system

Abstract

Provide a system. 【Solution means】 Means for obtaining identification information of network facilities, Means for analyzing the network configuration based on the obtained identification information, Means for generating a network configuration diagram using the analysis result, Means for detecting changes in the state of network facilities, Means for updating the network configuration diagram based on the detected state changes, Means for notifying the user of proposed changes to the network configuration diagram, Means for periodically updating the network configuration diagram based on the user's approval, A system including the above.

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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern IT infrastructures, configuration diagrams of networks and servers need to be updated regularly, but manual creation and update work require a great deal of effort and time. Also, when a network failure occurs, although it is required to quickly identify the location and the scope of influence of the failure, with conventional methods, these operations are complicated and there is a possibility of errors. As a result, problems such as inappropriate resource management and delayed response to failures occur. Therefore, it is necessary to solve these problems by performing automatic analysis of networks and automatic generation and update of configuration diagrams.

Means for Solving the Problems

[0005] This invention relates to a system that automatically acquires identification information of network equipment and analyzes the network configuration based on that information. It also generates a network configuration diagram from the analysis results and provides this configuration information to the user through a visualization engine. Furthermore, it has a function to automatically detect changes in the state of network equipment and update the network configuration diagram as needed. The system includes a process for notifying the user of proposed changes to the configuration diagram and updating it after obtaining their approval. This improves the efficiency of network management and enables rapid fault response.

[0006] "Network equipment" is a general term for hardware devices and software components that operate as building blocks of a network.

[0007] "Identification information" refers to unique data used to identify network equipment, and this includes IP addresses and server names.

[0008] "Analysis" is the process of understanding the structure and state of a network using acquired data and extracting meaningful information.

[0009] A "network diagram" is a diagram that visually represents the connection status and placement of each device within a network, and is used to understand the overall structure of the network.

[0010] A "visualization engine" is a software function that converts data into a visual representation, providing the network status and configuration in a format that is easy for users to understand.

[0011] "State change" refers to fluctuations in the operating state or configuration changes that occur in network equipment, and this includes function shutdowns and setting changes.

[0012] A "user" refers to a person who uses this system to manage and monitor the network. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference numeral (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.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a system that acquires identification information of network equipment, automatically analyzes the network configuration based on the acquired information, and visualizes it. This system works in cooperation with servers, terminals, and users, enabling efficient network management and rapid response in the event of failures.

[0035] First, the server uses a network scanning tool to collect identification information from all devices on the network. This collected information is stored in a database and used for configuration analysis. The server performs scans periodically to update information on newly added and modified devices.

[0036] The terminal retrieves configuration data provided by the server and generates a network diagram using a visualization engine. This diagram is displayed in an easy-to-understand format for the user, allowing for a visualization of the overall network status. In the event of a failure, the affected area is highlighted, helping the user quickly understand the problem.

[0037] As a concrete example, when a new server is added to the network, the server automatically detects this and updates its configuration information. The terminal then regenerates the configuration diagram reflecting the updates and notifies the user. The user approves or proposes modifications and sends feedback to the server through the terminal. This ensures that the network configuration is always up-to-date and supports rapid decision-making.

[0038] Furthermore, if users have any questions about the configuration diagram, they can ask directly through their terminal. The server will then collect relevant information in response to these requests and provide the user with a detailed explanation. This feature allows users to gain a deeper understanding and improve their confidence in network operations.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server uses network scanning tools to collect identification information for all devices on the network. This includes data such as IP addresses and server names.

[0042] Step 2:

[0043] The server stores the collected identification information in a database. This data is used for subsequent network configuration analysis.

[0044] Step 3:

[0045] The server periodically rescans the network to automatically detect new or modified devices. This detection updates the database information.

[0046] Step 4:

[0047] The terminal retrieves the latest configuration data from the server and generates a network diagram using a visualization engine. This diagram visually represents the network topology.

[0048] Step 5:

[0049] The terminal displays the generated configuration diagram to the user. By viewing the configuration diagram, the user can grasp the overall picture of the network.

[0050] Step 6:

[0051] When a failure occurs, the server identifies the affected device and sends that information to the terminal. This allows the terminal to highlight the problematic area in the configuration diagram.

[0052] Step 7:

[0053] Users receive proposed changes to the configuration diagram via their device. Users approve or modify the proposals and send their feedback from their device to the server.

[0054] Step 8:

[0055] When a user asks a question about something they don't understand, the device sends the request to the server. The server collects relevant information and provides the user with a detailed explanation.

[0056] (Example 1)

[0057] 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."

[0058] In managing communication systems, it is crucial to efficiently collect identification information for each device and visualize the network structure based on that information. However, conventional methods have presented challenges such as difficulty in responding quickly to status changes or failures, leading to delays in notifying users and updating the network structure. Furthermore, immediate responses to user inquiries were also difficult.

[0059] 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.

[0060] In this invention, the server includes means for acquiring information on all devices in the computer network using an identification information collection function, means for automatically analyzing the network structure based on the acquired information, and means for responding to user inquiries using a generative model. This enables more efficient network management, rapid response in the event of a failure, and immediate information provision and response to users.

[0061] The "identification information collection function" is a function that collects information from each device within the network and organizes and stores it.

[0062] A "computer network" is a system in which numerous computers and devices are interconnected and exchange information with each other.

[0063] "Means for automatically analyzing network structure" refers to methods for independently analyzing and understanding the overall configuration of a network based on acquired network information.

[0064] A "communication system structure diagram" is a diagram that visually represents the configuration and connection relationships of a network, and is useful for users to understand the state of the network.

[0065] "Means for automatically detecting changes in state" refers to means that can monitor changes in devices and connections within a network and immediately grasp any changes.

[0066] "Means of notifying users of proposed changes" refers to means of immediately communicating information to users regarding changes to the network structure.

[0067] A "generative model" is a model that uses artificial intelligence technology to automatically generate appropriate answers to user inquiries.

[0068] This invention is a system in which servers, terminals, and users cooperate to streamline network management and monitoring. The server uses a network scanning tool to collect identification information from all devices on the network. In this step, for example, an open-source network scanning tool is used to obtain the IP addresses and hostnames of all devices connected to the network. The collected information is stored in a database, and the server uses this information to analyze the network structure.

[0069] The terminal retrieves network structure data analyzed from the server and processes it using a visualization engine. The visualization engine generates a structure diagram that visualizes the network configuration, for example, using an open-source dashboard creation tool. The generated structure diagram visually shows the status and connections of each device in the network, helping the user understand the overall picture of the network.

[0070] Users can view a structural diagram provided via their terminal, enabling them to quickly understand network changes and failures. If an anomaly is highlighted in the structural diagram, users can immediately take corrective action. Furthermore, users can send questions via their terminal about areas they are unsure of, and the server provides immediate responses using a generative AI model. Specifically, commonly used natural language processing techniques are applied as the generative AI model to generate natural language answers to user inquiries.

[0071] As a concrete example, when a new device is added to the network, the server automatically detects it and updates the network structure information. The terminal then updates the structure diagram based on the latest information and notifies the user. The user can review the updated structure diagram and provide feedback to the server as needed. This leads to faster and more accurate network maintenance.

[0072] For example, a possible prompt to input into a generating AI model could be, "Please tell me the procedure to follow when a new device is added to the network." In this way, the entire system can work together, significantly improving the efficiency and reliability of network management.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server launches a network scanning tool and scans all devices on the network. The network's IP range is specified as input, and the output provides identification information for each device, including its IP address, hostname, and MAC address. This information is saved to a file and prepared for transfer to a database for post-processing.

[0076] Step 2:

[0077] The server stores the identification information obtained in step 1 in the database. The scan result file is used as input, and the information for each device is saved in the database table as output. This process maintains the consistency of the information and makes it easy to refer to it in conjunction with past records.

[0078] Step 3:

[0079] The server retrieves the latest identification information from the database and analyzes the network structure. The database contents are used as input, and the network topology is obtained as the output. Here, the connection relationships between devices and the network configuration are revealed. The program uses algorithms to evaluate the device hierarchy and the presence or absence of links.

[0080] Step 4:

[0081] The terminal retrieves analyzed network topology data from the server and generates a structural diagram using a visualization engine. The analysis results are used as input, and the output is a structural diagram in a user-friendly format. This program utilizes the visualization engine to arrange devices as shapes and represent their connection relationships with lines.

[0082] Step 5:

[0083] The terminal updates the structural diagram based on fault information and status changes. The analysis results from step 3 and alert information from the monitoring system are used as input. The output is an updated structural diagram, with fault areas highlighted. This allows the user to immediately identify network problems.

[0084] Step 6:

[0085] Users view the structural diagram via their terminal and send feedback to the server as needed. The input is the structural diagram and its description, and the output is user approval or modification suggestions sent via the terminal. This feedback allows for further refinement of the network configuration and improved reliability.

[0086] Step 7:

[0087] The user sends a question from their device to the server, which then uses a generative AI model to generate a response. The user's question is given as input, and the automatically generated answer is received as output. This interaction allows the user to deepen their understanding of network operations.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Modern information processing systems require the immediate understanding of network structure and status, particularly the scope of impact during failures, and the ability to respond quickly. However, conventional systems face challenges in real-time visual monitoring and immediate fault response. Furthermore, the increasing burden on engineers to continuously monitor the increasingly complex network status leads to a decline in efficiency.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for collecting identification data of network devices, means for analyzing the network structure based on the collected identification data, and means for creating a network structure diagram using the analysis results. This makes it possible to display the network status of the group of information processing devices in real time using visual devices. Furthermore, fault conditions can be highlighted using visual devices to facilitate a quick response by operators, resulting in more efficient network management and faster response to failures.

[0093] "Network devices" refer to various information devices connected to a network, and these are hardware devices used for sending and receiving data.

[0094] "Identification data" refers to information used to uniquely recognize network devices, and typically includes IP addresses and MAC addresses.

[0095] An "information processing system" refers to a group of information devices that are interconnected via a network and perform data processing and communication.

[0096] "Visual devices" are devices used to physically visualize digital information, and include smart glasses and displays.

[0097] A "visualization engine" is a software or hardware component that displays digital data in a visually understandable format.

[0098] "Fault status" refers to abnormalities or failures in the network, and is a factor that prevents normal operation.

[0099] The system for implementing this invention mainly consists of a server, terminals, and operators. The server collects identification data from network devices and stores it in a database. The hardware used includes network scanners and standard computer servers. Dedicated analysis software and a visualization engine are used to analyze this information to identify the network structure and generate a structural diagram.

[0100] This generated network structure diagram is provided to the operator via a terminal. The terminal includes visual devices such as smart glasses, which display the network status in real time. This display is dynamically updated by a visualization engine, and areas of failure are highlighted, especially when they occur. Based on this information, the operator can quickly respond to network problems.

[0101] For example, if a robot in a factory loses communication, this information is immediately detected by the server, and the network structure diagram is updated. This anomaly is visually highlighted to operators wearing smart glasses, allowing them to physically go to the location and resolve the problem.

[0102] An example of input to the generating AI model could be a prompt such as, "Design a smart glasses application that visualizes the network status of robots in a factory and detects faults."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server uses a network scanner to collect identification data from network devices. The input is set to the network's IP range, and the output is the collected identification data (IP address, MAC address, etc.). This data is stored in a database.

[0106] Step 2:

[0107] The server analyzes the identification data stored in the database. This analysis determines the connection relationships between devices and identifies the network structure. The identification data collected in the previous step is used as input, and network structure information is obtained as output.

[0108] Step 3:

[0109] The server uses a visualization engine to create a network structure diagram based on the analyzed network structure information. Network structure information is used as input, and the output is a network structure diagram that is easy for the user to understand visually.

[0110] Step 4:

[0111] The terminal retrieves the created network structure diagram and displays it to the operator using a visual device. The input is a visualized network structure diagram, and the output is the diagram displayed on a visual device such as smart glasses, which is provided to the operator.

[0112] Step 5:

[0113] The server constantly monitors network status changes and detects anomalies and failures. It monitors real-time network data as input and updates the information as output if an anomaly is detected.

[0114] Step 6:

[0115] When a fault is detected, the terminal highlights the information using a visual device. The input is fault information provided by the server, and the output is a network structure diagram with the problem area highlighted, which is shown to the operator.

[0116] Step 7:

[0117] Users respond quickly based on information displayed on smart glasses. The input is visual information provided from the device, and the output is actual troubleshooting and corrective actions.

[0118] These steps enable real-time monitoring of the network status and rapid fault response.

[0119] 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.

[0120] This invention combines a system that automatically identifies, analyzes, and visualizes network equipment to streamline network management with an emotion engine that recognizes the user's emotional state. This system aims to reduce stress and support decision-making by quickly identifying network operational challenges and providing information tailored to the user's emotions.

[0121] First, the server uses existing network scanning tools to collect identification information from devices within the network and stores it in a database. The server periodically performs scans to detect new devices and changes. Based on the collected information, the server analyzes the network configuration and forms data for generating a network diagram.

[0122] The terminal processes data received from the server via a visualization engine to generate a graphical network configuration diagram. This diagram is displayed to the user in an intuitive and easy-to-understand format. In the event of a failure, the affected area is highlighted to support a rapid response.

[0123] Furthermore, to recognize user emotions in real time, the device is equipped with an emotion engine. The emotion engine analyzes the emotional state using various sensor data and user interaction data. For example, if a user's stress level increases while viewing a network diagram, the emotion engine can detect that emotion, and the device can adjust how the information is displayed. Specifically, it can soften the tone of the information presentation or adjust the amount of information to provide it in a way that is less burdensome for the user.

[0124] As a concrete example, when a major network failure occurs, the server quickly identifies the faulty device, and the terminal notifies the user of the situation. If the emotion engine detects stress during the user's review of the notification, the terminal reduces the priority of the notification and presents solutions step by step, thereby reducing the user's burden. This entire process reduces the stress of network operations and improves the efficiency of decision-making.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server launches a network scanning tool to collect identification information for each device on the network. This information includes IP addresses and hostnames.

[0128] Step 2:

[0129] The server stores the collected data in a database and analyzes the network configuration. This analysis clarifies the connection relationships between each device.

[0130] Step 3:

[0131] The terminal generates a configuration diagram using a visualization engine based on the latest network configuration data obtained from the server. The configuration diagram is designed to allow the user to see the status of the entire network at a glance.

[0132] Step 4:

[0133] When a user views a diagram on their device, the device uses an emotion engine to evaluate the user's emotional state in real time. Data collected from the user's camera and microphone is used for analysis.

[0134] Step 5:

[0135] When the emotion engine detects user stress, the device dynamically adjusts the tone and style of information presentation. For example, it reduces the amount of information or highlights only the most important information to lessen the user's burden.

[0136] Step 6:

[0137] When a network failure occurs, the server quickly identifies the affected devices and sends that information to the terminals. The terminals then reflect this information in the configuration diagram and notify the user of the location and scope of the failure.

[0138] Step 7:

[0139] Users review the suggested solutions and configuration diagram changes from their terminals and provide feedback. This feedback is then relayed to the server, where corrective actions are implemented.

[0140] Step 8:

[0141] If a user requires further information or support, the device sends the request to the server, which collects and provides the relevant information to the user. This allows the user to efficiently work towards resolving the problem.

[0142] (Example 2)

[0143] 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".

[0144] Traditional network management systems often require manual processes for understanding network configurations and identifying the scope of impact in the event of a failure, placing significant stress on administrators. Furthermore, the lack of information tailored to users' emotional states can reduce the speed and accuracy of decision-making. There is a need to address these issues and improve the efficiency of network operations while reducing the burden on users.

[0145] 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.

[0146] In this invention, the server includes means for collecting identification information of network devices, means for analyzing the configuration of the communication network based on the collected identification information, and means for adjusting the information presentation method based on the recognized emotional state. This enables automatic identification of devices within the communication network and real-time configuration analysis, and allows for the provision of appropriate information according to the user's emotions.

[0147] A "network device" is a device used to send and receive digital data, and functions as a component of a communication network.

[0148] "Identification information" refers to data used to uniquely identify each network device, and mainly includes IP addresses, MAC addresses, and hostnames.

[0149] A "communication network" is a system or structure in which multiple network devices are connected for the exchange of digital data.

[0150] "Analysis" is the process of investigating and understanding the structure and characteristics of an object in detail based on collected data.

[0151] A "configuration diagram" is a diagram that visually represents the connection status and layout of the components of a communication network.

[0152] "State change" refers to any variation or transition in the attributes or behavior of a communication network or its components.

[0153] "User" refers to an individual or legal entity that manages, monitors, or uses a communication network.

[0154] "Emotional state" refers to a change in the user's psychological or emotional state, including internal conditions such as stress and a sense of security.

[0155] "Information presentation method" refers to the format and means of providing data and notifications to users, including text and visual displays.

[0156] This invention provides a system that streamlines the management of communication networks and reduces the psychological burden on users. In addition to automatic identification, configuration analysis, and visualization of network devices, it also includes a function to recognize the emotional state of users. This system is implemented using the hardware and software described below.

[0157] The server uses existing network scanning tools, such as general-purpose open-source IP scanners, to collect identification information from each device in the network. This identification information includes IP addresses, MAC addresses, and device names. Based on this information, the server analyzes the current state of the network and constructs a network topology.

[0158] The terminal runs a rendering engine to visualize the analysis data acquired from the server. This rendering engine uses open-source visual libraries and other resources to visually display the network configuration. This display includes a function to clarify the scope of impact in the event of a failure, helping users to quickly decide on a course of action.

[0159] Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's stress level and level of excitement based on sensor data such as camera and mouse movements and key input patterns. Based on the analysis results, the device adjusts the tone of information presentation and changes the way important information is presented to suit the user.

[0160] For example, in the event of a major network failure, the server quickly identifies the problematic device, and the terminal notifies the user of the situation. When the user reviews the notification, if the emotion engine detects a high-stress state, the terminal re-presents the notification in a gradual and gentler format. This series of functions makes it easier to make decisions that affect network operations and reduces the psychological burden on users.

[0161] An example of a prompt for a generative AI model might be, "Please suggest a presentation method that reduces user stress during network failures."

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server initiates a network scan. It uses existing database information about each network device as input. The server uses a scanning tool to obtain the latest identification information from each network device and stores it in the database. The output is the database containing the latest identification information. Specifically, it scans and collects data such as IP addresses and device IDs.

[0165] Step 2:

[0166] The server analyzes the collected identification information and constructs the topology of the communication network. The input is the identification information in the database. This analysis clarifies the connection relationships and placement of network devices and creates the basic data for generating a communication network configuration diagram. The output is a data model that reflects the specific connection patterns. The operation involves identifying connection paths and dependencies between devices.

[0167] Step 3:

[0168] The terminal uses topology data obtained from the server to run a rendering engine and generate a visual network configuration diagram. The input is network configuration analysis data sent from the server. This procedure generates an intuitive visual overview of the entire network, which the user can use for management and maintenance. The output is a graphical configuration diagram that the user can view on the terminal. Specifically, the process involves visualizing the analysis data on a coordinate system.

[0169] Step 4:

[0170] The terminal automatically identifies the affected area when a network failure occurs and notifies the user. The input is real-time network monitoring data. The terminal identifies the problematic device and its connections and overlays this information on a visualized configuration diagram. The output is a configuration diagram highlighting the scope of the failure. Specifically, it includes actions to make the affected area easily identifiable by displaying it with color or flags.

[0171] Step 5:

[0172] The device uses an emotion engine to recognize the user's real-time emotional state. Inputs include user operation patterns and sensor data (e.g., camera and mouse movements). The emotion engine analyzes this data to determine the user's level of stress and excitement. Outputs are analytical information regarding the emotional state. Specifically, this involves integrating sensor data and performing pattern analysis using algorithms.

[0173] Step 6:

[0174] The device adjusts how information is presented based on the recognized emotional state. The input is emotional state information output by the emotion engine. The device uses this information to adjust the tone and amount of notifications and information presented to the user. For example, if stress is detected, notifications may be simplified or information may be presented in stages to reduce the user's burden. The output is the adjusted information presentation, which is displayed on the user's screen. Specific actions include changing the screen layout and adjusting the text tone.

[0175] (Application Example 2)

[0176] 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".

[0177] While conventional network management systems comprehensively provide information on network configuration changes and failures, they lack optimization of information presentation that takes into account the stress and emotional state of administrators. This results in significant mental burden during network management and troubleshooting, leading to decreased decision-making efficiency. Therefore, a system is needed that dynamically adjusts information presentation according to the user's emotional state to reduce stress.

[0178] 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.

[0179] In this invention, the server includes means for acquiring identification information of network equipment, means for analyzing the network configuration and visualizing it as a diagram, and means for analyzing the user's emotional state and adjusting the displayed information. This enables more efficient network management and reduces stress for administrators.

[0180] "Network equipment" refers to the physical or logical components used for equipment and devices to communicate with one another.

[0181] "Identification information" refers to data or protocol information used to uniquely identify specific network equipment.

[0182] "Network configuration" refers to information that describes how devices and equipment are arranged and interconnected within a network.

[0183] A "configuration diagram" is a diagram or map that visually represents the physical or logical arrangement of a network.

[0184] "Emotional state" refers to the state of a user's psychological or emotional response, as evaluated in real time.

[0185] A "visualization engine" is a software module or component used to convert data into a format that can be visually displayed.

[0186] A "failure" refers to a state in which some kind of problem occurs with equipment or communication protocols within a network, preventing them from functioning normally.

[0187] "Dynamic adjustment" means changing a system or settings automatically or manually in response to changes in circumstances or conditions.

[0188] "Presenting solutions step by step" means presenting solutions to a problem sequentially to the user, rather than presenting all the solutions at once.

[0189] This invention is a system aimed at improving the efficiency of network management and reducing user stress. The server first uses a network scanning tool to collect identification information for each piece of equipment within the factory. This identification information is stored in a database and used for network configuration analysis. The data obtained from the network configuration analysis is then generated as a configuration diagram using a visualization engine.

[0190] The device receives a network configuration diagram sent from the server and presents it to the user in real time. Furthermore, the device is equipped with an emotion engine that analyzes data such as the user's facial expressions and voice tone. This allows the system to assess the user's stress and emotional state, dynamically adjusting how information is presented. For example, if stress is detected, the notification tone may be softened, or information may be presented in stages to reduce the user's psychological burden.

[0191] Specifically, when a problem is detected, the server immediately identifies the issue and highlights its scope of impact on the configuration diagram. The terminal informs the user of this information and provides an explanation in an appropriate tone using an emotion engine. For example, it provides step-by-step solutions such as, "First, please restart the robot. If that doesn't solve the problem, please consider calling a specialist."

[0192] An example of a prompt for a generative AI model would be: "When an emergency occurs in a factory, please show how to present information to mitigate the impact. Specifically, please describe the role of the emotion engine and provide step-by-step guidance for stress reduction."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server uses a network scanning tool to collect identification information from all devices within the factory. The collected data includes identification information such as the device's IP address, MAC address, and operating status. This information is stored in a database, which is then used for subsequent network configuration analysis.

[0196] Step 2:

[0197] The server analyzes the identification information stored in the database to understand the network configuration. Specifically, it analyzes communication protocols and connection status to clarify the connection relationships of each device. Based on these analysis results, it generates a dataset for creating a network configuration diagram.

[0198] Step 3:

[0199] The terminal inputs network configuration data sent from the server into a visualization engine and generates a graphical configuration diagram. The diagram is displayed to the user in an intuitive format using device icons and connection lines. The generated configuration diagram is provided to the user in real time.

[0200] Step 4:

[0201] The device analyzes the user's emotional state using an emotion engine. Inputs include the user's facial movements and voice tone, captured through the camera and microphone. Based on this data, an emotion recognition algorithm evaluates the user's emotional state and identifies their stress level and type of emotion.

[0202] Step 5:

[0203] The device dynamically adjusts the content and tone of the information it presents based on the analysis results of the emotion engine. Specifically, if stress levels are high, it will lower the volume of notifications and alerts, reduce the amount of information, and display solutions in stages. This adjustment aims to provide information in a way that is less burdensome for the user.

[0204] Step 6:

[0205] Users perform network management and troubleshooting based on the coordinated information. For example, they identify problem areas while looking at a visualized configuration diagram and proceed with the solution according to a step-by-step plan. This entire process reduces the psychological burden on users and enables more efficient decision-making.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention relates to a system that acquires identification information of network equipment, automatically analyzes the network configuration based on the acquired information, and visualizes it. This system works in cooperation with servers, terminals, and users, enabling efficient network management and rapid response in the event of failures.

[0223] First, the server uses a network scanning tool to collect identification information from all devices on the network. This collected information is stored in a database and used for configuration analysis. The server performs scans periodically to update information on newly added and modified devices.

[0224] The terminal retrieves configuration data provided by the server and generates a network diagram using a visualization engine. This diagram is displayed in an easy-to-understand format for the user, allowing for a visualization of the overall network status. In the event of a failure, the affected area is highlighted, helping the user quickly understand the problem.

[0225] As a concrete example, when a new server is added to the network, the server automatically detects this and updates its configuration information. The terminal then regenerates the configuration diagram reflecting the updates and notifies the user. The user approves or proposes modifications and sends feedback to the server through the terminal. This ensures that the network configuration is always up-to-date and supports rapid decision-making.

[0226] Furthermore, if users have any questions about the configuration diagram, they can ask directly through their terminal. The server will then collect relevant information in response to these requests and provide the user with a detailed explanation. This feature allows users to gain a deeper understanding and improve their confidence in network operations.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server uses network scanning tools to collect identification information for all devices on the network. This includes data such as IP addresses and server names.

[0230] Step 2:

[0231] The server stores the collected identification information in a database. This data is used for subsequent network configuration analysis.

[0232] Step 3:

[0233] The server periodically rescans the network to automatically detect new or modified devices. This detection updates the database information.

[0234] Step 4:

[0235] The terminal retrieves the latest configuration data from the server and generates a network diagram using a visualization engine. This diagram visually represents the network topology.

[0236] Step 5:

[0237] The terminal displays the generated configuration diagram to the user. By viewing the configuration diagram, the user can grasp the overall picture of the network.

[0238] Step 6:

[0239] When a failure occurs, the server identifies the affected device and sends that information to the terminal. This allows the terminal to highlight the problematic area in the configuration diagram.

[0240] Step 7:

[0241] Users receive proposed changes to the configuration diagram via their device. Users approve or modify the proposals and send their feedback from their device to the server.

[0242] Step 8:

[0243] When a user asks a question about something they don't understand, the device sends the request to the server. The server collects relevant information and provides the user with a detailed explanation.

[0244] (Example 1)

[0245] 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."

[0246] In managing communication systems, it is crucial to efficiently collect identification information for each device and visualize the network structure based on that information. However, conventional methods have presented challenges such as difficulty in responding quickly to status changes or failures, leading to delays in notifying users and updating the network structure. Furthermore, immediate responses to user inquiries were also difficult.

[0247] 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.

[0248] In this invention, the server includes means for acquiring information on all devices in the computer network using an identification information collection function, means for automatically analyzing the network structure based on the acquired information, and means for responding to user inquiries using a generative model. This enables more efficient network management, rapid response in the event of a failure, and immediate information provision and response to users.

[0249] The "identification information collection function" is a function that collects information from each device within the network and organizes and stores it.

[0250] A "computer network" is a system in which numerous computers and devices are interconnected and exchange information.

[0251] "Means for automatically analyzing network structure" refers to methods for independently analyzing and understanding the overall configuration of a network based on acquired network information.

[0252] A "communication system structure diagram" is a diagram that visually represents the configuration and connection relationships of a network, and is useful for users to understand the state of the network.

[0253] "Means for automatically detecting changes in state" refers to means that can monitor changes in devices and connections within a network and immediately grasp any changes.

[0254] "Means of notifying users of proposed changes" refers to means of immediately communicating information to users regarding changes to the network structure.

[0255] A "generative model" is a model that uses artificial intelligence technology to automatically generate appropriate answers to user inquiries.

[0256] This invention is a system in which servers, terminals, and users cooperate to streamline network management and monitoring. The server uses a network scanning tool to collect identification information from all devices on the network. In this step, for example, an open-source network scanning tool is used to obtain the IP addresses and hostnames of all devices connected to the network. The collected information is stored in a database, and the server uses this information to analyze the network structure.

[0257] The terminal retrieves network structure data analyzed from the server and processes it using a visualization engine. The visualization engine generates a structure diagram that visualizes the network configuration, for example, using an open-source dashboard creation tool. The generated structure diagram visually shows the status and connections of each device in the network, helping the user understand the overall picture of the network.

[0258] Users can view a structural diagram provided via their terminal, enabling them to quickly understand network changes and failures. If an anomaly is highlighted in the structural diagram, users can immediately take corrective action. Furthermore, users can send questions via their terminal about areas they are unsure of, and the server provides immediate responses using a generative AI model. Specifically, commonly used natural language processing techniques are applied as the generative AI model to generate natural language answers to user inquiries.

[0259] As a concrete example, when a new device is added to the network, the server automatically detects it and updates the network structure information. The terminal then updates the structure diagram based on the latest information and notifies the user. The user can review the updated structure diagram and provide feedback to the server as needed. This leads to faster and more accurate network maintenance.

[0260] For example, a possible prompt to input into a generating AI model could be, "Please tell me the procedure to follow when a new device is added to the network." In this way, the entire system can work together, significantly improving the efficiency and reliability of network management.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server launches a network scanning tool and scans all devices on the network. The network's IP range is specified as input, and the output provides identification information for each device, including its IP address, hostname, and MAC address. This information is saved to a file and prepared for transfer to a database for post-processing.

[0264] Step 2:

[0265] The server stores the identification information obtained in step 1 in the database. The scan result file is used as input, and the information for each device is saved in the database table as output. This process maintains the consistency of the information and makes it easy to refer to it in conjunction with past records.

[0266] Step 3:

[0267] The server retrieves the latest identification information from the database and analyzes the network structure. The database contents are used as input, and the network topology is obtained as the output. Here, the connection relationships between devices and the network configuration are revealed. The program uses algorithms to evaluate the device hierarchy and the presence or absence of links.

[0268] Step 4:

[0269] The terminal retrieves analyzed network topology data from the server and generates a structural diagram using a visualization engine. The analysis results are used as input, and the output is a structural diagram in a user-friendly format. This program utilizes the visualization engine to arrange devices as shapes and represent their connection relationships with lines.

[0270] Step 5:

[0271] The terminal updates the structural diagram based on fault information and status changes. The analysis results from step 3 and alert information from the monitoring system are used as input. The output is an updated structural diagram, with fault areas highlighted. This allows the user to immediately identify network problems.

[0272] Step 6:

[0273] Users view the structural diagram via their terminal and send feedback to the server as needed. The input is the structural diagram and its description, and the output is user approval or modification suggestions sent via the terminal. This feedback allows for further refinement of the network configuration and improved reliability.

[0274] Step 7:

[0275] The user sends a question from their device to the server, which then uses a generative AI model to generate a response. The user's question is given as input, and the automatically generated answer is received as output. This interaction allows the user to deepen their understanding of network operations.

[0276] (Application Example 1)

[0277] 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."

[0278] Modern information processing systems require the immediate understanding of network structure and status, particularly the scope of impact during failures, and the ability to respond quickly. However, conventional systems face challenges in real-time visual monitoring and immediate fault response. Furthermore, the increasing burden on engineers to continuously monitor the increasingly complex network status leads to a decline in efficiency.

[0279] 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.

[0280] In this invention, the server includes means for collecting identification data of network devices, means for analyzing the network structure based on the collected identification data, and means for creating a network structure diagram using the analysis results. This makes it possible to display the network status of the group of information processing devices in real time using visual devices. Furthermore, fault conditions can be highlighted using visual devices to facilitate a quick response by operators, resulting in more efficient network management and faster response to failures.

[0281] "Network devices" refer to various information devices connected to a network, and these are hardware devices used for sending and receiving data.

[0282] "Identification data" refers to information used to uniquely identify a network device, usually including an IP address, a MAC address, etc.

[0283] "Information processing device group" refers to a group of information devices that are interconnected through a network and perform data processing and communication.

[0284] "Visual device" refers to a device for physically visualizing digital information, including smart glasses, displays, etc.

[0285] "Visualization engine" refers to software or a hardware component for displaying digital data in a form that is easy to visually understand.

[0286] "Fault situation" refers to an abnormality or a fault in a network, which is a factor that hinders normal operation.

[0287] The system for implementing this invention mainly consists of a server, a terminal, and an operator. The server collects identification data from network devices and stores it in a database. The hardware used includes a network scanner and a standard computer server. In order to analyze this information to identify the network structure and generate a structure diagram, dedicated analysis software and a visualization engine are used.

[0288] The generated network structure diagram is provided to the operator through the terminal. The terminal includes visual devices such as smart glasses and displays the state of the network in real time. This display is dynamically updated by the visualization engine, and when a fault situation occurs, that part is emphasized. Based on this information, the operator can quickly respond to network problems.

[0289] For example, if a robot in a factory loses communication, this information is immediately detected by the server, and the network structure diagram is updated. This anomaly is visually highlighted to operators wearing smart glasses, allowing them to physically go to the location and resolve the problem.

[0290] An example of input to the generating AI model could be a prompt such as, "Design a smart glasses application that visualizes the network status of robots in a factory and detects faults."

[0291] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0292] Step 1:

[0293] The server uses a network scanner to collect identification data from network devices. The input is set to the network's IP range, and the output is the collected identification data (IP address, MAC address, etc.). This data is stored in a database.

[0294] Step 2:

[0295] The server analyzes the identification data stored in the database. This analysis determines the connection relationships between devices and identifies the network structure. The identification data collected in the previous step is used as input, and network structure information is obtained as output.

[0296] Step 3:

[0297] The server uses a visualization engine to create a network structure diagram based on the analyzed network structure information. Network structure information is used as input, and the output is a network structure diagram that is easy for the user to understand visually.

[0298] Step 4:

[0299] The terminal acquires the created network structure diagram and displays it to the operator using a visual device. The input is the visualized network structure diagram, and as output, a diagram displayed on a visual device such as smart glasses is provided to the operator.

[0300] Step 5:

[0301] The server constantly monitors the state changes of the network and detects abnormalities and failure situations. It monitors real-time network data as input, and as output, when an abnormality is detected, the information is updated.

[0302] Step 6:

[0303] When a failure situation is detected, the terminal highlights the information using a visual device. The input is the failure information provided by the server, and as output, a network structure diagram with the problem area highlighted is shown to the operator.

[0304] Step 7:

[0305] The user responds quickly based on the information displayed on the smart glasses. The input is the visual information provided by the terminal, and as output, actual failure response measures and correction processes are carried out.

[0306] Through these steps, the state of the network is monitored in real time, enabling rapid failure response.

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

[0308] This invention combines a system that automatically identifies, analyzes, and visualizes network equipment to streamline network management with an emotion engine that recognizes the user's emotional state. This system aims to reduce stress and support decision-making by quickly identifying network operational challenges and providing information tailored to the user's emotions.

[0309] First, the server uses existing network scanning tools to collect identification information from devices within the network and stores it in a database. The server periodically performs scans to detect new devices and changes. Based on the collected information, the server analyzes the network configuration and forms data for generating a network diagram.

[0310] The terminal processes data received from the server via a visualization engine to generate a graphical network configuration diagram. This diagram is displayed to the user in an intuitive and easy-to-understand format. In the event of a failure, the affected area is highlighted to support a rapid response.

[0311] Furthermore, to recognize user emotions in real time, the device is equipped with an emotion engine. The emotion engine analyzes the emotional state using various sensor data and user interaction data. For example, if a user's stress level increases while viewing a network diagram, the emotion engine can detect that emotion, and the device can adjust how the information is displayed. Specifically, it can soften the tone of the information presentation or adjust the amount of information to provide it in a way that is less burdensome for the user.

[0312] As a concrete example, when a major network failure occurs, the server quickly identifies the faulty device, and the terminal notifies the user of the situation. If the emotion engine detects stress during the user's review of the notification, the terminal reduces the priority of the notification and presents solutions step by step, thereby reducing the user's burden. This entire process reduces the stress of network operations and improves the efficiency of decision-making.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The server launches a network scanning tool to collect identification information for each device on the network. This information includes IP addresses and hostnames.

[0316] Step 2:

[0317] The server stores the collected data in a database and analyzes the network configuration. This analysis clarifies the connection relationships between each device.

[0318] Step 3:

[0319] The terminal generates a configuration diagram using a visualization engine based on the latest network configuration data obtained from the server. The configuration diagram is designed to allow the user to see the status of the entire network at a glance.

[0320] Step 4:

[0321] When a user views a diagram on their device, the device uses an emotion engine to evaluate the user's emotional state in real time. Data collected from the user's camera and microphone is used for analysis.

[0322] Step 5:

[0323] When the emotion engine detects user stress, the device dynamically adjusts the tone and style of information presentation. For example, it reduces the amount of information or highlights only the most important information to lessen the user's burden.

[0324] Step 6:

[0325] When a network failure occurs, the server quickly identifies the affected devices and sends that information to the terminals. The terminals then reflect this information in the configuration diagram and notify the user of the location and scope of the failure.

[0326] Step 7:

[0327] Users review the suggested solutions and configuration diagram changes from their terminals and provide feedback. This feedback is then relayed to the server, where corrective actions are implemented.

[0328] Step 8:

[0329] If a user requires further information or support, the device sends the request to the server, which collects and provides the relevant information to the user. This allows the user to efficiently work towards resolving the problem.

[0330] (Example 2)

[0331] 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".

[0332] Traditional network management systems often require manual processes for understanding network configurations and identifying the scope of impact in the event of a failure, placing significant stress on administrators. Furthermore, the lack of information tailored to users' emotional states can reduce the speed and accuracy of decision-making. There is a need to address these issues and improve the efficiency of network operations while reducing the burden on users.

[0333] 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.

[0334] In this invention, the server includes means for collecting identification information of network devices, means for analyzing the configuration of the communication network based on the collected identification information, and means for adjusting the information presentation method based on the recognized emotional state. This enables automatic identification of devices within the communication network and real-time configuration analysis, and allows for the provision of appropriate information according to the user's emotions.

[0335] A "network device" is a device used to send and receive digital data, and functions as a component of a communication network.

[0336] "Identification information" refers to data used to uniquely identify each network device, and mainly includes IP addresses, MAC addresses, and hostnames.

[0337] A "communication network" is a system or structure in which multiple network devices are connected for the exchange of digital data.

[0338] "Analysis" is the process of investigating and understanding the structure and characteristics of an object in detail based on collected data.

[0339] A "configuration diagram" is a diagram that visually represents the connection status and layout of the components of a communication network.

[0340] "State change" refers to any variation or transition in the attributes or behavior of a communication network or its components.

[0341] "User" refers to an individual or legal entity that manages, monitors, or uses a communication network.

[0342] "Emotional state" refers to a change in the user's psychological or emotional state, including internal conditions such as stress and a sense of security.

[0343] "Information presentation method" refers to the format and means of providing data and notifications to users, including text and visual displays.

[0344] This invention provides a system that streamlines the management of communication networks and reduces the psychological burden on users. In addition to automatic identification, configuration analysis, and visualization of network devices, it also includes a function to recognize the emotional state of users. This system is implemented using the hardware and software described below.

[0345] The server uses existing network scanning tools, such as general-purpose open-source IP scanners, to collect identification information from each device in the network. This identification information includes IP addresses, MAC addresses, and device names. Based on this information, the server analyzes the current state of the network and constructs a network topology.

[0346] The terminal runs a rendering engine to visualize the analysis data acquired from the server. This rendering engine uses open-source visual libraries and other resources to visually display the network configuration. This display includes a function to clarify the scope of impact in the event of a failure, helping users to quickly decide on a course of action.

[0347] Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's stress level and level of excitement based on sensor data such as camera and mouse movements and key input patterns. Based on the analysis results, the device adjusts the tone of information presentation and changes the way important information is presented to suit the user.

[0348] For example, in the event of a major network failure, the server quickly identifies the problematic device, and the terminal notifies the user of the situation. When the user reviews the notification, if the emotion engine detects a high-stress state, the terminal re-presents the notification in a gradual and gentler format. This series of functions makes it easier to make decisions that affect network operations and reduces the psychological burden on users.

[0349] An example of a prompt for a generative AI model might be, "Please suggest a presentation method that reduces user stress during network failures."

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The server initiates a network scan. It uses existing database information about each network device as input. The server uses a scanning tool to obtain the latest identification information from each network device and stores it in the database. The output is the database containing the latest identification information. Specifically, it scans and collects data such as IP addresses and device IDs.

[0353] Step 2:

[0354] The server analyzes the collected identification information and constructs the topology of the communication network. The input is the identification information in the database. This analysis clarifies the connection relationships and placement of network devices and creates the basic data for generating a communication network configuration diagram. The output is a data model that reflects the specific connection patterns. The operation involves identifying connection paths and dependencies between devices.

[0355] Step 3:

[0356] The terminal uses topology data obtained from the server to run a rendering engine and generate a visual network configuration diagram. The input is network configuration analysis data sent from the server. This procedure generates an intuitive visual overview of the entire network, which the user can use for management and maintenance. The output is a graphical configuration diagram that the user can view on the terminal. Specifically, the process involves visualizing the analysis data on a coordinate system.

[0357] Step 4:

[0358] The terminal automatically identifies the affected area when a network failure occurs and notifies the user. The input is real-time network monitoring data. The terminal identifies the problematic device and its connections and overlays this information on a visualized configuration diagram. The output is a configuration diagram highlighting the scope of the failure. Specifically, it includes actions to make the affected area easily identifiable by displaying it with color or flags.

[0359] Step 5:

[0360] The device uses an emotion engine to recognize the user's real-time emotional state. Inputs include user operation patterns and sensor data (e.g., camera and mouse movements). The emotion engine analyzes this data to determine the user's level of stress and excitement. Outputs are analytical information regarding the emotional state. Specifically, this involves integrating sensor data and performing pattern analysis using algorithms.

[0361] Step 6:

[0362] The device adjusts how information is presented based on the recognized emotional state. The input is emotional state information output by the emotion engine. The device uses this information to adjust the tone and amount of notifications and information presented to the user. For example, if stress is detected, notifications may be simplified or information may be presented in stages to reduce the user's burden. The output is the adjusted information presentation, which is displayed on the user's screen. Specific actions include changing the screen layout and adjusting the text tone.

[0363] (Application Example 2)

[0364] 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."

[0365] While conventional network management systems comprehensively provide information on network configuration changes and failures, they lack optimization of information presentation that takes into account the stress and emotional state of administrators. This results in significant mental burden during network management and troubleshooting, leading to decreased decision-making efficiency. Therefore, a system is needed that dynamically adjusts information presentation according to the user's emotional state to reduce stress.

[0366] 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.

[0367] In this invention, the server includes means for acquiring identification information of network equipment, means for analyzing the network configuration and visualizing it as a diagram, and means for analyzing the user's emotional state and adjusting the displayed information. This enables more efficient network management and reduces stress for administrators.

[0368] "Network equipment" refers to the physical or logical components used for equipment and devices to communicate with one another.

[0369] "Identification information" refers to data or protocol information used to uniquely identify specific network equipment.

[0370] "Network configuration" refers to information that describes how devices and equipment are arranged and interconnected within a network.

[0371] A "configuration diagram" is a diagram or map that visually represents the physical or logical arrangement of a network.

[0372] "Emotional state" refers to the state of a user's psychological or emotional response, as evaluated in real time.

[0373] A "visualization engine" is a software module or component used to convert data into a format that can be visually displayed.

[0374] A "failure" refers to a state in which some kind of problem occurs with equipment or communication protocols within a network, preventing them from functioning normally.

[0375] "Dynamic adjustment" means changing a system or settings automatically or manually in response to changes in circumstances or conditions.

[0376] "Presenting solutions step by step" means presenting solutions to a problem sequentially to the user, rather than presenting all the solutions at once.

[0377] This invention is a system aimed at improving the efficiency of network management and reducing user stress. The server first uses a network scanning tool to collect identification information for each piece of equipment within the factory. This identification information is stored in a database and used for network configuration analysis. The data obtained from the network configuration analysis is then generated as a configuration diagram using a visualization engine.

[0378] The device receives a network configuration diagram sent from the server and presents it to the user in real time. Furthermore, the device is equipped with an emotion engine that analyzes data such as the user's facial expressions and voice tone. This allows the system to assess the user's stress and emotional state, dynamically adjusting how information is presented. For example, if stress is detected, the notification tone may be softened, or information may be presented in stages to reduce the user's psychological burden.

[0379] Specifically, when a problem is detected, the server immediately identifies the issue and highlights its scope of impact on the configuration diagram. The terminal informs the user of this information and provides an explanation in an appropriate tone using an emotion engine. For example, it provides step-by-step solutions such as, "First, please restart the robot. If that doesn't solve the problem, please consider calling a specialist."

[0380] An example of a prompt for a generative AI model would be: "When an emergency occurs in a factory, please show how to present information to mitigate the impact. Specifically, please describe the role of the emotion engine and provide step-by-step guidance for stress reduction."

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The server uses a network scanning tool to collect identification information from all devices within the factory. The collected data includes identification information such as the device's IP address, MAC address, and operating status. This information is stored in a database, which is then used for subsequent network configuration analysis.

[0384] Step 2:

[0385] The server analyzes the identification information stored in the database to understand the network configuration. Specifically, it analyzes communication protocols and connection status to clarify the connection relationships of each device. Based on these analysis results, it generates a dataset for creating a network configuration diagram.

[0386] Step 3:

[0387] The terminal inputs network configuration data sent from the server into a visualization engine and generates a graphical configuration diagram. The diagram is displayed to the user in an intuitive format using device icons and connection lines. The generated configuration diagram is provided to the user in real time.

[0388] Step 4:

[0389] The device analyzes the user's emotional state using an emotion engine. Inputs include the user's facial movements and voice tone, captured through the camera and microphone. Based on this data, an emotion recognition algorithm evaluates the user's emotional state and identifies their stress level and type of emotion.

[0390] Step 5:

[0391] The device dynamically adjusts the content and tone of the information it presents based on the analysis results of the emotion engine. Specifically, if stress levels are high, it will lower the volume of notifications and alerts, reduce the amount of information, and display solutions in stages. This adjustment aims to provide information in a way that is less burdensome for the user.

[0392] Step 6:

[0393] Users perform network management and troubleshooting based on the coordinated information. For example, they identify problem areas while looking at a visualized configuration diagram and proceed with the solution according to a step-by-step plan. This entire process reduces the psychological burden on users and enables more efficient decision-making.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] This invention relates to a system that acquires identification information of network equipment, automatically analyzes the network configuration based on the acquired information, and visualizes it. This system works in cooperation with servers, terminals, and users, enabling efficient network management and rapid response in the event of failures.

[0411] First, the server uses a network scanning tool to collect identification information from all devices on the network. This collected information is stored in a database and used for configuration analysis. The server performs scans periodically to update information on newly added and modified devices.

[0412] The terminal retrieves configuration data provided by the server and generates a network diagram using a visualization engine. This diagram is displayed in an easy-to-understand format for the user, allowing for a visualization of the overall network status. In the event of a failure, the affected area is highlighted, helping the user quickly understand the problem.

[0413] As a concrete example, when a new server is added to the network, the server automatically detects this and updates its configuration information. The terminal then regenerates the configuration diagram reflecting the updates and notifies the user. The user approves or proposes modifications and sends feedback to the server through the terminal. This ensures that the network configuration is always up-to-date and supports rapid decision-making.

[0414] Furthermore, if users have any questions about the configuration diagram, they can ask directly through their terminal. The server will then collect relevant information in response to these requests and provide the user with a detailed explanation. This feature allows users to gain a deeper understanding and improve their confidence in network operations.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The server uses network scanning tools to collect identification information for all devices on the network. This includes data such as IP addresses and server names.

[0418] Step 2:

[0419] The server stores the collected identification information in a database. This data is used for subsequent network configuration analysis.

[0420] Step 3:

[0421] The server periodically rescans the network to automatically detect new or modified devices. This detection updates the database information.

[0422] Step 4:

[0423] The terminal retrieves the latest configuration data from the server and generates a network diagram using a visualization engine. This diagram visually represents the network topology.

[0424] Step 5:

[0425] The terminal displays the generated configuration diagram to the user. By viewing the configuration diagram, the user can grasp the overall picture of the network.

[0426] Step 6:

[0427] When a failure occurs, the server identifies the affected device and sends that information to the terminal. This allows the terminal to highlight the problematic area in the configuration diagram.

[0428] Step 7:

[0429] Users receive proposed changes to the configuration diagram via their device. Users approve or modify the proposals and send their feedback from their device to the server.

[0430] Step 8:

[0431] When a user asks a question about something they don't understand, the device sends the request to the server. The server collects relevant information and provides the user with a detailed explanation.

[0432] (Example 1)

[0433] 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."

[0434] In managing communication systems, it is crucial to efficiently collect identification information for each device and visualize the network structure based on that information. However, conventional methods have presented challenges such as difficulty in responding quickly to status changes or failures, leading to delays in notifying users and updating the network structure. Furthermore, immediate responses to user inquiries were also difficult.

[0435] 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.

[0436] In this invention, the server includes means for acquiring information on all devices in the computer network using an identification information collection function, means for automatically analyzing the network structure based on the acquired information, and means for responding to user inquiries using a generative model. This enables more efficient network management, rapid response in the event of a failure, and immediate information provision and response to users.

[0437] The "identification information collection function" is a function that collects information from each device within the network and organizes and stores it.

[0438] A "computer network" is a system in which numerous computers and devices are interconnected and exchange information.

[0439] "Means for automatically analyzing network structure" refers to methods for independently analyzing and understanding the overall configuration of a network based on acquired network information.

[0440] A "communication system structure diagram" is a diagram that visually represents the configuration and connection relationships of a network, and is useful for users to understand the state of the network.

[0441] "Means for automatically detecting changes in state" refers to means that can monitor changes in devices and connections within a network and immediately grasp any changes.

[0442] "Means of notifying users of proposed changes" refers to means of immediately communicating information to users regarding changes to the network structure.

[0443] A "generative model" is a model that uses artificial intelligence technology to automatically generate appropriate answers to user inquiries.

[0444] This invention is a system in which servers, terminals, and users cooperate to streamline network management and monitoring. The server uses a network scanning tool to collect identification information from all devices on the network. In this step, for example, an open-source network scanning tool is used to obtain the IP addresses and hostnames of all devices connected to the network. The collected information is stored in a database, and the server uses this information to analyze the network structure.

[0445] The terminal retrieves network structure data analyzed from the server and processes it using a visualization engine. The visualization engine generates a structure diagram that visualizes the network configuration, for example, using an open-source dashboard creation tool. The generated structure diagram visually shows the status and connections of each device in the network, helping the user understand the overall picture of the network.

[0446] Users can view a structural diagram provided via their terminal, enabling them to quickly understand network changes and failures. If an anomaly is highlighted in the structural diagram, users can immediately take corrective action. Furthermore, users can send questions via their terminal about areas they are unsure of, and the server provides immediate responses using a generative AI model. Specifically, commonly used natural language processing techniques are applied as the generative AI model to generate natural language answers to user inquiries.

[0447] As a concrete example, when a new device is added to the network, the server automatically detects it and updates the network structure information. The terminal then updates the structure diagram based on the latest information and notifies the user. The user can review the updated structure diagram and provide feedback to the server as needed. This leads to faster and more accurate network maintenance.

[0448] For example, a possible prompt to input into a generating AI model could be, "Please tell me the procedure to follow when a new device is added to the network." In this way, the entire system can work together, significantly improving the efficiency and reliability of network management.

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

[0450] Step 1:

[0451] The server launches a network scanning tool and scans all devices on the network. The network's IP range is specified as input, and the output provides identification information for each device, including its IP address, hostname, and MAC address. This information is saved to a file and prepared for transfer to a database for post-processing.

[0452] Step 2:

[0453] The server stores the identification information obtained in step 1 in the database. The scan result file is used as input, and the information for each device is saved in the database table as output. This process maintains the consistency of the information and makes it easy to refer to it in conjunction with past records.

[0454] Step 3:

[0455] The server retrieves the latest identification information from the database and analyzes the network structure. The database contents are used as input, and the network topology is obtained as the output. Here, the connection relationships between devices and the network configuration are revealed. The program uses algorithms to evaluate the device hierarchy and the presence or absence of links.

[0456] Step 4:

[0457] The terminal retrieves analyzed network topology data from the server and generates a structural diagram using a visualization engine. The analysis results are used as input, and the output is a structural diagram in a user-friendly format. This program utilizes the visualization engine to arrange devices as shapes and represent their connection relationships with lines.

[0458] Step 5:

[0459] The terminal updates the structural diagram based on fault information and status changes. The analysis results from step 3 and alert information from the monitoring system are used as input. The output is an updated structural diagram, with fault areas highlighted. This allows the user to immediately identify network problems.

[0460] Step 6:

[0461] Users view the structural diagram via their terminal and send feedback to the server as needed. The input is the structural diagram and its description, and the output is user approval or modification suggestions sent via the terminal. This feedback allows for further refinement of the network configuration and improved reliability.

[0462] Step 7:

[0463] The user sends a question from their device to the server, which then uses a generative AI model to generate a response. The user's question is given as input, and the automatically generated answer is received as output. This interaction allows the user to deepen their understanding of network operations.

[0464] (Application Example 1)

[0465] 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."

[0466] Modern information processing systems require the immediate understanding of network structure and status, particularly the scope of impact during failures, and the ability to respond quickly. However, conventional systems face challenges in real-time visual monitoring and immediate fault response. Furthermore, the increasing burden on engineers to continuously monitor the increasingly complex network status leads to a decline in efficiency.

[0467] 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.

[0468] In this invention, the server includes means for collecting identification data of network devices, means for analyzing the network structure based on the collected identification data, and means for creating a network structure diagram using the analysis results. This makes it possible to display the network status of the group of information processing devices in real time using visual devices. Furthermore, fault conditions can be highlighted using visual devices to facilitate a quick response by operators, resulting in more efficient network management and faster response to failures.

[0469] "Network devices" refer to various information devices connected to a network, and these are hardware devices used for sending and receiving data.

[0470] "Identification data" refers to information used to uniquely recognize network devices, and typically includes IP addresses and MAC addresses.

[0471] An "information processing system" refers to a group of information devices that are interconnected via a network and perform data processing and communication.

[0472] "Visual devices" are devices used to physically visualize digital information, and include smart glasses and displays.

[0473] A "visualization engine" is a software or hardware component that displays digital data in a visually understandable format.

[0474] "Fault status" refers to abnormalities or failures in the network, and is a factor that prevents normal operation.

[0475] The system for implementing this invention mainly consists of a server, terminals, and operators. The server collects identification data from network devices and stores it in a database. The hardware used includes network scanners and standard computer servers. Dedicated analysis software and a visualization engine are used to analyze this information to identify the network structure and generate a structural diagram.

[0476] This generated network structure diagram is provided to the operator via a terminal. The terminal includes visual devices such as smart glasses, which display the network status in real time. This display is dynamically updated by a visualization engine, and areas of failure are highlighted, especially when they occur. Based on this information, the operator can quickly respond to network problems.

[0477] For example, if a robot in a factory loses communication, this information is immediately detected by the server, and the network structure diagram is updated. This anomaly is visually highlighted to operators wearing smart glasses, allowing them to physically go to the location and resolve the problem.

[0478] An example of input to the generating AI model could be a prompt such as, "Design a smart glasses application that visualizes the network status of robots in a factory and detects faults."

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The server uses a network scanner to collect identification data from network devices. The input is set to the network's IP range, and the output is the collected identification data (IP address, MAC address, etc.). This data is stored in a database.

[0482] Step 2:

[0483] The server analyzes the identification data stored in the database. This analysis determines the connection relationships between devices and identifies the network structure. The identification data collected in the previous step is used as input, and network structure information is obtained as output.

[0484] Step 3:

[0485] The server uses a visualization engine to create a network structure diagram based on the analyzed network structure information. Network structure information is used as input, and the output is a network structure diagram that is easy for the user to understand visually.

[0486] Step 4:

[0487] The terminal retrieves the created network structure diagram and displays it to the operator using a visual device. The input is a visualized network structure diagram, and the output is the diagram displayed on a visual device such as smart glasses, which is provided to the operator.

[0488] Step 5:

[0489] The server constantly monitors network status changes and detects anomalies and failures. It monitors real-time network data as input and updates the information as output if an anomaly is detected.

[0490] Step 6:

[0491] When a fault is detected, the terminal highlights the information using a visual device. The input is fault information provided by the server, and the output is a network structure diagram with the problem area highlighted, which is shown to the operator.

[0492] Step 7:

[0493] Users respond quickly based on information displayed on smart glasses. The input is visual information provided from the device, and the output is actual troubleshooting and corrective actions.

[0494] These steps enable real-time monitoring of the network status and rapid fault response.

[0495] 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.

[0496] This invention combines a system that automatically identifies, analyzes, and visualizes network equipment to streamline network management with an emotion engine that recognizes the user's emotional state. This system aims to reduce stress and support decision-making by quickly identifying network operational challenges and providing information tailored to the user's emotions.

[0497] First, the server uses existing network scanning tools to collect identification information from devices within the network and stores it in a database. The server periodically performs scans to detect new devices and changes. Based on the collected information, the server analyzes the network configuration and forms data for generating a network diagram.

[0498] The terminal processes data received from the server via a visualization engine to generate a graphical network configuration diagram. This diagram is displayed to the user in an intuitive and easy-to-understand format. In the event of a failure, the affected area is highlighted to support a rapid response.

[0499] Furthermore, to recognize user emotions in real time, the device is equipped with an emotion engine. The emotion engine analyzes the emotional state using various sensor data and user interaction data. For example, if a user's stress level increases while viewing a network diagram, the emotion engine can detect that emotion, and the device can adjust how the information is displayed. Specifically, it can soften the tone of the information presentation or adjust the amount of information to provide it in a way that is less burdensome for the user.

[0500] As a concrete example, when a major network failure occurs, the server quickly identifies the faulty device, and the terminal notifies the user of the situation. If the emotion engine detects stress during the user's review of the notification, the terminal reduces the priority of the notification and presents solutions step by step, thereby reducing the user's burden. This entire process reduces the stress of network operations and improves the efficiency of decision-making.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The server launches a network scanning tool to collect identification information for each device on the network. This information includes IP addresses and hostnames.

[0504] Step 2:

[0505] The server stores the collected data in a database and analyzes the network configuration. This analysis clarifies the connection relationships between each device.

[0506] Step 3:

[0507] The terminal generates a configuration diagram using a visualization engine based on the latest network configuration data obtained from the server. The configuration diagram is designed to allow the user to see the status of the entire network at a glance.

[0508] Step 4:

[0509] When a user views a diagram on their device, the device uses an emotion engine to evaluate the user's emotional state in real time. Data collected from the user's camera and microphone is used for analysis.

[0510] Step 5:

[0511] When the emotion engine detects user stress, the device dynamically adjusts the tone and style of information presentation. For example, it reduces the amount of information or highlights only the most important information to lessen the user's burden.

[0512] Step 6:

[0513] When a network failure occurs, the server quickly identifies the affected devices and sends that information to the terminals. The terminals then reflect this information in the configuration diagram and notify the user of the location and scope of the failure.

[0514] Step 7:

[0515] Users review the suggested solutions and configuration diagram changes from their terminals and provide feedback. This feedback is then relayed to the server, where corrective actions are implemented.

[0516] Step 8:

[0517] If a user requires further information or support, the device sends the request to the server, which collects and provides the relevant information to the user. This allows the user to efficiently work towards resolving the problem.

[0518] (Example 2)

[0519] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0520] Traditional network management systems often require manual processes for understanding network configurations and identifying the scope of impact in the event of a failure, placing significant stress on administrators. Furthermore, the lack of information tailored to users' emotional states can reduce the speed and accuracy of decision-making. There is a need to address these issues and improve the efficiency of network operations while reducing the burden on users.

[0521] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0522] In this invention, the server includes means for collecting identification information of network devices, means for analyzing the configuration of the communication network based on the collected identification information, and means for adjusting the information presentation method based on the recognized emotional state. This enables automatic identification of devices within the communication network and real-time configuration analysis, and allows for the provision of appropriate information according to the user's emotions.

[0523] A "network device" is a device used to send and receive digital data, and functions as a component of a communication network.

[0524] "Identification information" refers to data used to uniquely identify each network device, and mainly includes IP addresses, MAC addresses, and hostnames.

[0525] A "communication network" is a system or structure in which multiple network devices are connected for the exchange of digital data.

[0526] "Analysis" is the process of investigating and understanding the structure and characteristics of an object in detail based on collected data.

[0527] A "configuration diagram" is a diagram that visually represents the connection status and layout of the components of a communication network.

[0528] "State change" refers to any variation or transition in the attributes or behavior of a communication network or its components.

[0529] "User" refers to an individual or legal entity that manages, monitors, or uses a communication network.

[0530] "Emotional state" refers to a change in the user's psychological or emotional state, including internal conditions such as stress and a sense of security.

[0531] "Information presentation method" refers to the format and means of providing data and notifications to users, including text and visual displays.

[0532] This invention provides a system that streamlines the management of communication networks and reduces the psychological burden on users. In addition to automatic identification, configuration analysis, and visualization of network devices, it also includes a function to recognize the emotional state of users. This system is implemented using the hardware and software described below.

[0533] The server uses existing network scanning tools, such as general-purpose open-source IP scanners, to collect identification information from each device in the network. This identification information includes IP addresses, MAC addresses, and device names. Based on this information, the server analyzes the current state of the network and constructs a network topology.

[0534] The terminal runs a rendering engine to visualize the analysis data acquired from the server. This rendering engine uses open-source visual libraries and other resources to visually display the network configuration. This display includes a function to clarify the scope of impact in the event of a failure, helping users to quickly decide on a course of action.

[0535] Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's stress level and level of excitement based on sensor data such as camera and mouse movements and key input patterns. Based on the analysis results, the device adjusts the tone of information presentation and changes the way important information is presented to suit the user.

[0536] For example, in the event of a major network failure, the server quickly identifies the problematic device, and the terminal notifies the user of the situation. When the user reviews the notification, if the emotion engine detects a high-stress state, the terminal re-presents the notification in a gradual and gentler format. This series of functions makes it easier to make decisions that affect network operations and reduces the psychological burden on users.

[0537] An example of a prompt for a generative AI model might be, "Please suggest a presentation method that reduces user stress during network failures."

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The server initiates a network scan. It uses existing database information about each network device as input. The server uses a scanning tool to obtain the latest identification information from each network device and stores it in the database. The output is the database containing the latest identification information. Specifically, it scans and collects data such as IP addresses and device IDs.

[0541] Step 2:

[0542] The server analyzes the collected identification information and constructs the topology of the communication network. The input is the identification information in the database. This analysis clarifies the connection relationships and placement of network devices and creates the basic data for generating a communication network configuration diagram. The output is a data model that reflects the specific connection patterns. The operation involves identifying connection paths and dependencies between devices.

[0543] Step 3:

[0544] The terminal uses topology data obtained from the server to run a rendering engine and generate a visual network configuration diagram. The input is network configuration analysis data sent from the server. This procedure generates an intuitive visual overview of the entire network, which the user can use for management and maintenance. The output is a graphical configuration diagram that the user can view on the terminal. Specifically, the process involves visualizing the analysis data on a coordinate system.

[0545] Step 4:

[0546] The terminal automatically identifies the affected area when a network failure occurs and notifies the user. The input is real-time network monitoring data. The terminal identifies the problematic device and its connections and overlays this information on a visualized configuration diagram. The output is a configuration diagram highlighting the scope of the failure. Specifically, it includes actions to make the affected area easily identifiable by displaying it with color or flags.

[0547] Step 5:

[0548] The device uses an emotion engine to recognize the user's real-time emotional state. Inputs include user operation patterns and sensor data (e.g., camera and mouse movements). The emotion engine analyzes this data to determine the user's level of stress and excitement. Outputs are analytical information regarding the emotional state. Specifically, this involves integrating sensor data and performing pattern analysis using algorithms.

[0549] Step 6:

[0550] The device adjusts how information is presented based on the recognized emotional state. The input is emotional state information output by the emotion engine. The device uses this information to adjust the tone and amount of notifications and information presented to the user. For example, if stress is detected, notifications may be simplified or information may be presented in stages to reduce the user's burden. The output is the adjusted information presentation, which is displayed on the user's screen. Specific actions include changing the screen layout and adjusting the text tone.

[0551] (Application Example 2)

[0552] 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."

[0553] While conventional network management systems comprehensively provide information on network configuration changes and failures, they lack optimization of information presentation that takes into account the stress and emotional state of administrators. This results in significant mental burden during network management and troubleshooting, leading to decreased decision-making efficiency. Therefore, a system is needed that dynamically adjusts information presentation according to the user's emotional state to reduce stress.

[0554] 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.

[0555] In this invention, the server includes means for acquiring identification information of network equipment, means for analyzing the network configuration and visualizing it as a diagram, and means for analyzing the user's emotional state and adjusting the displayed information. This enables more efficient network management and reduces stress for administrators.

[0556] "Network equipment" refers to the physical or logical components used for equipment and devices to communicate with one another.

[0557] "Identification information" refers to data or protocol information used to uniquely identify specific network equipment.

[0558] "Network configuration" refers to information that describes how devices and equipment are arranged and interconnected within a network.

[0559] A "configuration diagram" is a diagram or map that visually represents the physical or logical arrangement of a network.

[0560] "Emotional state" refers to the state of a user's psychological or emotional response, as evaluated in real time.

[0561] A "visualization engine" is a software module or component used to convert data into a format that can be visually displayed.

[0562] A "failure" refers to a state in which some kind of problem occurs with equipment or communication protocols within a network, preventing them from functioning normally.

[0563] "Dynamic adjustment" means changing a system or settings automatically or manually in response to changes in circumstances or conditions.

[0564] "Presenting solutions step by step" means presenting solutions to a problem sequentially to the user, rather than presenting all the solutions at once.

[0565] This invention is a system aimed at improving the efficiency of network management and reducing user stress. The server first uses a network scanning tool to collect identification information for each piece of equipment within the factory. This identification information is stored in a database and used for network configuration analysis. The data obtained from the network configuration analysis is then generated as a configuration diagram using a visualization engine.

[0566] The device receives a network configuration diagram sent from the server and presents it to the user in real time. Furthermore, the device is equipped with an emotion engine that analyzes data such as the user's facial expressions and voice tone. This allows the system to assess the user's stress and emotional state, dynamically adjusting how information is presented. For example, if stress is detected, the notification tone may be softened, or information may be presented in stages to reduce the user's psychological burden.

[0567] Specifically, when a problem is detected, the server immediately identifies the issue and highlights its scope of impact on the configuration diagram. The terminal informs the user of this information and provides an explanation in an appropriate tone using an emotion engine. For example, it provides step-by-step solutions such as, "First, please restart the robot. If that doesn't solve the problem, please consider calling a specialist."

[0568] An example of a prompt for a generative AI model would be: "When an emergency occurs in a factory, please show how to present information to mitigate the impact. Specifically, please describe the role of the emotion engine and provide step-by-step guidance for stress reduction."

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The server uses a network scanning tool to collect identification information from all devices within the factory. The collected data includes identification information such as the device's IP address, MAC address, and operating status. This information is stored in a database, which is then used for subsequent network configuration analysis.

[0572] Step 2:

[0573] The server analyzes the identification information stored in the database to understand the network configuration. Specifically, it analyzes communication protocols and connection status to clarify the connection relationships of each device. Based on these analysis results, it generates a dataset for creating a network configuration diagram.

[0574] Step 3:

[0575] The terminal inputs network configuration data sent from the server into a visualization engine and generates a graphical configuration diagram. The diagram is displayed to the user in an intuitive format using device icons and connection lines. The generated configuration diagram is provided to the user in real time.

[0576] Step 4:

[0577] The device analyzes the user's emotional state using an emotion engine. Inputs include the user's facial movements and voice tone, captured through the camera and microphone. Based on this data, an emotion recognition algorithm evaluates the user's emotional state and identifies their stress level and type of emotion.

[0578] Step 5:

[0579] The device dynamically adjusts the content and tone of the information it presents based on the analysis results of the emotion engine. Specifically, if stress levels are high, it will lower the volume of notifications and alerts, reduce the amount of information, and display solutions in stages. This adjustment aims to provide information in a way that is less burdensome for the user.

[0580] Step 6:

[0581] Users perform network management and troubleshooting based on the coordinated information. For example, they identify problem areas while looking at a visualized configuration diagram and proceed with the solution according to a step-by-step plan. This entire process reduces the psychological burden on users and enables more efficient decision-making.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] This invention relates to a system that acquires identification information of network equipment, automatically analyzes the network configuration based on the acquired information, and visualizes it. This system works in cooperation with servers, terminals, and users, enabling efficient network management and rapid response in the event of failures.

[0600] First, the server uses a network scanning tool to collect identification information from all devices on the network. This collected information is stored in a database and used for configuration analysis. The server performs scans periodically to update information on newly added and modified devices.

[0601] The terminal retrieves configuration data provided by the server and generates a network diagram using a visualization engine. This diagram is displayed in an easy-to-understand format for the user, allowing for a visualization of the overall network status. In the event of a failure, the affected area is highlighted, helping the user quickly understand the problem.

[0602] As a concrete example, when a new server is added to the network, the server automatically detects this and updates its configuration information. The terminal then regenerates the configuration diagram reflecting the updates and notifies the user. The user approves or proposes modifications and sends feedback to the server through the terminal. This ensures that the network configuration is always up-to-date and supports rapid decision-making.

[0603] Furthermore, if users have any questions about the configuration diagram, they can ask directly through their terminal. The server will then collect relevant information in response to these requests and provide the user with a detailed explanation. This feature allows users to gain a deeper understanding and improve their confidence in network operations.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The server uses network scanning tools to collect identification information for all devices on the network. This includes data such as IP addresses and server names.

[0607] Step 2:

[0608] The server stores the collected identification information in a database. This data is used for subsequent network configuration analysis.

[0609] Step 3:

[0610] The server periodically rescans the network to automatically detect new or modified devices. This detection updates the database information.

[0611] Step 4:

[0612] The terminal retrieves the latest configuration data from the server and generates a network diagram using a visualization engine. This diagram visually represents the network topology.

[0613] Step 5:

[0614] The terminal displays the generated configuration diagram to the user. By viewing the configuration diagram, the user can grasp the overall picture of the network.

[0615] Step 6:

[0616] When a failure occurs, the server identifies the affected device and sends that information to the terminal. This allows the terminal to highlight the problematic area in the configuration diagram.

[0617] Step 7:

[0618] Users receive proposed changes to the configuration diagram via their device. Users approve or modify the proposals and send their feedback from their device to the server.

[0619] Step 8:

[0620] When a user asks a question about something they don't understand, the device sends the request to the server. The server collects relevant information and provides the user with a detailed explanation.

[0621] (Example 1)

[0622] 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".

[0623] In managing communication systems, it is crucial to efficiently collect identification information for each device and visualize the network structure based on that information. However, conventional methods have presented challenges such as difficulty in responding quickly to status changes or failures, leading to delays in notifying users and updating the network structure. Furthermore, immediate responses to user inquiries were also difficult.

[0624] 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.

[0625] In this invention, the server includes means for acquiring information on all devices in the computer network using an identification information collection function, means for automatically analyzing the network structure based on the acquired information, and means for responding to user inquiries using a generative model. This enables more efficient network management, rapid response in the event of a failure, and immediate information provision and response to users.

[0626] The "identification information collection function" is a function that collects information from each device within the network and organizes and stores it.

[0627] A "computer network" is a system in which numerous computers and devices are interconnected and exchange information.

[0628] "Means for automatically analyzing network structure" refers to methods for independently analyzing and understanding the overall configuration of a network based on acquired network information.

[0629] A "communication system structure diagram" is a diagram that visually represents the configuration and connection relationships of a network, and is useful for users to understand the state of the network.

[0630] "Means for automatically detecting changes in state" refers to means that can monitor changes in devices and connections within a network and immediately grasp any changes.

[0631] "Means of notifying users of proposed changes" refers to means of immediately communicating information to users regarding changes to the network structure.

[0632] A "generative model" is a model that uses artificial intelligence technology to automatically generate appropriate answers to user inquiries.

[0633] This invention is a system in which servers, terminals, and users cooperate to streamline network management and monitoring. The server uses a network scanning tool to collect identification information from all devices on the network. In this step, for example, an open-source network scanning tool is used to obtain the IP addresses and hostnames of all devices connected to the network. The collected information is stored in a database, and the server uses this information to analyze the network structure.

[0634] The terminal retrieves network structure data analyzed from the server and processes it using a visualization engine. The visualization engine generates a structure diagram that visualizes the network configuration, for example, using an open-source dashboard creation tool. The generated structure diagram visually shows the status and connections of each device in the network, helping the user understand the overall picture of the network.

[0635] Users can view a structural diagram provided via their terminal, enabling them to quickly understand network changes and failures. If an anomaly is highlighted in the structural diagram, users can immediately take corrective action. Furthermore, users can send questions via their terminal about areas they are unsure of, and the server provides immediate responses using a generative AI model. Specifically, commonly used natural language processing techniques are applied as the generative AI model to generate natural language answers to user inquiries.

[0636] As a concrete example, when a new device is added to the network, the server automatically detects it and updates the network structure information. The terminal then updates the structure diagram based on the latest information and notifies the user. The user can review the updated structure diagram and provide feedback to the server as needed. This leads to faster and more accurate network maintenance.

[0637] For example, a possible prompt to input into a generating AI model could be, "Please tell me the procedure to follow when a new device is added to the network." In this way, the entire system can work together, significantly improving the efficiency and reliability of network management.

[0638] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0639] Step 1:

[0640] The server launches a network scanning tool and scans all devices on the network. The network's IP range is specified as input, and the output provides identification information for each device, including its IP address, hostname, and MAC address. This information is saved to a file and prepared for transfer to a database for post-processing.

[0641] Step 2:

[0642] The server stores the identification information obtained in step 1 in the database. The scan result file is used as input, and the information for each device is saved in the database table as output. This process maintains the consistency of the information and makes it easy to refer to it in conjunction with past records.

[0643] Step 3:

[0644] The server retrieves the latest identification information from the database and analyzes the network structure. The database contents are used as input, and the network topology is obtained as the output. Here, the connection relationships between devices and the network configuration are revealed. The program uses algorithms to evaluate the device hierarchy and the presence or absence of links.

[0645] Step 4:

[0646] The terminal retrieves analyzed network topology data from the server and generates a structural diagram using a visualization engine. The analysis results are used as input, and the output is a structural diagram in a user-friendly format. This program utilizes the visualization engine to arrange devices as shapes and represent their connection relationships with lines.

[0647] Step 5:

[0648] The terminal updates the structural diagram based on fault information and status changes. The analysis results from step 3 and alert information from the monitoring system are used as input. The output is an updated structural diagram, with fault areas highlighted. This allows the user to immediately identify network problems.

[0649] Step 6:

[0650] Users view the structural diagram via their terminal and send feedback to the server as needed. The input is the structural diagram and its description, and the output is user approval or modification suggestions sent via the terminal. This feedback allows for further refinement of the network configuration and improved reliability.

[0651] Step 7:

[0652] The user sends a question from their device to the server, which then uses a generative AI model to generate a response. The user's question is given as input, and the automatically generated answer is received as output. This interaction allows the user to deepen their understanding of network operations.

[0653] (Application Example 1)

[0654] 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".

[0655] Modern information processing systems require the immediate understanding of network structure and status, particularly the scope of impact during failures, and the ability to respond quickly. However, conventional systems face challenges in real-time visual monitoring and immediate fault response. Furthermore, the increasing burden on engineers to continuously monitor the increasingly complex network status leads to a decline in efficiency.

[0656] 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.

[0657] In this invention, the server includes means for collecting identification data of network devices, means for analyzing the network structure based on the collected identification data, and means for creating a network structure diagram using the analysis results. This makes it possible to display the network status of the group of information processing devices in real time using visual devices. Furthermore, fault conditions can be highlighted using visual devices to facilitate a quick response by operators, resulting in more efficient network management and faster response to failures.

[0658] "Network devices" refer to various information devices connected to a network, and these are hardware devices used for sending and receiving data.

[0659] "Identification data" refers to information used to uniquely recognize network devices, and typically includes IP addresses and MAC addresses.

[0660] An "information processing system" refers to a group of information devices that are interconnected via a network and perform data processing and communication.

[0661] "Visual devices" are devices used to physically visualize digital information, and include smart glasses and displays.

[0662] A "visualization engine" is a software or hardware component that displays digital data in a visually understandable format.

[0663] "Fault status" refers to abnormalities or failures in the network, and is a factor that prevents normal operation.

[0664] The system for implementing this invention mainly consists of a server, terminals, and operators. The server collects identification data from network devices and stores it in a database. The hardware used includes network scanners and standard computer servers. Dedicated analysis software and a visualization engine are used to analyze this information to identify the network structure and generate a structural diagram.

[0665] This generated network structure diagram is provided to the operator via a terminal. The terminal includes visual devices such as smart glasses, which display the network status in real time. This display is dynamically updated by a visualization engine, and areas of failure are highlighted, especially when they occur. Based on this information, the operator can quickly respond to network problems.

[0666] For example, if a robot in a factory loses communication, this information is immediately detected by the server, and the network structure diagram is updated. This anomaly is visually highlighted to operators wearing smart glasses, allowing them to physically go to the location and resolve the problem.

[0667] An example of input to the generating AI model could be a prompt such as, "Design a smart glasses application that visualizes the network status of robots in a factory and detects faults."

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1:

[0670] The server uses a network scanner to collect identification data from network devices. The input is set to the network's IP range, and the output is the collected identification data (IP address, MAC address, etc.). This data is stored in a database.

[0671] Step 2:

[0672] The server analyzes the identification data stored in the database. This analysis determines the connection relationships between devices and identifies the network structure. The identification data collected in the previous step is used as input, and network structure information is obtained as output.

[0673] Step 3:

[0674] The server uses a visualization engine to create a network structure diagram based on the analyzed network structure information. Network structure information is used as input, and the output is a network structure diagram that is easy for the user to understand visually.

[0675] Step 4:

[0676] The terminal retrieves the created network structure diagram and displays it to the operator using a visual device. The input is a visualized network structure diagram, and the output is the diagram displayed on a visual device such as smart glasses, which is provided to the operator.

[0677] Step 5:

[0678] The server constantly monitors network status changes and detects anomalies and failures. It monitors real-time network data as input and updates the information as output if an anomaly is detected.

[0679] Step 6:

[0680] When a fault is detected, the terminal highlights the information using a visual device. The input is fault information provided by the server, and the output is a network structure diagram with the problem area highlighted, which is shown to the operator.

[0681] Step 7:

[0682] Users respond quickly based on information displayed on smart glasses. The input is visual information provided from the device, and the output is actual troubleshooting and corrective actions.

[0683] These steps enable real-time monitoring of the network status and rapid fault response.

[0684] 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.

[0685] This invention combines a system that automatically identifies, analyzes, and visualizes network equipment to streamline network management with an emotion engine that recognizes the user's emotional state. This system aims to reduce stress and support decision-making by quickly identifying network operational challenges and providing information tailored to the user's emotions.

[0686] First, the server uses existing network scanning tools to collect identification information from devices within the network and stores it in a database. The server periodically performs scans to detect new devices and changes. Based on the collected information, the server analyzes the network configuration and forms data for generating a network diagram.

[0687] The terminal processes data received from the server via a visualization engine to generate a graphical network configuration diagram. This diagram is displayed to the user in an intuitive and easy-to-understand format. In the event of a failure, the affected area is highlighted to support a rapid response.

[0688] Furthermore, to recognize user emotions in real time, the device is equipped with an emotion engine. The emotion engine analyzes the emotional state using various sensor data and user interaction data. For example, if a user's stress level increases while viewing a network diagram, the emotion engine can detect that emotion, and the device can adjust how the information is displayed. Specifically, it can soften the tone of the information presentation or adjust the amount of information to provide it in a way that is less burdensome for the user.

[0689] As a concrete example, when a major network failure occurs, the server quickly identifies the faulty device, and the terminal notifies the user of the situation. If the emotion engine detects stress during the user's review of the notification, the terminal reduces the priority of the notification and presents solutions step by step, thereby reducing the user's burden. This entire process reduces the stress of network operations and improves the efficiency of decision-making.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The server launches a network scanning tool to collect identification information for each device on the network. This information includes IP addresses and hostnames.

[0693] Step 2:

[0694] The server stores the collected data in a database and analyzes the network configuration. This analysis clarifies the connection relationships between each device.

[0695] Step 3:

[0696] The terminal generates a configuration diagram using a visualization engine based on the latest network configuration data obtained from the server. The configuration diagram is designed to allow the user to see the status of the entire network at a glance.

[0697] Step 4:

[0698] When a user views a diagram on their device, the device uses an emotion engine to evaluate the user's emotional state in real time. Data collected from the user's camera and microphone is used for analysis.

[0699] Step 5:

[0700] When the emotion engine detects user stress, the device dynamically adjusts the tone and style of information presentation. For example, it reduces the amount of information or highlights only the most important information to lessen the user's burden.

[0701] Step 6:

[0702] When a network failure occurs, the server quickly identifies the affected devices and sends that information to the terminals. The terminals then reflect this information in the configuration diagram and notify the user of the location and scope of the failure.

[0703] Step 7:

[0704] Users review the suggested solutions and configuration diagram changes from their terminals and provide feedback. This feedback is then relayed to the server, where corrective actions are implemented.

[0705] Step 8:

[0706] If a user requires further information or support, the device sends the request to the server, which collects and provides the relevant information to the user. This allows the user to efficiently work towards resolving the problem.

[0707] (Example 2)

[0708] 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".

[0709] Traditional network management systems often require manual processes for understanding network configurations and identifying the scope of impact in the event of a failure, placing significant stress on administrators. Furthermore, the lack of information tailored to users' emotional states can reduce the speed and accuracy of decision-making. There is a need to address these issues and improve the efficiency of network operations while reducing the burden on users.

[0710] 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.

[0711] In this invention, the server includes means for collecting identification information of network devices, means for analyzing the configuration of the communication network based on the collected identification information, and means for adjusting the information presentation method based on the recognized emotional state. This enables automatic identification of devices within the communication network and real-time configuration analysis, and allows for the provision of appropriate information according to the user's emotions.

[0712] A "network device" is a device used to send and receive digital data, and functions as a component of a communication network.

[0713] "Identification information" refers to data used to uniquely identify each network device, and mainly includes IP addresses, MAC addresses, and hostnames.

[0714] A "communication network" is a system or structure in which multiple network devices are connected for the exchange of digital data.

[0715] "Analysis" is the process of investigating and understanding the structure and characteristics of an object in detail based on collected data.

[0716] A "configuration diagram" is a diagram that visually represents the connection status and layout of the components of a communication network.

[0717] "State change" refers to any variation or transition in the attributes or behavior of a communication network or its components.

[0718] "User" refers to an individual or legal entity that manages, monitors, or uses a communication network.

[0719] "Emotional state" refers to a change in the user's psychological or emotional state, including internal conditions such as stress and a sense of security.

[0720] "Information presentation method" refers to the format and means of providing data and notifications to users, including text and visual displays.

[0721] This invention provides a system that streamlines the management of communication networks and reduces the psychological burden on users. In addition to automatic identification, configuration analysis, and visualization of network devices, it also includes a function to recognize the emotional state of users. This system is implemented using the hardware and software described below.

[0722] The server uses existing network scanning tools, such as general-purpose open-source IP scanners, to collect identification information from each device in the network. This identification information includes IP addresses, MAC addresses, and device names. Based on this information, the server analyzes the current state of the network and constructs a network topology.

[0723] The terminal runs a rendering engine to visualize the analysis data acquired from the server. This rendering engine uses open-source visual libraries and other resources to visually display the network configuration. This display includes a function to clarify the scope of impact in the event of a failure, helping users to quickly decide on a course of action.

[0724] Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time. This emotion engine analyzes the user's stress level and level of excitement based on sensor data such as camera and mouse movements and key input patterns. Based on the analysis results, the device adjusts the tone of information presentation and changes the way important information is presented to suit the user.

[0725] For example, in the event of a major network failure, the server quickly identifies the problematic device, and the terminal notifies the user of the situation. When the user reviews the notification, if the emotion engine detects a high-stress state, the terminal re-presents the notification in a gradual and gentler format. This series of functions makes it easier to make decisions that affect network operations and reduces the psychological burden on users.

[0726] An example of a prompt for a generative AI model might be, "Please suggest a presentation method that reduces user stress during network failures."

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The server initiates a network scan. It uses existing database information about each network device as input. The server uses a scanning tool to obtain the latest identification information from each network device and stores it in the database. The output is the database containing the latest identification information. Specifically, it scans and collects data such as IP addresses and device IDs.

[0730] Step 2:

[0731] The server analyzes the collected identification information and constructs the topology of the communication network. The input is the identification information in the database. This analysis clarifies the connection relationships and placement of network devices and creates the basic data for generating a communication network configuration diagram. The output is a data model that reflects the specific connection patterns. The operation involves identifying connection paths and dependencies between devices.

[0732] Step 3:

[0733] The terminal uses topology data obtained from the server to run a rendering engine and generate a visual network configuration diagram. The input is network configuration analysis data sent from the server. This procedure generates an intuitive visual overview of the entire network, which the user can use for management and maintenance. The output is a graphical configuration diagram that the user can view on the terminal. Specifically, the process involves visualizing the analysis data on a coordinate system.

[0734] Step 4:

[0735] The terminal automatically identifies the affected area when a network failure occurs and notifies the user. The input is real-time network monitoring data. The terminal identifies the problematic device and its connections and overlays this information on a visualized configuration diagram. The output is a configuration diagram highlighting the scope of the failure. Specifically, it includes actions to make the affected area easily identifiable by displaying it with color or flags.

[0736] Step 5:

[0737] The device uses an emotion engine to recognize the user's real-time emotional state. Inputs include user operation patterns and sensor data (e.g., camera and mouse movements). The emotion engine analyzes this data to determine the user's level of stress and excitement. Outputs are analytical information regarding the emotional state. Specifically, this involves integrating sensor data and performing pattern analysis using algorithms.

[0738] Step 6:

[0739] The device adjusts how information is presented based on the recognized emotional state. The input is emotional state information output by the emotion engine. The device uses this information to adjust the tone and amount of notifications and information presented to the user. For example, if stress is detected, notifications may be simplified or information may be presented in stages to reduce the user's burden. The output is the adjusted information presentation, which is displayed on the user's screen. Specific actions include changing the screen layout and adjusting the text tone.

[0740] (Application Example 2)

[0741] 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".

[0742] While conventional network management systems comprehensively provide information on network configuration changes and failures, they lack optimization of information presentation that takes into account the stress and emotional state of administrators. This results in significant mental burden during network management and troubleshooting, leading to decreased decision-making efficiency. Therefore, a system is needed that dynamically adjusts information presentation according to the user's emotional state to reduce stress.

[0743] 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.

[0744] In this invention, the server includes means for acquiring identification information of network equipment, means for analyzing the network configuration and visualizing it as a diagram, and means for analyzing the user's emotional state and adjusting the displayed information. This enables more efficient network management and reduces stress for administrators.

[0745] "Network equipment" refers to the physical or logical components used for equipment and devices to communicate with one another.

[0746] "Identification information" refers to data or protocol information used to uniquely identify specific network equipment.

[0747] "Network configuration" refers to information that describes how devices and equipment are arranged and interconnected within a network.

[0748] A "configuration diagram" is a diagram or map that visually represents the physical or logical arrangement of a network.

[0749] "Emotional state" refers to the state of a user's psychological or emotional response, as evaluated in real time.

[0750] A "visualization engine" is a software module or component used to convert data into a format that can be visually displayed.

[0751] A "failure" refers to a state in which some kind of problem occurs with equipment or communication protocols within a network, preventing them from functioning normally.

[0752] "Dynamic adjustment" means changing a system or settings automatically or manually in response to changes in circumstances or conditions.

[0753] "Presenting solutions step by step" means presenting solutions to a problem sequentially to the user, rather than presenting all the solutions at once.

[0754] This invention is a system aimed at improving the efficiency of network management and reducing user stress. The server first uses a network scanning tool to collect identification information for each piece of equipment within the factory. This identification information is stored in a database and used for network configuration analysis. The data obtained from the network configuration analysis is then generated as a configuration diagram using a visualization engine.

[0755] The device receives a network configuration diagram sent from the server and presents it to the user in real time. Furthermore, the device is equipped with an emotion engine that analyzes data such as the user's facial expressions and voice tone. This allows the system to assess the user's stress and emotional state, dynamically adjusting how information is presented. For example, if stress is detected, the notification tone may be softened, or information may be presented in stages to reduce the user's psychological burden.

[0756] Specifically, when a problem is detected, the server immediately identifies the issue and highlights its scope of impact on the configuration diagram. The terminal informs the user of this information and provides an explanation in an appropriate tone using an emotion engine. For example, it provides step-by-step solutions such as, "First, please restart the robot. If that doesn't solve the problem, please consider calling a specialist."

[0757] An example of a prompt for a generative AI model would be: "When an emergency occurs in a factory, please show how to present information to mitigate the impact. Specifically, please describe the role of the emotion engine and provide step-by-step guidance for stress reduction."

[0758] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0759] Step 1:

[0760] The server uses a network scanning tool to collect identification information from all devices within the factory. The collected data includes identification information such as the device's IP address, MAC address, and operating status. This information is stored in a database, which is then used for subsequent network configuration analysis.

[0761] Step 2:

[0762] The server analyzes the identification information stored in the database to understand the network configuration. Specifically, it analyzes communication protocols and connection status to clarify the connection relationships of each device. Based on these analysis results, it generates a dataset for creating a network configuration diagram.

[0763] Step 3:

[0764] The terminal inputs network configuration data sent from the server into a visualization engine and generates a graphical configuration diagram. The diagram is displayed to the user in an intuitive format using device icons and connection lines. The generated configuration diagram is provided to the user in real time.

[0765] Step 4:

[0766] The device analyzes the user's emotional state using an emotion engine. Inputs include the user's facial movements and voice tone, captured through the camera and microphone. Based on this data, an emotion recognition algorithm evaluates the user's emotional state and identifies their stress level and type of emotion.

[0767] Step 5:

[0768] The device dynamically adjusts the content and tone of the information it presents based on the analysis results of the emotion engine. Specifically, if stress levels are high, it will lower the volume of notifications and alerts, reduce the amount of information, and display solutions in stages. This adjustment aims to provide information in a way that is less burdensome for the user.

[0769] Step 6:

[0770] Users perform network management and troubleshooting based on the coordinated information. For example, they identify problem areas while looking at a visualized configuration diagram and proceed with the solution according to a step-by-step plan. This entire process reduces the psychological burden on users and enables more efficient decision-making.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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."

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A means for obtaining identification information of network equipment,

[0795] A means for analyzing the network configuration based on acquired identification information,

[0796] A means for generating a network configuration diagram using the analysis results,

[0797] A means for detecting changes in the state of network equipment,

[0798] A means for updating the network configuration diagram based on detected state changes,

[0799] A means of notifying users of proposed changes to the network configuration diagram,

[0800] A means of periodically updating the network configuration diagram based on user approval,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, comprising a visualization engine for visualizing a network configuration diagram.

[0804] (Claim 3)

[0805] The system according to claim 1, which, in the event of a network failure, automatically identifies the scope of the failure and performs a process to notify the user of that information.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A means for acquiring information on all devices in a computer network using an identification information collection function,

[0809] A means for automatically analyzing the network structure based on acquired information,

[0810] A means of creating a communication system structure diagram based on the results of the analysis,

[0811] A means for automatically detecting changes in the state of communication equipment,

[0812] A means for updating the communication system structure diagram in response to detected changes,

[0813] A means of notifying users of proposals regarding changes to the communication system structure diagram,

[0814] A means of periodically updating the communication system structure diagram based on user approval,

[0815] A means of responding to user questions using a generative model,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising an image generation device for visually displaying a communication system structure diagram.

[0819] (Claim 3)

[0820] The system according to claim 1, which automatically identifies the affected area when an abnormality occurs in the communication system and performs a process to inform the user of the result.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means for collecting identification data of network devices,

[0824] A means for analyzing the network structure based on the collected identification data,

[0825] A method for creating a network structure diagram using the analysis results,

[0826] A means for detecting a change in the state of an information processing device,

[0827] A means for updating the network structure diagram based on detected state changes,

[0828] A means of notifying the operator of proposed modifications to the network structure diagram,

[0829] A means of periodically updating the network structure diagram based on the operator's approval,

[0830] A means of displaying the network status of a group of information processing devices in real time using a visual device,

[0831] A means of using visual aids to highlight the status of the malfunction and facilitate a quick response by the operator,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, comprising a display engine for visualizing a network structure diagram.

[0835] (Claim 3)

[0836] The system according to claim 1, which, in the event of a network failure, automatically identifies the scope of the failure and notifies the operator of that information.

[0837] "Example 2 of combining an emotion engine"

[0838] (Claim 1)

[0839] Means for collecting identification information of network devices,

[0840] A means for analyzing the configuration of the communication network based on the collected identification information,

[0841] A means for generating a network configuration diagram using the analysis results,

[0842] A means for detecting changes in the state of a communication network,

[0843] A means for updating the network configuration diagram based on detected state changes,

[0844] A means of notifying users of proposed changes to the network configuration diagram,

[0845] A means of periodically updating the network configuration diagram based on user permission,

[0846] A means of recognizing the emotional state of users in real time,

[0847] Means for adjusting the method of information presentation based on recognized emotional states,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising a drawing engine for visualizing a diagram of a communication network configuration.

[0851] (Claim 3)

[0852] The system according to claim 1, which automatically identifies the scope of the impact when a communication network failure occurs and performs a process to notify users of that information.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] A means for obtaining identification information of network equipment,

[0856] A means for analyzing the network configuration based on acquired identification information,

[0857] A means for generating a network configuration diagram using the analysis results,

[0858] A means for detecting changes in the state of network equipment,

[0859] A means for updating the network configuration diagram based on detected state changes,

[0860] A means of notifying users of proposed changes to the network configuration diagram,

[0861] A means of periodically updating the network configuration diagram based on user approval,

[0862] A means of analyzing the user's emotional state and adjusting the displayed information,

[0863] A means of presenting solutions to users step by step,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising a visualization engine for visualizing a network configuration diagram and an engine for optimizing information presentation based on the user's emotional state.

[0867] (Claim 3)

[0868] The system according to claim 1, which, in the event of a network failure, automatically identifies the scope of the failure, notifies the user of that information, and performs a process to convey the information in a tone appropriate to the user's emotions. [Explanation of Symbols]

[0869] 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 obtaining identification information of network equipment, A means for analyzing the network configuration based on acquired identification information, A means for generating a network configuration diagram using the analysis results, A means for detecting changes in the state of network equipment, A means for updating the network configuration diagram based on detected state changes, A means of notifying users of proposed changes to the network configuration diagram, A means of periodically updating the network configuration diagram based on user approval, A system that includes this.

2. The system according to claim 1, comprising a visualization engine for visualizing a network configuration diagram.

3. The system according to claim 1, which, in the event of a network failure, automatically identifies the scope of the failure and performs a process to notify the user of that information.

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