm signals received from a base station, means for acquiring relevant information based on the alarm signals, and means for analyzing the user's emotional state and optimizing notification content. This enables rapid and appropriate information provision and response in the event of a failure, reducing user stress and improving the efficiency of network operations.

[0366] A "warning signal" is a signal transmitted from a base station to indicate an anomaly or malfunction in the communication network and to prompt a quick response.

[0367] "Related information" refers to data necessary to identify the cause of the malfunction, and is obtained from the work registration system, supply system, and other communication devices.

[0368] "Processing means" refers to systems and algorithms used to analyze collected information and identify the cause of a problem.

[0369] "User emotional state" refers to information that indicates the user's psychological response when interacting with the system, and is analyzed by the emotion engine.

[0370] "Optimizing notification content" refers to the act of providing information in the most easily understandable and appropriate format, depending on the user's emotional state.

[0371] A "report" is a document generated after all incident response processes are completed, and it includes processing logs and analysis results from the sentiment engine.

[0372] The embodiments for carrying out the present invention are shown below.

[0373] The server plays a central role in this system, receiving alarm signals, collecting and integrating data, and performing analysis. The underlying hardware can be a standard server device, connecting to base stations and other information sources via a communication network. The software utilizes machine learning libraries such as TensorFlow for data analysis. Furthermore, natural language processing (NLP) techniques are used to enable an emotion engine that analyzes the user's emotional state.

[0374] The server first receives an alarm signal from the base station and collects relevant information based on it. Specifically, it obtains information from the supply system's API and the work registration system's database, and then centralizes and analyzes this information. In the analysis process, the collected information is fed into a TensorFlow model to derive the cause of the failure. In user sentiment analysis, NLP is executed based on the input text data to calculate a sentiment score.

[0375] The device receives notifications sent from the server and presents them to the user. The notifications are optimized based on the user's sentiment score and are presented in an easy-to-understand format. For example, if the user is feeling stressed, the server will send a notification with a more detailed and empathetic explanation.

[0376] For example, if a network failure occurs and is caused by a power outage, the server will quickly grasp the power outage information and activate the emergency power supply. The server will then provide users with detailed information about the power outage and, if necessary, escalate the issue to the appropriate personnel. This process incorporates considerations based on sentiment analysis to ensure users do not experience unnecessary stress.

[0377] The following are examples of prompts when using a generative AI model:

[0378] "Conduct sentiment analysis on user responses after a power outage alarm is detected, and propose an appropriate notification method."

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The server receives an alarm signal transmitted from the base station. The input is the alarm signal received over the network. The server uses this signal as a trigger to activate the next step, the data collection module. This action enables immediate fault response.

[0382] Step 2:

[0383] The server collects relevant information based on the received alarm signals. Specifically, it obtains status information by issuing SQL queries to the work registration system and collects power supply information by accessing the supply system's API. The input is the alarm signal, and the output is a set of collected relevant information. In this process, the server centralizes the information and passes it on to the next analysis stage.

[0384] Step 3:

[0385] The server uses the collected information to perform root cause analysis. It feeds the input data into a TensorFlow machine learning model to infer the root cause of the failure. Historical data is also referenced during this process. The input consists of collected relevant information and historical data, while the output is the identified cause of the failure. Once the analysis is complete, the necessary information for proceeding to the next step is available.

[0386] Step 4:

[0387] The server uses an emotion engine to analyze the user's emotional state. It analyzes the user's text input using natural language processing techniques and calculates an emotion score. The input is the user's text data, and the output is the emotion score. Based on these results, the notification content is optimized.

[0388] Step 5:

[0389] The server sends the most appropriate notification to the device based on the analysis results and sentiment analysis results. The notification is tailored to be friendly based on the user's emotional state. The input is the results of identifying the cause of the problem and the sentiment score, and the output is the customized notification. In this specific stage, the information is delivered to the user as email or app notification.

[0390] Step 6:

[0391] The server generates a detailed report after all processing is complete. It aggregates processing logs and sentiment analysis results to create a report in PDF format. The input is log data from all processes involved in the incident response, and the output is the final report. This report is sent to the relevant departments and used as feedback for future countermeasures.

[0392] (Application Example 2)

[0393] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0394] In modern data center operations, while rapid identification and response to technical problems are crucial, sufficient consideration is often given to the stress and emotional state of administrators. Even when there are no technical issues, administrator motivation and mental state can affect efficiency, resulting in a decline in operational efficiency.

[0395] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0396] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information, and means for obtaining information from a work registration system and power supply information. This enables not only technical fault response but also the provision of optimal information and notifications tailored to the emotional state of the administrator.

[0397] A "base station" is a piece of equipment that forms part of a communication network and transmits warning signals.

[0398] An "alarm signal" is a signal transmitted from a base station when a problem occurs in a communication network.

[0399] "Related information" refers to additional data collected based on alarm signals and is used to identify the cause of the malfunction.

[0400] A "work registration system" is a system for recording and managing the progress and status of work.

[0401] "Power supply information" refers to information about the status of electricity provided by power companies.

[0402] A "communication device" is a device that transmits and receives data.

[0403] "Analysis methods" refer to the methods and techniques used to handle collected information and identify the cause of a problem.

[0404] "Escalation" is the process of promptly and appropriately requesting a higher level of action depending on the severity of the problem.

[0405] "Emotional state" refers to the mental state of users and administrators, and includes factors such as stress and satisfaction.

[0406] "Optimization" is the process of pursuing the best possible state or result under given conditions.

[0407] The system in this invention is designed to efficiently and flexibly respond to alarm signals from base stations in data center management. This system simultaneously provides technical support and administrator assistance by considering the user's emotional state and providing optimal information.

[0408] The server first receives an alarm signal from a base station on the network via a communication method. Based on this signal, the server collects relevant information from the work registration system, power supply information, and other communication devices. The collected information is analyzed using machine learning algorithms to identify the cause of the failure. Frameworks such as TensorFlow are examples of analysis algorithms used.

[0409] Furthermore, the server acquires the administrator's voice and facial expression data from smart devices and analyzes their state using an emotion engine. The Google Cloud Vision API and other tools are used for this emotional state analysis. Based on the analysis results, the server provides appropriate notifications to the administrator, ensuring that the information does not overload the system.

[0410] For example, if the load on equipment in a data center suddenly increases, users wearing smart glasses can receive voice instructions in real time. If the user is detected as being under stress, the server simplifies the presentation of detailed information to help administrators respond quickly.

[0411] An example of a prompt statement using a generative AI model is as follows:

[0412] "Design a system that detects the emotional state of data center administrators and provides efficient workflow suggestions. Explain, with specific examples, what types of notifications and suggestions would be effective in reducing administrator fatigue and stress."

[0413] Thus, the present invention enables the operation of a data center that is optimized not only from a technical standpoint but also from an ergonomic perspective.

[0414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0415] Step 1:

[0416] The server receives an alarm signal from the base station via the network. The input is the alarm signal from the base station, and the output is the received alarm signal data. Based on this data, the server prepares to proceed to the next processing step.

[0417] Step 2:

[0418] Based on the alarm signals received by the server, it collects relevant information. Specifically, it collects necessary data from the work registration system, power supply information, and other communication devices. The input is the acquisition of alarm signals and related system data, and the output is the integrated information of this data. The server centrally manages this data, forming the basis for the subsequent analysis process.

[0419] Step 3:

[0420] The server analyzes the information it collects using machine learning algorithms. The input is integrated informational data, and the output is the result of identifying the cause of the failure. The server uses analysis tools such as TensorFlow to analyze the information in real time and identify potential problems.

[0421] Step 4:

[0422] The server acquires voice and facial expression data from the administrator's smart device and analyzes it using an emotion engine. The input is voice and video data from the smart device, and the output is the analysis result of the administrator's emotional state. The server uses the Google Cloud Vision API and other tools to evaluate the administrator's emotional state and determine levels of stress, dissatisfaction, etc.

[0423] Step 5:

[0424] The server provides administrators with optimal notifications and information based on machine learning and sentiment analysis results. Inputs are analysis results and sentiment analysis results, while output is the content of the notifications sent to administrators. The server adjusts the urgency and detail of the notifications according to the administrator's status.

[0425] Step 6:

[0426] The user receives a notification on their smart device. The input is the notification content from the server, and the output is the administrator's acceptance of the information and their subsequent actions. The user can then take appropriate action based on this notification.

[0427] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0428] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0429] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0433] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0434] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0435] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0436] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0437] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0438] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0439] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0440] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0441] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0442] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0443] This invention is an advanced server-based monitoring system for efficiently managing the operation of a communication network. The server automatically detects alarm signals from base stations and collects and analyzes relevant information. Its embodiments are described in detail below.

[0444] First, the server receives alarm signals transmitted in real time from each base station. These signals indicate that an anomaly has occurred in the network or equipment. The server analyzes the information in these alarm signals to determine their nature and priority. At this stage, the nature of the specific problem is estimated.

[0445] Next, the server proactively collects relevant information. This includes the current work status from the work registration system, the latest supply status and power outage information from the power company as power supply information, and real-time data obtained from other communication devices. This comprehensive information collection makes it possible to evaluate the underlying causes of the problem from multiple perspectives.

[0446] Subsequently, the server performs advanced analysis based on the collected information. Using machine learning algorithms, it compares past failure data with the current situation to quickly and accurately pinpoint the cause. This analysis identifies high-priority issues requiring immediate attention and cases that need escalation.

[0447] The server will send an escalation message to the user (administrator) as needed. This notification will include a summary of the problem and recommended actions to support quick decision-making.

[0448] Furthermore, the server can automatically take actions to respond to failures. For example, in the event of a power outage, it can activate emergency power supplies and establish alternative communication paths. This enables immediate response to problems and minimizes network downtime.

[0449] Finally, the server records all processing steps and generates a detailed report. This report is intended to help improve future operations and incident response. Users can use this report to further optimize and make decisions about their network, thereby improving network reliability and performance.

[0450] As a concrete example, consider a scenario where an unexpected power outage occurs at a base station. The server immediately detects the power outage alarm from this base station and identifies the cause as a power outage by checking the regional power supply information. Subsequently, it automatically issues a command to activate the emergency power supply and, after the response is complete, provides the user with a report containing detailed processing records. Once this process is complete, network stability is maintained.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The server receives alarm signals from the base station. Each time an alarm signal is generated, the server immediately analyzes the signal to determine its priority and nature.

[0454] Step 2:

[0455] The server accesses the work registration system to check the current work status. This confirms that the issue is not caused by maintenance work being carried out at the base station.

[0456] Step 3:

[0457] The server accesses power company APIs and websites to collect power supply information. It retrieves the latest information for the affected area to determine if a power outage is the cause.

[0458] Step 4:

[0459] The server retrieves relevant data from other communication devices. This includes information from the network core and neighboring base stations, which helps determine whether the anomaly is localized or global.

[0460] Step 5:

[0461] The server uses machine learning algorithms to analyze all the information it collects. It compares historical failure data with real-time data to identify the cause and scope of the failure.

[0462] Step 6:

[0463] The server will send a notification to the user if escalation is necessary. This notification will include a summary of the problem, its cause, and recommended actions.

[0464] Step 7:

[0465] The server performs automated fault response. Specifically, this includes activating emergency power in the event of a power outage and switching communication paths.

[0466] Step 8:

[0467] The server records the entire process and generates a detailed report. This report is presented to the user and used to improve future operations.

[0468] (Example 1)

[0469] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] In the operation and management of communication networks, it is necessary to quickly detect abnormal situations in real time, effectively collect and analyze information, and automate appropriate responses. Furthermore, it is crucial to efficiently record the results of these responses and utilize them for future operational improvements. A system that centrally and automatically manages these processes is needed.

[0471] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0472] In this invention, the server includes means for receiving alarm signals from a communication device, means for collecting information related to the alarm signals using a database, and means for acquiring data from various registration systems, supply status information, and other information devices. This enables the detection and response to anomalies in the communication network, as well as the recording and reporting of the results.

[0473] An "alarm signal" is a notification signal from a communication network or device indicating an anomaly, and is transmitted under specific conditions.

[0474] A "communication device" is a device that includes hardware or software for transmitting information and has the function of sending and receiving data over a network.

[0475] A "database" is a system that organizes and stores information, allowing for efficient searching and retrieval as needed.

[0476] A "various registration systems" is a mechanism that holds different management data and allows for monitoring of its status and progress.

[0477] "Supply status information" refers to data provided by external suppliers that shows the current state of resources and energy.

[0478] An "information device" is a device or system that processes, manages, or transmits information.

[0479] "Machine learning methods" are a collection of algorithms used to empirically create models based on data and perform predictions and classifications.

[0480] "Recovery processing" refers to a series of operations performed to restore a system that has experienced an abnormality or failure to a normal state.

[0481] A "report document" is a document that summarizes and describes the results of a specific activity or process, and communicates them to the relevant parties.

[0482] This invention is a monitoring system that rapidly detects anomalies in communication networks, automates effective information collection and analysis, and implements appropriate countermeasures. The server plays a central role in the network, receiving alarm signals from communication devices and aggregating and analyzing data from multiple information sources based on these signals.

[0483] Specifically, the server connects to the communication device via a wide-area network using a communication interface to receive alarm signals. TCP / IP is the primary communication protocol used in this process. After receiving the alarm signal, the server analyzes the signal using Python and related data analysis libraries.

[0484] Based on the analysis, the server collects relevant information using a database and also retrieves supply information and work status from external systems via APIs. Automated scripts are used to streamline information collection.

[0485] Machine learning techniques are used to analyze the information, and algorithms such as TensorFlow are executed to compare historical data with current data. This analysis helps identify the cause of failures and assists in determining their importance, such as urgency.

[0486] Subsequently, the server uses a notification system to report important information to the user. Sending emails via SMTP enables rapid escalation to administrators.

[0487] Furthermore, the server automatically performs recovery processes and controls the power management system as needed. This significantly reduces downtime, for example, by automatically operating the emergency power supply in the event of a power outage.

[0488] Finally, to record all processes, the server uses log management tools such as the ELK stack to meticulously record the processing steps and automatically generates a report. This report provides users with information to help monitor and improve network operations.

[0489] A concrete example is the case of an unexpected power outage at a base station. The server detects the power outage alarm signal, checks the power supply information for the area, activates the emergency power supply to stabilize the situation, and then automatically sends a detailed report to the administrator to maintain operations.

[0490] An example of a prompt is: "Propose a method for receiving alarm signals from a base station and identifying the type and priority of an anomaly using pattern recognition."

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The server receives alarm signals from the communication device. The input is real-time alarm signal data; upon receipt, the signal content is added to the analysis queue. The output is the alarm data requiring analysis. Specifically, the server waits for data packets from the base station via a continuously running socket.

[0494] Step 2:

[0495] The server analyzes the received alarm signals. In this step, it uses an analysis library such as Python based on the input alarm signals to identify the signal type and priority. The output is the characteristic data of the analyzed signals. In specific operation, the server compares the signal data with past logs to recognize anomaly patterns.

[0496] Step 3:

[0497] The server collects relevant information. This involves retrieving information from a database containing information about the underlying alarm signal and communicating with external systems via API. The input is initial analysis data related to the alarm, and the output is an aggregated set of information. In practice, API requests are issued via automated scripts to obtain information from the work registration system and supply status.

[0498] Step 4:

[0499] The server analyzes information using machine learning techniques. The input consists of collected relevant information and historical datasets. Based on this, it runs models such as TensorFlow to identify the cause of failures. The output is the result of the cause analysis. Specifically, the server performs scoring that suggests inferences based on a database of past failures.

[0500] Step 5:

[0501] The server sends escalation messages to the user as needed. The input to this process is analysis results indicating high-priority issues, and the output is a notification message to the user. Specifically, the server uses SMTP to send an email to the administrator, quickly informing them of the actions that need to be taken.

[0502] Step 6:

[0503] The server automatically performs the recovery process. Based on pre-configured recovery rules, it receives problem data requiring action as input and takes countermeasures such as activating the emergency power supply. The output indicates a recovery complete state. Specific actions include the server sending instructions to the power management system via an API.

[0504] Step 7:

[0505] The server records the process and generates a report. At this stage, it receives log information of the entire process as input and creates a detailed report as output. Specifically, it uses the ELK stack to organize the logs and sends the report to the user in PDF format.

[0506] (Application Example 1)

[0507] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] Modern communication networks are susceptible to anomalies due to a wide variety of factors, requiring monitoring and rapid response. However, conventional systems often suffer from delays in anomaly detection and reporting, leading to delayed responses. Furthermore, identifying the cause of anomalies can be time-consuming, potentially compromising network stability. To address these challenges, a system is needed that can detect anomalies in real time, immediately notify users, and enable appropriate countermeasures.

[0509] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0510] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information based on the alarm signals, and means for acquiring information from a work registration system, power supply information, and other communication devices. This enables the detection of anomalies in real time, rapid identification of their causes, immediate notification to users, and the suggestion of appropriate countermeasures.

[0511] A "base station" is a device in a communication network that transmits and receives wireless signals and maintains connections with devices.

[0512] An "alarm signal" is a signal that indicates an abnormality has occurred in the network or equipment, and it serves as an important trigger in monitoring systems.

[0513] A "work registration system" is a system for managing work schedules and statuses, and for recording related information.

[0514] "Power supply information" refers to data provided by power companies that shows the current power supply status and information on power outages.

[0515] A "communication device" is a device or apparatus that is connected to a network and transmits and receives data.

[0516] "Escalation" refers to the process of reporting information to higher management and requesting additional action when a problem is not resolved.

[0517] A "machine learning algorithm" is a mathematical model that learns patterns and rules from data and automatically makes predictions and decisions.

[0518] A "generative AI model" is a pre-trained model that uses artificial intelligence to generate text and images.

[0519] A "prompt" is a text of instructions or questions used to elicit a specific response or output from an AI.

[0520] In embodiments of this invention, the server functions as the primary hardware for monitoring the communication network. The server includes a network connectivity device for receiving alarm signals transmitted from a base station. It also has interfaces for acquiring necessary information from external systems, including communication devices, a work registration system, and power supply information.

[0521] The program runs on a server and is implemented using Python. MySQL is used as the database to properly store and manage received alarm signals and related information. This allows the server to analyze data and identify anomalies in real time.

[0522] Machine learning algorithms are used for data analysis. Machine learning frameworks such as TensorFlow are applied to the process of comparing historical failure data with data acquired in real time. This allows the server to quickly identify the cause of failures and immediately send notifications to users' mobile devices if an anomaly is detected.

[0523] The user's device has an application developed with Flutter installed, which displays alarm information in real time and suggests recommended countermeasures. By utilizing a generative AI model, appropriate countermeasures for identified anomalies can be provided to the device as prompt messages.

[0524] As a concrete example, if the server detects suspicious traffic on the network, it generates a prompt message saying, "Have you detected abnormal network traffic? Please tell me the cause and recommended course of action," and notifies the user's terminal. This process allows the user to immediately understand the situation and take appropriate action.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The server receives alarm signals transmitted from the base station. The input is the alarm signal emitted from the base station, and the output is the received signal information. The server temporarily stores the received alarm signal in memory.

[0528] Step 2:

[0529] The server collects relevant information based on the received alarm signal. The input is the alarm signal, and the output is a set of relevant information. The server obtains relevant data from the work registration system, the latest power supply information, and other communication devices, integrates them, and stores them in a database.

[0530] Step 3:

[0531] The server uses machine learning algorithms to analyze historical and real-time data to identify the root cause of failures. The input consists of collected data and historical data, while the output is the identified cause of the anomaly. The server utilizes TensorFlow for data analysis, enabling rapid identification of the cause of the anomaly.

[0532] Step 4:

[0533] Based on the identified anomaly, the server generates a prompt message using a generative AI model. The input is information about the cause of the anomaly, and the output is a prompt message to notify the user. The server generates an appropriate prompt message according to the anomaly and sends it to the user's terminal.

[0534] Step 5:

[0535] The terminal displays prompt messages received from the server on its screen, notifying the user of the anomaly and the necessary countermeasures. The input is prompt messages from the server, and the output is a visual display of information for the user. The terminal uses a Flutter application to inform the user of the situation in real time.

[0536] Step 6:

[0537] The user selects and executes the appropriate response based on the prompt messages provided by the terminal. The input is information from the terminal, and the output is the action taken based on the user's decision. The user takes the necessary actions to maintain network stability, referring to the provided countermeasures.

[0538] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0539] The system in this invention achieves efficient and adaptive operation of a communication network by combining multiple functions centered around a server. This system includes communication means for receiving alarm signals from base stations, functions for collecting related information, and machine learning analysis means, as well as an emotion engine for recognizing user emotions.

[0540] The server first receives alarm signals transmitted from the base station and collects various data based on these signals. It checks the work status from the work registration system, collects power supply information and information from other communication devices, and integrates it. This information is analyzed by machine learning algorithms to identify the cause of the failure.

[0541] The emotion engine analyzes a user's emotional state based on their words, actions, and responses when they interact with the system. This analysis is then used by the server to escalate or notify the user. For example, if a user is experiencing stress, the system may provide a more detailed and easier-to-understand explanation or optimize escalation alerts according to their urgency.

[0542] As a specific use case, let's consider a network failure. First, the server detects a power outage alarm from the base station and obtains power outage information from the power company. If the analysis determines that a power outage is the cause, it automatically activates the emergency power supply. While handling the failure, the emotion engine analyzes the user's responses and provides optimal information according to their stress level. Throughout this process, notifications are provided in a way that takes into consideration the user's ability to avoid unnecessary stress.

[0543] Furthermore, at the end of all processes, the server generates a detailed report that includes the results of the emotion engine's analysis. This allows for system improvements not only from a technical perspective but also from an ergonomic perspective during post-event reviews and the development of future countermeasures. This invention improves not only the technical aspects but also the user experience and is expected to make a significant contribution to next-generation network operations.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The server receives an alarm signal from the base station. This signal indicates that an anomaly has occurred within the network, and the server immediately analyzes the alarm to determine its nature.

[0547] Step 2:

[0548] The server accesses the work registration system to retrieve information on relevant work and maintenance. This allows it to verify whether any ongoing work is causing an alarm.

[0549] Step 3:

[0550] The server connects to the power company's API to check the power supply status and makes inquiries. It retrieves power outage information and evaluates its correlation with alarms.

[0551] Step 4:

[0552] The server collects real-time data from other communication devices to understand the environment around the base station and the network status. This allows for the assessment of the extent and impact of any anomalies.

[0553] Step 5:

[0554] The server uses machine learning models to analyze collected data and identify the root cause of failures. This analysis process references both historical and real-time data.

[0555] Step 6:

[0556] The emotion engine analyzes the user's emotional state. Based on the user's words and actions when interacting with the system, it evaluates stress levels and emotional intensity.

[0557] Step 7:

[0558] Based on the analysis results, the server notifies the user of escalation as needed. The notification content is customized according to the evaluation results of the emotion engine. The tone and level of detail of the notification are adjusted according to the user's emotional state.

[0559] Step 8:

[0560] The server automatically handles failures. For example, it immediately takes specific measures to resolve the problem, such as activating emergency power or setting up alternative routes.

[0561] Step 9:

[0562] The server records all processing steps and results and generates a detailed report. This report also includes the results of the user's sentiment analysis by the sentiment engine and is provided to the user to help with future improvements.

[0563] (Example 2)

[0564] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0565] In modern communication networks, rapid and effective responses to failures are essential. However, in conventional systems, identifying the cause of a failure and gathering information for resolving it are often done manually, which is time-consuming and labor-intensive. As a result, users tend to experience stress, and the efficiency of network operations decreases.

[0566] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0567] In this invention, the server includes means for monitoring alarm signals received from a base station, means for acquiring relevant information based on the alarm signals, and means for analyzing the user's emotional state and optimizing notification content. This enables rapid and appropriate information provision and response in the event of a failure, reducing user stress and improving the efficiency of network operations.

[0568] A "warning signal" is a signal transmitted from a base station to indicate an anomaly or malfunction in the communication network and to prompt a quick response.

[0569] "Related information" refers to data necessary to identify the cause of the malfunction, and is obtained from the work registration system, supply system, and other communication devices.

[0570] "Processing means" refers to systems and algorithms used to analyze collected information and identify the cause of a problem.

[0571] "User emotional state" refers to information that indicates the user's psychological response when interacting with the system, and is analyzed by the emotion engine.

[0572] "Optimizing notification content" refers to the act of providing information in the most easily understandable and appropriate format, depending on the user's emotional state.

[0573] A "report" is a document generated after all incident response processes are completed, and it includes processing logs and analysis results from the sentiment engine.

[0574] The embodiments for carrying out the present invention are shown below.

[0575] The server plays a central role in this system, receiving alarm signals, collecting and integrating data, and performing analysis. The underlying hardware can be a standard server device, connecting to base stations and other information sources via a communication network. The software utilizes machine learning libraries such as TensorFlow for data analysis. Furthermore, natural language processing (NLP) techniques are used to enable an emotion engine that analyzes the user's emotional state.

[0576] The server first receives an alarm signal from the base station and collects relevant information based on it. Specifically, it obtains information from the supply system's API and the work registration system's database, and then centralizes and analyzes this information. In the analysis process, the collected information is fed into a TensorFlow model to derive the cause of the failure. In user sentiment analysis, NLP is executed based on the input text data to calculate a sentiment score.

[0577] The device receives notifications sent from the server and presents them to the user. The notifications are optimized based on the user's sentiment score and are presented in an easy-to-understand format. For example, if the user is feeling stressed, the server will send a notification with a more detailed and empathetic explanation.

[0578] For example, if a network failure occurs and is caused by a power outage, the server will quickly grasp the power outage information and activate the emergency power supply. The server will then provide users with detailed information about the power outage and, if necessary, escalate the issue to the appropriate personnel. This process incorporates considerations based on sentiment analysis to ensure users do not experience unnecessary stress.

[0579] Examples of prompts when using a generative AI model are as follows:

[0580] "Conduct sentiment analysis on user responses after a power outage alarm is detected, and propose an appropriate notification method."

[0581] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0582] Step 1:

[0583] The server receives an alarm signal transmitted from the base station. The input is the alarm signal received over the network. The server uses this signal as a trigger to activate the next step, the data collection module. This action enables immediate fault response.

[0584] Step 2:

[0585] The server collects relevant information based on the received alarm signals. Specifically, it obtains status information by issuing SQL queries to the work registration system and collects power supply information by accessing the supply system's API. The input is the alarm signal, and the output is a set of collected relevant information. In this process, the server centralizes the information and passes it on to the next analysis stage.

[0586] Step 3:

[0587] The server uses the collected information to perform root cause analysis. It feeds the input data into a TensorFlow machine learning model to infer the root cause of the failure. Historical data is also referenced during this process. The input consists of collected relevant information and historical data, while the output is the identified cause of the failure. Once the analysis is complete, the necessary information for proceeding to the next step is available.

[0588] Step 4:

[0589] The server uses an emotion engine to analyze the user's emotional state. It analyzes the user's text input using natural language processing techniques and calculates an emotion score. The input is the user's text data, and the output is the emotion score. Based on these results, the notification content is optimized.

[0590] Step 5:

[0591] The server sends the most appropriate notification to the device based on the analysis results and sentiment analysis results. The notification is tailored to be friendly based on the user's emotional state. The input is the results of identifying the cause of the problem and the sentiment score, and the output is the customized notification. In this specific stage, the information is delivered to the user as email or app notification.

[0592] Step 6:

[0593] The server generates a detailed report after all processing is complete. It aggregates processing logs and sentiment analysis results to create a report in PDF format. The input is log data from all processes involved in the incident response, and the output is the final report. This report is sent to the relevant departments and used as feedback for future countermeasures.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0596] In modern data center operations, while rapid identification and response to technical problems are crucial, sufficient consideration is often given to the stress and emotional state of administrators. Even when there are no technical issues, administrator motivation and mental state can affect efficiency, resulting in a decline in operational efficiency.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0598] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information, and means for obtaining information from a work registration system and power supply information. This enables not only technical fault response but also the provision of optimal information and notifications tailored to the emotional state of the administrator.

[0599] A "base station" is a piece of equipment that forms part of a communication network and transmits warning signals.

[0600] An "alarm signal" is a signal transmitted from a base station when a problem occurs in a communication network.

[0601] "Related information" refers to additional data collected based on alarm signals and is used to identify the cause of the malfunction.

[0602] A "work registration system" is a system for recording and managing the progress and status of work.

[0603] "Power supply information" refers to information about the status of electricity provided by power companies.

[0604] A "communication device" is a device that transmits and receives data.

[0605] "Analysis methods" refer to the methods and techniques used to handle collected information and identify the cause of a problem.

[0606] "Escalation" is the process of promptly and appropriately requesting a higher level of action depending on the severity of the problem.

[0607] "Emotional state" refers to the mental state of users and administrators, and includes factors such as stress and satisfaction.

[0608] "Optimization" is the process of pursuing the best possible state or result under given conditions.

[0609] The system in this invention is designed to efficiently and flexibly respond to alarm signals from base stations in data center management. This system simultaneously provides technical support and administrator assistance by considering the user's emotional state and providing optimal information.

[0610] The server first receives an alarm signal from a base station on the network via a communication method. Based on this signal, the server collects relevant information from the work registration system, power supply information, and other communication devices. The collected information is analyzed using machine learning algorithms to identify the cause of the failure. Frameworks such as TensorFlow are examples of analysis algorithms used.

[0611] Furthermore, the server acquires the administrator's voice and facial expression data from smart devices and analyzes their state using an emotion engine. The Google Cloud Vision API and other tools are used for this emotional state analysis. Based on the analysis results, the server provides appropriate notifications to the administrator, ensuring that the information does not overload the system.

[0612] For example, if the load on equipment in a data center suddenly increases, users wearing smart glasses can receive voice instructions in real time. If the user is detected as being under stress, the server simplifies the presentation of detailed information to help administrators respond quickly.

[0613] An example of a prompt statement using a generative AI model is as follows:

[0614] "Design a system that detects the emotional state of data center administrators and provides efficient workflow suggestions. Explain, with specific examples, what types of notifications and suggestions would be effective in reducing administrator fatigue and stress."

[0615] Thus, the present invention enables the operation of a data center that is optimized not only from a technical standpoint but also from an ergonomic perspective.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The server receives an alarm signal from the base station via the network. The input is the alarm signal from the base station, and the output is the received alarm signal data. Based on this data, the server prepares to proceed to the next processing step.

[0619] Step 2:

[0620] Based on the alarm signals received by the server, it collects relevant information. Specifically, it collects necessary data from the work registration system, power supply information, and other communication devices. The input is the acquisition of alarm signals and related system data, and the output is the integrated information of this data. The server centrally manages this data, forming the basis for the subsequent analysis process.

[0621] Step 3:

[0622] The server analyzes the information it collects using machine learning algorithms. The input is integrated informational data, and the output is the result of identifying the cause of the failure. The server uses analysis tools such as TensorFlow to analyze the information in real time and identify potential problems.

[0623] Step 4:

[0624] The server acquires voice and facial expression data from the administrator's smart device and analyzes it using an emotion engine. The input is voice and video data from the smart device, and the output is the analysis result of the administrator's emotional state. The server uses the Google Cloud Vision API and other tools to evaluate the administrator's emotional state and determine levels of stress, dissatisfaction, etc.

[0625] Step 5:

[0626] The server provides administrators with optimal notifications and information based on machine learning and sentiment analysis results. Inputs are analysis results and sentiment analysis results, while output is the content of the notifications sent to administrators. The server adjusts the urgency and detail of the notifications according to the administrator's status.

[0627] Step 6:

[0628] The user receives a notification on their smart device. The input is the notification content from the server, and the output is the administrator's acceptance of the information and their subsequent actions. The user can then take appropriate action based on this notification.

[0629] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0630] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0631] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0635] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0636] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0637] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0638] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0639] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0640] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0641] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0642] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0643] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0644] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0645] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] This invention is an advanced server-based monitoring system for efficiently managing the operation of a communication network. The server automatically detects alarm signals from base stations and collects and analyzes relevant information. Its embodiments are described in detail below.

[0647] First, the server receives alarm signals transmitted in real time from each base station. These signals indicate that an anomaly has occurred in the network or equipment. The server analyzes the information in these alarm signals to determine their nature and priority. At this stage, the nature of the specific problem is estimated.

[0648] Next, the server proactively collects relevant information. This includes the current work status from the work registration system, the latest supply status and power outage information from the power company as power supply information, and real-time data obtained from other communication devices. This comprehensive information collection makes it possible to evaluate the underlying causes of the problem from multiple perspectives.

[0649] Subsequently, the server performs advanced analysis based on the collected information. Using machine learning algorithms, it compares past failure data with the current situation to quickly and accurately pinpoint the cause. This analysis identifies high-priority issues requiring immediate attention and cases that need escalation.

[0650] The server will send an escalation message to the user (administrator) as needed. This notification will include a summary of the problem and recommended actions to support quick decision-making.

[0651] Furthermore, the server can automatically take actions to respond to failures. For example, in the event of a power outage, it can activate emergency power supplies and establish alternative communication paths. This enables immediate response to problems and minimizes network downtime.

[0652] Finally, the server records all processing steps and generates a detailed report. This report is intended to help improve future operations and incident response. Users can use this report to further optimize and make decisions about their network, thereby improving network reliability and performance.

[0653] As a concrete example, consider a scenario where an unexpected power outage occurs at a base station. The server immediately detects the power outage alarm from this base station and identifies the cause as a power outage by checking the regional power supply information. Subsequently, it automatically issues a command to activate the emergency power supply and, after the response is complete, provides the user with a report containing detailed processing records. Once this process is complete, network stability is maintained.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The server receives alarm signals from the base station. Each time an alarm signal is generated, the server immediately analyzes the signal to determine its priority and nature.

[0657] Step 2:

[0658] The server accesses the work registration system to check the current work status. This confirms that the issue is not caused by maintenance work being carried out at the base station.

[0659] Step 3:

[0660] The server accesses power company APIs and websites to collect power supply information. It retrieves the latest information for the affected area to determine if a power outage is the cause.

[0661] Step 4:

[0662] The server retrieves relevant data from other communication devices. This includes information from the network core and neighboring base stations, which helps determine whether the anomaly is localized or global.

[0663] Step 5:

[0664] The server uses machine learning algorithms to analyze all the information it collects. It compares historical failure data with real-time data to identify the cause and scope of the failure.

[0665] Step 6:

[0666] The server will send a notification to the user if escalation is necessary. This notification will include a summary of the problem, its cause, and recommended actions.

[0667] Step 7:

[0668] The server performs automated fault response. Specifically, this includes activating emergency power in the event of a power outage and switching communication paths.

[0669] Step 8:

[0670] The server records the entire process and generates a detailed report. This report is presented to the user and used to improve future operations.

[0671] (Example 1)

[0672] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] In the operation and management of communication networks, it is necessary to quickly detect abnormal situations in real time, effectively collect and analyze information, and automate appropriate responses. Furthermore, it is crucial to efficiently record the results of these responses and utilize them for future operational improvements. A system that centrally and automatically manages these processes is needed.

[0674] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0675] In this invention, the server includes means for receiving alarm signals from a communication device, means for collecting information related to the alarm signals using a database, and means for acquiring data from various registration systems, supply status information, and other information devices. This enables the detection and response to anomalies in the communication network, as well as the recording and reporting of the results.

[0676] An "alarm signal" is a notification signal from a communication network or device indicating an anomaly, and is transmitted under specific conditions.

[0677] A "communication device" is a device that includes hardware or software for transmitting information and has the function of sending and receiving data over a network.

[0678] A "database" is a system that organizes and stores information, allowing for efficient searching and retrieval as needed.

[0679] A "various registration systems" is a mechanism that holds different management data and allows for monitoring of its status and progress.

[0680] "Supply status information" refers to data provided by external suppliers that shows the current state of resources and energy.

[0681] An "information device" is a device or system that processes, manages, or transmits information.

[0682] "Machine learning methods" are a collection of algorithms used to empirically create models based on data and perform predictions and classifications.

[0683] "Recovery processing" refers to a series of operations performed to restore a system that has experienced an abnormality or failure to a normal state.

[0684] A "report document" is a document that summarizes and describes the results of a specific activity or process, and communicates them to the relevant parties.

[0685] This invention is a monitoring system that rapidly detects anomalies in communication networks, automates effective information collection and analysis, and implements appropriate countermeasures. The server plays a central role in the network, receiving alarm signals from communication devices and aggregating and analyzing data from multiple information sources based on these signals.

[0686] Specifically, the server connects to the communication device via a wide-area network using a communication interface to receive alarm signals. TCP / IP is the primary communication protocol used in this process. After receiving the alarm signal, the server analyzes the signal using Python and related data analysis libraries.

[0687] Based on the analysis, the server collects relevant information using a database and also retrieves supply information and work status from external systems via APIs. Automated scripts are used to streamline information collection.

[0688] Machine learning techniques are used to analyze the information, and algorithms such as TensorFlow are executed to compare historical data with current data. This analysis helps identify the cause of failures and assists in determining their importance, such as urgency.

[0689] Subsequently, the server uses a notification system to report important information to the user. Sending emails via SMTP enables rapid escalation to administrators.

[0690] Furthermore, the server automatically performs recovery processes and controls the power management system as needed. This significantly reduces downtime, for example, by automatically operating the emergency power supply in the event of a power outage.

[0691] Finally, to record all processes, the server uses log management tools such as the ELK stack to meticulously record the processing steps and automatically generates a report. This report provides users with information to help monitor and improve network operations.

[0692] A concrete example is the case of an unexpected power outage at a base station. The server detects the power outage alarm signal, checks the power supply information for the area, activates the emergency power supply to stabilize the situation, and then automatically sends a detailed report to the administrator to maintain operations.

[0693] An example of a prompt is: "Propose a method for receiving alarm signals from a base station and identifying the type and priority of an anomaly using pattern recognition."

[0694] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0695] Step 1:

[0696] The server receives alarm signals from the communication device. The input is real-time alarm signal data; upon receipt, the signal content is added to the analysis queue. The output is the alarm data requiring analysis. Specifically, the server waits for data packets from the base station via a continuously running socket.

[0697] Step 2:

[0698] The server analyzes the received alarm signals. In this step, it uses an analysis library such as Python based on the input alarm signals to identify the signal type and priority. The output is the characteristic data of the analyzed signals. In specific operation, the server compares the signal data with past logs to recognize anomaly patterns.

[0699] Step 3:

[0700] The server collects relevant information. This involves retrieving information from a database containing information about the underlying alarm signal and communicating with external systems via API. The input is initial analysis data related to the alarm, and the output is an aggregated set of information. In practice, API requests are issued via automated scripts to obtain information from the work registration system and supply status.

[0701] Step 4:

[0702] The server analyzes information using machine learning techniques. The input consists of collected relevant information and historical datasets. Based on this, it runs models such as TensorFlow to identify the cause of failures. The output is the result of the cause analysis. Specifically, the server performs scoring that suggests inferences based on a database of past failures.

[0703] Step 5:

[0704] The server sends escalation messages to the user as needed. The input to this process is analysis results indicating high-priority issues, and the output is a notification message to the user. Specifically, the server uses SMTP to send an email to the administrator, quickly informing them of the actions that need to be taken.

[0705] Step 6:

[0706] The server automatically performs the recovery process. Based on pre-configured recovery rules, it receives problem data requiring action as input and takes countermeasures such as activating the emergency power supply. The output indicates a recovery complete state. Specific actions include the server sending instructions to the power management system via an API.

[0707] Step 7:

[0708] The server records the process and generates a report. At this stage, it receives log information of the entire process as input and creates a detailed report as output. Specifically, it uses the ELK stack to organize the logs and sends the report to the user in PDF format.

[0709] (Application Example 1)

[0710] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] Modern communication networks are susceptible to anomalies due to a wide variety of factors, requiring monitoring and rapid response. However, conventional systems often suffer from delays in anomaly detection and reporting, leading to delayed responses. Furthermore, identifying the cause of anomalies can be time-consuming, potentially compromising network stability. To address these challenges, a system is needed that can detect anomalies in real time, immediately notify users, and enable appropriate countermeasures.

[0712] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0713] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information based on the alarm signals, and means for acquiring information from a work registration system, power supply information, and other communication devices. This enables the detection of anomalies in real time, rapid identification of their causes, immediate notification to users, and the suggestion of appropriate countermeasures.

[0714] A "base station" is a device in a communication network that transmits and receives wireless signals and maintains connections with devices.

[0715] An "alarm signal" is a signal that indicates an abnormality has occurred in the network or equipment, and it serves as an important trigger in monitoring systems.

[0716] A "work registration system" is a system for managing work schedules and statuses, and for recording related information.

[0717] "Power supply information" refers to data provided by power companies that shows the current power supply status and information on power outages.

[0718] A "communication device" is a device or apparatus that is connected to a network and transmits and receives data.

[0719] "Escalation" refers to the process of reporting information to higher management and requesting additional action when a problem is not resolved.

[0720] A "machine learning algorithm" is a mathematical model that learns patterns and rules from data and automatically makes predictions and decisions.

[0721] A "generative AI model" is a pre-trained model that uses artificial intelligence to generate text and images.

[0722] A "prompt" is a text of instructions or questions used to elicit a specific response or output from an AI.

[0723] In embodiments of this invention, the server functions as the primary hardware for monitoring the communication network. The server includes a network connectivity device for receiving alarm signals transmitted from a base station. It also has interfaces for acquiring necessary information from external systems, including communication devices, a work registration system, and power supply information.

[0724] The program runs on a server and is implemented using Python. MySQL is used as the database to properly store and manage received alarm signals and related information. This allows the server to analyze data and identify anomalies in real time.

[0725] Machine learning algorithms are used for data analysis. Machine learning frameworks such as TensorFlow are applied to the process of comparing historical failure data with data acquired in real time. This allows the server to quickly identify the cause of failures and immediately send notifications to users' mobile devices if an anomaly is detected.

[0726] The user's device has an application developed with Flutter installed, which displays alarm information in real time and suggests recommended countermeasures. By utilizing a generative AI model, appropriate countermeasures for identified anomalies can be provided to the device as prompt messages.

[0727] As a concrete example, if the server detects suspicious traffic on the network, it generates a prompt message saying, "Have you detected abnormal network traffic? Please tell me the cause and recommended course of action," and notifies the user's terminal. This process allows the user to immediately understand the situation and take appropriate action.

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The server receives alarm signals transmitted from the base station. The input is the alarm signal emitted from the base station, and the output is the received signal information. The server temporarily stores the received alarm signal in memory.

[0731] Step 2:

[0732] The server collects relevant information based on the received alarm signal. The input is the alarm signal, and the output is a set of relevant information. The server obtains relevant data from the work registration system, the latest power supply information, and other communication devices, integrates them, and stores them in a database.

[0733] Step 3:

[0734] The server uses machine learning algorithms to analyze historical and real-time data to identify the root cause of failures. The input consists of collected data and historical data, while the output is the identified cause of the anomaly. The server utilizes TensorFlow for data analysis, enabling rapid identification of the cause of the anomaly.

[0735] Step 4:

[0736] Based on the identified anomaly, the server generates a prompt message using a generative AI model. The input is information about the cause of the anomaly, and the output is a prompt message to notify the user. The server generates an appropriate prompt message according to the anomaly and sends it to the user's terminal.

[0737] Step 5:

[0738] The terminal displays prompt messages received from the server on its screen, notifying the user of the anomaly and the necessary countermeasures. The input is prompt messages from the server, and the output is a visual display of information for the user. The terminal uses a Flutter application to inform the user of the situation in real time.

[0739] Step 6:

[0740] The user selects and executes the appropriate response based on the prompt messages provided by the terminal. The input is information from the terminal, and the output is the action taken based on the user's decision. The user takes the necessary actions to maintain network stability, referring to the provided countermeasures.

[0741] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0742] The system in this invention achieves efficient and adaptive operation of a communication network by combining multiple functions centered around a server. This system includes communication means for receiving alarm signals from base stations, functions for collecting related information, and machine learning analysis means, as well as an emotion engine for recognizing user emotions.

[0743] The server first receives alarm signals transmitted from the base station and collects various data based on these signals. It checks the work status from the work registration system, collects power supply information and information from other communication devices, and integrates it. This information is analyzed by machine learning algorithms to identify the cause of the failure.

[0744] The emotion engine analyzes a user's emotional state based on their words, actions, and responses when they interact with the system. This analysis is then used by the server to escalate or notify the user. For example, if a user is experiencing stress, the system may provide a more detailed and easier-to-understand explanation or optimize escalation alerts according to their urgency.

[0745] As a specific use case, let's consider a network failure. First, the server detects a power outage alarm from the base station and obtains power outage information from the power company. If the analysis determines that a power outage is the cause, it automatically activates the emergency power supply. While handling the failure, the emotion engine analyzes the user's responses and provides optimal information according to their stress level. Throughout this process, notifications are provided in a way that takes into consideration the user's ability to avoid unnecessary stress.

[0746] Furthermore, at the end of all processes, the server generates a detailed report that includes the results of the emotion engine's analysis. This allows for system improvements not only from a technical perspective but also from an ergonomic perspective during post-event reviews and the development of future countermeasures. This invention improves not only the technical aspects but also the user experience and is expected to make a significant contribution to next-generation network operations.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] The server receives an alarm signal from the base station. This signal indicates that an anomaly has occurred within the network, and the server immediately analyzes the alarm to determine its nature.

[0750] Step 2:

[0751] The server accesses the work registration system to retrieve information on relevant work and maintenance. This allows it to verify whether any ongoing work is causing an alarm.

[0752] Step 3:

[0753] The server connects to the power company's API to check the power supply status and makes inquiries. It retrieves power outage information and evaluates its correlation with alarms.

[0754] Step 4:

[0755] The server collects real-time data from other communication devices to understand the environment around the base station and the network status. This allows for the assessment of the extent and impact of any anomalies.

[0756] Step 5:

[0757] The server uses machine learning models to analyze collected data and identify the root cause of failures. This analysis process references both historical and real-time data.

[0758] Step 6:

[0759] The emotion engine analyzes the user's emotional state. Based on the user's words and actions when interacting with the system, it evaluates stress levels and emotional intensity.

[0760] Step 7:

[0761] Based on the analysis results, the server notifies the user of escalation as needed. The notification content is customized according to the evaluation results of the emotion engine. The tone and level of detail of the notification are adjusted according to the user's emotional state.

[0762] Step 8:

[0763] The server automatically handles failures. For example, it immediately takes specific measures to resolve the problem, such as activating emergency power or setting up alternative routes.

[0764] Step 9:

[0765] The server records all processing steps and results and generates a detailed report. This report also includes the results of the user's sentiment analysis by the sentiment engine and is provided to the user to help with future improvements.

[0766] (Example 2)

[0767] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0768] In modern communication networks, rapid and effective responses to failures are essential. However, in conventional systems, identifying the cause of a failure and gathering information for resolving it are often done manually, which is time-consuming and labor-intensive. As a result, users tend to experience stress, and the efficiency of network operations decreases.

[0769] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0770] In this invention, the server includes means for monitoring alarm signals received from a base station, means for acquiring relevant information based on the alarm signals, and means for analyzing the user's emotional state and optimizing notification content. This enables rapid and appropriate information provision and response in the event of a failure, reducing user stress and improving the efficiency of network operations.

[0771] A "warning signal" is a signal transmitted from a base station to indicate an anomaly or malfunction in the communication network and to prompt a quick response.

[0772] "Related information" refers to data necessary to identify the cause of the malfunction, and is obtained from the work registration system, supply system, and other communication devices.

[0773] "Processing means" refers to systems and algorithms used to analyze collected information and identify the cause of a problem.

[0774] "User emotional state" refers to information that indicates the user's psychological response when interacting with the system, and is analyzed by the emotion engine.

[0775] "Optimizing notification content" refers to the act of providing information in the most easily understandable and appropriate format, depending on the user's emotional state.

[0776] A "report" is a document generated after all incident response processes are completed, and it includes processing logs and analysis results from the sentiment engine.

[0777] The embodiments for carrying out the present invention are shown below.

[0778] The server plays a central role in this system, receiving alarm signals, collecting and integrating data, and performing analysis. The underlying hardware can be a standard server device, connecting to base stations and other information sources via a communication network. The software utilizes machine learning libraries such as TensorFlow for data analysis. Furthermore, natural language processing (NLP) techniques are used to enable an emotion engine that analyzes the user's emotional state.

[0779] The server first receives an alarm signal from the base station and collects relevant information based on it. Specifically, it obtains information from the supply system's API and the work registration system's database, and then centralizes and analyzes this information. In the analysis process, the collected information is fed into a TensorFlow model to derive the cause of the failure. In user sentiment analysis, NLP is executed based on the input text data to calculate a sentiment score.

[0780] The device receives notifications sent from the server and presents them to the user. The notifications are optimized based on the user's sentiment score and are presented in an easy-to-understand format. For example, if the user is feeling stressed, the server will send a notification with a more detailed and empathetic explanation.

[0781] For example, if a network failure occurs and is caused by a power outage, the server will quickly grasp the power outage information and activate the emergency power supply. The server will then provide users with detailed information about the power outage and, if necessary, escalate the issue to the appropriate personnel. This process incorporates considerations based on sentiment analysis to ensure users do not experience unnecessary stress.

[0782] Examples of prompts when using a generative AI model are as follows:

[0783] "Conduct sentiment analysis on user responses after a power outage alarm is detected, and propose an appropriate notification method."

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] The server receives an alarm signal transmitted from the base station. The input is the alarm signal received over the network. The server uses this signal as a trigger to activate the next step, the data collection module. This action enables immediate fault response.

[0787] Step 2:

[0788] The server collects relevant information based on the received alarm signals. Specifically, it obtains status information by issuing SQL queries to the work registration system and collects power supply information by accessing the supply system's API. The input is the alarm signal, and the output is a set of collected relevant information. In this process, the server centralizes the information and passes it on to the next analysis stage.

[0789] Step 3:

[0790] The server uses the collected information to perform root cause analysis. It feeds the input data into a TensorFlow machine learning model to infer the root cause of the failure. Historical data is also referenced during this process. The input consists of collected relevant information and historical data, while the output is the identified cause of the failure. Once the analysis is complete, the necessary information for proceeding to the next step is available.

[0791] Step 4:

[0792] The server uses an emotion engine to analyze the user's emotional state. It analyzes the user's text input using natural language processing techniques and calculates an emotion score. The input is the user's text data, and the output is the emotion score. Based on these results, the notification content is optimized.

[0793] Step 5:

[0794] The server sends the most appropriate notification to the device based on the analysis results and sentiment analysis results. The notification is tailored to be friendly based on the user's emotional state. The input is the results of identifying the cause of the problem and the sentiment score, and the output is the customized notification. In this specific stage, the information is delivered to the user as email or app notification.

[0795] Step 6:

[0796] The server generates a detailed report after all processing is complete. It aggregates processing logs and sentiment analysis results to create a report in PDF format. The input is log data from all processes involved in the incident response, and the output is the final report. This report is sent to the relevant departments and used as feedback for future countermeasures.

[0797] (Application Example 2)

[0798] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0799] In modern data center operations, while rapid identification and response to technical problems are crucial, sufficient consideration is often given to the stress and emotional state of administrators. Even when there are no technical issues, administrator motivation and mental state can affect efficiency, resulting in a decline in operational efficiency.

[0800] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0801] In this invention, the server includes means for monitoring alarm signals received from a base station, means for collecting relevant information, and means for obtaining information from a work registration system and power supply information. This enables not only technical fault response but also the provision of optimal information and notifications tailored to the emotional state of the administrator.

[0802] A "base station" is a piece of equipment that forms part of a communication network and transmits warning signals.

[0803] An "alarm signal" is a signal transmitted from a base station when a problem occurs in a communication network.

[0804] "Related information" refers to additional data collected based on alarm signals and is used to identify the cause of the malfunction.

[0805] A "work registration system" is a system for recording and managing the progress and status of work.

[0806] "Power supply information" refers to information about the status of electricity provided by power companies.

[0807] A "communication device" is a device that transmits and receives data.

[0808] "Analysis methods" refer to the methods and techniques used to handle collected information and identify the cause of a problem.

[0809] "Escalation" is the process of promptly and appropriately requesting a higher level of action depending on the severity of the problem.

[0810] "Emotional state" refers to the mental state of users and administrators, and includes factors such as stress and satisfaction.

[0811] "Optimization" is the process of pursuing the best possible state or result under given conditions.

[0812] The system in this invention is designed to efficiently and flexibly respond to alarm signals from base stations in data center management. This system simultaneously provides technical support and administrator assistance by considering the user's emotional state and providing optimal information.

[0813] The server first receives an alarm signal from a base station on the network via a communication method. Based on this signal, the server collects relevant information from the work registration system, power supply information, and other communication devices. The collected information is analyzed using machine learning algorithms to identify the cause of the failure. Frameworks such as TensorFlow are examples of analysis algorithms used.

[0814] Furthermore, the server acquires the administrator's voice and facial expression data from smart devices and analyzes their state using an emotion engine. The Google Cloud Vision API and other tools are used for this emotional state analysis. Based on the analysis results, the server provides appropriate notifications to the administrator, ensuring that the information does not overload the system.

[0815] For example, if the load on equipment in a data center suddenly increases, users wearing smart glasses can receive voice instructions in real time. If the user is detected as being under stress, the server simplifies the presentation of detailed information to help administrators respond quickly.

[0816] An example of a prompt statement using a generative AI model is as follows:

[0817] "Design a system that detects the emotional state of data center administrators and provides efficient workflow suggestions. Explain, with specific examples, what types of notifications and suggestions would be effective in reducing administrator fatigue and stress."

[0818] Thus, the present invention enables the operation of a data center that is optimized not only from a technical standpoint but also from an ergonomic perspective.

[0819] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0820] Step 1:

[0821] The server receives an alarm signal from the base station via the network. The input is the alarm signal from the base station, and the output is the received alarm signal data. Based on this data, the server prepares to proceed to the next processing step.

[0822] Step 2:

[0823] Based on the alarm signals received by the server, it collects relevant information. Specifically, it collects necessary data from the work registration system, power supply information, and other communication devices. The input is the acquisition of alarm signals and related system data, and the output is the integrated information of this data. The server centrally manages this data, forming the basis for the subsequent analysis process.

[0824] Step 3:

[0825] The server analyzes the information it collects using machine learning algorithms. The input is integrated informational data, and the output is the result of identifying the cause of the failure. The server uses analysis tools such as TensorFlow to analyze the information in real time and identify potential problems.

[0826] Step 4:

[0827] The server acquires voice and facial expression data from the administrator's smart device and analyzes it using an emotion engine. The input is voice and video data from the smart device, and the output is the analysis result of the administrator's emotional state. The server uses the Google Cloud Vision API and other tools to evaluate the administrator's emotional state and determine levels of stress, dissatisfaction, etc.

[0828] Step 5:

[0829] The server provides administrators with optimal notifications and information based on machine learning and sentiment analysis results. Inputs are analysis results and sentiment analysis results, while output is the content of the notifications sent to administrators. The server adjusts the urgency and detail of the notifications according to the administrator's status.

[0830] Step 6:

[0831] The user receives a notification on their smart device. The input is the notification content from the server, and the output is the administrator's acceptance of the information and their subsequent actions. The user can then take appropriate action based on this notification.

[0832] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0833] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0834] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0835] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0836] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0837] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0838] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0839] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0840] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0841] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0842] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0843] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0844] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0845] 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.

[0846] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0847] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0848] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0849] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0850] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0851] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0852] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0853] The following is further disclosed regarding the embodiments described above.

[0854] (Claim 1)

[0855] A means for monitoring alarm signals received from a base station,

[0856] means for collecting relevant information based on the aforementioned alarm signal,

[0857] A means for acquiring information from a work registration system, power supply information, and other communication devices,

[0858] A means of analyzing the collected information to identify the cause,

[0859] A means of notifying users of information that requires escalation,

[0860] A means of automatically performing fault response,

[0861] A system that includes means for recording the results of the response and generating a report.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising communication means for receiving the aforementioned alarm signal, wherein the communication means is connected to a base station via a network.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the analysis means uses a machine learning algorithm to identify the cause of the failure by combining historical data and real-time data.

[0866] "Example 1"

[0867] (Claim 1)

[0868] A means for receiving an alarm signal from a communication device,

[0869] A means for collecting information related to the aforementioned alarm signal using a database,

[0870] Means for acquiring data from various registration systems, supply status information, and other information devices,

[0871] This is a means of analyzing accumulated information to identify the cause, and includes the use of data comparison algorithms.

[0872] A means of reporting to users the results of the analysis that indicate the need for escalation,

[0873] A means of automatically performing the recovery process,

[0874] A system that includes means for recording execution results and generating report documents.

[0875] (Claim 2)

[0876] The system according to claim 1, characterized in that it has a communication means, and the communication means is connected to a communication device via a wide-area network.

[0877] (Claim 3)

[0878] The system according to claim 1, characterized in that the analysis means combines past information and present information and uses machine learning techniques to identify the cause of the failure.

[0879] "Application Example 1"

[0880] (Claim 1)

[0881] A means for monitoring alarm signals received from a base station,

[0882] means for collecting relevant information based on the aforementioned alarm signal,

[0883] A means for acquiring information from a work registration system, power supply information, and other communication devices,

[0884] A means of analyzing the collected information to identify the cause,

[0885] A means of notifying users of information that requires escalation,

[0886] A means of automatically performing fault response,

[0887] A means of recording the results of the response and generating a report,

[0888] A means to detect network anomalies in real time and issue an immediate alert,

[0889] A means of quickly identifying the cause by comparing past data with the current situation using machine learning algorithms,

[0890] A system that includes means for displaying abnormal situations on the user's mobile device and suggesting recommended countermeasures.

[0891] (Claim 2)

[0892] The system according to claim 1, further comprising communication means for receiving the aforementioned alarm signal, wherein the communication means is connected to a base station via a network.

[0893] (Claim 3)

[0894] The system according to claim 1, characterized in that the analysis means generates prompt sentences using a generative AI model, identifies anomalies, and proposes appropriate countermeasures.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] A means for monitoring alarm signals received from a base station,

[0898] Means for acquiring relevant information based on the aforementioned alarm signal,

[0899] Means for acquiring information from a work registration system, a supply system, and other communication devices,

[0900] The collected information is processed using a means to identify the cause,

[0901] A means of analyzing the user's emotional state and optimizing notification content,

[0902] A means of automatically performing fault response,

[0903] A system that includes means for recording the results of actions taken and creating reports.

[0904] (Claim 2)

[0905] The system according to claim 1, further comprising communication means for receiving the aforementioned alarm signal, wherein the communication means is connected to a base station via an information network.

[0906] (Claim 3)

[0907] The system according to claim 1, characterized in that the processing means uses a learning algorithm to combine past information and current information to identify the cause of the failure.

[0908] "Application example 2 when combining with an emotional engine"

[0909] (Claim 1)

[0910] A means for monitoring alarm signals received from a base station,

[0911] means for collecting relevant information based on the aforementioned alarm signal,

[0912] A means for acquiring information from a work registration system, power supply information, and other communication devices,

[0913] A means of analyzing the collected information to identify the cause,

[0914] A means of notifying users of information that requires escalation,

[0915] A means of automatically performing fault response,

[0916] A means of recording the results of the response and generating a report,

[0917] A means of analyzing the emotional state of the manager,

[0918] A system including means for optimizing notification content based on the aforementioned emotional state.

[0919] (Claim 2)

[0920] The system according to claim 1, further comprising communication means for receiving the aforementioned alarm signal, wherein the communication means is connected to a base station via a network.

[0921] (Claim 3)

[0922] The system according to claim 1, characterized in that the analysis means uses a machine learning algorithm to combine historical data and real-time data to identify the cause of the failure and adjusts the response based on the emotional state of the administrator. [Explanation of Symbols]

[0923] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for monitoring alarm signals received from a base station, means for collecting relevant information based on the aforementioned alarm signal, A means for acquiring information from a work registration system, power supply information, and other communication devices, A means of analyzing the collected information to identify the cause, A means of notifying users of information that requires escalation, A means of automatically performing fault response, A means of recording the results of the response and generating a report, A means to detect network anomalies in real time and issue an immediate alert, A means of quickly identifying the cause by comparing past data with the current situation using machine learning algorithms, A system that includes means for displaying abnormal situations on the user's mobile device and suggesting recommended countermeasures.

2. The system according to claim 1, further comprising communication means for receiving the aforementioned alarm signal, wherein the communication means is connected to a base station via a network.

3. The system according to claim 1, characterized in that the analysis means generates prompt sentences using a generative AI model, identifies anomalies, and proposes appropriate countermeasures.

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