system

Abstract

We provide the system. [Solution] A means for automatically detecting the current state of the network and generating configuration information, A means of receiving and analyzing queries in natural language from users, A means of providing appropriate troubleshooting information based on the analyzed query, A means of monitoring the network status in real time and detecting anomalies, A means of notifying users of detected anomalies and providing recommended countermeasures, A means of analyzing past data to predict future network failures and propose preventative measures, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a construction site, network management in the absence of IT personnel is very difficult. In particular, due to the complexity of the network configuration and the need for quick response to abnormalities, many sites are managed by IT-inexperienced personnel, and appropriate responses are often not possible. In addition, real-time network monitoring, quick troubleshooting when a failure occurs, and even prediction and prevention of future problems are often not carried out, which has an adverse impact on work efficiency and safety. Therefore, a method that allows IT-inexperienced personnel to easily perform network management and quickly and effectively solve problems is required.

Means for Solving the Problems

[0005] This invention provides a means for automatically detecting the current state of a network and generating its configuration information, enabling even those without specialized knowledge to instantly grasp the overall picture of the network. Furthermore, it enables rapid problem resolution by receiving inquiries from users in natural language, analyzing them, and providing appropriate troubleshooting information. It also monitors the network status in real time, notifies users if an anomaly is detected, and proposes specific countermeasures. This allows for the prediction of failures in advance and the provision of preventative measures. Additionally, by coordinating with external support centers and dispatching technicians as needed, it facilitates smooth on-site follow-up.

[0006] "Network status" refers to information indicating the current network connectivity, configuration, and operational status.

[0007] "Automatic detection" refers to a process in which a system independently collects and analyzes information without human intervention.

[0008] "Configuration information" refers to information that indicates the types of devices that make up the network, their connection status, and their relationships to each other.

[0009] A "natural language query" refers to a question or request expressed in everyday language, without using technical jargon.

[0010] "Analysis" refers to the process of analyzing received information and understanding its meaning and content.

[0011] "Troubleshooting" refers to a series of steps taken to identify a problem and provide a solution.

[0012] "Real-time monitoring" refers to a process that constantly checks the network status and immediately detects any anomalies.

[0013] "Detecting an anomaly" refers to a system detecting actions or behaviors that differ from the normal state.

[0014] "Prediction" refers to estimating future states and possibilities based on past data and current situations.

[0015] "Preventive measures" refer to specific means and methods taken to prevent potential future problems.

[0016] "External support center" refers to an organization or department, often located remotely, that provides support related to the operation of a network system.

[0017] "Engineer dispatch" refers to the act of dispatching local experts with technical knowledge to solve problems and perform maintenance.

Brief Description of Drawings

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

Mode for Carrying Out the Invention

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

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[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 provides a system for effective network management in environments where dedicated IT personnel are unavailable, such as construction sites. This system automatically generates network configuration information, accepts natural language inquiries from users, analyzes them, and performs troubleshooting. Furthermore, it enables rapid notification and response by monitoring the network in real time and detecting anomalies.

[0040] Specifically, the server scans all devices connected to the network and visualizes their configuration information. This allows the network diagram displayed on the terminal to help users intuitively understand the current network status. Inquiries sent by users via LINEWORKS, etc., are analyzed by the server using natural language processing technology to identify the type of problem and provide corresponding solutions.

[0041] For example, if a user reports a slow internet connection, the server measures network throughput and connection latency, and reconfirms available bandwidth. Furthermore, if a problem is detected, the server immediately sends an appropriate notification to the user's device, guiding them through possible solutions and recovery procedures to support a quick response.

[0042] The server analyzes data collected through daily monitoring and predicts future risks based on this analysis. Based on this predictive information, it can prevent potential problems by proactively proposing appropriate maintenance and equipment upgrades. Furthermore, in the event of particularly critical issues, it can quickly resolve problems on-site by coordinating with an external support center and arranging for the dispatch of technicians as needed.

[0043] Thus, the system of the present invention simplifies complex network management even for those without IT experience, reducing labor and costs while supporting safe and efficient operations at construction sites.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The terminal connects to the on-site network. Specifically, the terminal physically connects to the network using Wi-Fi or LAN cables, performing basic preparations to obtain an overview of the network.

[0047] Step 2:

[0048] The server scans the entire network. It detects all connected devices and collects configuration information such as their IP addresses, MAC addresses, and device types. A network map is then generated based on this information.

[0049] Step 3:

[0050] The user checks the network map. The network map generated by the server is displayed on the terminal, allowing the user to visually confirm the network configuration. This enables the user to perform actions to understand the current state of the network.

[0051] Step 4:

[0052] Users submit inquiries. If a user has questions or problems regarding the network, they enter their questions in natural language via LINEWORKS and send them to the server.

[0053] Step 5:

[0054] The server analyzes the query. The server uses natural language processing technology to analyze the user's query and identify the problem. Based on the results, it prepares the necessary troubleshooting information.

[0055] Step 6:

[0056] The server provides troubleshooting information. Based on the analysis results, the server responds to the user in a chat format with specific solutions and troubleshooting steps. The user can then resolve the problem by following the provided steps.

[0057] Step 7:

[0058] The server monitors the network in real time. The server constantly monitors the network's operational status and operates a system that automatically detects anomalies. A system is in place to immediately initiate action when a problem is detected.

[0059] Step 8:

[0060] The server notifies the user of any abnormalities. When an abnormality is detected in the network, the server immediately notifies the user's terminal of this information and provides specific countermeasures.

[0061] Step 9:

[0062] The server analyzes past data and makes predictions. Based on the accumulated data, the server performs statistical analysis to predict potential problems that may occur in the future and proposes necessary maintenance in advance.

[0063] Step 10:

[0064] The server will coordinate with external support centers as needed. In the event of an emergency or critical problem, the server will contact the external support center and arrange for the dispatch of technicians to the site to ensure a rapid response.

[0065] (Example 1)

[0066] 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."

[0067] In environments utilizing communication infrastructure, the challenge lies in effectively managing and monitoring the network, even in the absence of dedicated IT personnel, and responding quickly when problems arise. Furthermore, it's necessary to enable users, even without specialized knowledge, to understand and grasp the network's configuration and status. Additionally, there's a need to quickly and appropriately analyze issues such as communication delays and instability, and propose countermeasures. Achieving these goals requires real-time monitoring, AI-driven analysis, and visualization techniques.

[0068] 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.

[0069] In this invention, the server includes means for automatically detecting the current state of the communication infrastructure and generating configuration information, means for receiving queries from users in natural language and analyzing them using generative artificial intelligence, and means for providing appropriate problem-solving information based on the analyzed queries. This enables users to intuitively understand the network configuration and status, and to quickly analyze and troubleshoot problems, even in the absence of dedicated IT personnel.

[0070] "Communication infrastructure" is a general term for the hardware and software that make up a digital network, and it is the infrastructure that enables data transmission and reception and internet access.

[0071] "Configuration information" refers to data that shows the settings and connection status of individual devices connected to a network, and is information that helps to understand the overall picture of the network.

[0072] "Natural language" refers to the language that humans use on a daily basis, and is a method of communication and giving instructions without relying on a specific programming language.

[0073] "Generative artificial intelligence" refers to algorithms or systems that learn from large datasets and can automatically perform natural language analysis, generation, and decision-making.

[0074] "Problem-solving information" refers to information that outlines specific countermeasures and procedures for particular troubles or problems, and functions as a guideline for users.

[0075] "Real-time monitoring" is a process that constantly monitors the status of the communication infrastructure, immediately detects abnormalities and malfunctions, and enables immediate response.

[0076] "Visual information" refers to data and structural information that are visually represented as graphs or diagrams, helping users intuitively understand the situation.

[0077] This system is designed to effectively manage communication infrastructure even in the absence of IT specialists. It primarily consists of three elements: servers, terminals, and users. These elements work together to monitor the network, analyze problems, and suggest appropriate solutions.

[0078] The server first scans all information devices connected to the communication infrastructure and obtains configuration information for each device. A general-purpose network management program (e.g., Nagios, Zabbix) is used for this scan. The server then generates a network visualization based on the obtained configuration information and visualizes it using a GUI library (e.g., D3.js, Chart.js). This visualization is provided to the user via a terminal, allowing the user to easily check the network status.

[0079] The server also receives text-based inquiries from users, such as specific problems like "my internet is slow." In response to these inquiries, the server uses a generative AI model (e.g., a large-scale language model) to analyze the text, identify the cause of the problem, and automatically suggest solutions.

[0080] Furthermore, the server continuously monitors the status of the communication infrastructure and immediately sends a notification to the terminal if an anomaly occurs. By performing predictive analysis based on past data and identifying potential problems in advance, a rapid response becomes possible.

[0081] For example, if a user reports "unstable internet connection," the server measures the throughput and latency of the connection in question and identifies that the problem is due to bandwidth limitations. It then recommends readjusting the available bandwidth as a solution.

[0082] Examples of prompt statements are as follows:

[0083] "Please identify the cause of the unstable network at the construction site and provide a solution."

[0084] "Based on user feedback 'Slow Internet,' please report your findings and provide recommended corrective actions."

[0085] This system makes network monitoring and management easy for users without specialized knowledge, and efficiently supports communication infrastructure failure response while minimizing effort and cost.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The server scans all information devices connected to the communication infrastructure. Configuration information such as the IP address, MAC address, and connection status of each device is used as input. This information is collected using a network management program and stored in a database as configuration information. A list of configuration information for all devices is generated as output. Specifically, it uses SNMP or ICMP protocols to query each device in the network.

[0089] Step 2:

[0090] The server creates a network visualization based on the collected configuration information. The input is a list of device configuration information generated in the previous step. As data processing, this is visualized using a GUI library to generate a network configuration diagram. The output is the network configuration diagram displayed on the terminal. The specific operation includes generating nodes and edges to illustrate the visualization information as a hierarchical structure.

[0091] Step 3:

[0092] The user sends network-related inquiries to the server in natural language. The input is a text message, such as "My internet is slow." The server uses a generative AI model to analyze this message and perform data calculations to identify the problem. The output is the identified problem and its details. Specifically, natural language processing techniques are used to analyze the text, extract keywords, and perform semantic analysis.

[0093] Step 4:

[0094] The server generates appropriate problem-solving information based on the problem. The input is the problem identified in the previous step. The problem-solving information includes troubleshooting steps and recommended configuration changes. The output is this solution information. In terms of specific actions, it refers to similar past cases and solutions from the database and presents the specific steps the user should take.

[0095] Step 5:

[0096] The server monitors the network status in real time. Inputs are performance and status information for each device in the network. Data processing executes algorithms to detect anomalies based on this information. Outputs are the anomaly detection results. Specifically, it monitors the performance metrics of each device and detects behavior that exceeds a threshold.

[0097] Step 6:

[0098] If the server detects an anomaly, it immediately sends a notification to the terminal. The input is the anomaly detection result. The output is a notification message to the user and recommended actions. Specifically, it notifies the terminal in the form of a pop-up or alert, providing information to take the necessary corrective action.

[0099] Step 7:

[0100] The server analyzes historical monitoring data to predict future risks to the communication infrastructure. Inputs are real-time monitoring and historical data. Data processing, including trend analysis and predictive models, is used to derive risk predictions. Outputs include predicted risk information and recommended preventative measures. Specific operations include statistical analysis and future predictions using machine learning models.

[0101] (Application Example 1)

[0102] 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."

[0103] In real-world environments, network management often requires the intervention of IT specialists, and immediate response is frequently difficult, especially in workplaces such as factories. Furthermore, while there is a need for rapid response to complex network configurations and sudden anomalies, the number of personnel capable of providing appropriate technical support is limited. In such environments, a system is needed that allows users to understand the network status and perform rapid troubleshooting through intuitive operation using natural language. Moreover, there is a need for autonomous robots operating within factories to handle network management, proactively detect problems, and propose solutions.

[0104] 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.

[0105] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for monitoring the network status in real time and detecting anomalies; means for notifying users of detected anomalies and providing recommended countermeasures; and means for a robot that detects a network anomaly to automatically monitor the network status and send a notification to the user based on the analyzed information. This makes it possible to effectively and efficiently manage the network within a factory even in the absence of dedicated IT personnel.

[0106] A "network" is a communication infrastructure that connects multiple electronic devices and other equipment to send and receive data.

[0107] "Configuration information" refers to setting information such as details of each device connected to the network and communication routes.

[0108] "Analysis" is the process of carefully examining given information or data and transforming it into an easily understandable format.

[0109] "Troubleshooting information" refers to information about the steps and methods for resolving problems that have occurred.

[0110] "Real-time monitoring" means immediately checking and processing the current state or events.

[0111] "Detecting an anomaly" means identifying behavior or conditions that are different from the normal state.

[0112] "Notification" is the act of conveying information or messages to a user.

[0113] "Recommended solutions" refer to methods that indicate the optimal solution to a problem that has occurred.

[0114] A "robot" is an automated mechanical device that performs tasks based on a specified program.

[0115] The system implementing this invention includes a program that automatically collects configuration information for a network to which multiple devices are connected, and uses that information to respond quickly when a problem occurs. The central server of the system scans the network and collects configuration information for all devices. The server uses Python and network management libraries to process the collected information into a form that is easy to visualize. The visualized information is displayed on the user's terminal, allowing them to intuitively understand the network status.

[0116] When a user submits a query in natural language, the content is sent to the server via a messaging application such as LINE. The server uses natural language processing technology to analyze this query, identify the problem, and suggest solutions. Specifically, it uses libraries such as NLTK and Spacy to analyze the query content and generate relevant troubleshooting information.

[0117] If an anomaly is detected, the terminal immediately sends a notification to the user. Network anomalies are checked in real time using libraries such as psutil and scapy. The server can quickly send notifications and suggest recommended actions in response to detected anomalies. Furthermore, robots can monitor the network, automatically detect anomalies, and send appropriate notifications.

[0118] As a concrete example, a robot used at a construction site may detect a network anomaly and send a notification via LINE such as, "A connection problem has been found with the router at site A. Please check the cable connection status."

[0119] To utilize this system more efficiently, an example of a prompt statement that leverages the generated AI model would be, "Please explain the cause of the slow network speed and suggest solutions." By using this prompt statement, the AI ​​can provide more accurate advice.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server scans the network and collects configuration information for all connected devices. The input is the network environment, and the output is a list of device information. This process uses libraries such as psutil and scapy to detect network devices, obtain their protocols, IP addresses, and MAC addresses, and save them as a list.

[0123] Step 2:

[0124] The server receives inquiries from users via messaging apps such as LINE using natural language. The input is the inquiry message from the user, and the output is the parsed problem description. In this step, the message content is taken into the server as a string, its context is parsed using NLTK or Spacy, and the type of problem is identified.

[0125] Step 3:

[0126] The server generates appropriate troubleshooting information based on the analyzed problem and provides it to the user. The input is the analysis result, and the output is a suggested solution to the problem. In this process, the server refers to a pre-prepared database of solutions, selects solutions and hints corresponding to the identified problem, and formats them in a format to be returned to the user.

[0127] Step 4:

[0128] The server monitors the network status in real time and detects anomalies. The input is real-time data from the network, and the output is the result of anomaly detection. In this step, network throughput and latency are continuously tracked, and if they exceed a defined threshold, they are detected as an anomaly and recorded within the system.

[0129] Step 5:

[0130] If an anomaly is detected, the server immediately sends a notification to the user's terminal and provides recommended countermeasures. The input is the result of the anomaly detection, and the output is the notification message. This process creates a message to inform the user about the detected anomaly and sends it to the user along with suggested countermeasures.

[0131] Step 6:

[0132] The server automatically monitors the network status when a robot detects a network anomaly, analyzes the information, and notifies the user. The input is monitoring data from the robot, and the output is the notification to the user. In this process, the information provided by the robot is analyzed to identify the anomaly, create the notification content, and provide the user with information quickly.

[0133] 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.

[0134] This invention combines an emotional engine with a system for effectively managing networks in environments where dedicated IT personnel are absent, such as construction sites. This system automatically detects the current state of the network and generates configuration information. It can also accept and analyze natural language inquiries from users and provide appropriate troubleshooting information. Furthermore, it monitors the network status in real time and provides rapid notification and solutions when an anomaly is detected.

[0135] In addition, this invention integrates an emotion engine that recognizes the user's emotions and adjusts the response based on those emotions. Specifically, the server evaluates the user's stress level through the emotion engine and adjusts the priority of network problem resolution and support accordingly. In this way, the aim is to provide more personalized support and improve the user experience.

[0136] For example, if a user is in a situation where they urgently need to resolve a network problem, and they send a message expressing their stress such as, "I can't use the internet at all, please fix it quickly," the server will use its emotion engine to recognize the user's urgency. As a result, the server will prioritize immediate action and arrange emergency support for the user.

[0137] The server performs statistical analysis based on data collected daily to predict future network failures. Based on this information, it proposes preventative measures and, when necessary, collaborates with external support centers to achieve early problem resolution. By utilizing an emotion engine, more granular management is possible than with conventional systems, and problems faced by users can be mitigated more effectively.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The terminal connects to the on-site network. The terminal physically connects to the network via Wi-Fi or LAN cable and performs authentication for basic communication operations.

[0141] Step 2:

[0142] The server scans the entire network. The server collects information such as the IP addresses, MAC addresses, and device types of all connected devices and generates a network configuration diagram.

[0143] Step 3:

[0144] The user checks the network configuration. The user views the network configuration diagram on their terminal to understand the overall picture of the network at the site.

[0145] Step 4:

[0146] Users submit inquiries. If there are network problems or questions, users can send questions to the server in natural language via LINEWORKS.

[0147] Step 5:

[0148] The server analyzes the query. Using natural language processing techniques, the server analyzes the query content and identifies the type of problem. This allows it to begin preparing for appropriate troubleshooting.

[0149] Step 6:

[0150] The server recognizes the user's emotions through an emotion engine. It evaluates the user's emotions and stress level based on the content of the inquiry and adjusts the priority of the response accordingly.

[0151] Step 7:

[0152] The server provides troubleshooting based on emotions. Based on the analyzed emotions and the nature of the problem, the server provides the user with the best solution and course of action via chat.

[0153] Step 8:

[0154] The server monitors the network in real time. The server continuously monitors the network status and establishes the infrastructure to immediately initiate response if an anomaly is detected.

[0155] Step 9:

[0156] The server notifies the user of any detected anomalies. When an anomaly is detected, the server sends a notification to the user and arranges specific countermeasures and emergency support.

[0157] Step 10:

[0158] The server analyzes past data and predicts future problems. Based on the data collected daily, the server performs analysis and suggests preventative measures against potential risks to the user.

[0159] Step 11:

[0160] The server will coordinate with an external support center as needed. In emergencies, the server will contact the support center and arrange for the dispatch of technicians or remote assistance to help resolve the problem quickly.

[0161] (Example 2)

[0162] 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".

[0163] In network management, users without specialized knowledge face the challenge of quickly and efficiently understanding the network's status and finding appropriate solutions. In particular, there is a need to provide individualized support that addresses users' emotional stress. Furthermore, there is a demand for proactively predicting and preventing future network failures.

[0164] 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.

[0165] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for evaluating the emotional state of the receiving user and adjusting the response content; and means for analyzing past data, predicting future network failures, and suggesting preventive measures. This enables even users without specialized knowledge to efficiently understand the problems they face, receive appropriate support tailored to their emotions, and prevent network failures from occurring.

[0166] "Means for automatically detecting the current state of the network" refers to the ability to automatically identify the connected devices and topology of the network using network scanning technology.

[0167] "Means for generating configuration information" refers to a function that organizes information based on the detected network state and configuration, and outputs it in a visualized format.

[0168] "Means for receiving and analyzing inquiries from users in natural language" refers to a function that receives inquiry content written by users in natural language and analyzes its intent using natural language processing technology.

[0169] "Means of providing appropriate troubleshooting information" refers to a function that provides users with information useful for resolving network problems based on analysis results.

[0170] "A means of monitoring the network status in real time and detecting anomalies" refers to a function that continuously monitors network performance and immediately detects anomalies or failures.

[0171] "Means for notifying users of detected anomalies and providing recommended countermeasures" refers to a function that informs users of detected anomalies and proposes corresponding solutions.

[0172] "Means for evaluating the emotional state of the receiving user and adjusting the response content" refers to a function that uses an emotion engine to analyze the user's emotions and respond accordingly.

[0173] "A means of analyzing past data to predict future network failures and propose preventive measures" refers to a function that analyzes past network data to identify failure patterns and take countermeasures in advance.

[0174] This system is a server-centric network management system that enables efficient operation, especially in environments without dedicated IT personnel. The server first automatically scans the network status and collects necessary network information. This involves using specialized software (e.g., monitoring tools such as Nagios or Zabbix) to analyze the network topology and connection data.

[0175] The server then visualizes configuration information based on the generated network information and also has the functionality to receive inquiries from users. Inquiries in natural language are received via the terminal and analyzed on the server using natural language processing libraries (e.g., spaCy or Transformers). This analysis extracts the gist of the problem and generates appropriate troubleshooting information.

[0176] Furthermore, the server evaluates the user's emotions through its emotion engine and adjusts its response to suit the user's situation. If the server detects that the user is in a hurry to resolve the issue, it prioritizes a rapid response and immediately presents the user with a solution.

[0177] The server also performs real-time network monitoring, constantly detecting anomalies. If an anomaly occurs, the server automatically issues an alert, enabling a rapid response. Furthermore, the server analyzes historical data to predict future network failures. Based on these predictions, it can proactively offer users appropriate preventative measures.

[0178] For example, if a user sends a message expressing stress, such as "I can't use the internet at all, please fix it quickly," the server uses the emotion engine to determine the user's situation is urgent and enables immediate action. Another example of a prompt to the generative AI model is, "How does the emotion engine operate when a network failure occurs?"

[0179] Thus, the present invention efficiently solves network problems faced by users and provides a more comfortable user environment.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The server detects the current state of the network. It collects data on connected devices and their communication status as input. Using a dedicated network monitoring tool, the server analyzes this input data and automatically generates a network topology. The output is comprehensive configuration information that shows the current state of the network.

[0183] Step 2:

[0184] The terminal receives inquiries from the user in natural language. The user inputs a problem using the terminal's interface. The server receives this inquiry and applies natural language processing techniques to analyze the intent of the inquiry. The input is the user's inquiry, and the output is data containing the gist of the analyzed problem.

[0185] Step 3:

[0186] The server generates appropriate troubleshooting information based on the analyzed data. It uses the essence of the analyzed problem and network configuration information as input. Based on this information, the server searches the database for the corresponding solution and presents it to the user as output.

[0187] Step 4:

[0188] The server evaluates the user's emotions and adjusts the response accordingly. It uses a message received from the terminal as input and analyzes the user's emotional state using an emotion engine. The output is a customized response tailored to the user's emotions. Specifically, if the stress level is high, a rapid support action is generated.

[0189] Step 5:

[0190] The server monitors the network in real time and detects anomalies. It uses continuously collected communication data as input and evaluates the situation using an anomaly detection algorithm. The output is the anomaly detection result and a notification to the user. If an anomaly occurs, the server generates an alert and immediately notifies the user.

[0191] Step 6:

[0192] The server analyzes historical data to predict future network failures. It uses historically collected network performance data as input. Based on this, it performs statistical analysis to predict the likelihood of future failures. The output is a risk assessment report including preventative measures.

[0193] Step 7:

[0194] The server uses a generative AI model to generate prompt messages to predict future anomalies. It takes user scenarios and potential anomaly events as input to form prompt messages, and then considers a trial-and-error process based on these. The output is a proposal for prompt messages to prevent anomalies.

[0195] (Application Example 2)

[0196] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0197] In physical stores, proper management of communication network systems is crucial for improving customer satisfaction and operational efficiency. However, store operators do not necessarily possess advanced technical knowledge, often facing difficulties in troubleshooting network issues and responding to emergencies. Furthermore, appropriately responding to customer inquiries and providing support tailored to customer emotions and urgency directly contributes to an improved customer experience, but current systems are insufficient in this regard. Therefore, a network management system that is sensitive to the emotions of users is needed.

[0198] 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.

[0199] In this invention, the server includes means for monitoring the network status in real time and detecting anomalies, means for recognizing the emotions of users and adjusting the response content, and means for setting priority of responses according to the urgency of the user. This makes it possible to constantly understand the status of the communication system within the store and to perform detailed network management that responds to the emotions of customers and users.

[0200] "Current network status" refers to the connection and performance status of the current communication system.

[0201] "Configuration information" refers to a set of data that represents the network connection structure, settings, and the status of the devices being used.

[0202] A "natural language query" is a question or request made in the language that users use on a daily basis.

[0203] An "analyzed query" is a query written in natural language that has been processed and transformed into an understandable form.

[0204] "Troubleshooting information" refers to information that includes the steps and advice necessary to resolve problems in communication systems.

[0205] "Real-time monitoring" refers to the operation of constantly observing the status of a communication system and being able to immediately grasp any changes.

[0206] "Anomaly detection" refers to a system finding deviations from the normal state of the network.

[0207] "Recommended solutions" refer to the methods offered as the most effective solutions to the discovered communication system problems.

[0208] "Analyzing past data" is the process of statistically evaluating previously recorded information to identify trends and the causes of problems.

[0209] "Predicting future network failures" involves forecasting potential communication system problems based on past patterns and data.

[0210] "User emotions" refers to the psychological state of a person operating or using the system.

[0211] "Adjusting response content" means changing the reply or the information presented according to the user's emotional state.

[0212] "Urgency" refers to the degree to which user inquiries or the status of communication systems require immediate attention.

[0213] "Priority of responses" refers to a hierarchy that determines the order in which multiple issues or inquiries are processed.

[0214] "Real-time management" refers to an operational method that manages the status of the communication system in real time and takes immediate action as needed.

[0215] To realize this invention, the system mainly consists of a server, a user terminal, and a communication network environment. The server is responsible for automatically detecting the current state of the network and generating its configuration information. It also monitors the network in real time, and if an anomaly is detected, it promptly notifies the user and provides recommended countermeasures.

[0216] The server receives queries sent by users in natural language and analyzes them using natural language processing (NLTK) technologies. This involves using natural language processing libraries such as NLTK and spaCy. Based on the analysis results, it also provides troubleshooting information. This process incorporates an AI model, specifically a generative AI model capable of generating interactive responses.

[0217] Furthermore, the server incorporates an emotion engine that analyzes user emotions using APIs such as the Sentiment Analysis API and adjusts responses accordingly. It also provides personalized support by assessing the urgency of the user's request and prioritizing responses. Leveraging the advantages of real-time management, the server constantly monitors network health and provides means to avoid future failures through predictive analytics.

[0218] As a concrete example, if a customer in a physical store encounters a Wi-Fi connection problem, the device sends a natural language inquiry to the server stating "Wi-Fi is not connecting." The server analyzes this message and, if it indicates a high level of stress, instructs the server to provide prompt service. Furthermore, preventative measures are also considered, such as predicting future network risks and arranging for technicians to work with external service centers as needed.

[0219] An example of a prompt for a generative AI model is: "We have received a communication inquiry from a customer. Please tell us how to analyze the text as follows to identify and respond to the appropriate sentiment." In this way, communication network problems faced by users can be mitigated more effectively.

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The server automatically detects the current state of the network. It receives connection status and performance data from network devices as input. This data is processed to generate network configuration information. This process uses monitoring tools to analyze the data and assess network health, providing real-time status. The output is recorded as network configuration information.

[0223] Step 2:

[0224] The server receives queries sent by users in natural language. The input is the query text sent from the user's terminal, which is parsed using natural language processing techniques. For parsing, libraries such as NLTK and spaCy are used to generate data structures for understanding grammar and context. The output is the parsed query content.

[0225] Step 3:

[0226] Based on the analyzed query, the server provides appropriate troubleshooting information. The input is the analysis result generated in the previous step. Using the AI ​​model, the corresponding troubleshooting information is formed, and the solutions proposed by the generating AI model are also considered. The output is specific solution steps and advice.

[0227] Step 4:

[0228] The server monitors the network status in real time and detects anomalies. The input is continuously acquired network monitoring data. An anomaly detection algorithm performs data calculations on this data to automatically identify anomalies. The output is either an alert when an anomaly is detected or a confirmation of normal operation.

[0229] Step 5:

[0230] If an anomaly is detected, the server promptly notifies the user and provides recommended actions. The input is the result of the anomaly detection, which is used to generate a notification message for the user. Using an emotion engine, the user's emotional state is also considered, and the content of the notification and the appropriate actions are adjusted according to their urgency. The output is the notification message sent to the user's device.

[0231] Step 6:

[0232] The server analyzes historical data to predict future network failures. The input is historically collected network log data, which is used for predictive analysis using statistical models. The data calculations employed include regression analysis and machine learning techniques. The output presents predicted failure trends and suggested preventative measures.

[0233] Step 7:

[0234] This system recognizes user emotions and adjusts responses accordingly. Input includes analyzing user inquiry text and behavioral data. It evaluates emotional states using tools like the Sentiment Analysis API and adjusts responses using an AI model. The output is the emotion-based, adjusted response. Because emotion recognition directly influences response adjustment in this step, a personalized experience is provided.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] [Second Embodiment]

[0239] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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".

[0251] This invention provides a system for effective network management in environments where dedicated IT personnel are unavailable, such as construction sites. This system automatically generates network configuration information, accepts natural language inquiries from users, analyzes them, and performs troubleshooting. Furthermore, it enables rapid notification and response by monitoring the network in real time and detecting anomalies.

[0252] Specifically, the server scans all devices connected to the network and visualizes their configuration information. This allows the network diagram displayed on the terminal to help users intuitively understand the current network status. Inquiries sent by users via LINEWORKS, etc., are analyzed by the server using natural language processing technology to identify the type of problem and provide corresponding solutions.

[0253] For example, if a user reports a slow internet connection, the server measures network throughput and connection latency, and reconfirms available bandwidth. Furthermore, if a problem is detected, the server immediately sends an appropriate notification to the user's device, guiding them through possible solutions and recovery procedures to support a quick response.

[0254] The server analyzes data collected through daily monitoring and predicts future risks based on this analysis. Based on this predictive information, it can prevent potential problems by proactively proposing appropriate maintenance and equipment upgrades. Furthermore, in the event of particularly critical issues, it can quickly resolve problems on-site by coordinating with an external support center and arranging for the dispatch of technicians as needed.

[0255] Thus, the system of the present invention simplifies complex network management even for those without IT experience, reducing labor and costs while supporting safe and efficient operations at construction sites.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] The terminal connects to the on-site network. Specifically, the terminal physically connects to the network using Wi-Fi or LAN cables, performing basic preparations to obtain an overview of the network.

[0259] Step 2:

[0260] The server scans the entire network. It detects all connected devices and collects configuration information such as their IP addresses, MAC addresses, and device types. A network map is then generated based on this information.

[0261] Step 3:

[0262] The user views the network map. The server generates a network map which is displayed on the terminal, allowing the user to visually confirm the network configuration. This enables the user to perform actions to understand the current state of the network.

[0263] Step 4:

[0264] Users submit inquiries. If a user has questions or problems regarding the network, they enter their questions in natural language via LINEWORKS and send them to the server.

[0265] Step 5:

[0266] The server analyzes the query. The server uses natural language processing technology to analyze the user's query and identify the problem. Based on the results, it prepares the necessary troubleshooting information.

[0267] Step 6:

[0268] The server provides troubleshooting information. Based on the analysis results, the server responds to the user in a chat format with specific solutions and troubleshooting steps. The user can then resolve the problem by following the provided steps.

[0269] Step 7:

[0270] The server monitors the network in real time. The server constantly monitors the network's operational status and operates a system that automatically detects anomalies. A system is in place to immediately initiate action when a problem is detected.

[0271] Step 8:

[0272] The server notifies the user of any abnormalities. When an abnormality is detected in the network, the server immediately notifies the user's terminal of this information and provides specific countermeasures.

[0273] Step 9:

[0274] The server analyzes past data and makes predictions. Based on the accumulated data, the server performs statistical analysis to predict potential problems that may occur in the future and proposes necessary maintenance in advance.

[0275] Step 10:

[0276] The server will coordinate with external support centers as needed. In the event of an emergency or critical problem, the server will contact the external support center and arrange for the dispatch of technicians to the site to ensure a rapid response.

[0277] (Example 1)

[0278] 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."

[0279] In environments utilizing communication infrastructure, the challenge lies in effectively managing and monitoring the network, even in the absence of dedicated IT personnel, and responding quickly when problems arise. Furthermore, it's necessary to enable users, even without specialized knowledge, to understand and grasp the network configuration and status. Additionally, there's a need to quickly and appropriately analyze issues such as communication delays and instability, and propose countermeasures. Achieving these goals requires real-time monitoring, AI-driven analysis, and visualization techniques.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0281] In this invention, the server includes means for automatically detecting the current state of the communication infrastructure and generating configuration information, means for receiving inquiries from users in natural language and analyzing them using a generative artificial intelligence, and means for providing appropriate problem-solving information based on the analyzed inquiries. As a result, even when there is no IT professional, users can intuitively understand the network configuration and status, and quickly analyze problems and troubleshoot.

[0282] The "communication infrastructure" is a general term for the hardware and software that make up a digital network, and is an infrastructure that enables data transmission and reception and Internet access.

[0283] The "configuration information" is data indicating the settings and connection status of individual devices connected to the network, and is information for grasping the overall picture of the network.

[0284] "Natural language" refers to the language that people use in daily life, and is a method of communicating and giving instructions without depending on a specific programming language.

[0285] The "generative artificial intelligence" is an algorithm or system that can learn based on a large-scale dataset and automatically perform natural language analysis, generation, and judgment.

[0286] The "problem-solving information" is information indicating specific countermeasures and procedures for specific troubles and failures, and functions as a guideline for users.

[0287] "Real-time monitoring" is a process of constantly monitoring the state of the communication infrastructure and immediately detecting abnormalities and malfunctions, and enables immediate response.

[0288] "Visual information" refers to data and structural information that are visually represented as graphs or diagrams, helping users intuitively understand the situation.

[0289] This system is designed to effectively manage communication infrastructure even in the absence of IT specialists. It primarily consists of three elements: servers, terminals, and users. These elements work together to monitor the network, analyze problems, and suggest appropriate solutions.

[0290] The server first scans all information devices connected to the communication infrastructure and obtains configuration information for each device. A general-purpose network management program (e.g., Nagios, Zabbix) is used for this scan. The server then generates a network visualization based on the obtained configuration information and visualizes it using a GUI library (e.g., D3.js, Chart.js). This visualization is provided to the user via a terminal, allowing the user to easily check the network status.

[0291] The server also receives text-based inquiries from users, such as specific problems like "my internet is slow." In response to these inquiries, the server uses a generative AI model (e.g., a large-scale language model) to analyze the text, identify the cause of the problem, and automatically suggest solutions.

[0292] Furthermore, the server continuously monitors the status of the communication infrastructure and immediately sends a notification to the terminal if an anomaly occurs. By performing predictive analysis based on past data and identifying potential problems in advance, a rapid response becomes possible.

[0293] For example, if a user reports "unstable internet connection," the server measures the throughput and latency of the connection in question and identifies that the problem is due to bandwidth limitations. It then recommends readjusting the available bandwidth as a solution.

[0294] Examples of prompt statements are as follows:

[0295] "Please identify the cause of the unstable network at the construction site and provide a solution."

[0296] "Based on user feedback 'Slow Internet,' please report your findings and provide recommended corrective actions."

[0297] This system makes network monitoring and management easy for users without specialized knowledge, and efficiently supports communication infrastructure failure response while minimizing effort and cost.

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] The server scans all information devices connected to the communication infrastructure. Configuration information such as the IP address, MAC address, and connection status of each device is used as input. This information is collected using a network management program and stored in a database as configuration information. A list of configuration information for all devices is generated as output. Specifically, it uses SNMP or ICMP protocols to query each device in the network.

[0301] Step 2:

[0302] The server creates a network visualization based on the collected configuration information. The input is a list of device configuration information generated in the previous step. As data processing, this is visualized using a GUI library to generate a network configuration diagram. The output is the network configuration diagram displayed on the terminal. The specific operation includes generating nodes and edges to illustrate the visualization information as a hierarchical structure.

[0303] Step 3:

[0304] The user sends an inquiry about the network to the server in natural language. The input is a text message such as "The Internet is slow". The server analyzes this message using a generative AI model and performs data operations to identify the problem. The output is the identified problem and its details. In a specific operation, the text is analyzed using natural language processing technology to perform keyword extraction and semantic analysis.

[0305] Step 4:

[0306] The server generates appropriate problem-solving information based on the problem. The input is the problem content identified in the previous step. The problem-solving information includes troubleshooting procedures and recommended setting changes. The output is this solution information. In a specific operation, past similar cases and solutions are referred to from the database, and specific procedures for the user to follow are presented.

[0307] Step 5:

[0308] The server monitors the network status in real time. The input is the performance and status information of each device in the network. In data operations, an algorithm for detecting anomalies is executed based on this information. The output is the anomaly detection result. As a specific operation, the performance metrics of each device are monitored, and operations exceeding the threshold are detected.

[0309] Step 6:

[0310] When the server detects an anomaly, it immediately sends a notification to the terminal. The input is the anomaly detection result. The output is a notification message to the user and recommended countermeasures. As a specific operation, the terminal is notified in the form of a pop-up or alert, and information for taking necessary measures is provided.

[0311] Step 7:

[0312] The server analyzes historical monitoring data to predict future risks to the communication infrastructure. Inputs are real-time monitoring and historical data. Data processing, including trend analysis and predictive models, is used to derive risk predictions. Outputs include predicted risk information and recommended preventative measures. Specific operations include statistical analysis and future predictions using machine learning models.

[0313] (Application Example 1)

[0314] 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."

[0315] In real-world environments, network management often requires the intervention of IT specialists, and immediate response is frequently difficult, especially in workplaces such as factories. Furthermore, while there is a need for rapid response to complex network configurations and sudden anomalies, the number of personnel capable of providing appropriate technical support is limited. In such environments, a system is needed that allows users to understand the network status and perform rapid troubleshooting through intuitive operation using natural language. Moreover, there is a need for autonomous robots operating within factories to handle network management, proactively detect problems, and propose solutions.

[0316] 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.

[0317] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for monitoring the network status in real time and detecting anomalies; means for notifying users of detected anomalies and providing recommended countermeasures; and means for a robot that detects a network anomaly to automatically monitor the network status and send a notification to the user based on the analyzed information. This makes it possible to effectively and efficiently manage the network within a factory even in the absence of dedicated IT personnel.

[0318] A "network" is a communication infrastructure that connects multiple electronic devices and other equipment to send and receive data.

[0319] "Configuration information" refers to setting information such as details of each device connected to the network and communication routes.

[0320] "Analysis" is the process of carefully examining given information or data and transforming it into an easily understandable format.

[0321] "Troubleshooting information" refers to information about the steps and methods for resolving problems that have occurred.

[0322] "Real-time monitoring" means immediately checking and processing the current state or events.

[0323] "Detecting an anomaly" means identifying behavior or conditions that are different from the normal state.

[0324] "Notification" is the act of conveying information or messages to a user.

[0325] "Recommended solutions" refer to methods that indicate the optimal solution to a problem that has occurred.

[0326] A "robot" is an automated mechanical device that performs tasks based on a specified program.

[0327] The system implementing this invention includes a program that automatically collects configuration information for a network to which multiple devices are connected, and uses that information to respond quickly when a problem occurs. The central server of the system scans the network and collects configuration information for all devices. The server uses Python and network management libraries to process the collected information into a form that is easy to visualize. The visualized information is displayed on the user's terminal, allowing them to intuitively understand the network status.

[0328] When a user submits a query in natural language, the content is sent to the server via a messaging application such as LINE. The server uses natural language processing technology to analyze this query, identify the problem, and suggest solutions. Specifically, it uses libraries such as NLTK and Spacy to analyze the query content and generate relevant troubleshooting information.

[0329] If an anomaly is detected, the terminal immediately sends a notification to the user. Network anomalies are checked in real time using libraries such as psutil and scapy. The server can quickly send notifications and suggest recommended actions in response to detected anomalies. Furthermore, robots can monitor the network, automatically detect anomalies, and send appropriate notifications.

[0330] As a concrete example, a robot used at a construction site may detect a network anomaly and send a notification via LINE such as, "A connection problem has been found with the router at site A. Please check the cable connection status."

[0331] To utilize this system more efficiently, an example of a prompt statement that leverages the generated AI model would be, "Please explain the cause of the slow network speed and suggest solutions." By using this prompt statement, the AI ​​can provide more accurate advice.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The server scans the network and collects configuration information for all connected devices. The input is the network environment, and the output is a list of device information. This process uses libraries such as psutil and scapy to detect network devices, obtain their protocols, IP addresses, and MAC addresses, and save them as a list.

[0335] Step 2:

[0336] The server receives inquiries from users via messaging apps such as LINE using natural language. The input is the inquiry message from the user, and the output is the parsed problem description. In this step, the message content is taken into the server as a string, its context is parsed using NLTK or Spacy, and the type of problem is identified.

[0337] Step 3:

[0338] The server generates appropriate troubleshooting information based on the analyzed problem and provides it to the user. The input is the analysis result, and the output is a suggested solution to the problem. In this process, the server refers to a pre-prepared database of solutions, selects solutions and hints corresponding to the identified problem, and formats them in a format to be returned to the user.

[0339] Step 4:

[0340] The server monitors the network status in real time and detects anomalies. The input is real-time data from the network, and the output is the result of anomaly detection. In this step, network throughput and latency are continuously tracked, and if they exceed a defined threshold, they are detected as an anomaly and recorded within the system.

[0341] Step 5:

[0342] If an anomaly is detected, the server immediately sends a notification to the user's terminal and provides recommended countermeasures. The input is the result of the anomaly detection, and the output is the notification message. This process creates a message to inform the user about the detected anomaly and sends it to the user along with suggested countermeasures.

[0343] Step 6:

[0344] The server automatically monitors the network status when a robot detects a network anomaly, analyzes the information, and notifies the user. The input is monitoring data from the robot, and the output is the notification to the user. In this process, the information provided by the robot is analyzed to identify the anomaly, create the notification content, and provide the user with information quickly.

[0345] 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.

[0346] This invention combines an emotional engine with a system for effectively managing networks in environments where dedicated IT personnel are absent, such as construction sites. This system automatically detects the current state of the network and generates configuration information. It can also accept and analyze natural language inquiries from users and provide appropriate troubleshooting information. Furthermore, it monitors the network status in real time and provides rapid notification and solutions when an anomaly is detected.

[0347] In addition, this invention integrates an emotion engine that recognizes the user's emotions and adjusts the response based on those emotions. Specifically, the server evaluates the user's stress level through the emotion engine and adjusts the priority of network problem resolution and support accordingly. In this way, the aim is to provide more personalized support and improve the user experience.

[0348] For example, if a user is in a situation where they urgently need to resolve a network problem, and they send a message expressing their stress such as, "I can't use the internet at all, please fix it quickly," the server will use its emotion engine to recognize the user's urgency. As a result, the server will prioritize immediate action and arrange emergency support for the user.

[0349] The server performs statistical analysis based on data collected daily to predict future network failures. Based on this information, it proposes preventative measures and, when necessary, collaborates with external support centers to achieve early problem resolution. By utilizing an emotion engine, more granular management is possible than with conventional systems, and problems faced by users can be mitigated more effectively.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The terminal connects to the on-site network. The terminal physically connects to the network via Wi-Fi or LAN cable and performs authentication for basic communication operations.

[0353] Step 2:

[0354] The server scans the entire network. The server collects information such as the IP addresses, MAC addresses, and device types of all connected devices and generates a network configuration diagram.

[0355] Step 3:

[0356] The user checks the network configuration. The user views the network configuration diagram on their terminal to understand the overall picture of the network at the site.

[0357] Step 4:

[0358] Users submit inquiries. If there are network problems or questions, users can send questions to the server in natural language via LINEWORKS.

[0359] Step 5:

[0360] The server analyzes the query. Using natural language processing techniques, the server analyzes the query content and identifies the type of problem. This allows it to begin preparing for appropriate troubleshooting.

[0361] Step 6:

[0362] The server recognizes the user's emotions through an emotion engine. It evaluates the user's emotions and stress level based on the content of the inquiry and adjusts the priority of the response accordingly.

[0363] Step 7:

[0364] The server provides troubleshooting based on emotions. Based on the analyzed emotions and the nature of the problem, the server provides the user with the best solution and course of action via chat.

[0365] Step 8:

[0366] The server monitors the network in real time. The server continuously monitors the network status and establishes the infrastructure to immediately initiate response if an anomaly is detected.

[0367] Step 9:

[0368] The server notifies the user of any detected anomalies. When an anomaly is detected, the server sends a notification to the user and arranges specific countermeasures and emergency support.

[0369] Step 10:

[0370] The server analyzes past data and predicts future problems. Based on the data collected daily, the server performs analysis and suggests preventative measures against potential risks to the user.

[0371] Step 11:

[0372] The server will coordinate with an external support center as needed. In emergencies, the server will contact the support center and arrange for the dispatch of technicians or remote assistance to help resolve the problem quickly.

[0373] (Example 2)

[0374] 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".

[0375] In network management, users without specialized knowledge face the challenge of quickly and efficiently understanding the network's status and finding appropriate solutions. In particular, there is a need to provide individualized support that addresses users' emotional stress. Furthermore, there is a demand for proactively predicting and preventing future network failures.

[0376] 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.

[0377] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for evaluating the emotional state of the receiving user and adjusting the response content; and means for analyzing past data, predicting future network failures, and suggesting preventive measures. This enables even users without specialized knowledge to efficiently understand the problems they face, receive appropriate support tailored to their emotions, and prevent network failures from occurring.

[0378] "Means for automatically detecting the current state of the network" refers to the ability to automatically identify the connected devices and topology of the network using network scanning technology.

[0379] "Means for generating configuration information" refers to a function that organizes information based on the detected network state and configuration, and outputs it in a visualized format.

[0380] "Means for receiving and analyzing inquiries from users in natural language" refers to a function that receives inquiry content written by users in natural language and analyzes its intent using natural language processing technology.

[0381] "Means of providing appropriate troubleshooting information" refers to a function that provides users with information useful for resolving network problems based on analysis results.

[0382] "A means of monitoring the network status in real time and detecting anomalies" refers to a function that continuously monitors network performance and immediately detects anomalies or failures.

[0383] "Means for notifying users of detected anomalies and providing recommended countermeasures" refers to a function that informs users of detected anomalies and proposes corresponding solutions.

[0384] "Means for evaluating the emotional state of the receiving user and adjusting the response content" refers to a function that uses an emotion engine to analyze the user's emotions and respond accordingly.

[0385] "A means of analyzing past data to predict future network failures and propose preventive measures" refers to a function that analyzes past network data to identify failure patterns and take countermeasures in advance.

[0386] This system is a server-centric network management system that enables efficient operation, especially in environments without dedicated IT personnel. The server first automatically scans the network status and collects necessary network information. This involves using specialized software (e.g., monitoring tools such as Nagios or Zabbix) to analyze the network topology and connection data.

[0387] The server then visualizes configuration information based on the generated network information and also has the functionality to receive inquiries from users. Inquiries in natural language are received via the terminal and analyzed on the server using natural language processing libraries (e.g., spaCy or Transformers). This analysis extracts the gist of the problem and generates appropriate troubleshooting information.

[0388] Furthermore, the server evaluates the user's emotions through its emotion engine and adjusts its response to suit the user's situation. If the server detects that the user is in a hurry to resolve the issue, it prioritizes a rapid response and immediately presents the user with a solution.

[0389] The server also performs real-time network monitoring, constantly detecting anomalies. If an anomaly occurs, the server automatically issues an alert, enabling a rapid response. Furthermore, the server analyzes historical data to predict future network failures. Based on these predictions, it can proactively offer users appropriate preventative measures.

[0390] For example, if a user sends a message expressing stress, such as "I can't use the internet at all, please fix it quickly," the server uses the emotion engine to determine the user's situation is urgent and enables immediate action. Another example of a prompt to the generative AI model is, "How does the emotion engine operate when a network failure occurs?"

[0391] Thus, the present invention efficiently solves network problems faced by users and provides a more comfortable user environment.

[0392] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0393] Step 1:

[0394] The server detects the current state of the network. It collects data on connected devices and their communication status as input. Using a dedicated network monitoring tool, the server analyzes this input data and automatically generates a network topology. The output is comprehensive configuration information that shows the current state of the network.

[0395] Step 2:

[0396] The terminal receives inquiries from the user in natural language. The user inputs a problem using the terminal's interface. The server receives this inquiry and applies natural language processing techniques to analyze the intent of the inquiry. The input is the user's inquiry, and the output is data containing the gist of the analyzed problem.

[0397] Step 3:

[0398] The server generates appropriate troubleshooting information based on the analyzed data. It uses the essence of the analyzed problem and network configuration information as input. Based on this information, the server searches the database for the corresponding solution and presents it to the user as output.

[0399] Step 4:

[0400] The server evaluates the user's emotions and adjusts the response accordingly. It uses a message received from the terminal as input and analyzes the user's emotional state using an emotion engine. The output is a customized response tailored to the user's emotions. Specifically, if the stress level is high, a rapid support action is generated.

[0401] Step 5:

[0402] The server monitors the network in real time and detects anomalies. It uses continuously collected communication data as input and evaluates the situation using an anomaly detection algorithm. The output is the anomaly detection result and a notification to the user. If an anomaly occurs, the server generates an alert and immediately notifies the user.

[0403] Step 6:

[0404] The server analyzes historical data to predict future network failures. It uses historically collected network performance data as input. Based on this, it performs statistical analysis to predict the likelihood of future failures. The output is a risk assessment report including preventative measures.

[0405] Step 7:

[0406] The server uses a generative AI model to generate prompt messages to predict future anomalies. It takes user scenarios and potential anomaly events as input to form prompt messages, and then considers a trial-and-error process based on these. The output is a proposal for prompt messages to prevent anomalies.

[0407] (Application Example 2)

[0408] 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."

[0409] In physical stores, proper management of communication network systems is crucial for improving customer satisfaction and operational efficiency. However, store operators do not necessarily possess advanced technical knowledge, often facing difficulties in troubleshooting network issues and responding to emergencies. Furthermore, appropriately responding to customer inquiries and providing support tailored to customer emotions and urgency directly contributes to an improved customer experience, but current systems are insufficient in this regard. Therefore, a network management system that is sensitive to the emotions of users is needed.

[0410] 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.

[0411] In this invention, the server includes means for monitoring the network status in real time and detecting anomalies, means for recognizing the emotions of users and adjusting the response content, and means for setting priority of responses according to the urgency of the user. This makes it possible to constantly understand the status of the communication system within the store and to perform detailed network management that responds to the emotions of customers and users.

[0412] "Current network status" refers to the connection and performance status of the current communication system.

[0413] "Configuration information" refers to a set of data that represents the network connection structure, settings, and the status of the devices being used.

[0414] A "natural language query" is a question or request made in the language that users use on a daily basis.

[0415] An "analyzed query" is a query written in natural language that has been processed and transformed into an understandable form.

[0416] "Troubleshooting information" refers to information that includes the steps and advice necessary to resolve problems in communication systems.

[0417] "Real-time monitoring" refers to the operation of constantly observing the status of a communication system and being able to immediately grasp any changes.

[0418] "Anomaly detection" refers to a system finding deviations from the normal state of the network.

[0419] "Recommended solutions" refer to the methods offered as the most effective solutions to the discovered communication system problems.

[0420] "Analyzing past data" is the process of statistically evaluating previously recorded information to identify trends and the causes of problems.

[0421] "Predicting future network failures" involves forecasting potential communication system problems based on past patterns and data.

[0422] "User emotions" refers to the psychological state of a person operating or using the system.

[0423] "Adjusting response content" means changing the reply or the information presented according to the user's emotional state.

[0424] "Urgency" refers to the degree to which user inquiries or the status of communication systems require immediate attention.

[0425] "Priority of responses" refers to a hierarchy that determines the order in which multiple issues or inquiries are processed.

[0426] "Real-time management" refers to an operational method that manages the status of the communication system in real time and takes immediate action as needed.

[0427] To realize this invention, the system mainly consists of a server, a user terminal, and a communication network environment. The server is responsible for automatically detecting the current state of the network and generating its configuration information. It also monitors the network in real time, and if an anomaly is detected, it promptly notifies the user and provides recommended countermeasures.

[0428] The server receives queries sent by users in natural language and analyzes them using natural language processing (NLTK) technologies. This involves using natural language processing libraries such as NLTK and spaCy. Based on the analysis results, it also provides troubleshooting information. This process incorporates an AI model, specifically a generative AI model capable of generating interactive responses.

[0429] Furthermore, the server incorporates an emotion engine that analyzes user emotions using APIs such as the Sentiment Analysis API and adjusts responses accordingly. It also provides personalized support by assessing the urgency of the user's request and prioritizing responses. Leveraging the advantages of real-time management, the server constantly monitors network health and provides means to avoid future failures through predictive analytics.

[0430] As a concrete example, if a customer in a physical store encounters a Wi-Fi connection problem, the device sends a natural language inquiry to the server stating "Wi-Fi is not connecting." The server analyzes this message and, if it indicates a high level of stress, instructs the server to provide prompt service. Furthermore, preventative measures are also considered, such as predicting future network risks and arranging for technicians to work with external service centers as needed.

[0431] An example of a prompt for a generative AI model is: "We have received a communication inquiry from a customer. Please tell us how to analyze the text as follows to identify and respond to the appropriate sentiment." In this way, communication network problems faced by users can be mitigated more effectively.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The server automatically detects the current state of the network. It receives connection status and performance data from network devices as input. This data is processed to generate network configuration information. This process uses monitoring tools to analyze the data and assess network health, providing real-time status. The output is recorded as network configuration information.

[0435] Step 2:

[0436] The server receives queries sent by users in natural language. The input is the query text sent from the user's terminal, which is parsed using natural language processing techniques. For parsing, libraries such as NLTK and spaCy are used to generate data structures for understanding grammar and context. The output is the parsed query content.

[0437] Step 3:

[0438] Based on the analyzed query, the server provides appropriate troubleshooting information. The input is the analysis result generated in the previous step. Using the AI ​​model, the corresponding troubleshooting information is formed, and the solutions proposed by the generating AI model are also considered. The output is specific solution steps and advice.

[0439] Step 4:

[0440] The server monitors the network status in real time and detects anomalies. The input is continuously acquired network monitoring data. An anomaly detection algorithm performs data calculations on this data to automatically identify anomalies. The output is either an alert when an anomaly is detected or a confirmation of normal operation.

[0441] Step 5:

[0442] If an anomaly is detected, the server promptly notifies the user and provides recommended actions. The input is the result of the anomaly detection, which is used to generate a notification message for the user. Using an emotion engine, the user's emotional state is also considered, and the content of the notification and the appropriate actions are adjusted according to their urgency. The output is the notification message sent to the user's device.

[0443] Step 6:

[0444] The server analyzes historical data to predict future network failures. The input is historically collected network log data, which is used for predictive analysis using statistical models. The data calculations employed include regression analysis and machine learning techniques. The output presents predicted failure trends and suggested preventative measures.

[0445] Step 7:

[0446] This system recognizes user emotions and adjusts responses accordingly. Input includes analyzing user inquiry text and behavioral data. It evaluates emotional states using tools like the Sentiment Analysis API and adjusts responses using an AI model. The output is the emotion-based, adjusted response. Because emotion recognition directly influences response adjustment in this step, a personalized experience is provided.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] [Third Embodiment]

[0451] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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".

[0463] This invention provides a system for effective network management in environments where dedicated IT personnel are unavailable, such as construction sites. This system automatically generates network configuration information, accepts natural language inquiries from users, analyzes them, and performs troubleshooting. Furthermore, it enables rapid notification and response by monitoring the network in real time and detecting anomalies.

[0464] Specifically, the server scans all devices connected to the network and visualizes their configuration information. This allows the network diagram displayed on the terminal to help users intuitively understand the current network status. Inquiries sent by users via LINEWORKS, etc., are analyzed by the server using natural language processing technology to identify the type of problem and provide corresponding solutions.

[0465] For example, if a user reports a slow internet connection, the server measures network throughput and connection latency, and reconfirms available bandwidth. Furthermore, if a problem is detected, the server immediately sends an appropriate notification to the user's device, guiding them through possible solutions and recovery procedures to support a quick response.

[0466] The server analyzes data collected through daily monitoring and predicts future risks based on this analysis. Based on this predictive information, it can prevent potential problems by proactively proposing appropriate maintenance and equipment upgrades. Furthermore, in the event of particularly critical issues, it can quickly resolve problems on-site by coordinating with an external support center and arranging for the dispatch of technicians as needed.

[0467] Thus, the system of the present invention simplifies complex network management even for those without IT experience, reducing labor and costs while supporting safe and efficient operations at construction sites.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The terminal connects to the on-site network. Specifically, the terminal physically connects to the network using Wi-Fi or LAN cables, performing basic preparations to obtain an overview of the network.

[0471] Step 2:

[0472] The server scans the entire network. It detects all connected devices and collects configuration information such as their IP addresses, MAC addresses, and device types. A network map is then generated based on this information.

[0473] Step 3:

[0474] The user checks the network map. The network map generated by the server is displayed on the terminal, allowing the user to visually confirm the network configuration. This enables the user to perform actions to understand the current state of the network.

[0475] Step 4:

[0476] Users submit inquiries. If a user has questions or problems regarding the network, they enter their questions in natural language via LINEWORKS and send them to the server.

[0477] Step 5:

[0478] The server analyzes the query. The server uses natural language processing technology to analyze the user's query and identify the problem. Based on the results, it prepares the necessary troubleshooting information.

[0479] Step 6:

[0480] The server provides troubleshooting information. Based on the analysis results, the server responds to the user in a chat format with specific solutions and troubleshooting steps. The user can then resolve the problem by following the provided steps.

[0481] Step 7:

[0482] The server monitors the network in real time. The server constantly monitors the network's operational status and operates a system that automatically detects anomalies. A system is in place to immediately initiate action when a problem is detected.

[0483] Step 8:

[0484] The server notifies the user of any abnormalities. When an abnormality is detected in the network, the server immediately notifies the user's terminal of this information and provides specific countermeasures.

[0485] Step 9:

[0486] The server analyzes past data and makes predictions. Based on the accumulated data, the server performs statistical analysis to predict potential problems that may occur in the future and proposes necessary maintenance in advance.

[0487] Step 10:

[0488] The server will coordinate with external support centers as needed. In the event of an emergency or critical problem, the server will contact the external support center and arrange for the dispatch of technicians to the site to ensure a rapid response.

[0489] (Example 1)

[0490] 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."

[0491] In environments utilizing communication infrastructure, the challenge lies in effectively managing and monitoring the network, even in the absence of dedicated IT personnel, and responding quickly when problems arise. Furthermore, it's necessary to enable users, even without specialized knowledge, to understand and grasp the network's configuration and status. Additionally, there's a need to quickly and appropriately analyze issues such as communication delays and instability, and propose countermeasures. Achieving these goals requires real-time monitoring, AI-driven analysis, and visualization techniques.

[0492] 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.

[0493] In this invention, the server includes means for automatically detecting the current state of the communication infrastructure and generating configuration information, means for receiving queries from users in natural language and analyzing them using generative artificial intelligence, and means for providing appropriate problem-solving information based on the analyzed queries. This enables users to intuitively understand the network configuration and status, and to quickly analyze and troubleshoot problems, even in the absence of dedicated IT personnel.

[0494] "Communication infrastructure" is a general term for the hardware and software that make up a digital network, and it is the infrastructure that enables data transmission and reception and internet access.

[0495] "Configuration information" refers to data that shows the settings and connection status of individual devices connected to a network, and is information that helps to understand the overall picture of the network.

[0496] "Natural language" refers to the language that humans use on a daily basis, and is a method of communication and giving instructions without relying on a specific programming language.

[0497] "Generative artificial intelligence" refers to algorithms or systems that learn from large datasets and can automatically perform natural language analysis, generation, and decision-making.

[0498] "Problem-solving information" refers to information that outlines specific countermeasures and procedures for particular troubles or problems, and functions as a guideline for users.

[0499] "Real-time monitoring" is a process that constantly monitors the status of the communication infrastructure, immediately detects abnormalities and malfunctions, and enables immediate response.

[0500] "Visual information" refers to data and structural information that are visually represented as graphs or diagrams, helping users intuitively understand the situation.

[0501] This system is designed to effectively manage communication infrastructure even in the absence of IT specialists. It primarily consists of three elements: servers, terminals, and users. These elements work together to monitor the network, analyze problems, and suggest appropriate solutions.

[0502] The server first scans all information devices connected to the communication infrastructure and obtains configuration information for each device. A general-purpose network management program (e.g., Nagios, Zabbix) is used for this scan. The server then generates a network visualization based on the obtained configuration information and visualizes it using a GUI library (e.g., D3.js, Chart.js). This visualization is provided to the user via a terminal, allowing the user to easily check the network status.

[0503] The server also receives text-based inquiries from users, such as specific problems like "my internet is slow." In response to these inquiries, the server uses a generative AI model (e.g., a large-scale language model) to analyze the text, identify the cause of the problem, and automatically suggest solutions.

[0504] Furthermore, the server continuously monitors the status of the communication infrastructure and immediately sends a notification to the terminal if an anomaly occurs. By performing predictive analysis based on past data and identifying potential problems in advance, a rapid response becomes possible.

[0505] For example, if a user reports "unstable internet connection," the server measures the throughput and latency of the connection in question and identifies that the problem is due to bandwidth limitations. It then recommends readjusting the available bandwidth as a solution.

[0506] Examples of prompt statements are as follows:

[0507] "Please identify the cause of the unstable network at the construction site and provide a solution."

[0508] "Based on user feedback 'Slow Internet,' please report your findings and provide recommended corrective actions."

[0509] This system makes network monitoring and management easy for users without specialized knowledge, and efficiently supports communication infrastructure failure response while minimizing effort and cost.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The server scans all information devices connected to the communication infrastructure. Configuration information such as the IP address, MAC address, and connection status of each device is used as input. This information is collected using a network management program and stored in a database as configuration information. A list of configuration information for all devices is generated as output. Specifically, it uses SNMP or ICMP protocols to query each device in the network.

[0513] Step 2:

[0514] The server creates a network visualization based on the collected configuration information. The input is a list of device configuration information generated in the previous step. As data processing, this is visualized using a GUI library to generate a network configuration diagram. The output is the network configuration diagram displayed on the terminal. The specific operation includes generating nodes and edges to illustrate the visualization information as a hierarchical structure.

[0515] Step 3:

[0516] The user sends network-related inquiries to the server in natural language. The input is a text message, such as "My internet is slow." The server uses a generative AI model to analyze this message and perform data calculations to identify the problem. The output is the identified problem and its details. Specifically, natural language processing techniques are used to analyze the text, extract keywords, and perform semantic analysis.

[0517] Step 4:

[0518] The server generates appropriate problem-solving information based on the problem. The input is the problem identified in the previous step. The problem-solving information includes troubleshooting steps and recommended configuration changes. The output is this solution information. In terms of specific actions, it refers to similar past cases and solutions from the database and presents the specific steps the user should take.

[0519] Step 5:

[0520] The server monitors the network status in real time. Inputs are performance and status information for each device in the network. Data processing executes algorithms to detect anomalies based on this information. Outputs are the anomaly detection results. Specifically, it monitors the performance metrics of each device and detects behavior that exceeds a threshold.

[0521] Step 6:

[0522] If the server detects an anomaly, it immediately sends a notification to the terminal. The input is the anomaly detection result. The output is a notification message to the user and recommended actions. Specifically, it notifies the terminal in the form of a pop-up or alert, providing information to take the necessary corrective action.

[0523] Step 7:

[0524] The server analyzes historical monitoring data to predict future risks to the communication infrastructure. Inputs are real-time monitoring and historical data. Data processing, including trend analysis and predictive models, is used to derive risk predictions. Outputs include predicted risk information and recommended preventative measures. Specific operations include statistical analysis and future predictions using machine learning models.

[0525] (Application Example 1)

[0526] 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."

[0527] In real-world environments, network management often requires the intervention of IT specialists, and immediate response is frequently difficult, especially in workplaces such as factories. Furthermore, while there is a need for rapid response to complex network configurations and sudden anomalies, the number of personnel capable of providing appropriate technical support is limited. In such environments, a system is needed that allows users to understand the network status and perform rapid troubleshooting through intuitive operation using natural language. Moreover, there is a need for autonomous robots operating within factories to handle network management, proactively detect problems, and propose solutions.

[0528] 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.

[0529] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for monitoring the network status in real time and detecting anomalies; means for notifying users of detected anomalies and providing recommended countermeasures; and means for a robot that detects a network anomaly to automatically monitor the network status and send a notification to the user based on the analyzed information. This makes it possible to effectively and efficiently manage the network within a factory even in the absence of dedicated IT personnel.

[0530] A "network" is a communication infrastructure that connects multiple electronic devices and other equipment to send and receive data.

[0531] "Configuration information" refers to setting information such as details of each device connected to the network and communication routes.

[0532] "Analysis" is the process of carefully examining given information or data and transforming it into an easily understandable format.

[0533] "Troubleshooting information" refers to information about the steps and methods for resolving problems that have occurred.

[0534] "Real-time monitoring" means immediately checking and processing the current state or events.

[0535] "Detecting an anomaly" means identifying behavior or conditions that are different from the normal state.

[0536] "Notification" is the act of conveying information or messages to a user.

[0537] "Recommended solutions" refer to methods that indicate the optimal solution to a problem that has occurred.

[0538] A "robot" is an automated mechanical device that performs tasks based on a specified program.

[0539] The system implementing this invention includes a program that automatically collects configuration information for a network to which multiple devices are connected, and uses that information to respond quickly when a problem occurs. The central server of the system scans the network and collects configuration information for all devices. The server uses Python and network management libraries to process the collected information into a form that is easy to visualize. The visualized information is displayed on the user's terminal, allowing them to intuitively understand the network status.

[0540] When a user submits a query in natural language, the content is sent to the server via a messaging application such as LINE. The server uses natural language processing technology to analyze this query, identify the problem, and suggest solutions. Specifically, it uses libraries such as NLTK and Spacy to analyze the query content and generate relevant troubleshooting information.

[0541] If an anomaly is detected, the terminal immediately sends a notification to the user. Network anomalies are checked in real time using libraries such as psutil and scapy. The server can quickly send notifications and suggest recommended actions in response to detected anomalies. Furthermore, robots can monitor the network, automatically detect anomalies, and send appropriate notifications.

[0542] As a concrete example, a robot used at a construction site may detect a network anomaly and send a notification via LINE such as, "A connection problem has been found with the router at site A. Please check the cable connection status."

[0543] To utilize this system more efficiently, an example of a prompt statement that leverages the generated AI model would be, "Please explain the cause of the slow network speed and suggest solutions." By using this prompt statement, the AI ​​can provide more accurate advice.

[0544] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0545] Step 1:

[0546] The server scans the network and collects configuration information for all connected devices. The input is the network environment, and the output is a list of device information. This process uses libraries such as psutil and scapy to detect network devices, obtain their protocols, IP addresses, and MAC addresses, and save them as a list.

[0547] Step 2:

[0548] The server receives inquiries from users via messaging apps such as LINE using natural language. The input is the inquiry message from the user, and the output is the parsed problem description. In this step, the message content is taken into the server as a string, its context is parsed using NLTK or Spacy, and the type of problem is identified.

[0549] Step 3:

[0550] The server generates appropriate troubleshooting information based on the analyzed problem and provides it to the user. The input is the analysis result, and the output is a suggested solution to the problem. In this process, the server refers to a pre-prepared database of solutions, selects solutions and hints corresponding to the identified problem, and formats them in a format to be returned to the user.

[0551] Step 4:

[0552] The server monitors the network status in real time and detects anomalies. The input is real-time data from the network, and the output is the result of anomaly detection. In this step, network throughput and latency are continuously tracked, and if they exceed a defined threshold, they are detected as an anomaly and recorded within the system.

[0553] Step 5:

[0554] If an anomaly is detected, the server immediately sends a notification to the user's terminal and provides recommended countermeasures. The input is the result of the anomaly detection, and the output is the notification message. This process creates a message to inform the user about the detected anomaly and sends it to the user along with suggested countermeasures.

[0555] Step 6:

[0556] The server automatically monitors the network status when a robot detects a network anomaly, analyzes the information, and notifies the user. The input is monitoring data from the robot, and the output is the notification to the user. In this process, the information provided by the robot is analyzed to identify the anomaly, create the notification content, and provide the user with information quickly.

[0557] 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.

[0558] This invention combines an emotional engine with a system for effectively managing networks in environments where dedicated IT personnel are absent, such as construction sites. This system automatically detects the current state of the network and generates configuration information. It can also accept and analyze natural language inquiries from users and provide appropriate troubleshooting information. Furthermore, it monitors the network status in real time and provides rapid notification and solutions when an anomaly is detected.

[0559] In addition, this invention integrates an emotion engine that recognizes the user's emotions and adjusts the response based on those emotions. Specifically, the server evaluates the user's stress level through the emotion engine and adjusts the priority of network problem resolution and support accordingly. In this way, the aim is to provide more personalized support and improve the user experience.

[0560] For example, if a user is in a situation where they urgently need to resolve a network problem, and they send a message expressing their stress such as, "I can't use the internet at all, please fix it quickly," the server will use its emotion engine to recognize the user's urgency. As a result, the server will prioritize immediate action and arrange emergency support for the user.

[0561] The server performs statistical analysis based on data collected daily to predict future network failures. Based on this information, it proposes preventative measures and, when necessary, collaborates with external support centers to achieve early problem resolution. By utilizing an emotion engine, more granular management is possible than with conventional systems, and problems faced by users can be mitigated more effectively.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] The terminal connects to the on-site network. The terminal physically connects to the network via Wi-Fi or LAN cable and performs authentication for basic communication operations.

[0565] Step 2:

[0566] The server scans the entire network. The server collects information such as the IP addresses, MAC addresses, and device types of all connected devices and generates a network configuration diagram.

[0567] Step 3:

[0568] The user checks the network configuration. The user views the network configuration diagram on their terminal to understand the overall picture of the network at the site.

[0569] Step 4:

[0570] Users submit inquiries. If there are network problems or questions, users can send questions to the server in natural language via LINEWORKS.

[0571] Step 5:

[0572] The server analyzes the query. Using natural language processing techniques, the server analyzes the query content and identifies the type of problem. This allows it to begin preparing for appropriate troubleshooting.

[0573] Step 6:

[0574] The server recognizes the user's emotions through an emotion engine. It evaluates the user's emotions and stress level based on the content of the inquiry and adjusts the priority of the response accordingly.

[0575] Step 7:

[0576] The server provides troubleshooting based on emotions. Based on the analyzed emotions and the nature of the problem, the server provides the user with the best solution and course of action via chat.

[0577] Step 8:

[0578] The server monitors the network in real time. The server continuously monitors the network status and establishes the infrastructure to immediately initiate response if an anomaly is detected.

[0579] Step 9:

[0580] The server notifies the user of any detected anomalies. When an anomaly is detected, the server sends a notification to the user and arranges specific countermeasures and emergency support.

[0581] Step 10:

[0582] The server analyzes past data and predicts future problems. Based on the data collected daily, the server performs analysis and suggests preventative measures against potential risks to the user.

[0583] Step 11:

[0584] The server will coordinate with an external support center as needed. In emergencies, the server will contact the support center and arrange for the dispatch of technicians or remote assistance to help resolve the problem quickly.

[0585] (Example 2)

[0586] 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."

[0587] In network management, users without specialized knowledge face the challenge of quickly and efficiently understanding the network's status and finding appropriate solutions. In particular, there is a need to provide individualized support that addresses users' emotional stress. Furthermore, there is a demand for proactively predicting and preventing future network failures.

[0588] 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.

[0589] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for evaluating the emotional state of the receiving user and adjusting the response content; and means for analyzing past data, predicting future network failures, and suggesting preventive measures. This enables even users without specialized knowledge to efficiently understand the problems they face, receive appropriate support tailored to their emotions, and prevent network failures from occurring.

[0590] "Means for automatically detecting the current state of the network" refers to the ability to automatically identify the connected devices and topology of the network using network scanning technology.

[0591] "Means for generating configuration information" refers to a function that organizes information based on the detected network state and configuration, and outputs it in a visualized format.

[0592] "Means for receiving and analyzing inquiries from users in natural language" refers to a function that receives inquiry content written by users in natural language and analyzes its intent using natural language processing technology.

[0593] "Means of providing appropriate troubleshooting information" refers to a function that provides users with information useful for resolving network problems based on analysis results.

[0594] "A means of monitoring the network status in real time and detecting anomalies" refers to a function that continuously monitors network performance and immediately detects anomalies or failures.

[0595] "Means for notifying users of detected anomalies and providing recommended countermeasures" refers to a function that informs users of detected anomalies and proposes corresponding solutions.

[0596] "Means for evaluating the emotional state of the receiving user and adjusting the response content" refers to a function that uses an emotion engine to analyze the user's emotions and respond accordingly.

[0597] "A means of analyzing past data to predict future network failures and propose preventive measures" refers to a function that analyzes past network data to identify failure patterns and take countermeasures in advance.

[0598] This system is a server-centric network management system that enables efficient operation, especially in environments without dedicated IT personnel. The server first automatically scans the network status and collects necessary network information. This involves using specialized software (e.g., monitoring tools such as Nagios or Zabbix) to analyze the network topology and connection data.

[0599] The server then visualizes configuration information based on the generated network information and also has the functionality to receive inquiries from users. Inquiries in natural language are received via the terminal and analyzed on the server using natural language processing libraries (e.g., spaCy or Transformers). This analysis extracts the gist of the problem and generates appropriate troubleshooting information.

[0600] Furthermore, the server evaluates the user's emotions through its emotion engine and adjusts its response to suit the user's situation. If the server detects that the user is in a hurry to resolve the issue, it prioritizes a rapid response and immediately presents the user with a solution.

[0601] The server also performs real-time network monitoring, constantly detecting anomalies. If an anomaly occurs, the server automatically issues an alert, enabling a rapid response. Furthermore, the server analyzes historical data to predict future network failures. Based on these predictions, it can proactively offer users appropriate preventative measures.

[0602] For example, if a user sends a message expressing stress, such as "I can't use the internet at all, please fix it quickly," the server uses the emotion engine to determine the user's situation is urgent and enables immediate action. Another example of a prompt to the generative AI model is, "How does the emotion engine operate when a network failure occurs?"

[0603] Thus, the present invention efficiently solves network problems faced by users and provides a more comfortable user environment.

[0604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0605] Step 1:

[0606] The server detects the current state of the network. It collects data on connected devices and their communication status as input. Using a dedicated network monitoring tool, the server analyzes this input data and automatically generates a network topology. The output is comprehensive configuration information that shows the current state of the network.

[0607] Step 2:

[0608] The terminal receives inquiries from the user in natural language. The user inputs a problem using the terminal's interface. The server receives this inquiry and applies natural language processing techniques to analyze the intent of the inquiry. The input is the user's inquiry, and the output is data containing the gist of the analyzed problem.

[0609] Step 3:

[0610] The server generates appropriate troubleshooting information based on the analyzed data. It uses the essence of the analyzed problem and network configuration information as input. Based on this information, the server searches the database for the corresponding solution and presents it to the user as output.

[0611] Step 4:

[0612] The server evaluates the user's emotions and adjusts the response accordingly. It uses a message received from the terminal as input and analyzes the user's emotional state using an emotion engine. The output is a customized response tailored to the user's emotions. Specifically, if the stress level is high, a rapid support action is generated.

[0613] Step 5:

[0614] The server monitors the network in real time and detects anomalies. It uses continuously collected communication data as input and evaluates the situation using an anomaly detection algorithm. The output is the anomaly detection result and a notification to the user. If an anomaly occurs, the server generates an alert and immediately notifies the user.

[0615] Step 6:

[0616] The server analyzes historical data to predict future network failures. It uses historically collected network performance data as input. Based on this, it performs statistical analysis to predict the likelihood of future failures. The output is a risk assessment report including preventative measures.

[0617] Step 7:

[0618] The server uses a generative AI model to generate prompt messages to predict future anomalies. It takes user scenarios and potential anomaly events as input to form prompt messages, and then considers a trial-and-error process based on these. The output is a proposal for prompt messages to prevent anomalies.

[0619] (Application Example 2)

[0620] 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."

[0621] In physical stores, proper management of communication network systems is crucial for improving customer satisfaction and operational efficiency. However, store operators do not necessarily possess advanced technical knowledge, often facing difficulties in troubleshooting network issues and responding to emergencies. Furthermore, appropriately responding to customer inquiries and providing support tailored to customer emotions and urgency directly contributes to an improved customer experience, but current systems are insufficient in this regard. Therefore, a network management system that is sensitive to the emotions of users is needed.

[0622] 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.

[0623] In this invention, the server includes means for monitoring the network status in real time and detecting anomalies, means for recognizing the emotions of users and adjusting the response content, and means for setting priority of responses according to the urgency of the user. This makes it possible to constantly understand the status of the communication system within the store and to perform detailed network management that responds to the emotions of customers and users.

[0624] "Current network status" refers to the connection and performance status of the current communication system.

[0625] "Configuration information" refers to a set of data that represents the network connection structure, settings, and the status of the devices being used.

[0626] A "natural language query" is a question or request made in the language that users use on a daily basis.

[0627] An "analyzed query" is a query written in natural language that has been processed and transformed into an understandable form.

[0628] "Troubleshooting information" refers to information that includes the steps and advice necessary to resolve problems in communication systems.

[0629] "Real-time monitoring" refers to the operation of constantly observing the status of a communication system and being able to immediately grasp any changes.

[0630] "Anomaly detection" refers to a system finding deviations from the normal state of the network.

[0631] "Recommended solutions" refer to the methods offered as the most effective solutions to the discovered communication system problems.

[0632] "Analyzing past data" is the process of statistically evaluating previously recorded information to identify trends and the causes of problems.

[0633] "Predicting future network failures" involves forecasting potential communication system problems based on past patterns and data.

[0634] "User emotions" refers to the psychological state of a person operating or using the system.

[0635] "Adjusting response content" means changing the reply or the information presented according to the user's emotional state.

[0636] "Urgency" refers to the degree to which user inquiries or the status of communication systems require immediate attention.

[0637] "Priority of responses" refers to a hierarchy that determines the order in which multiple issues or inquiries are processed.

[0638] "Real-time management" refers to an operational method that manages the status of the communication system in real time and takes immediate action as needed.

[0639] To realize this invention, the system mainly consists of a server, a user terminal, and a communication network environment. The server is responsible for automatically detecting the current state of the network and generating its configuration information. It also monitors the network in real time, and if an anomaly is detected, it promptly notifies the user and provides recommended countermeasures.

[0640] The server receives queries sent by users in natural language and analyzes them using natural language processing (NLTK) technologies. This involves using natural language processing libraries such as NLTK and spaCy. Based on the analysis results, it also provides troubleshooting information. This process incorporates an AI model, specifically a generative AI model capable of generating interactive responses.

[0641] Furthermore, the server incorporates an emotion engine that analyzes user emotions using APIs such as the Sentiment Analysis API and adjusts responses accordingly. It also provides personalized support by assessing the urgency of the user's request and prioritizing responses. Leveraging the advantages of real-time management, the server constantly monitors network health and provides means to avoid future failures through predictive analytics.

[0642] As a concrete example, if a customer in a physical store encounters a Wi-Fi connection problem, the device sends a natural language inquiry to the server stating "Wi-Fi is not connecting." The server analyzes this message and, if it indicates a high level of stress, instructs the server to provide prompt service. Furthermore, preventative measures are also considered, such as predicting future network risks and arranging for technicians to work with external service centers as needed.

[0643] An example of a prompt for a generative AI model is: "We have received a communication inquiry from a customer. Please tell us how to analyze the text as follows to identify and respond to the appropriate sentiment." In this way, communication network problems faced by users can be mitigated more effectively.

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The server automatically detects the current state of the network. It receives connection status and performance data from network devices as input. This data is processed to generate network configuration information. This process uses monitoring tools to analyze the data and assess network health, providing real-time status. The output is recorded as network configuration information.

[0647] Step 2:

[0648] The server receives queries sent by users in natural language. The input is the query text sent from the user's terminal, which is parsed using natural language processing techniques. For parsing, libraries such as NLTK and spaCy are used to generate data structures for understanding grammar and context. The output is the parsed query content.

[0649] Step 3:

[0650] Based on the analyzed query, the server provides appropriate troubleshooting information. The input is the analysis result generated in the previous step. Using the AI ​​model, the corresponding troubleshooting information is formed, and the solutions proposed by the generating AI model are also considered. The output is specific solution steps and advice.

[0651] Step 4:

[0652] The server monitors the network status in real time and detects anomalies. The input is continuously acquired network monitoring data. An anomaly detection algorithm performs data calculations on this data to automatically identify anomalies. The output is either an alert when an anomaly is detected or a confirmation of normal operation.

[0653] Step 5:

[0654] If an anomaly is detected, the server promptly notifies the user and provides recommended actions. The input is the result of the anomaly detection, which is used to generate a notification message for the user. Using an emotion engine, the user's emotional state is also considered, and the content of the notification and the appropriate actions are adjusted according to their urgency. The output is the notification message sent to the user's device.

[0655] Step 6:

[0656] The server analyzes historical data to predict future network failures. The input is historically collected network log data, which is used for predictive analysis using statistical models. The data calculations employed include regression analysis and machine learning techniques. The output presents predicted failure trends and suggested preventative measures.

[0657] Step 7:

[0658] This system recognizes user emotions and adjusts responses accordingly. Input includes analyzing user inquiry text and behavioral data. It evaluates emotional states using tools like the Sentiment Analysis API and adjusts responses using an AI model. The output is the emotion-based, adjusted response. Because emotion recognition directly influences response adjustment in this step, a personalized experience is provided.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] [Fourth Embodiment]

[0663] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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".

[0676] This invention provides a system for effective network management in environments where dedicated IT personnel are unavailable, such as construction sites. This system automatically generates network configuration information, accepts natural language inquiries from users, analyzes them, and performs troubleshooting. Furthermore, it enables rapid notification and response by monitoring the network in real time and detecting anomalies.

[0677] Specifically, the server scans all devices connected to the network and visualizes their configuration information. This allows the network diagram displayed on the terminal to help users intuitively understand the current network status. Inquiries sent by users via LINEWORKS, etc., are analyzed by the server using natural language processing technology to identify the type of problem and provide corresponding solutions.

[0678] For example, if a user reports a slow internet connection, the server measures network throughput and connection latency, and reconfirms available bandwidth. Furthermore, if a problem is detected, the server immediately sends an appropriate notification to the user's device, guiding them through possible solutions and recovery procedures to support a quick response.

[0679] The server analyzes data collected through daily monitoring and predicts future risks based on this analysis. Based on this predictive information, it can prevent potential problems by proactively proposing appropriate maintenance and equipment upgrades. Furthermore, in the event of particularly critical issues, it can quickly resolve problems on-site by coordinating with an external support center and arranging for the dispatch of technicians as needed.

[0680] Thus, the system of the present invention simplifies complex network management even for those without IT experience, reducing labor and costs while supporting safe and efficient operations at construction sites.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The terminal connects to the on-site network. Specifically, the terminal physically connects to the network using Wi-Fi or LAN cables, performing basic preparations to obtain an overview of the network.

[0684] Step 2:

[0685] The server scans the entire network. It detects all connected devices and collects configuration information such as their IP addresses, MAC addresses, and device types. A network map is then generated based on this information.

[0686] Step 3:

[0687] The user checks the network map. The network map generated by the server is displayed on the terminal, allowing the user to visually confirm the network configuration. This enables the user to perform actions to understand the current state of the network.

[0688] Step 4:

[0689] Users submit inquiries. If a user has questions or problems regarding the network, they enter their questions in natural language via LINEWORKS and send them to the server.

[0690] Step 5:

[0691] The server analyzes the query. The server uses natural language processing technology to analyze the user's query and identify the problem. Based on the results, it prepares the necessary troubleshooting information.

[0692] Step 6:

[0693] The server provides troubleshooting information. Based on the analysis results, the server responds to the user in a chat format with specific solutions and troubleshooting steps. The user can then resolve the problem by following the provided steps.

[0694] Step 7:

[0695] The server monitors the network in real time. The server constantly monitors the network's operational status and operates a system that automatically detects anomalies. A system is in place to immediately initiate action when a problem is detected.

[0696] Step 8:

[0697] The server notifies the user of any abnormalities. When an abnormality is detected in the network, the server immediately notifies the user's terminal of this information and provides specific countermeasures.

[0698] Step 9:

[0699] The server analyzes past data and makes predictions. Based on the accumulated data, the server performs statistical analysis to predict potential problems that may occur in the future and proposes necessary maintenance in advance.

[0700] Step 10:

[0701] The server will coordinate with external support centers as needed. In the event of an emergency or critical problem, the server will contact the external support center and arrange for the dispatch of technicians to the site to ensure a rapid response.

[0702] (Example 1)

[0703] 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".

[0704] In environments utilizing communication infrastructure, the challenge lies in effectively managing and monitoring the network, even in the absence of dedicated IT personnel, and responding quickly when problems arise. Furthermore, it's necessary to enable users, even without specialized knowledge, to understand and grasp the network's configuration and status. Additionally, there's a need to quickly and appropriately analyze issues such as communication delays and instability, and propose countermeasures. Achieving these goals requires real-time monitoring, AI-driven analysis, and visualization techniques.

[0705] 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.

[0706] In this invention, the server includes means for automatically detecting the current state of the communication infrastructure and generating configuration information, means for receiving queries from users in natural language and analyzing them using generative artificial intelligence, and means for providing appropriate problem-solving information based on the analyzed queries. This enables users to intuitively understand the network configuration and status, and to quickly analyze and troubleshoot problems, even in the absence of dedicated IT personnel.

[0707] "Communication infrastructure" is a general term for the hardware and software that make up a digital network, and it is the infrastructure that enables data transmission and reception and internet access.

[0708] "Configuration information" refers to data that shows the settings and connection status of individual devices connected to a network, and is information that helps to understand the overall picture of the network.

[0709] "Natural language" refers to the language that humans use on a daily basis, and is a method of communication and giving instructions without relying on a specific programming language.

[0710] "Generative artificial intelligence" refers to algorithms or systems that learn from large datasets and can automatically perform natural language analysis, generation, and decision-making.

[0711] "Problem-solving information" refers to information that outlines specific countermeasures and procedures for particular troubles or problems, and functions as a guideline for users.

[0712] "Real-time monitoring" is a process that constantly monitors the status of the communication infrastructure, immediately detects abnormalities and malfunctions, and enables immediate response.

[0713] "Visual information" refers to data and structural information that are visually represented as graphs or diagrams, helping users intuitively understand the situation.

[0714] This system is designed to effectively manage communication infrastructure even in the absence of IT specialists. It primarily consists of three elements: servers, terminals, and users. These elements work together to monitor the network, analyze problems, and suggest appropriate solutions.

[0715] The server first scans all information devices connected to the communication infrastructure and obtains configuration information for each device. A general-purpose network management program (e.g., Nagios, Zabbix) is used for this scan. The server then generates a network visualization based on the obtained configuration information and visualizes it using a GUI library (e.g., D3.js, Chart.js). This visualization is provided to the user via a terminal, allowing the user to easily check the network status.

[0716] The server also receives text-based inquiries from users, such as specific problems like "my internet is slow." In response to these inquiries, the server uses a generative AI model (e.g., a large-scale language model) to analyze the text, identify the cause of the problem, and automatically suggest solutions.

[0717] Furthermore, the server continuously monitors the status of the communication infrastructure and immediately sends a notification to the terminal if an anomaly occurs. By performing predictive analysis based on past data and identifying potential problems in advance, a rapid response becomes possible.

[0718] For example, if a user reports "unstable internet connection," the server measures the throughput and latency of the connection in question and identifies that the problem is due to bandwidth limitations. It then recommends readjusting the available bandwidth as a solution.

[0719] Examples of prompt statements are as follows:

[0720] "Please identify the cause of the unstable network at the construction site and provide a solution."

[0721] "Based on user feedback 'Slow Internet,' please report your findings and provide recommended corrective actions."

[0722] This system makes network monitoring and management easy for users without specialized knowledge, and efficiently supports communication infrastructure failure response while minimizing effort and cost.

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] The server scans all information devices connected to the communication infrastructure. Configuration information such as the IP address, MAC address, and connection status of each device is used as input. This information is collected using a network management program and stored in a database as configuration information. A list of configuration information for all devices is generated as output. Specifically, it uses SNMP or ICMP protocols to query each device in the network.

[0726] Step 2:

[0727] The server creates a network visualization based on the collected configuration information. The input is a list of device configuration information generated in the previous step. As data processing, this is visualized using a GUI library to generate a network configuration diagram. The output is the network configuration diagram displayed on the terminal. The specific operation includes generating nodes and edges to illustrate the visualization information as a hierarchical structure.

[0728] Step 3:

[0729] The user sends network-related inquiries to the server in natural language. The input is a text message, such as "My internet is slow." The server uses a generative AI model to analyze this message and perform data calculations to identify the problem. The output is the identified problem and its details. Specifically, natural language processing techniques are used to analyze the text, extract keywords, and perform semantic analysis.

[0730] Step 4:

[0731] The server generates appropriate problem-solving information based on the problem. The input is the problem identified in the previous step. The problem-solving information includes troubleshooting steps and recommended configuration changes. The output is this solution information. In terms of specific actions, it refers to similar past cases and solutions from the database and presents the specific steps the user should take.

[0732] Step 5:

[0733] The server monitors the network status in real time. Inputs are performance and status information for each device in the network. Data processing executes algorithms to detect anomalies based on this information. Outputs are the anomaly detection results. Specifically, it monitors the performance metrics of each device and detects behavior that exceeds a threshold.

[0734] Step 6:

[0735] If the server detects an anomaly, it immediately sends a notification to the terminal. The input is the anomaly detection result. The output is a notification message to the user and recommended actions. Specifically, it notifies the terminal in the form of a pop-up or alert, providing information to take the necessary corrective action.

[0736] Step 7:

[0737] The server analyzes historical monitoring data to predict future risks to the communication infrastructure. Inputs are real-time monitoring and historical data. Data processing, including trend analysis and predictive models, is used to derive risk predictions. Outputs include predicted risk information and recommended preventative measures. Specific operations include statistical analysis and future predictions using machine learning models.

[0738] (Application Example 1)

[0739] 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".

[0740] In real-world environments, network management often requires the intervention of IT specialists, and immediate response is frequently difficult, especially in workplaces such as factories. Furthermore, while there is a need for rapid response to complex network configurations and sudden anomalies, the number of personnel capable of providing appropriate technical support is limited. In such environments, a system is needed that allows users to understand the network status and perform rapid troubleshooting through intuitive operation using natural language. Moreover, there is a need for autonomous robots operating within factories to handle network management, proactively detect problems, and propose solutions.

[0741] 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.

[0742] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for monitoring the network status in real time and detecting anomalies; means for notifying users of detected anomalies and providing recommended countermeasures; and means for a robot that detects a network anomaly to automatically monitor the network status and send a notification to the user based on the analyzed information. This makes it possible to effectively and efficiently manage the network within a factory even in the absence of dedicated IT personnel.

[0743] A "network" is a communication infrastructure that connects multiple electronic devices and other equipment to send and receive data.

[0744] "Configuration information" refers to setting information such as details of each device connected to the network and communication routes.

[0745] "Analysis" is the process of carefully examining given information or data and transforming it into an easily understandable format.

[0746] "Troubleshooting information" refers to information about the steps and methods for resolving problems that have occurred.

[0747] "Real-time monitoring" means immediately checking and processing the current state or events.

[0748] "Detecting an anomaly" means identifying behavior or conditions that are different from the normal state.

[0749] "Notification" is the act of conveying information or messages to a user.

[0750] "Recommended solutions" refer to methods that indicate the optimal solution to a problem that has occurred.

[0751] A "robot" is an automated mechanical device that performs tasks based on a specified program.

[0752] The system implementing this invention includes a program that automatically collects configuration information for a network to which multiple devices are connected, and uses that information to respond quickly when a problem occurs. The central server of the system scans the network and collects configuration information for all devices. The server uses Python and network management libraries to process the collected information into a form that is easy to visualize. The visualized information is displayed on the user's terminal, allowing them to intuitively understand the network status.

[0753] When a user submits a query in natural language, the content is sent to the server via a messaging application such as LINE. The server uses natural language processing technology to analyze this query, identify the problem, and suggest solutions. Specifically, it uses libraries such as NLTK and Spacy to analyze the query content and generate relevant troubleshooting information.

[0754] If an anomaly is detected, the terminal immediately sends a notification to the user. Network anomalies are checked in real time using libraries such as psutil and scapy. The server can quickly send notifications and suggest recommended actions in response to detected anomalies. Furthermore, robots can monitor the network, automatically detect anomalies, and send appropriate notifications.

[0755] As a concrete example, a robot used at a construction site may detect a network anomaly and send a notification via LINE such as, "A connection problem has been found with the router at site A. Please check the cable connection status."

[0756] To utilize this system more efficiently, an example of a prompt statement that leverages the generated AI model would be, "Please explain the cause of the slow network speed and suggest solutions." By using this prompt statement, the AI ​​can provide more accurate advice.

[0757] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0758] Step 1:

[0759] The server scans the network and collects configuration information for all connected devices. The input is the network environment, and the output is a list of device information. This process uses libraries such as psutil and scapy to detect network devices, obtain their protocols, IP addresses, and MAC addresses, and save them as a list.

[0760] Step 2:

[0761] The server receives inquiries from users via messaging apps such as LINE using natural language. The input is the inquiry message from the user, and the output is the parsed problem description. In this step, the message content is taken into the server as a string, its context is parsed using NLTK or Spacy, and the type of problem is identified.

[0762] Step 3:

[0763] The server generates appropriate troubleshooting information based on the analyzed problem and provides it to the user. The input is the analysis result, and the output is a suggested solution to the problem. In this process, the server refers to a pre-prepared database of solutions, selects solutions and hints corresponding to the identified problem, and formats them in a format to be returned to the user.

[0764] Step 4:

[0765] The server monitors the network status in real time and detects anomalies. The input is real-time data from the network, and the output is the result of anomaly detection. In this step, network throughput and latency are continuously tracked, and if they exceed a defined threshold, they are detected as an anomaly and recorded within the system.

[0766] Step 5:

[0767] If an anomaly is detected, the server immediately sends a notification to the user's terminal and provides recommended countermeasures. The input is the result of the anomaly detection, and the output is the notification message. This process creates a message to inform the user about the detected anomaly and sends it to the user along with suggested countermeasures.

[0768] Step 6:

[0769] The server automatically monitors the network status when a robot detects a network anomaly, analyzes the information, and notifies the user. The input is monitoring data from the robot, and the output is the notification to the user. In this process, the information provided by the robot is analyzed to identify the anomaly, create the notification content, and provide the user with information quickly.

[0770] 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.

[0771] This invention combines an emotional engine with a system for effectively managing networks in environments where dedicated IT personnel are absent, such as construction sites. This system automatically detects the current state of the network and generates configuration information. It can also accept and analyze natural language inquiries from users and provide appropriate troubleshooting information. Furthermore, it monitors the network status in real time and provides rapid notification and solutions when an anomaly is detected.

[0772] In addition, this invention integrates an emotion engine that recognizes the user's emotions and adjusts the response based on those emotions. Specifically, the server evaluates the user's stress level through the emotion engine and adjusts the priority of network problem resolution and support accordingly. In this way, the aim is to provide more personalized support and improve the user experience.

[0773] For example, if a user is in a situation where they urgently need to resolve a network problem, and they send a message expressing their stress such as, "I can't use the internet at all, please fix it quickly," the server will use its emotion engine to recognize the user's urgency. As a result, the server will prioritize immediate action and arrange emergency support for the user.

[0774] The server performs statistical analysis based on data collected daily to predict future network failures. Based on this information, it proposes preventative measures and, when necessary, collaborates with external support centers to achieve early problem resolution. By utilizing an emotion engine, more granular management is possible than with conventional systems, and problems faced by users can be mitigated more effectively.

[0775] The following describes the processing flow.

[0776] Step 1:

[0777] The terminal connects to the on-site network. The terminal physically connects to the network via Wi-Fi or LAN cable and performs authentication for basic communication operations.

[0778] Step 2:

[0779] The server scans the entire network. The server collects information such as the IP addresses, MAC addresses, and device types of all connected devices and generates a network configuration diagram.

[0780] Step 3:

[0781] The user checks the network configuration. The user views the network configuration diagram on their terminal to understand the overall picture of the network at the site.

[0782] Step 4:

[0783] Users submit inquiries. If there are network problems or questions, users can send questions to the server in natural language via LINEWORKS.

[0784] Step 5:

[0785] The server analyzes the query. Using natural language processing techniques, the server analyzes the query content and identifies the type of problem. This allows it to begin preparing for appropriate troubleshooting.

[0786] Step 6:

[0787] The server recognizes the user's emotions through an emotion engine. It evaluates the user's emotions and stress level based on the content of the inquiry and adjusts the priority of the response accordingly.

[0788] Step 7:

[0789] The server provides troubleshooting based on emotions. Based on the analyzed emotions and the nature of the problem, the server provides the user with the best solution and course of action via chat.

[0790] Step 8:

[0791] The server monitors the network in real time. The server continuously monitors the network status and establishes the infrastructure to immediately initiate response if an anomaly is detected.

[0792] Step 9:

[0793] The server notifies the user of any detected anomalies. When an anomaly is detected, the server sends a notification to the user and arranges specific countermeasures and emergency support.

[0794] Step 10:

[0795] The server analyzes past data and predicts future problems. Based on the data collected daily, the server performs analysis and suggests preventative measures against potential risks to the user.

[0796] Step 11:

[0797] The server will coordinate with an external support center as needed. In emergencies, the server will contact the support center and arrange for the dispatch of technicians or remote assistance to help resolve the problem quickly.

[0798] (Example 2)

[0799] 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".

[0800] In network management, users without specialized knowledge face the challenge of quickly and efficiently understanding the network's status and finding appropriate solutions. In particular, there is a need to provide individualized support that addresses users' emotional stress. Furthermore, there is a demand for proactively predicting and preventing future network failures.

[0801] 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.

[0802] In this invention, the server includes means for automatically detecting the current state of the network and generating configuration information; means for receiving and analyzing queries in natural language from users; means for providing appropriate troubleshooting information based on the analyzed queries; means for evaluating the emotional state of the receiving user and adjusting the response content; and means for analyzing past data, predicting future network failures, and suggesting preventive measures. This enables even users without specialized knowledge to efficiently understand the problems they face, receive appropriate support tailored to their emotions, and prevent network failures from occurring.

[0803] "Means for automatically detecting the current state of the network" refers to the ability to automatically identify the connected devices and topology of the network using network scanning technology.

[0804] "Means for generating configuration information" refers to a function that organizes information based on the detected network state and configuration, and outputs it in a visualized format.

[0805] "Means for receiving and analyzing inquiries from users in natural language" refers to a function that receives inquiry content written by users in natural language and analyzes its intent using natural language processing technology.

[0806] "Means of providing appropriate troubleshooting information" refers to a function that provides users with information useful for resolving network problems based on analysis results.

[0807] "A means of monitoring the network status in real time and detecting anomalies" refers to a function that continuously monitors network performance and immediately detects anomalies or failures.

[0808] "Means for notifying users of detected anomalies and providing recommended countermeasures" refers to a function that informs users of detected anomalies and proposes corresponding solutions.

[0809] "Means for evaluating the emotional state of the receiving user and adjusting the response content" refers to a function that uses an emotion engine to analyze the user's emotions and respond accordingly.

[0810] "A means of analyzing past data to predict future network failures and propose preventive measures" refers to a function that analyzes past network data to identify failure patterns and take countermeasures in advance.

[0811] This system is a server-centric network management system that enables efficient operation, especially in environments without dedicated IT personnel. The server first automatically scans the network status and collects necessary network information. This involves using specialized software (e.g., monitoring tools such as Nagios or Zabbix) to analyze the network topology and connection data.

[0812] The server then visualizes configuration information based on the generated network information and also has the functionality to receive inquiries from users. Inquiries in natural language are received via the terminal and analyzed on the server using natural language processing libraries (e.g., spaCy or Transformers). This analysis extracts the gist of the problem and generates appropriate troubleshooting information.

[0813] Furthermore, the server evaluates the user's emotions through its emotion engine and adjusts its response to suit the user's situation. If the server detects that the user is in a hurry to resolve the issue, it prioritizes a rapid response and immediately presents the user with a solution.

[0814] The server also performs real-time network monitoring, constantly detecting anomalies. If an anomaly occurs, the server automatically issues an alert, enabling a rapid response. Furthermore, the server analyzes historical data to predict future network failures. Based on these predictions, it can proactively offer users appropriate preventative measures.

[0815] For example, if a user sends a message expressing stress, such as "I can't use the internet at all, please fix it quickly," the server uses the emotion engine to determine the user's situation is urgent and enables immediate action. Another example of a prompt to the generative AI model is, "How does the emotion engine operate when a network failure occurs?"

[0816] Thus, the present invention efficiently solves network problems faced by users and provides a more comfortable user environment.

[0817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0818] Step 1:

[0819] The server detects the current state of the network. It collects data on connected devices and their communication status as input. Using a dedicated network monitoring tool, the server analyzes this input data and automatically generates a network topology. The output is comprehensive configuration information that shows the current state of the network.

[0820] Step 2:

[0821] The terminal receives inquiries from the user in natural language. The user inputs a problem using the terminal's interface. The server receives this inquiry and applies natural language processing techniques to analyze the intent of the inquiry. The input is the user's inquiry, and the output is data containing the gist of the analyzed problem.

[0822] Step 3:

[0823] The server generates appropriate troubleshooting information based on the analyzed data. It uses the essence of the analyzed problem and network configuration information as input. Based on this information, the server searches the database for the corresponding solution and presents it to the user as output.

[0824] Step 4:

[0825] The server evaluates the user's emotions and adjusts the response accordingly. It uses a message received from the terminal as input and analyzes the user's emotional state using an emotion engine. The output is a customized response tailored to the user's emotions. Specifically, if the stress level is high, a rapid support action is generated.

[0826] Step 5:

[0827] The server monitors the network in real time and detects anomalies. It uses continuously collected communication data as input and evaluates the situation using an anomaly detection algorithm. The output is the anomaly detection result and a notification to the user. If an anomaly occurs, the server generates an alert and immediately notifies the user.

[0828] Step 6:

[0829] The server analyzes historical data to predict future network failures. It uses historically collected network performance data as input. Based on this, it performs statistical analysis to predict the likelihood of future failures. The output is a risk assessment report including preventative measures.

[0830] Step 7:

[0831] The server uses a generative AI model to generate prompt messages to predict future anomalies. It takes user scenarios and potential anomaly events as input to form prompt messages, and then considers a trial-and-error process based on these. The output is a proposal for prompt messages to prevent anomalies.

[0832] (Application Example 2)

[0833] 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".

[0834] In physical stores, proper management of communication network systems is crucial for improving customer satisfaction and operational efficiency. However, store operators do not necessarily possess advanced technical knowledge, often facing difficulties in troubleshooting network issues and responding to emergencies. Furthermore, appropriately responding to customer inquiries and providing support tailored to customer emotions and urgency directly contributes to an improved customer experience, but current systems are insufficient in this regard. Therefore, a network management system that is sensitive to the emotions of users is needed.

[0835] 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.

[0836] In this invention, the server includes means for monitoring the network status in real time and detecting anomalies, means for recognizing the emotions of users and adjusting the response content, and means for setting priority of responses according to the urgency of the user. This makes it possible to constantly understand the status of the communication system within the store and to perform detailed network management that responds to the emotions of customers and users.

[0837] "Current network status" refers to the connection and performance status of the current communication system.

[0838] "Configuration information" refers to a set of data that represents the network connection structure, settings, and the status of the devices being used.

[0839] A "natural language query" is a question or request made in the language that users use on a daily basis.

[0840] An "analyzed query" is a query written in natural language that has been processed and transformed into an understandable form.

[0841] "Troubleshooting information" refers to information that includes the steps and advice necessary to resolve problems in communication systems.

[0842] "Real-time monitoring" refers to the operation of constantly observing the status of a communication system and being able to immediately grasp any changes.

[0843] "Anomaly detection" refers to a system finding deviations from the normal state of the network.

[0844] "Recommended solutions" refer to the methods offered as the most effective solutions to the discovered communication system problems.

[0845] "Analyzing past data" is the process of statistically evaluating previously recorded information to identify trends and the causes of problems.

[0846] "Predicting future network failures" involves forecasting potential communication system problems based on past patterns and data.

[0847] "User emotions" refers to the psychological state of a person operating or using the system.

[0848] "Adjusting response content" means changing the reply or the information presented according to the user's emotional state.

[0849] "Urgency" refers to the degree to which user inquiries or the status of communication systems require immediate attention.

[0850] "Priority of responses" refers to a hierarchy that determines the order in which multiple issues or inquiries are processed.

[0851] "Real-time management" refers to an operational method that manages the status of the communication system in real time and takes immediate action as needed.

[0852] To realize this invention, the system mainly consists of a server, a user terminal, and a communication network environment. The server is responsible for automatically detecting the current state of the network and generating its configuration information. It also monitors the network in real time, and if an anomaly is detected, it promptly notifies the user and provides recommended countermeasures.

[0853] The server receives queries sent by users in natural language and analyzes them using natural language processing (NLTK) technologies. This involves using natural language processing libraries such as NLTK and spaCy. Based on the analysis results, it also provides troubleshooting information. This process incorporates an AI model, specifically a generative AI model capable of generating interactive responses.

[0854] Furthermore, the server incorporates an emotion engine that analyzes user emotions using APIs such as the Sentiment Analysis API and adjusts responses accordingly. It also provides personalized support by assessing the urgency of the user's request and prioritizing responses. Leveraging the advantages of real-time management, the server constantly monitors network health and provides means to avoid future failures through predictive analytics.

[0855] As a concrete example, if a customer in a physical store encounters a Wi-Fi connection problem, the device sends a natural language inquiry to the server stating "Wi-Fi is not connecting." The server analyzes this message and, if it indicates a high level of stress, instructs the server to provide prompt service. Furthermore, preventative measures are also considered, such as predicting future network risks and arranging for technicians to work with external service centers as needed.

[0856] An example of a prompt for a generative AI model is: "We have received a communication inquiry from a customer. Please tell us how to analyze the text as follows to identify and respond to the appropriate sentiment." In this way, communication network problems faced by users can be mitigated more effectively.

[0857] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0858] Step 1:

[0859] The server automatically detects the current state of the network. It receives connection status and performance data from network devices as input. This data is processed to generate network configuration information. This process uses monitoring tools to analyze the data and assess network health, providing real-time status. The output is recorded as network configuration information.

[0860] Step 2:

[0861] The server receives queries sent by users in natural language. The input is the query text sent from the user's terminal, which is parsed using natural language processing techniques. For parsing, libraries such as NLTK and spaCy are used to generate data structures for understanding grammar and context. The output is the parsed query content.

[0862] Step 3:

[0863] Based on the analyzed query, the server provides appropriate troubleshooting information. The input is the analysis result generated in the previous step. Using the AI ​​model, the corresponding troubleshooting information is formed, and the solutions proposed by the generating AI model are also considered. The output is specific solution steps and advice.

[0864] Step 4:

[0865] The server monitors the network status in real time and detects anomalies. The input is continuously acquired network monitoring data. An anomaly detection algorithm performs data calculations on this data to automatically identify anomalies. The output is either an alert when an anomaly is detected or a confirmation of normal operation.

[0866] Step 5:

[0867] If an anomaly is detected, the server promptly notifies the user and provides recommended actions. The input is the result of the anomaly detection, which is used to generate a notification message for the user. Using an emotion engine, the user's emotional state is also considered, and the content of the notification and the appropriate actions are adjusted according to their urgency. The output is the notification message sent to the user's device.

[0868] Step 6:

[0869] The server analyzes historical data to predict future network failures. The input is historically collected network log data, which is used for predictive analysis using statistical models. The data calculations employed include regression analysis and machine learning techniques. The output presents predicted failure trends and suggested preventative measures.

[0870] Step 7:

[0871] This system recognizes user emotions and adjusts responses accordingly. Input includes analyzing user inquiry text and behavioral data. It evaluates emotional states using tools like the Sentiment Analysis API and adjusts responses using an AI model. The output is the emotion-based, adjusted response. Because emotion recognition directly influences response adjustment in this step, a personalized experience is provided.

[0872] 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.

[0873] 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.

[0874] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0875] 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.

[0876] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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."

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0893] The following is further disclosed regarding the embodiments described above.

[0894] (Claim 1)

[0895] A means for automatically detecting the current state of the network and generating configuration information,

[0896] A means of receiving and analyzing queries in natural language from users,

[0897] A means of providing appropriate troubleshooting information based on the analyzed query,

[0898] A means of monitoring the network status in real time and detecting anomalies,

[0899] A means of notifying users of detected anomalies and providing recommended countermeasures,

[0900] A means of analyzing past data to predict future network failures and propose preventative measures,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, characterized in that it includes means for arranging the dispatch of a technician in cooperation with an external support center when a network anomaly is detected.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for automatically visualizing the network configuration simply by connecting a user to the network.

[0906] "Example 1"

[0907] (Claim 1)

[0908] A means for automatically detecting the current status of the communication infrastructure and generating configuration information,

[0909] A means of receiving and analyzing queries in natural language from users,

[0910] A means of providing appropriate problem-solving information based on the analyzed query,

[0911] A means of monitoring the status of the communication infrastructure in real time and detecting anomalies,

[0912] A means of notifying users of detected anomalies and providing recommended countermeasures,

[0913] A means of analyzing past information to predict future communication infrastructure failures and propose preventive measures,

[0914] A means of providing visual information of the communication infrastructure to the user terminal,

[0915] A means of analyzing user inquiries using artificial intelligence,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, characterized in that it includes means for arranging the dispatch of engineers in cooperation with an external support base when an abnormality in the communication infrastructure is detected.

[0919] (Claim 3)

[0920] The system according to claim 1, further comprising means for automatically visualizing the communication infrastructure configuration simply by connecting a user to the communication infrastructure.

[0921] "Application Example 1"

[0922] (Claim 1)

[0923] A means for automatically detecting the current state of the network and generating configuration information,

[0924] A means of receiving and analyzing queries in natural language from users,

[0925] A means of providing appropriate troubleshooting information based on the analyzed query,

[0926] A means of monitoring the network status in real time and detecting anomalies,

[0927] A means of notifying users of detected anomalies and providing recommended countermeasures,

[0928] A means of analyzing past data to predict future network failures and propose preventative measures,

[0929] A robot that detects network anomalies automatically monitors the network status and sends notifications to users based on the analyzed information.

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, characterized in that it includes means for arranging the dispatch of a technician in cooperation with an external support organization when a network anomaly is detected.

[0933] (Claim 3)

[0934] The system according to claim 1, further comprising means for automatically visualizing the network configuration simply by connecting a user to the network.

[0935] "Example 2 of combining an emotion engine"

[0936] (Claim 1)

[0937] A means for automatically detecting the current state of the network and generating configuration information,

[0938] A means of receiving and analyzing queries in natural language from users,

[0939] A means of providing appropriate troubleshooting information based on the analyzed query,

[0940] A means of monitoring the network status in real time and detecting anomalies,

[0941] A means of notifying users of detected anomalies and providing recommended countermeasures,

[0942] A means of evaluating the emotional state of the recipient and adjusting the response content,

[0943] A means of analyzing past data to predict future network failures and propose preventative measures,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, characterized in that it includes means for arranging the dispatch of a technician in cooperation with an external support center when a network anomaly is detected.

[0947] (Claim 3)

[0948] The system according to claim 1, further comprising means for automatically visualizing the network configuration simply by connecting a user to the network.

[0949] "Application example 2 when combining with an emotional engine"

[0950] (Claim 1)

[0951] A means for automatically detecting the current state of the network and generating configuration information,

[0952] A means of receiving and analyzing queries in natural language from users,

[0953] A means of providing appropriate troubleshooting information based on the analyzed query,

[0954] A means of monitoring the network status in real time and detecting anomalies,

[0955] A means of notifying users of detected anomalies and providing recommended countermeasures,

[0956] A means of analyzing past data to predict future network failures and propose preventative measures,

[0957] A means of recognizing the user's emotions and adjusting the response content,

[0958] A means of setting priorities for responses according to the urgency of the user,

[0959] A means of managing the status of a store's communication system in real time,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, characterized in that it includes means for coordinating with an external service center to dispatch a technician when a network anomaly is detected.

[0963] (Claim 3)

[0964] The system according to claim 1, further comprising means for automatically visualizing the network configuration simply by connecting a user to a communication network. [Explanation of Symbols]

[0965] 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 automatically detecting the current state of the network and generating configuration information, A means of receiving and analyzing queries in natural language from users, A means of providing appropriate troubleshooting information based on the analyzed query, A means of monitoring the network status in real time and detecting anomalies, A means of notifying users of detected anomalies and providing recommended countermeasures, A means of analyzing past data to predict future network failures and propose preventative measures, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for arranging for the dispatch of a technician in cooperation with an external support center when a network anomaly is detected.

3. The system according to claim 1, further comprising means for automatically visualizing the network configuration simply by connecting a user to the network.

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